Advanced Sensor Technology: Biomedical, Environmental, and Construction Applications 0323902227, 9780323902229

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ADVANCED SENSOR TECHNOLOGY

ADVANCED SENSOR TECHNOLOGY BIOMEDICAL, ENVIRONMENTAL, AND CONSTRUCTION APPLICATIONS Edited by

AHMED BARHOUM NanoStruc Research Group, Chemistry Department, Faculty of Science, Helwan University, Cairo, Egypt; National Centre for Sensor Research, School of Chemical Sciences, Dublin City University, Dublin, Ireland

ZEYNEP ALTINTAS Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany; Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

Preface Advanced Sensor Technology: Biomedical, Environmental, and Construction Applications is appropriate for the interdisciplinary community of researchers and practitioners interested in developing sensor technologies. The book is a valuable reference for materials scientists, biologists, and medical, chemical, biomedical, manufacturing, and mechanical engineers working in the research and development industry, as well as academics interested in learning more about future global markets, emerging applications, and the technology of sensor-based systems. The authors address the economics of sensor technology, including the fabrication of chemical sensors, biosensors, and nanosensors. Topics are covered comprehensively and presented in a logical manner for the benefit of the reader.

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List of contributors Abdelwaheb Chatti Laboratory of Biochemistry and Molecular Biology, Faculty of Sciences of Bizerte, University of Carthage, Jarzouna, Tunisia Abdul Shaban Research Centre for Natural Sciences, Institute of Materials and Environmental Chemistry, Budapest, Hungary Afef Gamraoui Laboratory of Biochemistry and Molecular Biology, Faculty of Sciences of Bizerte, University of Carthage, Jarzouna, Tunisia Ahmed Barhoum NanoStruc Research Group, Chemistry Department, Faculty of Science, Helwan University, Cairo, Egypt; National Centre for Sensor Research, School of Chemical Sciences, Dublin City University, Dublin, Ireland Aida Mousavi Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Amina Othmani Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia Aysu Tolun Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany Azam Bagheri Pebdeni Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran, Iran Aziz Amine Laboratory of Process Engineering and Environment, Faculty of Sciences and Techniques, Hassan II University of Casablanca, Mohammedia, Morocco Bahar Saboorizadeh Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Bora Garipcan Institute of Biomedical Engineering, Bog˘azic¸i University, I˙stanbul, Turkey Cansu I˙lke Kuru Ege University, Faculty of Science, Department of Biochemistry, Izmir, Turkey Ecenaz Bilgen Department of Chemistry, Middle East Technical University, C ¸ ankaya, Ankara, Turkey Ekin Sehit Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany; Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany Fahimeh Nojoki Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran, Iran

xiii

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List of contributors

Fereshteh Amourizi Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Francisco J. Barba Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de Vale`ncia, Vale`ncia, Spain Fulden Ulucan-Karnak Ege University, Faculty of Science, Department of Biochemistry, Izmir, Turkey Gilbert Tang School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, United Kingdom ¨ ztatlı Institute of Biomedical Engineering, Bog˘azic¸i University, Hayriye O I˙stanbul, Turkey Ivan Petrunin School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, United Kingdom Javad Shabani Shayeh Tehran, Iran

Protein Research Center, Shahid Beheshti University,

Judit Telegdi Research Centre for Natural Sciences, Institute of Materials and ´ buda University, Faculty of Environmental Chemistry, Budapest, Hungary; O Light Industry and Environmental Engineering, Budapest, Hungary Kaiyu He State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products; Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, P.R. China Kakali Ghoshal Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, Nashville, TN, United States Kheibar Dashtian Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran ´ buda University, Faculty of Light Industry and Environmental Larbi Eddaif O Engineering, Budapest, Hungary; Research Centre for Natural Sciences, Institute of Materials and Environmental Chemistry, Budapest, Hungary Liu Wang State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products; Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, P.R. China Maria Helena de Sa´ CIQUP-Chemistry Research Centre of the University of Porto, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal Maryam Amoo Nanotechnology Group, Department of Material Engineering, Isfahan University of Technology, Isfahan, Iran Mehran Habibi Rezaei Department of Cell & Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran Mehrdad Forough Department of Chemistry, University, C ¸ ankaya, Ankara, Turkey

Middle

East

Technical

List of contributors

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Mehrnoush Dianatkhah Department of Clinical Pharmacy, Faculty of Pharmacy, Isfahan University of Medical Science, Isfahan, Iran Muqsit Pirzada Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany; Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany Narjiss Seddaoui Laboratory of Process Engineering and Environment, Faculty of Sciences and Techniques, Hassan II University of Casablanca, Mohammedia, Morocco Navvabeh Salarizadeh Department of Cell & Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran; Department of Biochemistry, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran Neda Shahbazi Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Nissem Abdeljelil Laboratory of Biochemistry and Molecular Biology, Faculty of Sciences of Bizerte, University of Carthage, Jarzouna, Tunisia Omer Sadak Department of Electrical and Electronics Engineering, Ardahan University, Ardahan, Turkey ¨ Ozgu¨l Persil C ¸ etinkol Department of Chemistry, Middle East Technical University, C ¸ ankaya, Ankara, Turkey Qingxin Hui Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom Richard Luxton Institute of Bio-Sensing Technology, University of the West of England, Bristol, United Kingdom Rouholah Zare-Dorabei Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Sajjad Shojai Department of Animal Science, School of Biology, College of Science, University of Tehran, Tehran, Iran Sana Safari Astaraei Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran Selma Hamimed Laboratory of Biochemistry and Molecular Biology, Faculty of Sciences of Bizerte, University of Carthage, Jarzouna, Tunisia Seyed Jalal Zargar Department of Cell & Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran Seyed Mohammad Taghi Gharibzahedi IInstitute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany; Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany Sinan Akgo¨l Ege University, Faculty of Science, Department of Biochemistry, Izmir, Turkey

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List of contributors

Vahid Mofid Department of Food Sciences & Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran Wenliang Li Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom Xiahong Xu State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products; Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, P.R. China Yethreb Mahjoubi Laboratory of Plant Toxicology and Environmental Microbiology, Faculty of Sciences of Bizerte, University of Carthage, Zarzouna, Tunisia Yuwei Pan Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom Zahra Goli-Malekabadi Bioengineering Center for Cancer, Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran; Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran Zeynep Altintas Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany; Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany Zhugen Yang Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-323-90222-9 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Susan Dennis Acquisitions Editor: Charlotte Rowley Editorial Project Manager: Tim Eslava Production Project Manager: R. Vijay Bharath Cover Designer: Greg Harris Typeset by MPS Limited, Chennai, India

Contents

List of contributors About the editors Preface

xiii xvii xxi

1 Fundamental aspects 1. Sensor technology: past, present, and future

3

MUQSIT PIRZADA AND ZEYNEP ALTINTAS

1.1 Introduction 1.2 Milestones in sensor development 1.3 State-of-the-art in sensor technology 1.4 The way ahead in sensing opportunities 1.5 Conclusions and remarks Acknowledgments References

3 5 7 9 11 12 12

2. Fundamentals of sensor technology

17

LARBI EDDAIF AND ABDUL SHABAN

2.1 Sensor, actuator, and transducer fundamentals 2.2 Sensors’ classification 2.3 Sensor applications 2.4 Innovative sensor technologies 2.5 Conclusion and future aspects References

17 23 31 39 41 42

2 Biomedical applications 3. Biosensors for virus detection

53

EKIN SEHIT AND ZEYNEP ALTINTAS

3.1 Introduction 3.2 Antibody-based biosensors for virus detection 3.3 Nucleic acid-based biosensors for virus detection

v

53 57 60

vi

Contents

3.4 Peptide-based biosensors for virus detection 3.5 Molecularly imprinted polymer-based biosensors for virus detection 3.6 Conclusion and remarks Acknowledgments References

65 69 73 74 74

4. Biosensors for bacteria detection

81

YUWEI PAN, WENLIANG LI, QINGXIN HUI AND ZHUGEN YANG

4.1 Introduction 4.2 Whole-cell biosensors for bacteria detection 4.3 Nanomaterials-based biosensors for bacteria detection 4.4 Various biosensors for bacteria detection 4.5 Integrated biosensing platforms for multiplexed bacteria detection 4.6 Conclusion and perspectives References

5. Biosensors for drug of abuse detection

81 82 85 90 112 115 116

125

KHEIBAR DASHTIAN, FERESHTEH AMOURIZI, NEDA SHAHBAZI, AIDA MOUSAVI, BAHAR SABOORIZADEH, SANA SAFARI ASTARAEI AND ROUHOLAH ZARE-DORABEI

5.1 Introduction 5.2 Drug biosensing 5.3 Conclusion and remarks References Further reading

6. Biosensors for nucleic acid detection

125 126 160 161 172

173

¨ ZGU ¨ L PERSIL C MEHRDAD FOROUGH, ECENAZ BILGEN AND O ¸ ETINKOL

6.1 Introduction 6.2 Optical nucleic acid biosensors: principles and feasibilities 6.3 Electrochemical nucleic acid biosensors 6.4 Strategies for improving the sensitivity of nucleic acid biosensors 6.5 CRISPR/Cas-assisted biosensing platforms for nucleic acid detection 6.6 Biosensor applications based on the nucleic acid structure 6.7 Conclusion and outlook References

7. Biosensors for glucose detection

173 175 188 193 202 205 212 213

235

EKIN SEHIT AND ZEYNEP ALTINTAS

7.1 Introduction 7.2 Electrochemical glucose biosensors 7.3 Optical glucose biosensors 7.4 Other glucose biosensors 7.5 Conclusion and remarks Acknowledgments References

235 237 244 251 254 254 254

Contents

8. Recent advances in biosensing technologies for detecting hormones

vii 261

KAKALI GHOSHAL

8.1 Introduction 8.2 Biosensor types based on biorecognition elements 8.3 Biosensors based on transducers in hormone detection 8.4 Discussion and conclusion Acknowledgment Conflicts of interest References

9. Biosensors for cancer biomarker detection

261 263 264 286 290 290 290

297

MUQSIT PIRZADA AND ZEYNEP ALTINTAS

9.1 Introduction 9.2 Cancer progress and biomarkers 9.3 Electrochemical biosensors for cancer biomarker detection 9.4 Optical biosensors for cancer biomarker detection 9.5 Piezoelectric biosensors for cancer biomarker detection 9.6 Other biosensors for cancer biomarker detection 9.7 Conclusion and remarks Acknowledgments References

10. Classical and new candidate biomarkers for developing biosensors in diagnosing diabetes and prediabetes; past, present and future

297 300 306 311 316 319 320 322 322

337

NAVVABEH SALARIZADEH, SAJJAD SHOJAI, AZAM BAGHERI PEBDENI, FAHIMEH NOJOKI, SEYED JALAL ZARGAR AND MEHRAN HABIBI REZAEI

10.1 Introduction to diabetes mellitus 10.2 Pathophysiology of diabetes 10.3 Glucose as a diabetes biomarker (history, accuracy, advantages, and disadvantages) 10.4 Glycated hemoglobin and glycated albumin as diabetes biomarkers 10.5 Novel biomarkers/metabolites in diabetes and associated complications 10.6 Conclusion References

11. Biosensors for drug detection

337 339 345 355 363 372 373

383

ZAHRA GOLI-MALEKABADI, NAVVABEH SALARIZADEH, MEHRNOUSH DIANATKHAH, MARYAM AMOO AND JAVAD SHABANI SHAYEH

11.1 Introduction 11.2 Criteria of an ideal method for drug analysis

383 387

viii

Contents

11.3 Biosensor design 11.4 Biosensors for drug detection 11.5 Recent trends in biosensors for drug detection 11.6 Conclusion References

12. Micro alcohol fuel cells towards autonomous electrochemical sensors

389 392 406 407 408

413

´ MARIA HELENA DE SA

12.1 Introduction 12.2 Fundamentals 12.3 Design and flow considerations 12.4 Fuels electrooxidation and micropower generation 12.5 Examples toward sensing applications 12.6 Conclusion and future outlook References

13. Biosensors for organs-on-a-chip and organoids

413 418 423 435 452 458 458

471

¨ ZTATLI, ZEYNEP ALTINTAS AND BORA GARIPCAN HAYRIYE O

13.1 Introduction 13.2 The use of biosensors in organotypic models 13.3 Biosensing technologies for monitoring organotypic models 13.4 Applications of biosensors in in vitro culture platforms of organotypic models 13.5 Conclusion and future perspectives Acknowledgments References

471 473 476 488 500 501 501

3 Environmental applications 14. Sensors for water and wastewater monitoring

517

ABDUL SHABAN, LARBI EDDAIF AND JUDIT TELEGDI

14.1 Wastewater pollutants 14.2 Sources of water pollutants 14.3 Types of water pollutants 14.4 Indicators of water pollution 14.5 Analytical methods for the detection of wastewater pollutants 14.6 Chemical sensors in water pollutant detection 14.7 Electrochemical sensors in water pollutant detection 14.8 Optical biosensors for water pollution detection 14.9 Conclusion References

517 517 518 522 528 537 541 546 555 556

Contents

15. Chemical sensing of heavy metals in water

ix 565

OMER SADAK

15.1 Introduction 15.2 Heavy metal toxicity ranges and mechanism in living cells 15.3 Heavy metal measurement methods in water and their performance 15.4 Current trends in heavy metal monitoring 15.5 Current limitations and future prospective 15.6 Conclusion References

16. Chemical sensing of food phenolics and antioxidant capacity

565 567 569 582 584 585 585

593

AYSU TOLUN AND ZEYNEP ALTINTAS

16.1 Introduction 16.2 Conventional methods for the determination of total phenolics and antioxidant capacity 16.3 Novel sensing methods of total phenolics and antioxidant capacity 16.4 Conclusion Acknowledgments References

17. Chemical sensing of pesticides in water

593 596 597 635 636 636

647

KAIYU HE, LIU WANG AND XIAHONG XU

17.1 Introduction 17.2 Colorimetric sensors for detection of pesticides 17.3 Fluorescent sensors for detection of pesticides 17.4 Raman sensors for detection of pesticides 17.5 Electrochemical sensors for detection of pesticides 17.6 Chemiluminescent sensors for detection of pesticides 17.7 Electrochemiluminescent sensors for detection of pesticides 17.8 Piezoelectric sensors for detection of pesticides 17.9 Conclusion and future perspectives References

18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

647 649 652 654 657 660 662 663 665 666

669

SELMA HAMIMED, YETHREB MAHJOUBI, NISSEM ABDELJELIL, AFEF GAMRAOUI, AMINA OTHMANI, AHMED BARHOUM AND ABDELWAHEB CHATTI

18.1 18.2 18.3 18.4 18.5 18.6

Introduction Detection of soil nutrients Detection of pH Detection of soil moisture Detection of organic matter Detection of inorganic pollutants

669 671 673 675 676 680

x

Contents

18.7 Soil-borne disease using a microbial biosensor 18.8 Challenges and future perspectives 18.9 Conclusion References

19. Recent advances in sensor and biosensor technologies for adulteration detection

681 692 693 693

699

NARJISS SEDDAOUI AND AZIZ AMINE

19.1 Introduction 19.2 Adulteration: a global scam and health threat 19.3 Conventional analytical techniques for adulterants detection 19.4 Recent trends in adulteration detection 19.5 Conclusions and remarks References

699 700 707 708 725 726

4 Construction and other applications 20. Biosensing technology in food production and processing

743

SEYED MOHAMMAD TAGHI GHARIBZAHEDI, FRANCISCO J. BARBA, VAHID MOFID AND ZEYNEP ALTINTAS

20.1 Introduction 20.2 Biosensors and food quality 20.3 Biosensors and food safety 20.4 Future prospectives 20.5 Conclusion Acknowledgments References

21. Sensors for aerial, automotive, and robotic applications

743 758 786 798 799 799 800

825

IVAN PETRUNIN AND GILBERT TANG

21.1 Introduction 21.2 Optical sensors 21.3 Inertial sensors 21.4 Radio frequency sensors 21.5 Magnetic and acoustic sensors 21.6 Timing sources 21.7 Final remarks References

22. Challenges and future aspects of sensor technology

825 826 831 833 839 842 845 846

853

RICHARD LUXTON

22.1 Introduction

853

Contents

22.2 Technology drivers 22.3 Commercialization 22.4 In conclusion References Further reading

23. Sensor commercialization and global market

xi 855 865 869 870 874

879

¨L FULDEN ULUCAN-KARNAK, CANSU ˙ILKE KURU AND SINAN AKGO

23.1 Introduction 23.2 Trends in sensing technologies 23.3 Sensing research and development 23.4 Commercialization pathway 23.5 Sensors in various industrial areas and global market shares 23.6 Conclusion References

Index

879 881 889 890 899 906 907

917

About the editors Prof. Dr. Ahmed Barhoum: NanoStruc Research Group, Chemistry Department, Faculty of Science, Helwan University, Cairo, Egypt; National Centre for Sensor Research, School of Chemical Sciences, Dublin City University, Dublin, Ireland Ahmed Barhoum is the head of the Nanostruc Research Group, Chemistry Department at the Helwan University (Egypt). He leads an interdisciplinary research group in the synthesis of nanoparticles, imprinted polymers, nanofibers, and thin films for catalysis, drug delivery, and electrochemical biosensing. He obtained his PhD and postdoc fellow in chemical sciences from the Department of Materials and Chemistry (MACH), Vrije Universiteit Brussel (Belgium). He is currently working at the School of Chemical Sciences (SCS), and a member of the National Centre for Sensor Research (NCSR), Fraunhofer Project Centre (FPC), and Nano Research Facility (NRF) at Dublin City University (Ireland). He has received several scientific awards and prizes for his academic excellence: Helwan University Prizes (2020 and 2019), Irish Research Council (2020), Chinese Academy of Science Fellowship (China, 2019), Institut franc¸ais d’E´gypte Fellowships (France, 2018 and 2020), Research Foundation Flanders Fellowships (Belgium, 2015 and 2016), Medastar Erasmus Mundus (Belgium, 2012), Welcome Erasmus Mundus (Italy, 2012), Gold Medal from the Egyptian Syndicate of Scientific Professions (2007), Gold Medal from the Helwan University (2007), and many more. He also serves as an expert evaluator for the National Science Centre (Poland), Czech Science Foundation (Russia), Swiss National Science Foundation (SNSF, Switzerland), and Innovators Support Fund (ISF, Egypt) and examiner for international student’s work (Egypt, India, Australia, etc.). He is on the editorial board of Frontiers in Bioengineering and Biotechnology, Frontiers in Nanotechnology, Nanomaterials, and Frontiers in Materials and editor of 12 handbooks published by Elsevier and Springer Nature. He has secured 18 research grants (PI/Co-PI of 10 funded projects and member of 8 projects) from Egypt (ASRT & STDF), China (CAS), Japan (JSPS), the United States (NSF & US-Aid), Belgium (SIM & FWO), Germany (AGYA), and France (Imhotep), among others.

xvii

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About the editors

He has coauthored 150 papers and published in top-tier journals, including Journal of Materials Chemistry A, ACS Applied Materials & Interfaces, Applied Materials Today, Nanoscale, Carbohydrate Polymers, Materials Science and Engineering: C, Journal of Colloid and Interface Science, out of which many have been highlighted in research highlights, news, and journal cover articles. His handbook Emerging Applications of Nanoparticles, Elsevier, has been featured on CNN Forbes, and Inc, and is among the top best nanostructures books of all time.

Prof. Dr. Zeynep Altintas: Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany; Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany Prof. Zeynep Altintas is the chair of Bioinspired Materials and Biosensor Technologies at the University of Kiel. She has been the head of Biosensors Group at the Technical University of Berlin since 2016. She completed her PhD at the age of 25 with the outstanding PhD student award. Her PhD period brought her several other research prizes and fellowships. Following a 1-year postdoc position at the Cranfield Biotechnology Centre, she continued her academic career as a faculty member of biomedical engineering at the Cranfield University (the United Kingdom) until 2016. She leads an interdisciplinary research group in the domains of biosensor technologies, computational chemistry, receptor design, functional polymers and their applications in (bio) chemical sciences, nanomaterials applications, and design, synthesis, and characterization of biomimetic materials. She has .170 publications in these fields, including books, journal articles, book chapters, patent applications, and conference papers. She has supervised more than 35 PhD and MSc students and mentored 7 postdoctoral fellows. She has delivered plenary and invited talks at numerous international conferences and world-renowned institutes to date. Her reputation is recognized by many prestigious international awards and grants for her research. Among others are the Life Science Bridge Award (along with h100,000 prize money), the Royal Society of Chemistry Research Award, the Marie Curie Individual Fellowship for Experienced Researchers, the TUBITAK Fellowship for Internationally Recognised Scientists, the Travel Grants from the British Council (2014a16), and several best posters, oral presentation, and paper awards. She serves as a referee for numerous high-impact journals and for several funding research institutions including the European Union (EU), the German Research

About the editors

xix

Foundation (DFG), the Dutch Research Council (NWO), the Israel Science Foundation (ISF), the French National Research Agency (ANR), the German Federal Ministry of Education and Research (BMBF) & EU Cofund Projects, and the Wisconsin Groundwater Coordinating Council (United States). She is an editorial board member of Biosensors and Bioelectronics, Sensing and Bio-Sensing Research, Scientific Reports, Micromachines, and Materials. She takes part in the organization and scientific committees of several international conferences. She is also a member of RSC since 2012 and holds visiting professorships in various EU countries.

C H A P T E R

1 Sensor technology: past, present, and future Muqsit Pirzada1,2 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

1.1 Introduction Modern sensors are vastly different from the primitive oxygen electrode developed by Clark nearly seven decades ago [1]. A survey of all databases from Web of Science (keywords: sensor/s OR biosensor/s) reveals that interest in sensor technology has exploded in recent times with nearly 1.5 million papers published within the last 40 years. Of these, more than a million have just been published since 2009. This exponential progress (Fig. 1.1) can be attributed to the multifold improvement in several scientific disciplines such as electrochemistry, optics, nanotechnology, molecular dynamics, and proteomics. Another trigger for this success is the rising demand for analyte recognition across various fields of application. For example, identifying and analyzing contaminants in food production is a prerequisite to ensuring a long shelf-life as well as consumer safety. Accumulation of microbial metabolic byproducts helps identify the freshness of meat and fish [2]. The levels of such metabolites and proteins in living beings may also act as early indicators of different health conditions [3]. Quantifying these markers helps identify diseases, risk factors, pregnancy as well as drug efficacy. Biosensors also help monitor the effectiveness of clinical therapies thereby enabling healthcare professionals to customize patient-specific treatments. The trend is also reflected in the commercial progress of sensing instruments where the contribution of biosensors has swelled from US$ 5 million to US$ 13 billion within three

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00006-6

3

© 2023 Elsevier Inc. All rights reserved.

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1. Sensor technology: past, present, and future 140000

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Point of care

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0 1990

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FIGURE 1.1 Research works on sensors published between 1975 and 2020. Inset: Research works on sensors for point-of-care testing purposes from 1990 to 2020. Source: Data from Web of Science.

decades [4]. Since the development of the first commercial biosensor in 1975, the number and variety of sensors have consistently increased to more than 40,000 products for in vitro diagnostics alone [5]. Although conventional diagnostics are still performed at centralized facilities by experienced professionals, the crippling deficiency of material and financial resources in underdeveloped and developing nations makes timely disease recognition not only time-consuming but also expensive and inaccessible to the general public. Therefore the World Health Organization has encouraged the development of point-of-care-tests (POCT) that meet the ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free and Deliverable to end-users) criteria and can be performed by the consumer in a nonclinical setting. As a result, the research and development of POCT devices have multiplied 130-fold from 1990 to 2020 (Fig. 1.1, inset). Sensor technology is therefore gravitating toward paper-based assays, lateral flow-tests, smartphone-based detection, and many similar approaches that rely on ubiquitous raw materials and instruments. Today, arduous experimental optimizations are being replaced with computational simulations. Several different types of nanomaterials are being incorporated in biosensors to amplify their signal, reduce the response time and improve the binding affinity [6]. Miniaturization of biosensors using microfluidic technology helps in reducing the sample volume required for analysis. Microfluidic technology is an integral part of lab-ona-chip and organ-on-a-chip systems. The specificity of sensing systems to recognize a typical analyte can be exploited for targeted drug delivery and therapy. Such approaches reduce the adverse side-effects that are quotidian in the treatment of

1. Fundamental aspects

1.2 Milestones in sensor development

5

cancer patients. In vivo sensing is also useful in the development of artificial organs to regulate organ behavior in response to various intracellular signaling events [7]. The ongoing global coronavirus pandemic has also directed renewed interest toward biosensors as potential tools for identifying and curbing water as well as air pollution [8,9].

1.2 Milestones in sensor development The current ubiquity of sensors should not be considered as a standalone success resulting solely from Cramer’s work on the glass electrode [10] or even Clark’s seminal research on biosensors [1,11]. While these works laid the groundwork on which sensor technology is built, the latest developments in biosensors are the products of advancements, discoveries, and inventions across a gamut of scientific areas. A few of these concepts even predate Clark’s work but have still emerged useful in sensor development. For example, while Polyakov’s group was working with silica particles in 1931, they observed that particles from which the additives were removed showed enhanced adsorption for the additives in comparison to structurally analogous ligands [12]. Dickey substituted these additives with dyes as templates [13]. This concept of additive memory evolved into molecular imprinting technology, a popular technique for cost-effective molecular recognition. Molecular imprinting has itself undergone several improvements in the nine decades since its inception. The silica particles have largely been replaced with organic polymers since 1972 [14]. Therefore Mosbach’s group proposed using such molecularly imprinted polymers (MIPs) to mimic antibodies [15]. Molecular imprinting is not a singular field. The impetus to make sensors “more intelligent” was the driving force behind the diversification of sensor technology. In its early phase, sensor development was an eccentric notion involving the integration of biomolecules such as enzymes to electrochemical sensing elements such as electrodes. Although the first batch of sensors relied on either voltammetry or amperometry, other electrochemical methods such as potentiometry were soon explored. The primitive enzyme electrodes of the 1960s were improvised by Professor Rechnitz in 1971 by placing them in an inverted configuration for betaglucosidase-mediated amygdaline sensing [16]. This ion-selective electrode sparked a renaissance age in sensor development where multiple combinations of the transducer and the biological element were probed. The variations in these elements and their combinations are still ongoing. Sensors have been developed with piezoelectric, thermal, and then optical transducers. This continuous evolution facilitated the

1. Fundamental aspects

6

1. Sensor technology: past, present, and future

development of the first in vivo ultrasound biosensor in 2020 [17]. The selection of receptors has also evolved from enzymes to antibodies, nucleic acids, whole cells, aptamers, affibodies, phages, and synthetic ligands. Although the enzymes were the receptor of choice in the first few years of sensor development and were used by several eminent researchers like Clark [11], Updike and Hicks [18], Guilbault and Montalvo [19], antibody-assisted biomolecule recognition was also being investigated in parallel. The antibody-antigen interaction had remained an enigma since the discovery of antibodies in 1890 [20]. Once these interactions were theorized by Goldberg in 1952 [21], their suitability for disease recognition was soon investigated. Within four years, the latex agglutination assay was developed that relied on these interactions to diagnose rheumatoid arthritis. Yalow and Berson subsequently developed the first immunoassay, a radioimmunoassay, in 1959 for which Yalow was awarded the Nobel Prize in 1977 [22]. Since the radioimmunoassay was not patented, assays for several different analytes were soon developed. As this technique involved antibody labeling with radioactive isotopes, alternative labels were studied in the 1960s and antibody-enzyme links were reported in 1966 [23,24], which laid the groundwork for the first enzyme-linked immunosorbent assay within the next 5 years [25]. Another pivotal milestone in immunosensing was the development of monoclonal antibodies by Ko¨hler and Milstein in 1975 [26]. The process was revolutionary and mitigated the pertinent issues of limited antibody supply and unavailability culminating in a Nobel Prize in medicine for both the researchers in 1984. Some of the popular assays developed during this time are illustrated in Fig. 1.2. Similar milestones were also achieved in the field of detection using nucleic acids as well as whole cells or microbes. Enzymes, antibodies, nucleic acids, and whole cells are generally considered conventional

FIGURE 1.2 (A) Different types of enzyme-linked immunosorbent assays (ELISA); (B) Gel electrophoresis, which is the basis of Southern, Western, Northern, and Eastern blot tests; (C) Antigen recognition by radioimmunoassay.

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1.3 State-of-the-art in sensor technology

7

receptors. While enzymatic receptors function due to catalysis, all other elements perform affinity-based recognition. Recent receptors such as synthetic antibodies, phages, affibodies, and aptamers are emerging as promising substitutes to the conventional receptors and may entirely replace them in the future.

1.3 State-of-the-art in sensor technology The prevalence and successful application of sensors are incumbent on several factors such as their cost, response time, sensitivity, specificity in the sensing medium, and their ability to detect multiple analytes. The drive to attain ultrasensitivity has resulted in the integration of several unconventional technologies into sensor development. For example, the incorporation of nanomaterials to sensing platforms has enabled the fabrication of sensors with high sensitivity and specificity. Nanomaterials are materials with at least one of their dimensions between 1 and 100 nm [6]. Owing to their small size, nanomaterials exhibit an exceptionally high surface area-to-volume ratio. Smaller nanomaterials such as nanoclusters and quantum dots have more atoms on their surface in comparison to the bulk. The high-energy electrons from these surfaces give rise to interesting plasmonic and electronic properties. Nanomaterials can act as synthetic atoms and their properties can be tuned by controlling their size, shape, or interparticle distance. For example, the distance between a quantum dot and a gold nanoparticle can be modulated with linkers to determine the optimal distance for maximum localized surface plasmon resonance [27]. The incorporation of nanoparticles into the sensing platform also helps amplify the signal response significantly [3]. Nanomaterials hold the key to sensor miniaturization since they can be exploited as receptors, transducers, labels, fluorophores, and signal amplification tags [28–31]. Nanotechnology has therefore achieved popularity not only among sensor developers but also among the scientific community as a whole. This recognition is evident with the multitude of Nobel Prizes awarded for research in this area such as Kroto, Curl, and Smalley’s discovery of buckminsterfullerene (Chemistry, 1996), Fert and Gruenberg’s discovery of giant magnetoresistance (Physics, 2007), Geim and Noselov’s experiments on graphene (Physics, 2010), and Sauvage, Stoddart, and Feringa’s synthesis and research on molecular machines (Chemistry, 2016). A brief survey of the Scopus database (keywords: sensor/s OR biosensor/s AND nanomaterial/s) across all fields reveals that just within the two decades since 2001, 167,774 documents were published on nanomaterial-mediated sensors and 129,300 are research articles. While only 85 of these documents belong to 2001, the number rose

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1. Sensor technology: past, present, and future

35000 30000 Publications Publications

25000 20000 15000 10000 5000 0 2000

2005

2010 Year

2015

2020

FIGURE 1.3 Research works using nanomaterials in sensor technology from 2000 to 2020. Source: Data from Scopus.

rapidly with 4183 and 31,174 documents reported annually in 2010 and 2020, respectively (Fig. 1.3). Mathematical modeling is another emerging tool that is useful for fabricating biosensing platforms with desired features. It allows the optimization of multiple criteria within the biochemical system such as enhancing the productivity of enzymes by determining the accurate temperature and pH required for catalytic recognition. Computational selection of nucleic acid probes by quantifying the interactions between Watson-Crick base pairs facilitates the generation of target-specific and ultrasensitive genosensors [32]. Cascade networks can thus be generated to perform divergent reactions by exploiting the compatibility between various biomolecules. In silico biocomputation has also been adopted for systems that involve proteins [33], aptamers [34], whole cells [35] as well as MIPs [36]. Molecular simulations often compute the attractive and repulsive forces of biomolecular electron clouds to predict the state of material during the sensing event. For example, the group of Altintas simulated different surface epitopes of a protein to select the most stabile peptide sequence as templates for molecular imprints [36]. Such simulations aid in elucidating solvent effects [37] as well as the nature of receptor-ligand interactions [38]. Biocomputation in association with logic-mediated operations encourages the development of artificial neural networks. These intelligent systems, when integrated into smart stimuli-responsive materials, are capable of sensing and subsequently actuating [32]. In silico designing of biosensors considerably reduces the experimental load, material consumption as well as optimization and development time.

1. Fundamental aspects

1.4 The way ahead in sensing opportunities

9

The binding affinity and specificity are two essential features of the receptor that enable molecule recognition at low concentrations in the presence of competent interfering molecules. Aptamers are oligonucleotides or peptides that specifically bind to a target nucleotide, protein, or cell. They belong to the class of novel receptors along with phages, MIPs, and affibodies. Although the technology for aptamer synthesis and enrichment (systematic evolution of ligands by exponential enrichment or “SELEX”) was already developed in 1990, they were first employed for biosensing only in 1998 [39,40]. However, aptamers have become commonplace as receptors since then and have already surpassed nucleotides such as deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). The ongoing trend on Scopus (keywords: biosensor “AND” ,receptor type.) reflects the potential for aptamers to surpass antibodies as the receptor of choice within the next few decades. Similarly, bacteriophages are also suitable candidates due to their high affinity for bacterial proteins. The phage display technique was first reported by Gregory Smith in 1985 [41] and further developed by Gregory Winter for which both of them shared the Nobel Prize in chemistry in 2018. The phage display technique involves the insertion of a gene, which encodes a protein or antibody of interest, in bacteriophage capsid protein. Analogous to SELEX, phage display is a promising in vitro technique for protein selection.

1.4 The way ahead in sensing opportunities The ongoing pandemic and the pressure on the healthcare system resulting from it have accelerated the research on POCT. Microfluidic systems can be easily patterned on inexpensive substrates such as papers or membranes to generate lateral flow assays. The simplicity, mobility, and affordability of lateral flow assays make them ideal for disease detection in resource-limited settings. For example, the necessity of a coronavirus disease (COVID-19) test report has become a prerequisite for travel, work as well as schools and the usual polymerase chain reaction assay is expensive, time-consuming, and complicated. Rapid lateral flow immunoassays for detecting the coronavirus antigen can be mass-produced and are very simple to operate by the end-consumer [42]. They are therefore transforming into excellent COVID-19 screening tests. It is expected that just like the case of home pregnancy kits, disease testing on lateral flow strips may also become quotidian especially in developing nations (Fig. 1.4). Another promising strategy is the use of MIPs as synthetic antibodies for protein recognition. Though Mosbach proposed the idea for drug recognition back in the 1990s, protein recognition using their

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1. Sensor technology: past, present, and future

(A) SARS-CoV-2 Nitrocellulose strip LFA

(B) Bare gold

Epitope adsorption

Electropolymerization

Template removal

Protein Epitope 1 Epitope 2 AuNPs

Protein capture

FIGURE 1.4 (A) Portable membrane-based virus detection strip [42]; (B) Imprinting process for generating nanomaterial amplified polyclonal synthetic antibodies [3]. AuNPs, Gold nanoparticles; LFA, lateral flow assay; SARS-CoV-2, severe acute respiratory coronavirus 2.

antibody-mimicking properties has been particularly challenging. Lower affinity, template bleeding, denaturation, and conformational fluctuations of proteinaceous templates during imprinting, and nonspecific binding are some of the obstacles that MIP-based protein detection has faced. However, the integration of novel technologies to molecular imprinting has led to promising results. Some of these auspicious approaches include computationally selecting stabile epitopes as templates [36], imprinting with multiple epitopes to introduce polyclonality [3], incorporation of conductive or plasmonic nanomaterials to promote signal response at lower concentrations of the target protein [43], introducing multifunctionalities via postimprinting modifications [44], spatial confinement of epitopes during imprinting [45] as well as using dual epitope MIPs in a plasmonic immunosandwich assay [46]. Molecular machines based on DNA walkers [47] as well as the nucleotide cleavage activity of CRISPR/Cas (clustered regularly interspaced short palindromic repeats/ CRISPR-associated proteins) [48] effectors are both exciting neoteric approaches to enhance nucleic acid-mediated biosensing. In addition to high-affinity receptors, the future of sensor technology may also include more propitious detection mechanisms. An excellent example is the use of molecular holography, so-called “molography,” to circumvent the issue of nonspecific binding in clinical biosensing [49]. Another encouraging strategy for analyte detection is the use of smartphones since they offer international connectivity and exhibit enormous potential for POCT and point-of-need systems. Smartphones can act as

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1.5 Conclusions and remarks

11

detectors or as instrumental interfaces. Owing to their compatibility across colorimetric, luminescent, and electrochemical setups, smartphone-based assays are considered highly versatile and suitable for widespread futuristic applications [50]. The future of biosensing extrapolates the concept of recognition itself to in vivo events such as pathogen transport and dissemination [51]. Nanomaterial-mediated response amplification may also be harnessed for high-throughput drug-screening [52]. The advent of in vitro translation for cell-free protein synthesis has elucidated the synthesis of sensor arrays relying on the difficult-to-express membrane proteins [53]. Biosensor miniaturization into microfluidic total analysis systems or the modern lab-on-a-chip and the even more recent organ-on-a-chip may soon become critical for multiple stages in the process of drug discovery as well as the development [54]. They can facilitate a better understanding of the pathophysiology of human diseases and organ function. Implantable, subcutaneous biosensors are tools for continuous monitoring of various metabolites generated in the human body and with rising interest in artificial organs, such sensors are expected to achieve rapid progress and commercialization. The development of wearable sensors is also expected to transform noninvasive analyte quantification, especially in the area of clinical diagnostics and sports. Due to all these developments, biosensors are projected to value over USD 40 billion within the next half-decade [55].

1.5 Conclusions and remarks Sensor technology in the past revolved around the stimuli-response behavior between the target and the receptor enzyme. These sensors were often electrochemical and capable of detecting a singular target. The targets were themselves small molecules such as blood oxygen, glucose, lactates, urea, etc. Furthermore, conventional receptors were sourced from nature and were therefore subjected to complex, expensive, and time-consuming extraction and purification steps. Although research on synthetic recognition elements was simultaneously underway, they were rigid silicon particles with an affinity for small organic molecules that are irrelevant for clinical, food, or defense purposes. Though these initial few decades since Clark’s first biosensor laid the foundation of sensors as we know today, their translation to industrial mass-production as well as ease of operation for the end-consumer was extremely limited. To address these concerns, several emerging concepts were assimilated into sensor technology. The most prominent of these is nanotechnology with nanomaterial-based sensors yielding multifold higher

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sensitivity and specificity with accelerated kinetics in addition to superior control of the sensor design and performance. Computational modeling is another vital tool that enables the fabrication of sophisticated biosensing systems by allowing in silico optimization thereby reducing valuable experimental time and research cost. Novel materials such as aptamers and intelligent MIPs are rapidly replacing conventional receptors owing to their durability, robustness, low cost, and efficient synthesis. Today, interest in vitro methods such as SELEX and phage display is expanding at an exponential rate. The global strain on healthcare systems is driving the development of POCT to ease the burden on centralized laboratories. Inexpensive, ubiquitous, and easy-to-operate platforms such as microfluidic paper assays, smartphone-mediated detection, wearable sensors, etc., are expected to lead the future of sensing technology. Intelligent polyclonal synthetic ligands, as well as DNA molecular machines, are capable of introducing multifunctionality and even multiple analyte detection. With these promising developments at the helm, the ultimate future of biosensing is the precise mimicry of natural recognition events that are tailored according to the targeted analytes and therefore efficiently discriminate against all nonspecific interferents while allowing single particle detection.

Acknowledgments Z.A. thanks the German Research Foundation (DFG, Grant number: 428780268) for the financial support as the principle investigator.

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C H A P T E R

2 Fundamentals of sensor technology Larbi Eddaif1,2 and Abdul Shaban1 1

Research Centre for Natural Sciences, Institute of Materials and ´ buda University, Faculty Environmental Chemistry, Budapest, Hungary, 2O of Light Industry and Environmental Engineering, Budapest, Hungary

2.1 Sensor, actuator, and transducer fundamentals 2.1.1 Introduction Components of a detection system typically consist of sensors, actuators, transducers, output display, and electronics. The terms sensors and transducers are generally related to measurement arrangements where the sensor is an element that delivers functional output signal as a response to a quantified substance being sensed. Simply, a sensor is a device that perceives alterations in a physical stimulus and produces a measurable analogous output signal that can be evaluated and further treated [1]. In other terms, a sensor is an electronic device capable of transforming a physical, chemical, or biological quantity into an electrical one (signal) (e.g., a frequency, a potential, or a current) (Fig. 2.1). Sensors are composed of many parts, namely: The test body is a sensitive element that transforms the measured magnitude to a measurable physical quantity. The transducer translates the physical quantity into an electrical one (output signal). The housing box is a mechanical element for protecting, holding, and fixing the sensor. And the packaging/ conditioning electronics are a device that converts the sensor’s output signal into a standard measurement signal and are the link between the sensor and the control system since they amplify and process the electrical signal.

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00003-0

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Physical, chemical, or biological quantity

Electrical quantity (Signal)

Sensor

Test body

Transducer

Intermediate physical quantity

Exploitable physical quantity

Conditioning electronics Housing box

FIGURE 2.1 Working principle and different components of a sensor.

Sensors differ from actuators in the sense that an actuator is an element that functions in a reversible mechanism to sensors. In other words, the actuator transforms an electrical output signal into a physical event, whereas the sensor converts physical results into an electrical output signal. While sensors are used at the input of a system, actuators are used to perform output functions in a system as they control an external device. Transducers are devices that convert energy in one form into another form. Generally, the energy is in the form of a signal. A transducer is a term collectively used for both sensors and actuators [1,2]. Selecting an appropriate sensor must fulfill certain criteria, where certain features must be taken into consideration such as the form of sensing, working principle, power consumption, precision, environmental circumstances, price, sensitivity and selectivity, the limit of detection (LOD) and range, and device calibration. Commonly, sensors are characterized by metrological parameters that are experimentally evaluated based on various factors as shown in Table 2.1. By constructing the calibration curve (Fig. 2.2) of a sensor, it is possible to determine and understand the relationship between the input and the output quantities, in terms of metrological parameters and detection features (sensitivity, detection limit, dynamic and linear ranges, etc.).

2.1.2 Sensor characteristics Owing to the growing demand for sensors in new applications, innovative devices for sensor technologies are pursued where new technologies can offer exceptional impacts like reduced sensor size, low price, high effectiveness, more stability, and shorter response time [3]. The increasing awareness of important application fields like environmental contamination, dangerous vapors and fumes, led to the development

1. Fundamental aspects

2.1 Sensor, actuator, and transducer fundamentals

TABLE 2.1

19

Metrological parameters of a sensor.

Characteristic

Description

Sensitivity

The quotient of the output quantity ΔY and the corresponding input quantity ΔX: S 5 ΔY=ΔX (Output signal variations/Input signal variations).

Selectivity

The ability of a sensor to detect a target element in the presence of many others contained in the same medium. The selectivity translates the sensor’s capability to be insensitive toward elements that are not the object of the measurement, but which influence its output.

Saturation

The stage when the output signal cannot exceed a maximum value, regardless of the input value.

Limit of Detection (LOD)

The lowest concentration of an analyte can be detected with an acceptable uncertainty. It is calculated based on the equation: LOD 5 3σ=Swhere σ and S are, respectively, the standard deviation and the slope of the calibration curve’s linear range.

Reproducibility

The agreement of closeness among the results of the same magnitude measurements, where individual experiments are carried out according to different methods, using various instruments, by several persons, in different laboratories, and after fairly long time intervals compared to a single measurement duration.

Repeatability

The agreement of nearness between successful measurement results of the same quantity with the same method, by the same person, with the same measuring instruments, in the same laboratory, and at fairly short time intervals.

Speed

This quality expresses the manner of monitoring input variations over time.

Influencing quantities

The quantities that, when applied, are liable to modify the sensor’s metrological characteristics. They can be of different origins, such as mechanical, chemical, thermal, electrical, etc.

Range

This specifies the limits of the input in which it can vary.

Accuracy

The degree of precision between actual measurement and exact value (%).

Stability

The reproducibility of the sensor for constant input over time.

Response time

The speed of alteration in output on a stepwise change in input.

Linearity

This is specified in terms of percentage of nonlinearity showing deviation from ideal situation.

Ruggedness

The degree of durability under hard operational circumstances.

Hysteresis

The characteristic that a transducer has in being unable to repeat its functionality dependably when used in the opposite direction of operation.

Drift

The long-term stability of the output without changing the input.

1. Fundamental aspects

20

2. Fundamentals of sensor technology

FIGURE 2.2 Sensor signal calibration curve.

of gas-detection apparatus. Several devices are functional in numerous industries like biomedical applications, chemical sensing, information storage, etc. [4]. The nanotechnology effect on the development of sensors is huge, where nanoparticles with predefined composition, grain size, and shape are assisting the innovative development of sensors and are increasing the list of detectable analytes. For selecting the appropriate sensor/transducer for any particular detection arrangement, it is very significant to study its performance characteristics. Several characteristics must be considered before choosing a transducer such as static and dynamic features. The selective performance properties of sensors and transducers might be classified under two groups: static and dynamic physical characteristics [5]: • Static characteristics, which are the established performance standards through static calibration. The most important static characteristics of sensors and transducers re listed in Table 2.1. • Dynamic characteristics, which are extracted from dynamic measurements in the function of time. In summary, the static features describe the performance when the detected quantity is constant, while the dynamic features correspond to dynamic inputs. The important dynamic features that must be put into consideration when choosing a sensor transducer are dynamic inaccuracy, fidelity, response time, and bandwidth. Generally, both the static and dynamic characteristics of sensors and transducers define the effectiveness and indicate how to accept the preferred input signals and discard undesirable inputs.

1. Fundamental aspects

2.1 Sensor, actuator, and transducer fundamentals

21

2.1.3 Signal processing of sensors The signal conditioning unit is employed to convert the physical output signal (nonelectrical) of the sensor to a digital output signal with an electrical quantity. Applied signal processing units include A/D converters, amplifiers, filters, rectifiers, and modulators. 2.1.3.1 Signals of sensors and transducers The physical (analog) signals must be properly prepared before conversion into a useful digital signal. Sensors necessitate signal conditioning before the data acquisition element effectively and precisely receives the output signal. Signal conditioning consists of electronic circuits that prepare the measured signal for further processing. Transduction is attaining energy in one form and converting it into another one, besides quantifying the energy change or energy input. Numerous measured signals can be quantified via a variety of transduction approaches. Transduction principles can be categorized according to the received and generated energy form of sensor signals [3,6]. According to their transduction principles, transducers can be classified as: • Passive transducers: Resistance variation type, capacitance variation type, inductance variation type, voltage, and current type. • Active transducers: Photo-voltaic cell, thermopile, moving coil type, piezoelectric type. Transducers sense physical phenomena and transform the measurand into an electrical signal. However, possibly these signals will not be in the appropriate forms. Signals given by a transducer could be nonlinear or noisy. Therefore before further conversion, it is crucial to eliminate the noise and nonlinearity related to the output from a sensor or a transducer. It is also desirable to adjust the amplitude (high/low) to the desired (analog/digital) form of the produced signals into adequate limits and form. These activities are accomplished by a process designated as “signal processing.” Signal processing affects the sensor output signal in numerous ways [6]: • To guard the sensor elements from any loss due to high current or voltage signals; • To transform the output signal from a transducer into the foreseen form (i.e., voltage/current); • To amplify/attenuate the signals to a suitable level for the next process; • To reduce noise from a signal and improve the S/N ratio; • And, to manipulate the signal from its nonlinear to a linear form.

1. Fundamental aspects

22

2. Fundamentals of sensor technology

2.1.3.2 Signal conditioning of sensors Signal conditioning is one of the essential building blocks of up-todate data acquisition systems, also known as DAS or DAQ. To perform physical sensing, the DAS consists of four main modules: sensors, signal conditioning, an analog-to-digital converter (ADC), and a personal computer with DAQ software for signal logging and analysis. Therefore signal conditioners receive the analog signal from the sensor, and after manipulation, the signal is transferred to the ADC subsystem where digitization and processing take place by specialized software, and then displayed on liquid crystal display. screens, saved to storage facilities, examined, and assessed. Signal conditionings require some essential elements to be useful for data acquisition systems such as electrical isolation, appropriate sensor connection accessories, detection range setup, signal filtering, and the conformation of sensor requirements. The most useful signal conditioners offer electrical isolation between inputs and outputs, which reduces noise, averts ground loops, and guarantees precise sensing tests. Modern signal conditioners are completely adjustable to the applied sensors by employing proper connectors. The capability to pick and set up the suitable sensing range and signal filtration are very indispensable functions in the process of signal conditioning. Practical signal conditioners are requisite to be perfectly adjustable to the intended sensor. To deliver any useful signal, a sensor output signal needs to be amplified with an amplifier that has a voltage gain up to 104 and a current gain up to 106. The amplification of a linear signal with the output signal is an exact reproduction of the input, just changed in amplitude, thus amplification is part of signal conditioning. When using analog sensors, generally some form of amplification (gain), impedance matching, isolation between the input and output, or perhaps filtering (frequency selection) may be required before the signal can be used; this is conveniently performed by operational amplifiers. Also, when measuring very small physical changes, the output signal of a sensor can become “contaminated” with undesirable signals or voltages that prevent the required actual signal from being measured properly. These unwanted signals are called “noise.” This noise or interference can be either significantly reduced or even eliminated by using signal conditioning or filtering techniques. By using either a low pass or a high pass or even a bandpass filter, the “bandwidth” of the noise can be reduced enormously, and only the required output signal is left. Both amplification and filtering play a significant role in interfacing both sensors and transducers to microprocessor and electronics-based systems in “real world” conditions. Increasing the signal-to-noise ratio

1. Fundamental aspects

2.2 Sensors’ classification

23

is usually performed by taking several measurements (data) and then averaging them to obtain the final value.

2.2 Sensors’ classification As deliberated earlier, a transducer is an element, which translates a definite measurand into a functional output signal employing a transduction mechanism [2,6]. Another useful definition is: a transducer is a device that transforms a signal from one form of energy to another. A transducer consists mainly of two elements: • Sensing element, which detects and reacts to the physical magnitude. • Transduction element, which converts the nonelectrical signal into a proportional electrical one. Sensors can be classified according to several criteria: (i) primary measured quantity, (ii) transduction principles, (iii) material and technology, (iv) property, and (v) applications. Out of these criteria, the transduction principle is the essential standard of classification. Another method for classifying sensors is based on the output signals, which is the stimulus being sensed, and is generally associated with the following properties [1,2]: • Acoustic: Wave characteristics (amplitude, phase polarization), spectrum, wave velocity, etc. • Biological/Chemical: pH, the concentration of species in the environment, additives or constituents in liquid, etc. • Electrical: Conductivity, voltage, resistance, charge, current, inductance, etc. • Optical: Refractive index, reflectivity, polarization, light intensity, wavelength, etc. • Magnetic: Magnetic moment, magnetic flux density, etc. • Mechanical: Strain, stress, torque, flow, length, force, pressure, acceleration, etc. • Thermal: Flux, thermal conductivity, heat flow, specific heat, temperature, etc. Sensors can also be classified as active or passive. For active sensors, a power (excitation) signal is needed from an external source. However, passive sensors directly produce the output signal in response to the input stimulus. In this contribution, the classification overview includes the transducers, for active transducers, the input energy is applied as a control signal in the course of conveying energy from the power source to a proportional output signal, while in passive transducers, the input energy is directly converted to the output one.

1. Fundamental aspects

24

2. Fundamentals of sensor technology

2.2.1 Chemical sensors 2.2.1.1 Overview A chemical sensor is a device that transforms chemical data into a valuable output signal. Generally, it converts physicochemical properties or chemical interactions (e.g., concentration or total composition of specific species into an output signal) [6]. Chemical sensors are consisting of two essential functional parts: the receptor and the transducer. The transformation of chemical data into an energy form, which can be measured by the transducer, takes place at the receptor element of the sensor. The receptor elements of chemical sensors can be classified according to different operating principles: • Physical: a physical phenomenon is taking place where no chemical reaction is involved such as measurement of conductivity, temperature, or mass variation. • Chemical: a chemical reaction takes place providing the output signal. • Biochemical: a biochemical process is a cause behind the analytical signal. The report of Scopus data proves that nonstop growth in chemical sensor-related publications has occurred in the last two decades, reaching B 9070 papers in 2020 (Fig. 2.3). This analysis intensely shows the

FIGURE 2.3 The progressive increase in the number of papers related to chemical sensors from 2001 to 2020 (Scopus database, 3 November 2021, keywords for search: chemical sensors).

1. Fundamental aspects

25

2.2 Sensors’ classification

TABLE 2.2

Chronological progress of different sensors and materials development.

Sensor development

Year

References

• Bacterial cellulose nanopaper-based optical sensors

2015

[7]

• Epidermal electronics

2011

[8]

• Microfluidic paper-based analytical devices

2007

[9]

• PCB-based sensor with integrated microfluidics

1997

[10]

• Soft lithography for PDMS microfluidics

1995

[11]

• Mediated enzyme electrode for amperometric biosensors

1984

[12]

• Polysilicon surface micromachinery technology

1982

[13]

• Dry film photoresists development

1968

[14]

• Microtiter plates

1955

[15]

• First positive photoresist

1940

[14]

• First negative photoresist

1935

[14]

• pH glass electrode

1906

[14]

• Photolithography development

1826

[14]

• Litmus paper for pH sensing

1800

[14]

• Pulp papermaking process

105

[14]

• Development of SU-8

significance of studies connected to chemical sensors and proves that they are trending in the field of sensors. Thus the key historical development and discovery of various sensor and biosensor technologies are presented in Tables 2.2 and 2.3. 2.2.1.2 Types of chemosensors Chemical sensors may be classified according to the employed transducer; Fig. 2.4 shows a simple classification of chemosensors based on the utilized transduction mode. In general, the widely employed chemosensors are named according to the involved transduction principle in the recognition application: optical transduction for optical chemosensors, electrochemical transduction for electrochemical sensors, or else piezoelectric transduction for piezoelectric/gravimetric chemosensors: • Optical chemosensors are based on optical phenomena resulting from the interaction between the analyte species with the receptor element. This sensor type can be further divided depending on the type of

1. Fundamental aspects

26

2. Fundamentals of sensor technology

TABLE 2.3 Historical advances in the development of sensor technology and biotechnology. Sensor advance

Year

References

• CRISPR-powered nucleic acid detection

2017

[16]

• “FreeStyleLibre”: Wearable glucose monitoring system

2014

[14]

• Organ-On-a-Chip devices

2010

[17]

• “ripeSense”: Commercial fruit ripeness sensor

2004

[14]

• Digital PCR discovery

1999

[18]

• Molecularly imprinted polymers as antibody mimics

1993

[19]

• Miniaturized total analysis systems

1990

[20]

• Multianalyte immunoassays’ conception

1989

[21]

• Lateral flow technology-pregnancy home test: first time

1988

[14]

• The technique of phase display

1985

[14]

• SPR-based gas and biosensors

1983

[22]

• Wearable heart rate monitor

1982

[14]

• First miniaturized ISFET-pH sensor

1972

[23]

• First glucose biosensor

1962

[24]

• Primary radioimmunoassay

1959

[25]

• Temperature and pressure reading-ingestible capsule

1957

[26]

• Electrical thermostat invention

1883

[14]

• “Davy Lamp”: First gas sensor

1815

[14]

• Barometer discovery

1643

[14]

• Thermometer invention

1612

[14]

optical properties: absorbance, reflectance, luminescence, fluorescence, refractive index, opto-thermal effect, light scattering. • Electrochemical sensors are based on transforming the effect of an electrochemical interaction between an analyte and the receptor at an electrode into a useful output signal. Further subgroups can be distinguished: voltammetric sensors, amperometric devices, potentiometric sensors, and chemically sensitized field-effect transistors. • Mass-sensitive chemosensors are based on transforming the mass alteration at a modified surface into an output signal as frequency. The mass alteration is triggered by the accumulation or loss of the analyte on the surface. Subgroups of these types of sensors are piezoelectric and surface acoustic wave sensors.

1. Fundamental aspects

27

2.2 Sensors’ classification

Chemical Sensors

Optical

Electrochemical

Piezoelectric

SPR

OWLS

Ellipsometry

Fluorescence

Potentiometric

Amperometric

Conductometric

Impedimetric

Surface Acoustic Wave

Guided Wave

Bulk Acoustic Wave: QCM

FIGURE 2.4 Classification of chemical sensors based on transduction mode.

2.2.2 Biosensors 2.2.2.1 Overview A biosensor is an analytical instrument that converts a biological process into an electrical signal, employing biological elements, including enzymes, tissues, microorganisms, cells, amino acids, etc., which are generally attached to a transducer converting the biological information into an electrical signal (Fig. 2.5). The choice of transducer depends on the biological reaction taking place. The most commonly employed material is typically an enzyme, and one of the frequently utilized feedbacks is the enzyme oxidation reaction, where the latter is considered as a catalyst directly affecting the current transport capacity of the enzyme being tested. The output of the transducer (typically a current) is commonly transformed into a voltage output signal to be suitably evaluated and presented. 2.2.2.2 Types of biosensors Biosensors can be categorized into two classes: either related to the biological recognition element: DNA, enzymes, antibodies, microorganisms, tissues, cell receptors, etc. Or related to the transduction principle: optical, electrochemical, and mass-based biosensors: • Optical biosensors permit the detection of target elements centered on light phenomena as absorption, scattering, or fluorescence, as a result

1. Fundamental aspects

28

2. Fundamentals of sensor technology

FIGURE 2.5 Working principle and different components of a biosensor.

of interactions between the elements to be detected and the recognition layer at the sensor’s surface [27]. A focal advantage of optical biosensors is their nonelectrical nature, providing the ability to sense multiple elements only by varying the light’s wavelength. • Electrochemical biosensors, based on the electrochemical interaction between the sensing biomaterial at the sensor’s surface and the species to be detected, yielding to an electrical signal, employ numerous transduction principles, such as potentiometric, impedimetric, voltammetric, and amperometric transducers. • Acoustic biosensors, or piezoelectric biosensors, are a subgroup of mass biosensors. They are based on the frequency shifts at different overtones when the biological sensing platform is immobilized onto the biosensor’s surface (usually Au). The biosensor then transforms the mechanical vibrations related to the detection of target elements into proportional electrical signals (mass to frequency).

2.2.3 Electrochemical sensors 2.2.3.1 Overview The growing concern to limit the contamination of air, water, soil, and food products, besides making biomedical analyzes more reliable, has created a need for the development of rapid, sensitive, and safe processes for detecting various chemical and biological pollutants [28,29]. Electrochemical sensors can provide inexpensive and sensitive tools based on portable devices capable of rapidly detecting a set of analytes along with decent sensing characteristics [30].

1. Fundamental aspects

2.2 Sensors’ classification

29

Recently, electrochemical sensors have been making a huge impact on multiple fields, including drug monitoring [31], early diagnosis of diseases [32], precise and fast environmental pollutants’ recognition and quantification [33
35], etc., and have been the subject of a great deal of research and development to provide simple, reliable, and low-cost systems via experiencing considerable emergence owing to their simplicity and portability, allowing the transformation of an electrochemical signal into an exploitable in situ electrical signal related to the target element’s quantification, making them better alternatives to rather complicated traditional analytical techniques [34]. 2.2.3.2 Types of electrochemical sensors The fundamentals of electrochemical sensors lie in the mechanism of transforming the electrochemical interaction happening between the target analyte and the electrode into an exploitable electrical signal [31], which is based on the fact that electroactive molecules in solution exchange electrons with the electrode surface depending on the potential at which this exchange takes place; the electrochemical transduction remains the most widely used in electroanalysis and biosensing applications thanks to its simplicity, low cost, and rapidity of response [36]. Electrochemical sensors can be classified as follows: • Amperometric electrochemical sensors: During a redox reaction, amperometric transducers measure the generated current at the working electrode at a constant potential; the current value is proportional to the analyte’s concentration in solution. Amperometric transducers are well-known for their wide linear ranges and high sensitivities compared to potentiometric ones [28]. • Potentiometric electrochemical sensors: Based on measuring the potential difference between a working electrode under zero current intensity and a constant potential reference electrode, the measured potentials are proportional to the analyte’s concentration in solution. Potentiometric sensors are generally known as ion selective electrodes (i.e., pH electrodes) [37,38]. • Conductometric electrochemical sensors: The conductivity of a medium is linearly related to the nature of ions in solution, their charge, mobility, and concentration. Conductometric sensors are based on the measurement of conductometric properties between two electrodes under an alternating current system [30]. • Voltammetric electrochemical sensors: Voltammetric transduction is based on the measurement of the current flow resulting from the analytes’ redox reactions in solution under a controlled variation of a potential difference between the reference and the working electrodes. The applied potential varies as a function of time and the

1. Fundamental aspects

30

2. Fundamentals of sensor technology

current is measured between the working and the counter electrodes as a function of potential, and the intensity-potential curves are named voltammograms, which are a sum of the faradic current resulting from the electrons transfer between the analytes in solution and the working electrode, and the capacitive current arising from the electrode charging. Thanks to their robustness, simplicity, and high sensitivity/selectivity, voltammetric transducers are frequently employed in all aspects of electroanalysis [39,40] and biosensing applications [41].

2.2.4 Optical sensors 2.2.4.1 Overview Optical sensors are based on several phenomena, such as surface plasmon resonance (SPR), fluorescence, radiolabeling, refractive index variations or else refractometry, ellipsometry, and evanescent waves [42]. Optical transduction systems allow real-time in situ detection analyses; along with high sensitivity, robustness, and low response times, another feature that strengthens their employment is the possibility of miniaturization and integration, as well as their simultaneous detection capability for quantifying several analytes of interest. Thanks to their enormous advantages, optical sensors have become popular lately with numerous commercially accessible devices [42]. 2.2.4.2 Types of optical sensors The principles of optical sensors lie in the mechanism of transforming a given optical interaction into an exploitable readout, and the first step in the development of an optical sensor consists in choosing the chemical, physical, or biological property of the species to be detected. Depending on the interaction between the light probe and the medium to be analyzed, the sensors can be based on the intensity modulation, the wavelength modulation, or the phase modulation, which is sensitive to the refractive index variation during a given measurement. Different categories of optical sensors are based on the employed transduction mechanism: • Refractometric optical sensors: Refractometric transduction is the most commonly used transducing mechanism in integrated optics, and refractometric sensors are based on the detection of the refractive index variations of the measurement medium in contact with an optical waveguide’s surface or a propagating evanescent wave [43]. • SPR optical sensors: The SPR transduction consists of immobilizing, on the metalized interface of a prism, a chemo(bio)reception platform for

1. Fundamental aspects

2.3 Sensor applications

31

detecting the target analytes that are injected via a continuous flow using a microfluidic system. The resonance phenomenon of surface plasmon, generated at the metal-dielectric interface using a light beam, is then detected and its variation reflects the interaction between the sensing platform and the target analytes at the chemo (bio)sensor interface [44]. However, the SPR transduction mode is limited due to the transducer’s nature, which can only be a noble metal such as gold, which limits the choice of immobilization procedures. A very shallow penetration depth, of a few nanometers, is also an element to be taken into account depending on the size of the elements involved in the application [45]. Undoubtedly, the SPR is the most suitable and employed optical method for chemo(bio)sensors, which allows employing miniaturized devices. The SPR has been utilized in the food industry, immunogenicity, proteomics, and drug discovery [44
47].

2.3 Sensor applications Recently, the employment of sensors has seen huge progress in various fields including the medical sector for early point-of-care diagnosis, disease detection, and glucose monitoring. They are also used in environmental monitoring and food safety to quantify toxicants such as heavy metals (HMs) ions, pesticides, etc. Table 2.4 lists recent sensor applications in the mentioned fields.

2.3.1 Applications of electrochemical sensors Selected studies of electrochemical sensors for detecting target elements are given in Table. 2.5. Zhang et al. fabricated a label-free tetracycline (TC) aptasensor via the deposition of anti-TC aptamer on a glassy carbon electrode (GCE), and the electrochemical sensor reached a detection limit (LOD) of 2.25 nM and seemed to be sensitive, efficient, and inexpensive [58]. An additional electrochemical sensor was constructed for the detection of TC, based on a modified carbon screenprinted electrode (SPE) employing a 50 -amino-modified 76-mer oligonucleotide, and a LOD of 78.6 pM was acquired [59]. Multiwalled carbon nanotubes and horseradish peroxidase electrocatalytic enzyme have been employed for building oxytetracycline (OTC) biosensors via modifying a graphite paste electrode (GPE). The proposed OTC detection platform achieved a LOD of 35 nM [60]. Metal ions, especially HMs, were the subject of enormous studies aiming at their detection and recognition (i.e., an ion-selective electrode

1. Fundamental aspects

32

2. Fundamentals of sensor technology

TABLE 2.4 Recent sensor applications in food, healthcare, environmental, animal, and agricultural fields. Application sector

Sensor type

Target analyte

References

Food monitoring

Fluorescent sensor based on gold nanoparticles and carbon dots

Acetamiprid

[48]

Gold nanoparticles-based colorimetric sensor

Carbendazim

[49]

Gold nanoparticles array sensor

Human IgG

[50]

Peptide nanotube gold nanoparticle sensor

Prostatespecific antigen

[51]

Epitaxial graphene-based sensor

NO2

[52], p. 2]

Spider dragline silk-based sensor

Humidity

[53]

Phenoxazine-Meldrum’s acid D-π-A fluorescent sensor

Hydrazine

[54]

Aminothiophenol sensor

Amantadine

[55]

Automatic colorimetric sensor

Phosphate and nitrite

[56]

Thiol-functionalized polydiacetylenebased flexible sensors

Ethylene

[57]

Healthcare monitoring

Environmental monitoring

Animals

Agriculture monitoring

composed of meso-octamethylcalix[4]pyrrole matrix was employed for the quantification of Ti31, and the LOD was found to be 0.8 μM based on potentiometric sensing) [61]. A voltammetric sensor for simultaneous detection of Pb21, Cd21, and Fe31 was fabricated based on the synergetic deposition of reduced graphene oxide (RGO) and calix[4]arene derivative on a GCE surface, reaching a LOD of 20 pM [62]. And a cyrhetrenyl-calix[4]arene at GCE-based electrochemical sensor was developed for detecting Cu21, adm the voltammetric sensor achieved a detection limit of 0.47 nM [63]. SPE covered by an ionophore (C-dec-9-enylcalix[4]resorcinarene-O(R 1 )-α-methylbenzylamine) can be applied for HM ion detection using the cyclic voltammetry (CV), the square wave voltammetry (SWV) as well as electrochemical impedance spectroscopy (EIS) techniques. Both CV and SWV techniques can sensitively quantity the HM ions when the sensor surface is properly pretreated with analytes capable of complexing with these ions.

1. Fundamental aspects

33

2.3 Sensor applications

TABLE 2.5

Selected applications of electrochemical sensors. Electrochemical transduction technique

The detection limit (M)

References

Tetracycline (TC) antibiotic

Cyclic voltammetry (CV)

2.25 3 1029

[58]

76-mer-50 -aminomodified oligonucleotide @ Carbon screen printed electrode (CSPE)

TC

CV

7.86 3 10211

[59]

Horseradish peroxidase (HRP) @ Multiwalled carbon nanotubes graphite paste electrode (MWCNTs-GPE)

Oxytetracycline (OTC) antibiotic

Differential pulse voltammetry (DPV)

3.5 3 1028

[60]

Meso-octamethyl calix [4]pyrrole @ Ionselective electrode

Ti31

Potentiometry

8 3 1027

[61]

Calix[4]arene derivative/Reduced graphene oxide @GCE

Pb21, Cd21 and Fe31

Square wave voltammetry (SWV)

2 3 10211

[62]

Cyrhetrenyl- calix[4] arene@ GCE

Cu21

Square wave anodic stripping voltammetry (SWASV)

4.7 3 10210

[63]

CuS dendrite electrode

Glucose

Amperometry

5 3 1028

[64]

Sensing platform @ electrode

Detection target element

Anti-tetracycline aptamer @ Glassy carbon electrode (GCE)

26

3D mesoporous samarium oxide hydrangea microspheres@ GCE

H202

Amperometry

1 3 10

[65]

Ultrafine Wavy PtRu Nanowires @ GCE

Dopamine

Amperometry

5 3 1028

[66]

Ferrocyanyl tethered dendrimer @ Indium tin oxide electrode (ITO)

Hydrazine

CV

3.12 3 10212

[67]

The CV plots (Fig. 2.6) clearly show that the presence of HMs alone does not produce analytical signals on the bare electrode. But when the SPE is coated by an ionophore that can complex with the HMs, immediately peaks appear that represent the presence of metal ions.

1. Fundamental aspects

34

2. Fundamentals of sensor technology

A B C D

0.10

Pb2+

Cu2+

0.05

I (mA)

Hg2+ Cd2+ 0.00

-0.05

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

E (V) Vs. Ag/AgCl FIGURE 2.6 Voltammograms of (A) bare SPEs in 0.2 M HCl, (B) bare SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl, (C) modified SPEs in 0.2 M HCl, and (D) modified SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl, for ionophore (C-dec-9-enylcalix[4]resorcinarene-O-(R 1 )-α-methylbenzylamine).

In this example, the electrolyte was 0.2 M HCl with or without HMs. The influence of the character of the ionophores is also well demonstrated by the well-defined oxidation current peaks (that correspond to the fast electron-transfer rate at the measuring platform) measured on the potential characteristics for the metal ions. SWV tests were performed to characterize the chemically modified sensors under optimal conditions (0.2 M HCl (pH 5 0.7), with accumulation potential of 21.2 V for 90 s). Fig. 2.7 illustrates the overlaid SWV curves for the simultaneous electrochemical determination of HMs in the concentration range from 1 to 100 ppb based on the I4@SPEs, whereas the corresponding calibration curves are displayed as onsets. The peak separation in the SWV signals is large to quantify each metal ion distinctly. Based on the constructed calibration curves, a perfect linear relationship between the concentration of HMs and the SWV currents was established. The EIS technique was employed for characterizing the interface (solution/electrodes surfaces), and the gained EIS outcomes were plotted in a Nyquist diagram form showing the real and imaginary parts of the impedance (Fig. 2.8). The smallest Rct values were associated with the modified SPEs in the presence of 1 ppm of HMs in 0.2 M HCl.

1. Fundamental aspects

35

2.3 Sensor applications

1.2 1.0

I4

A B C D

Pb2+

0.8 0.6

Cu2+

0.4

Hg2+

Cd2+ 0.2 0.0 -1.0

-0.5

0.0

0.5

E (V) Vs. Ag/AgCl FIGURE 2.7 SWV signatures of (A) bare SPEs in 0.2 M HCl, (B) bare SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl, (C) modified SPEs in 0.2 M HCl, and (D) modified SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl.

FIGURE 2.8 EIS Nyquist plots of (A) bare SPEs in 0.2 M HCl, (B) bare SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl, (C) modified SPEs in 0.2 M HCl, and (D) modified SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl.

1. Fundamental aspects

36

2. Fundamentals of sensor technology

The decrease in resistivity values of the modified SPEs indicates the electrode surface conductivity improvement from one side and the enhanced electron transfer properties from another. Other target elements such as glucose, H2O2, dopamine, and hydrazine were the subject of detection and sensing employing electrochemical sensors. For example, the work of Kim et al. appraised a CuS dendrite electrode for the nonenzymatic recognition of glucose, and the sensor achieved a detection limit up to 50 nM [64]. Yan et al. fabricated an enzyme-free amperometric H2O2 sensor with a LOD of 1 μM, in their work, and the sensitivity of a GCE was enhanced by depositing 3D mesoporous Sm2O3 microspheres [65]. Zhao et al. modified a GCE employing wavy PtRu nanowires for the construction of a dopamine sensor, and the amperometric sensing attained a LOD of 50 nM [66]. Das et al. modified an indium tin oxide (ITO) electrode utilizing a ferrocenyl-tethered dendrimer for the development of a hydrazine immune-sensor, and the voltammetric detection platform reached a 3.12 pM as detection limit [67].

2.3.2 Applications of optical sensors Optical systems require powerful light sources, long optical paths, and the use of multipass cells. However, they allow specific, nondestructive, and quantitative measurements, especially when employed as sensors for the recognition, quantification, imaging, or screening elements of interest. The mentioned advantages are translated by the commercialization and utilization of optical detection systems as real-life sensing platforms. Table 2.6 gives insights on selected studies of optical sensors. Semwal et al. developed a SPR-based H2O2 sensor via depositing a catalase enzyme on gold and graphene oxide optical fiber, and the detection limit was calculated to be 55 μM [44]. Sarikaya et al. fabricated a uric acid SPR sensor by employing a uric acid molecularly imprinted template, and the optical sensor achieved a LOD of 1.46 μM [46]. Qian et al. functionalized an optical fiber SPR sensor utilizing boronic acid, aimed at constructing a glycoprotein sensor, and the sensitive optical detection resulted in a LOD of 0.29 nM [47]. Another SPR sensor was designed through the layer-by-layer assembling of polyelectrolyte complexes to quantify the doxorubicin drug, and a detection limit of 0.07 pM was reached [45]. Lotfi et al. employed a 1,3-alternate calix[4]arene bearing aminothiadiazole tails for the design of a Ag1 highly selective fluorescent sensor, and the detection of the silver ions in aqueous and physiological samples was accomplished. A LOD of 6.29 μM was reached along with the formation of 1:1 complex [68]. For the quantification of mercuric ions, Dhir et al. developed a fluorescent sensor by employing a calix[4]arene bearing dansyl moieties, and the sensor reached a LOD of 0.1 μM [69].

1. Fundamental aspects

37

2.3 Sensor applications

TABLE 2.6

Selected applications of optical sensors.

Modifier

Target element

Optical transduction technique

LOD (M)

References

Catalase enzyme

H202

SPR

5.5 3 1025

[44]

Uric acid molecularly imprinted nanoparticles

Uric acid

SPR

1.46 3 1026

[46]

Functionalized boronic acid

Glycoprotein

SPR

0.29 3 1029

[47]

Polyelectrolyte complexes

Doxorubicin

SPR

7 3 10213

[45]

1,3-Alternate calix[4]arene bearing amino-thiadiazole tails

Ag1

Fluorescence

6.29 3 1026

[68]

Calix[4]arene bearing dansyl groups

Hg21

Fluorescence

1 3 1027

[69]

1,3-Alternate thiacalix[4] arene bearing naphthyl groups

F2

Fluorescence

2.6 3 1027

[70]

Amino-pillar[5]arene

Au31

Fluorescence

7 3 1028

[71]

1,3-Alternate calix[4] arenes containing pyrene moieties

Wide range of anions and cations

Colorimetry

—

[72]

Quinaldinep-tertbutylcalix[4]arene

Hg21

Colorimetry

2.95 3 1026

[73]

Kumar et al. designed an F2 induced turn-on fluorescent sensor by utilizing 1,3-alternate thiacalix[4]arene bearing naphthyl groups as fluoroionophore, and the sensor could achieve a detection limit of 0.26 μM along with forming a 1:1 complex [70]. Yang et al. employed a novel amino-pillar[5]arene as fluoro-ionophore for sensing Au31, and the fluorescent chemosensor reached a LOD of 70 nM and showed further sensing capability in a wide pH range from 1 to 13.5; the formed complex was in a 2:1 ratio (Amino-pillar[5]arene: Au31) [71]. Maity et al. utilized a series of 1,3-alternate calix[4]arenes containing pyrene moieties for fabricating colorimetric chemosensors. Besides studying the solvent effect on the selective colorimetric detection of a wide range of anions and cations, considering anions, the developed sensors were F2 selective, and for cations, selectivity towards Cu21 was shown in a (THF-H2O) solvent, while in an (acetonitrile-chloroform) mixture, the sensors were selective to Hg21, Pb21, Ni21, and Cu21 [72].

1. Fundamental aspects

38

2. Fundamentals of sensor technology

Likewise, Maya et al. constructed a Hg21 selective colorimetric sensor based on quinaldine-p-tert-butylcalix[4]arene, and the sensor achieved a detection limit of 2.95 μM and showed a reversible sensing process [73].

2.3.3 Applications of nanomaterial-based-sensors for water monitoring Several detection networks, namely nanomaterials (nanowires, nanoparticles, nanorods, and carbon nanotubes, bioplatforms, and polymers), have been explored by scientists as thin films for electroanalytical and biosensing applications [74,75], aiming at enhancing the sensing characteristics and detection capabilities for some developed sensors. Selected studies of nanostructured (metal and carbon), biological, and polymericbased sensors for water monitoring are discussed here. 2.3.3.1 Metal and carbon-based sensors for water monitoring The achieved scientific advancement in the application of nanostructured-based sensors rests on the improvement of the nanostructures’ surface area, and their noteworthy conductive and catalytic properties, besides their outstanding binding preferences to several target elements to be detected and quantified [76
78]. The way of depositing and structuring the nanomaterials on the transducer device is the critical parameter in attaining decent analytical and detection characteristics. Gold nanoparticles (AuNPs) are broadly employed in sensing applications for trace elemental analysis. For example, AuNPs were combined with RGO to detect As31 in real water, and the proposed sensor reached a detection limit of 1.3 nM [79]. The same ion was analyzed by Kumar et al. who employed a Nafion-L-Leucine-graphene oxide (GO) layer as a detection platform, and the electrochemical sensor reached a detection limit of 6.7 μM and was applied to quantify these ions in river water [80]. The synergistic effect of an ionic liquid, a bismuth film, and an electrochemically reduced graphene oxide (ERGO) were explored for sensing Cd21, and the sensor achieved a detection limit of 26.7 nM [81]. A nitrogen-doped graphene detection platform was applied for the quantification of several metal ions, and the sensor attained detection limits up to 30, 2, 1, and 10 nM, for Cd21, Pb21, Cu21, and Hg21, respectively [82]. A detection network composed of chitosan, DNA, and RGO was utilized to sense Hg21, and the impedimetric sensor achieved a decent detection limit of 0.016 nM [83,84]. Wang et al. detected the same ion via combining ERGO, AuNPs, and thymine-1-acetic acid-cysteamine as a sensing network, and the sensor successfully detected mercuric ions and attained a LOD of 7.5 pM [85].

1. Fundamental aspects

2.4 Innovative sensor technologies

39

Pioneering biosensors have been applied in constructing specific silver ions detection platforms. For example, Lee et al. employed a piezogravimetric cytosine DNA-based-biosensor for detecting Ag1 ions in aqueous media, and the sensor could reach a lower detection limit in the order of 0.1 nM within 10 min [86]. Silver ions have been sensed in real and model systems utilizing a hybridized DNA biosensor platform, and the latter displayed a detection limit of 26.4 pM on GPE and 104 pM on combined AuNPs and GPE [87]. The synergistic effect of DNA, 3D graphene oxide, and Fe3O4 was studied for the construction of an Ag1-based DNA biosensor, and the latter reached a detection limit of 2 pM [88]. For the quantification of Hg21 in aqueous media, an oligonucleotideAuNPs piezogravimetric sensor was designed, and it reached detection limits of 4 and 7 nM based on frequency and dissipation shifts [89,90]. For detecting Cu21 in lake and tap waters, an AuNPs-aptasensor was constructed reaching a LOD of 0.1 pM [89,90], and a single-stranded DNAzyme-based sensor for sensing Pb21 was fabricated and attained a lower detection limit of 0.25 nM [83,84]. 2.3.3.2 Polymer-based sensors for water monitoring Polymeric sensors are often employed for the detection and quantification of copper ions in water sources. For example, Deshmukh et al. deposited polyaniline (PANI) on an ITO electrode for the voltammetric and impedimetric sensing of Cu21 [91]. Later, they combined PANI, single-walled nanotubes, and ethylenediaminetetraacetic acid for modifying a stainless steel electrode aimed at sensing Cu21, and the voltammetric sensor achieved a detection limit of 1.4 μM [92]. Recently, ion-imprinted polymeric templates served as a novel innovative technology for the design and fabrication of selective detection networks. For example, Wei and coworkers combined graphene oxide and chitosan to form a Cu21IP sensor that exhibited a detection limit of 0.15 μM [93]. Another Cu21IP sensor was constructed and based on voltammetry studies, and it displayed a low detection limit of 74 pM for copper ions in water samples [94]. A polymeric film of phenylenediamine for the development of a Cu21IP sensor was employed, and the voltammetric sensor exhibited decent sensing properties: LOD 5 2.7 nM [95]. Also, a polymeric-based piezogravimetric sensor was fabricated for copper ions detection achieving a detection limit of 0.8 nM [96].

2.4 Innovative sensor technologies Innovative sensor technologies are being explored for novel safetyrelated and environmental applications.

1. Fundamental aspects

40

2. Fundamentals of sensor technology

Sensors that detect dangerous chemical, physical, or biologic pollutes in the environment are indicated as environmental sensors, while sensors that detect a physiological state to evaluate the effects of exposure to dangerous pollutants are designated as biosensors. Sensor technologies can be classified into three groups: placeable, implantable, and wearable strategies. Table 2.7 shows the advantages and disadvantages of each type. TABLE 2.7 Characterization of broad categories of current and novel sensor technologies: advantages and limitations. Innovative Sensor strategies

Advantages

Disadvantages

References

Placeable sensors

• A network of multiple sensor nodes can measure several different hazards • Low sample size • Cost-effective • High grade of spatiotemporal resolution

• Ineffective processing of data from different nodes into sensible, actionable info

[97,98,102]

Implantable sensors

• Accurate measurements • Practicality • Ingestible

• The perception that this is an invasive medical procedure, raising ethical and legal issues

[97,99,100]

Wearable sensorsElectronic epidermal wearables smart textiles etextiles.

• Real-time monitoring • Ultra-low-cost • Simple and automatic operation • Empowered by electricity • High-quality data processing • Instant sensing • Accurate data collection

• Dangerous due to physical hazards caused by wearable technologies (irritation, thermal burns, auditory damage) • Numerous obstacles such as stability, cost, production, user awareness • Analytical requirements: power supply, data acquisition, and processing system, communication, and maintaining the functionality of epidermal materials during use in relevant environments

[101,103,104]

1. Fundamental aspects

2.5 Conclusion and future aspects

41

• Placeable sensors can be easily placed and applied as networks of multiple sensor nodes that can measure the same or several different hazards. Economical wireless sensor networks can monitor multiple analytes in real-time [97]. The newest application of wireless area sensor networks is the wireless body sensor network using wearable instead of placeable sensors [98]. • Implantable sensors can be implemented into the skin via microneedles, microchips, or can be swallowed (i.e., a cardiac pacemaker) [99,100]. These sensors can offer more accurate data. The ingestible sensor has the greatest likelihood of moving from research into clinical and workplace applications. • Wearable sensors can be worn on or over clothing such as t-shirts, head coverings, shoes, or spectacles or placed in the external auditory canal [101]. Future applications of wearable sensors include sensors that are knitted into fabrics that can be worn by someone as clothing (electronic textile wearables) and sensors incorporated in thin “skin-like” films or tattoos that can be used directly to the epidermis (electronic epidermal wearables) [103]. Wearable textile sensors signify the integration of material science and electronics to implant electronic circuitry within textiles to produce a new type of textiles called smart textiles or e-textiles. Electronic epidermal wearables are ultra-thin, “skin-like” membranes, with tattoo-like conformability and stretchability that can noninvasively gather physiological info from within the body through the epidermis [104]. Epidermal wearable sensors are used to measure pH, various electrolytes, and other metabolites on or under the skin physically, chemically, or electrochemically as point-of-care applications.

2.5 Conclusion and future aspects Numerous advances are ongoing involving a new generation of sensors that are smarter, more precise, faster, cheaper, wireless, safer, self-learning, smaller, standardized, etc. Sensors are increasingly being used to examine soil and water quality, climate, crops, diseases, and plagues. Humans are already encountering sensor technology in their daily lives. And most of all sensor technology will develop into smart sensors (e.g., smart testing units that self-monitor, transfer status analyses to the operating system, and generate a dependable network of detection and calibration data). The integration of various chemical and biological modifiers for fabricating sensing platforms aimed at the ultra-trace detection and

1. Fundamental aspects

42

2. Fundamentals of sensor technology

quantification of HMs, biological and chemical pollutants in water sources, besides the construction of point-of-care devices for the early diagnosis and monitoring of drugs and several diseases, has exposed intensifying progress in electroanalytical and biosensing studies due to the advantages of sensors having quality sensing characteristics, costeffectiveness, portability, and simplicity. Regardless of the considerable advancements in the research area dealing with the design and development of sensors, future research and further focus must be aimed at addressing disadvantages such as price of assembly of bulk and surface modifiers along with evaluating their toxicity before potential commercial applications. It would be beneficial for the incorporation and synergy of microfluidics and nanotechnology to be further studied and developed for the construction of lab-on-a-chip affordable detection platforms as an example. Nonetheless, the real challenge is developing realistic sensing prototypes and their commercialization afterward, so they can compete with traditional detection methods and analytical devices. It should be noted that the market development of sensors depends on regulations imposing controls and restrictions for evaluating chemical and biological qualities. Thus the need to make the necessary decisions in terms of environmental standards and constraints on the monitoring of water bodies, drug control, and the early diagnosis of diseases should be a priority.

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C H A P T E R

3 Biosensors for virus detection Ekin Sehit1,2 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

3.1 Introduction Several disease outbreaks caused by pathogenic viruses like West Nile Virus, severe acute respiratory syndrome coronavirus (SARS-CoV), influenza, Ebola, and Zika virus have struck the world in recent decades [1]. The most recent example is the pandemic of COVID-19, which caused almost 3 million deaths and an estimated economic burden of more than US$ 1 trillion worldwide [2,3]. These experiences have proved the importance of detection and quantification of pathogenic viruses for medical diagnostic, sanitation, food, and watertreatment applications. The diagnostic tools used during the management of recent pandemics and many other viral diseases mostly rely on conventional diagnostic tools such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). Although these methods are accepted as the gold standard for virus detection, they require experienced technical staff, expensive laboratory equipment, and time. Biosensors can be a more rapid, cost-effective, and easy-to-use alternative to traditional viral detection methods and can be used for point-ofcare applications in resource-limited settings. Therefore a vast number of scientific studies have been performed on biosensors for virus diagnostics. In this direction, various recognition elements (e.g., antibodies, nucleic acids, aptamers, peptides, peptide nucleic acids, molecularly imprinted polymers) are combined with several transducing systems to obtain high sensitivity and specificity. Additionally, nanostructures are often incorporated into the sensing platform to achieve this goal. Such biosensors can target the whole virus, a surface protein, and genetic

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material or its antibody depending on the design. The structural properties, the interactions with the host cell, and life cycle of the targeted virus must be studied in order to develop an effective biosensing platform.

3.1.1 Structure and infection mechanism of common viruses Human pathogenic viruses are infectious agents that exploit the molecular machinery of the living cells in order to reproduce. They enter a healthy host cell via specific binding event to certain receptor structures on the cell membrane. Upon entering, the replication of the viral genome and protein synthesis take place in the cell leading to creation of progeny viruses. The structure of the virus particles, infected host-cell type, and molecular interactions with the receptor vary between different virus species. Such characteristics of commonly studied human pathogenic viruses are reviewed in this section. Strains of influenza A viruses (IAV) are commonly studied in the biosensor field as the analyte since it can cause seasonal influenza outbreaks affecting millions of people worldwide. Moreover, influenza viruses cannot be eradicated by vaccine due to the frequent antigenic shift and drift. IAVs belong to the family Orthomyxoviridae with virions in the shape of filamentous rods longer than 300 nm or spheres with 100 nm diameter. The IAV virions are enveloped by host-cell derived lipid membrane in which transmembrane proteins hemagglutinin (HA), neuraminidase (NA), and M2 exist. Below the envelope and its transmembrane proteins lies the matrix of M1 protein enclosing the virion core [4]. For IAVs there are 18 subtypes of HA and 11 subtypes of NA that are used for defining a particular influenza A virus (e.g., H1N1) [5]. HA is a trimeric glycoprotein consisting of two structural regions: a stem and a globular head. The globular head part carries the binding site for sialic acid as well as various antigenic sites that antibodies specifically recognize. Sialic acids are monosaccharides commonly found in many different cells of different animal species. The HA spikes on IAV specifically interact with the α-2,3- or α-2,6-linkages of sialic acid on the host cell starting the endocytosis of the virus [6]. The endosomal vesicle is acidified leading to fusion of the viral envelope and vesicle membrane. The M2 transmembrane protein serves as an ion channel to facilitate the acidification of the virion core resulting in release of viral ribonucleoprotein (vRNP) into the cell cytoplasm, which is then transported into the host-cell nucleus for viral mRNA transcription and viral RNA replication. Upon the translation of the virus originated mRNA by the ribosome in the cytoplasm, mature viral transmembrane proteins are carried to the cell membrane while the nucleoproteins and subunits of RNA polymerase are transported back to the nucleus to form new

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vRNP complexes, which later move to the transmembrane proteins near the cell membrane to start the budding process. The release of the progeny virions from the host cell is facilitated by the NA by removing sialic acid residues on the cell membrane [7]. The Flaviviridae family includes many pathogenic flaviviruses such as Zika virus, Dengue virus, Japanese encephalitis virus (JEV) as well as hepacivirus causing Hepatitis C infection. Flaviviruses are often transmitted by arthopods and symptoms may vary from mild fever to fatal encephalitis and hemorrhagic fever [8]. Their transmission cycle includes an amplifying vertebrate reservoir and an insect vector. Commonly humans are infected by mosquito and tick bites [7]. Flavivirus virions are particles with a diameter of 40 60 nm carrying a positive sense ssRNA in a icosahedral-shaped nucleocapsid covered by a host-derived lipid envelope accommodating 180 copies of two glycoproteins [8]. The genome encodes three structural (i.e., capsid, membrane prM, and envelope E) and seven nonstructural proteins. E and prM (precursor to M) are the two glycoproteins containing two transmembrane helices. prM may assist in folding and assembly of the E protein prior to its cleavage during maturation yielding to pr-peptide and the M protein while the E protein is mainly responsible for cellular binding and entry. The virus enters the cell via endocytosis following binding the cellular receptors such as dendritic-cell-specific ICAMgrabbing nonintegrin, glucose-regulating protein 78, CD14-associated molecules, αvβ3 integrins, C-type lectin receptors, phosphatidylserine receptors TIM (T-cell immunoglobulin and mucin domain) and TYRO3, AXL, and MER (TAM) [8,9]. Acidification induced endosome disintegration releases the viral genome into the cytoplasm to be translated into a single polyprotein, which is then modified by proteases while the viral genome is replicated in intracellular membranes. The immature and noninfectious virions containing E and prM proteins, nucleocapsid, and lipid bilayer are formed initially in the lumen of endoplasmic reticulum, which are then matured by cleavage of prM and released by exocytosis [8]. Nonstructural protein 1 (NS1) of flaviviruses is targeted by many sensors as it is an important biomarker in early diagnosis of viral infection [10 14]. NS1 is a highly conserved, immunogenic glycoprotein claimed to work in viral RNA replication, virion morphogenesis, and regulation of immune response [15]. Hepatitis B virus (HBV) is a human pathogen that infects hepatocytes in the liver causing chronic diseases with high risk of death. HBV is a small virus of Hepadnaviridae family carrying double-stranded DNA as genomic material [16]. Three surface proteins are located on the envelope: small (S), medium (M), and large (L) sharing C-terminal S domain. Initial low affinity interaction with the host cell facilitated by heparan sulfate proteoglycans (HSPGs) is prerequisite to highly specific binding

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of preS1 domain of L protein to the human sodium taurocholate cotransporting polypeptide (NTCP), which is specifically expressed by liver [17]. Following the NTCP receptor binding, virus is taken up into the cell via endocytosis and nucleocapsids are carried into the host nucleus in which relaxed circular DNA (rcDNA) is converted to covalently closed circular DNA (cccDNA) to serve as a template for viral RNA production. The synthesized RNAs are translated into viral proteins in cytoplasm while pgRNA is packed inside core particles and later converted to positive-sense DNA as progeny genome. The core particles are then enveloped by surface protein and released as infectious virions [18].

3.1.2 Current methods in virus detection The diagnostic methods for viruses have evolved significantly with the technological developments. Current techniques are mainly based on specific detection of viral antigens or genome. Enzyme linked immunosorbent assay (ELISA) is a commonly used tool for viral diagnostics. ELISA was developed in 1971 by the two independent research groups namely, Engvall and Perlmann and Van Weemen and Schuurs [19]. It can be used for the detection of antigens or antibodies in a sample. There are different types of ELISAs such as direct, indirect, sandwich, and competitive assays. In a direct assay, the analyte is bound to the surface of a 96-well plate via electrostatic interactions. Following the introduction of nonreacting proteins and blocking buffers to prevent the nonspecific interactions, enzyme conjugated antibody is incubated with the antigen to allow specific binding (Fig. 3.1A). Substrate for the enzyme is then added to create a color formation that is detected by a microplate reader [19]. Although it is a relatively fast approach, it lacks (A) Direct assay

(B) Indirect assay

(C) Sandwich assay

Secondary antibody Secondary antibody

Detection antibody

Primary antibody

Antigen

Enzyme

Substrate

Capture antibody

Product

FIGURE 3.1 Three different methods of ELISA: Direct assay (A), indirect assay (B), and sandwich assay (C).

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sensitivity. In an indirect assay a primary antibody for the antigen is introduced to the antigen-bound and blocked surface. Different from the direct assay, a secondary antibody carrying the enzyme is used for the detection (Fig. 3.1B). Indirect assays are generally used for testing of HIV antibodies in clinics. A third method to run ELISA is sandwich assay, where a capture antibody is first applied to the surface. Following the antigen binding onto the capture antibody, a detection antibody is used to sandwich the antigen in between. The secondary antibody with the enzyme is then added for the enzymatic detection (Fig. 3.1C). The sandwich assay provides higher specificity thanks to the two-step antibody-based recognition, thus sample purification is not required prior to detection. However, it is more time-consuming and costly assay compared to direct and indirect assays. Furthermore, two particular antibodies binding on the different epitopes of the antigen must be used for a successful detection [19]. ELISA is not a suitable virus detection method in certain cases such as lack of strain-specific antibodies or highly conserved coat proteins [20]. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is another commonly used virus detection tool that allows specific recognition of viral genetic material and is considered to be the golden standard of current diagnostic technologies. PCR is a method consisting of several steps to amplify the DNA. Firstly, the template DNA is denatured by heat and primers are added with deoxyribonuclotide triphosphates. While cooling, the primers anneal to the desired sections of the template and polymerase enzyme catalyzes the replication of the DNA strand. Several iterations of PCR steps are required to obtain a significant amount of detectable genetic material. The detection is then performed by gel electrophoresis [21]. RTPCR is a similar method to amplify and detect RNA by initially synthesizing a complimentary DNA of it using reverse transcriptase enzyme. The template is then amplified by following the denaturation, annealing, and copying steps of PCR in repetition. The amplified sample can be analyzed real-time for viral genome using fluorescent molecules [22]. In contrast to time-consuming and expensive virus detection methods robust, accurate, and cost-effective biosensors have recently been developed for rapid point-of-care virus diagnosis. In this chapter, we examine the recent studies on biosensing platforms for virus detection based on the recognition elements, namely antibody, nucleic acids, peptides, and molecularly imprinted polymers.

3.2 Antibody-based biosensors for virus detection Antibodies are natural recognition elements produced by a living organism as an immune response to the infection. They detect the

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corresponding antigen with high selectivity and sensitivity with the aid of lock-key assembly. Highly sensitive immunoassays are developed using sandwich assays in which a secondary antibody is utilized in addition to capturing antibodies for signal detection and amplification. The specific antigen-antibody interaction has been employed in combination with photonic crystals to develop a label-free optical biosensor for H1N1 detection [23]. The antibody against HA, a major surface glycoprotein of H1N1, was immobilized on SiO2-based inverse opal nanostructure (a photonic crystal) with the aid of chemical and biological linkers. The inverse opal photonic crystal was constructed by sol-gel process of silica precursor around closely packed polystyrene beads that are later removed by calcination. The reflectance of antibody immobilized sensing platform was measured with increasing concentrations of virus to obtain a calibration curve in an investigation range of 103a105 PFUs. Selectivity of the sensor was further confirmed with another subtype H3N2 and influenza B virus, which showed insignificant readings as expected. Another antibody-based detection system was developed using an intensity-modulated SPR sensor, which was immobilized with a novel monoclonal antibody targeting the HA of avian influenza A H7N9 virus [24]. The proposed sensor could detect the virus in a range of 2.3 3 102 to 2.3 3 105 copies mL21 with a limit of detection (LOD) of 144 copies mL21. Thanks to the fast and accurate measurement characteristics of electrochemical transducers, they are often used in virus sensing technologies. For instance, nanopatterned electrodes provide high sensitivity due to increased surface area resulting in accelerated charge transfer rates. Boron doped diamond (BDD) electrodes are of great interest in electrochemical detection technologies due to their wide potential window, fast response, great stability, and biocompatibility. Thus BDD electrode on Si surface was employed in detection of M1 protein of influenza virus subsequent to anti-M1 immobilization [25]. As the protein bound to antibodies on the electrode, a denser surface was formed in which the electron transfer between the redox active species and the electrode was blocked resulting in increased charge transfer resistance. The impedimetric BDD-based sensor showed high sensitivity with an LOD of 1 fg/mL. Also, it provided a good sensing performance in a solution consisting of BSA, artificial saliva, and pathogenic bacteria, which mimics a real sample. In addition to BDD, electrodes modified with nanomaterials such as AuNPs [26], ZnO NPs [27], carbon nanotubes [28], polymeric nanostructures, and graphene quantum dots [29] are commonly employed in electrochemical virus detection as they provide a large surface area for improved sensitivity. For instance, a nanocomposite sensor composed of AuNP-embedded polyaniline (AuNP-PAni) nanowires along with

2. Biomedical applications

59

3.2 Antibody-based biosensors for virus detection

antibody immobilized nitrogen and sulfur-doped graphene quantum dots (Ab-N,S-GQD) was fabricated for the detection of Hepatitis E virus. An additional PAni layer was initially formed on glassy carbon electrode forming a durable and adhesive layer to allocate Ab-N,S-GQD-AuNPPAni nanocomposite on electrode surface. The sulfur doping of GQDs allowed strong attachment to AuNPs on PAni nanowires via thiol-gold interaction while the nitrogen impurities enhanced the electrochemical properties of the electrode for higher sensitivity. Further improvement in sensing performance was obtained by application of external pulses at 10.8 V during virus loading step to promote the electrostatic interactions between negatively charged virus and positively charged PAni via expansion of the polymer chain length. The sensor revealed a linear detection range of 1 fg/mL 100 pg/mL with an LOD of 0.8 fg/mL. Another electrochemical immunosensor was fabricated by immobilizing human enterovirus 71 antibody on AuNP deposited ITO surface [26]. A sandwich assay was conducted for improved sensitivity, where magnetic nanobeads (MNBs) with horseradish peroxidase (HRP) and secondary antibody were used as detection agent (Fig. 3.2). Here, the secondary antibody on the MNBs facilitates the binding of nanobeads to the viruses while the enzyme serves as a catalyst for the redox reactions of TMB and H2O2 providing current flow that is later detected by chronoamperometry. This enzyme labeled immunosensor exhibited a detection range of 0.1a600 ng/mL with an LOD of 0.01 ng/mL. Furthermore, the proposed sensor also served for colorimetric detection since the HRP catalyzed redox reaction of TMB and H2O2 forms a colored product. MPA / MUA EDC / NHS

Electrochemical deposition ITO

ITO

ITO

TMB / H2O2 TMBox / H2O

ITO

AuNPs

Human enterovirus

TMB / H2O2

Antibody

Magnetic nanobeads

ITO

HRP

FIGURE 3.2 Detection mechanism of human enterovirus by antibody immobilized AuNPs/ITO electrode and dual-labeled magnetic nanobeads [26].

2. Biomedical applications

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3. Biosensors for virus detection

Colorimetric detection methods are often preferred for point-of-care diagnostics due to their convenience, simplicity, and robustness. For fast virus recognition in environments with limited resources, they can be employed rapidly with naked eye. Recently, a colorimetric sensor for influenza virus A and B has been developed utilizing recombinant antibody immobilized cotton-swabs [30]. A sandwich assay was conducted with the detection antibody carrying colored nanobeads causing colorchange of the cotton-swabs in the presence of targeted viruses. Another colorimetric immunosensor for H5N1virus was fabricated by utilizing the alkaline phosphatase (ALP) and gold nanobipyramids [31]. The enzyme linked to secondary antibody in the sandwich immunoassay catalyzed the hydrolysis of 4-aminophenyl phosphate to 4-aminophenol, which reduces the silver nitrate to silver monomer. The newly formed silver monomer was coated on the surface of gold nanobipyramids leading to a blue-shifted LSPR and visible color change in the sample solution. The proposed sensor exhibited a linear range of 0.001 2 2.5 ng/mL with an LOD of 1 pg/mL. Antibodies are produced as an immune system response in infected host humans/animals. Therefore they can serve as a biomarker for a viral infection that can be detected by biosensors. For instance, the antibody for E2 glycoprotein of classical swine fever virus (CSFV) was detected by enzyme-labeled magnetoelastic biosensor utilizing E2 glycoprotein as the capturing receptor [32]. The ALP enzyme linked to secondary antibody returns the reaction hydrolysis of 5-bromo-4-chloro-3indolyl phosphate/nitro blue tetrazolium chloride mixture into a precipitated product, which was then detected by a change in the resonance frequency of the magnetoelastic sensor revealing a linear range of 5 ng/mL 10 μg/mL with an LOD of 2.466 ng/mL. In another study, recombinant spike protein S1 of Middle East respiratory syndrome coronavirus (MERS-CoV) was immobilized on AuNPs deposited on an array of carbon electrodes to construct an electrochemical immunosensor [33]. The detection principle in this study is based on the competition between the free viruses in the sample and the immobilized protein for the antibody introduced into the sample at a fixed concentration. Such a sensor exhibited a linear detection range of 0.001 ng/mL 100 ng/mL and an LOD of 1.04 pg/mL. Further examples of antibody-based biosensors for viruses are listed in Table 3.1.

3.3 Nucleic acid-based biosensors for virus detection Two main classes of nucleic acids (i.e., DNA and RNA) are polymeric species made of sugar-phosphate backbone with nucleic bases as side chains. Every living organism has either DNA or RNA for molecular

2. Biomedical applications

TABLE 3.1 Overview of the antibody-based biosensors for viruses. Sensor platform

Target

Detection method

Detection range

Cysteamine-AuNPs/IgGanti-T7

T7 bacteriophage

Colorimetric

Up to 5.72 3 10

Anti-GVA /ZnO thin film

Grapevine virus A-type

Optical

1 pg mL 10 ng mL

Zev-Abs/DTSP/IDE-Au

Zika virus

Electrochemical

10 pM 1 nM

NS1/CNT/GCE/PP-NHS

Dengue virus NS1 antibody

Electrochemical

Anti-NS1/Graphene

Zika virus (NS1 protein)

FET

Anti-NDV/AuNPs/Ex-TFG

Newcastle disease virus

Optical

Anti-PPV/Protein G on Au

Plum Pox Virus

Anti-Measles/NHC/Au

10

212

10

25

10

PFU/mL

g/mL

LOD

Ref.

18 pM

[34] [35]

10 pM 212

10

g/mL

[36] [12]

0.45 nM

[13]

0 1000 pg/mL

25 pg/mL

[37]

EGOFET

5 ng/mL 50 μg/mL

180 pg/mL

[38]

Measles virus

Electrochemical

10 100 μg/mL

6 μg/mL

[39]

Anti-FAdV/AuNBs and GQDs

Fowl Adenovirus

Optoelectronical

10 10.000 PFU/mL

8.75 PFU/mL

[40]

Ab/TrGO/ITO/glass

H1N1

Electrochemical

0 10000 PFU/mL

26 PFU/mL

[41]

Ab/LFIA strip and Fe3O4@AgNPs

H1N1, Human adenovirus

Optical

10 107 PFU/mL

50, 10 PFU/mL

[42]

Anti-HA@QD-peptide-AuNP

H1N1

Optical

0 100 ng/mL

17.02 fg/mL

[43]

Au/DSU/NH2 rGO-PAMAM/Anti-E

Dengue virus type 2

Optical

0.08 0.5 pM

0.08 pM

[44]

Ab, Antibody; AuNBs, gold nanobundles; AuNPs, gold nanoparticles; CNT, carbon nanotubes; DSU, dithiobis (succinimidyl undecanoate); DTSP, dithiobis (succinimidyl propionate); EGOFET, electrolyte-gated organic field-effect transistor; Ex-TFG, excessively tilted fiber grating; FAdV, fowl adenovirus; FET, field effect transistor; GCE, glassy carbon electrode; GQDs, graphene quantum dots; GVA, grapevine virus A-type; HA, hemagglutinin; IDE-Au, interdigitated microelectrode of gold array; IgGanti-T7, immunoglobulin G antibody for T7 bacteriophage; ITO, indium tin oxide; LFIA, lateral flow immunoassay; NDV, newcastle disease virus; NHC, N-heterocyclic carbene; NS1, non-structural protein 1; PAMAM, polyamidoamine; PP-NHS, polypyrrole (N-hydroxysuccinimido 11-(pyrrol-1-yl) undecanoate); PPV, plum pox virus; QD, quantum dots; rGO, reduced graphene oxide; SERS, surface-enhanced Raman spectroscopy; TrGO, thermally decomposed reduced graphene oxide; Zev-Abs, Zika virus specific envelope protein antibody.

62

3. Biosensors for virus detection

information storage and regulation of gene expression [45]. Genomic biosensors mainly benefit from specific hybridization of nucleic acid strands by formation of base pairs via hydrogen bonding. This simple yet effective mechanism can distinguish any differentiation in gene sequences leading to high specificity and selectivity [46]. Although nucleic acid-based affinity ligands are highly sensitive and selective, they are susceptible to disintegration by nucleases and proteases. Furthermore, they are easily affected by pH changes hindering their application range [47]. Nucleic acid-based biosensing platforms are further enriched with nanomaterials to enhance their sensitivity. Such an approach was used in the fabrication of an electrochemical disposable genosensor for the detection of Dengue virus DNA [48]. The electrode was modified with MoS2 nanosheets in order to improve the adsorption of single-stranded probe DNA (ss-pDNA) via van der Waals interactions. The sensing platform successfully detects the target viral DNA in a linear range from 0.1 nM to 100 μM with an LOD of 3.4 nM. In another study, oxidized glassy carbon electrode was modified with silsesquioxane-functionalized gold nanoparticles to improve the electroactivity of the sensor and immobilization efficiency of ss-pDNA for Zika virus detection [49]. As a result of nanomaterial modification on the electrode, high sensitivity and selectivity were achieved with an LOD of 0.82 pM. AuNPs are also useful for colorimetric assays due to their distinctive optical properties depending on their size and shape. Shawky and colleagues utilized the probe RNAmodified citrate capped AuNPs as nanoprobes for hepatitis C together with cationic AuNPs to visualize the hybridization between the probe and the target RNA [50]. As the hybridization occurs, the negatively charged nanoprobes are agglomerated with the cationic AuNPs altering the optical properties of the solution. Such a sensor exhibited an LOD of 4.57 IU/μL. Similarly, AuNPs were capped with antisense oligonucleotides (ASOs) to assemble a colorimetric biosensor for SARS-CoV-2 [51]. The thiol modified ASOs were virtually selected for a high-affinity recognition of N gene from two regions, simultaneously resulting in agglomeration of ASO-capped AuNPs in the presence of the target. Following the RNase H enzyme incubation with agglomerated ASO-capped AuNPs for 5 min at 65 C, the conjugated RNA strands were cleaved from the ASOcapped AuNPs triggering further aggregation of AuNPs, which led to a visible color change detectable by the naked eye. Such a colorimetric sensor achieved an LOD of 0.18 ng/μL. Nanomaterials can be combined as multicomponent nanocomposites to achieve ultrahigh sensitivity. In a recent study, ternary nanocubes of noble metals (AgAuPt NCs) were employed in an electrochemiluminescence (ECL) biosensor for the determination of Hepatitis B virus [52]. The GCE electrode was modified with a nanocomposite made of

2. Biomedical applications

3.3 Nucleic acid-based biosensors for virus detection

63

cross-linked polyethyleneimine (PEI), ABEI luminophore, ternary NCs, and chitosan. The thiolated hair probe was anchored on the functionalized GCE to be hybridized with ferrocene-labeled ss-DNA. In the absence of analyte, the ferrocene quenches the ECL activity of luminophore leading to the “off” state of the biosensor. The ECL signal is recovered in the presence of analyte as the viral DNA replaces the ferrocene-labeled ss-DNA by hybridizing with the hair probe creating a strong ECL response as “on” state. Here, the biosensor also benefits from Pb21-requiring DNAzyme recycling amplification of viral DNA to enhance the biosensor sensitivity resulting in an LOD of 65 aM. This offon blinking biosensor can detect the target virus in a range of 0.1 fMa1 nM. A similar strategy was followed by Sighal and coworkers to fabricate an ZnO/Pt-Pd nanocomposite-based electrochemical genosensor for Dengue virus detection [53]. A conserved consensus sequence, which is common in all serotypes of Dengue virus, was used as ssDNA probe to prevent false results. Methylene blue was employed as redox reporter due to intercalation with the dsDNA formed by hybridization of probe and target DNA. The resulting nanocomposite genosensor could detect viral DNA in a concentration range of 1 100 μM with an LOD of 43 μM. Another genosensor was developed using magnetic nanoparticles and probe DNA anchored carbon dots for fluorescence resonance energy transfer (FRET)-based detection of Human T-lymphotropic virus type 1 (HTLV-1) [54]. Probe A immobilized CDs were adsorbed on Fe2O3@Au in the absence of analyte resulting in a quenched CDs response. However, in the presence of targeted genetic material and probe B, the CDs desorbed from the surface of magnetic nanoparticles to form a hybridized structure of CDs with probe A, target DNA, and probe B. The fluorescent emission response of CDs showed a linear increase for the concentration range of 10 320 nM with an LOD of 10 nM. Aptamers are nucleic acid-based synthetic recognition elements that are specifically chosen for an analyte by the Sequential Evolution of Ligands by Exponential Enrichment (SELEX) procedure allowing selection of high affinity binders for the target from a library of oligonucleotides. Aptamers, targeting various compounds from inorganic ions to whole organisms, have widely been used in biosensing applications [46]. The main disadvantage of aptamers is their inclination toward enzymatic digestion [55]. In virus biosensing applications, aptamers are often combined with nanostructures to enhance the capacity for ligand immobilization leading to increased sensitivity. Wang and coworkers fabricated a nanowell structure on gold QCM chip, which was functionalized with ssDNA aptamer to AIV H5N1 [56]. The nanopatterned electrode was constructed by covalently attaching a nanoporous film on gold electrode via dithiol crosslinker. Subsequently, the chip surface was treated with 16-mercaptohexadecanoic acid to introduce carboxylic

2. Biomedical applications

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3. Biosensors for virus detection

acid functionality and ssDNA aptamer was conjugated on the nanowell surface from its 5’-terminal via amine coupling chemistry. The piezoelectric aptasensor could detect the targeted viruses in a concentration range of 224a24 hemagglutination units per 50 μL with a detection time of 10 min. Similarly, a 3D nano-popcorn substrate was built to establish a Surface Enhanced Raman Spectroscopy (SERS)-based aptasensor for H1N1 recognition by manipulating the surface energy difference between PFDT spacer and the Au layer [57]. The resulted nanopatterned surface accommodates hot spots that improve the Raman intensity via formation of localized concentrated electric field. The formed surface is modified with HA-aptamer carrying a Raman reporter with the aid of a capture DNA as a linker. As the target virus is introduced into the sensing platform, the aptamer is detached from the sensing surface and binds to virus leading to significant decrease in Raman intensity. The proposed assay achieved an LOD of 97 PFU/mL in a total assay time of 20 min (Fig. 3.3).

(A)

(I)

HA-aptamer

(B)

Capture ssDNA

(I)

HA target

Raman reporter

(II)

(II)

HA-aptamer

FIGURE 3.3 Detection mech of SERS-based aptasensor. Raman reporter-labeled aptamer probe and capture DNA hybridizes on nanopopcorn surface (I) leading to significant Raman signal (II) (A). H1N1 presence triggers an alteration in aptamer conformation (I) resulting in a lower Raman response (II) (B) [57].

2. Biomedical applications

3.4 Peptide-based biosensors for virus detection

65

In another study, a colorimetric aptasensor for murine norovirus (MNV) benefited from catalytic activity of AuNPs against oxidation of TMB releasing of a blue-colored product [58]. The nanozyme characteristics of AuNPs was suppressed by conjugation of capsid-specific aptamer resulting in no color observation. The enzyme-like characteristics were recovered in the presence of target virus since the aptamers dissociate from the nanozymes and adsorb to the virus capsid due to four orders of higher affinity toward the capsid protein than of the AuNPs. This nanozyme aptasensor is able to quantify MNV in a linear dynamic range of 20a1000 MNV per assay and a calculated LOD of 3 MNV per assay. Further examples of nucleic acid-based biosensors targeting pathogenic viruses are listed in Table 3.2.

3.4 Peptide-based biosensors for virus detection Peptides are natural or synthetic short polymers of amino acid units. Being made of the same building units enables them to substitute for proteins in bioanalytical applications. They are utilized in biosensors as recognition units since they are highly stable, easily modified, and chemically versatile alternatives to protein-based affinity elements [72]. Arya and colleagues employed a peptide chain as a ligand for impedimetric detection of HA-antibody [73]. The microelectrode array with comb structure (MACS) electrode was modified with capture peptide carrying two-stranded α-helical coiled coil peptides via thiol-gold interaction. This impedimetric sensor can quantify HA-antibody of influenza virus in a linear concentration range of 1 pg/mL 100 ng/mL with a sample incubation time of 20 min. Thanks to the chemical versatility of affinity peptides, they are often modified for the enhancement of biosensing performance. For instance, an electrochemical biosensor targeting human norovirus was developed by modifying the affinity peptide with different peptide linkers [74]. The unstructured flexible linker (-GGGS) and zwitterionic-based nonfouling linker (-EKEKEKE) ameliorate the sensing capability while the α-helical rigid linkers deteriorate it. The best performance was obtained by addition of one nonfouling and two flexible peptide linkers with an LOD of 1.7 copies mL21. Furthermore, such a sensor could detect norovirus extracted from oyster with an LOD of 2.47 copies mL21. In a recent study, three affinity peptide fragments obtained from antibody targeting glycoprotein were labeled with fluorescent dye for optical detection of Ebola virus [75]. The peptide fragments were immobilized on graphene oxide (GO) surface to benefit from fluorescence quenching properties. The immobilized peptides were removed from GO surface due to complex formation with the viruses resulting in fluorescence signal recovery.

2. Biomedical applications

TABLE 3.2 Other examples of nucleic acid-based biosensors for viruses. Sensor platform

Target

Detection method

Detection range 3

105 EID50/mL

Aptamer/Au SPR chip, aptamer@AuNPs

H5N1

Optical

10

ITO/PEI/V46 aptamer

H1N1

Electrochemical

1 106 PFU/mL 2

105 PFU/mL

LOD

Ref.

200 EID50/mL

[59]

3.7 PFU/mL

[60]

20 PFU/mL

[61]

NH2-pDNA/MHA/MCH/Au

DENV

Electrochemical

10

CdTe QDs-cpDNA/GNs/GCE, AuNPs/pDNA

HBV HCV

Electrochemiluminescence

0.0005 0.5 nM 0.001 1 nM

0.082 pM 0.34 pM

[62]

rHA aptamer/Ag@SiO2

H5N1

Metal enhanced fluorescence

2 200 ng/mL

2 ng/mL

[63]

Aptamer@AuNPs

MDPV

Optical

0.74 24 EID50

1.5 EID50

[64]

PE/PEI/AuNPs/aptamer

NS1

Electrochemical

3 160 ng/mL

0.3 ng/mL

[65]

ss-DNA/Au/ITO nanowires

HBV

FET

1 fM 10 μM

1 fM

[66]

AMs/cpDNA/Zn(II) salphen complex

DENV

Optical

10215 1023 M

1.21 3 10216 M

[67]

PCGE/poly(3 4-AHBA)/probe ssDNA

Zika

Electrochemical

84 pM

25.4 pM

[68]

CPE-HT18C6(Ag)/chitosan/SiQDs@PAMAM/ssDNA probe

SARS-CoV2

Electrochemical

1 pM 8 nM

0.3 pM

[69]

ssDNA/MB@SiNPs/FTO

HCV

Electrochemical

100 106 copies mL21

90 copies mL21

[70]

Aptamer/PEG/Au POF

SARS-CoV2

Optical

25 100 nM

37 nM

[71]

1.41 nM

3 4-AHBA, 3-amino-4-hydroxybenzoic acid; AMs, acrylic microspheres; AuNPs, gold nanoparticles; cpDNA, capture DNA; CPE, carbon paste electrode; DENV, dengue virus; EID50, 50% egg infection dose; GCE, glassy carbon electrode; GNs, graphene nanosheets; FET, field effect transistor; FTO, fluorine doped tin oxide; HBV, hepatitis B virus; HCV, hepatitis C virus; ITO, indium tin oxide; MB@SiNPs, methylene blue doped silica nanoparticles; MCH, 6-mercapto-1-hexanol; MDPV, Muscovy duck parvovirus; MHA, 6mercaptohexanoic acid; NS1, non-structural protein 1 of dengue virus; PAMAM, poly(amidoamine); PCGE, pencil carbon graphite electrode; pDNA, probe DNA; PE, pencil electrode; PEG, polyethylene glycol; PEI, Polyethyleneimine; PFU, plaque forming unit; POF, plastic optical fiber; rHA, recombinant hemagglutinin; SARS-CoV-2, severe acute respiratory syndrome coronavirus-2; SiQDs, silicon quantum dots; ssDNA, single stranded DNA.

3.4 Peptide-based biosensors for virus detection

67

However, the sensor failed to differentiate between different virus species. The weak selectivity of short peptides was addressed by utilizing Principal Component Analysis, which allows reduction of dimensionality in analysis of complex data. Similarly, Wu and coworkers employed GO to design an FRET-based biosensor to detect HIV antibody using Lanthanide-doped upconversion nanoparticles (UCNPs) modified with HIV gp120-derived peptide as recognition units [76]. Here, the peptide was modified with phospholipid to facilitate the hydrophobic van der Waals interaction between the oleate functionalities of UCNPs and hydrophobic tail of the phospholipids. The UCNPs exhibited strong fluorescent emission when excited by near IR, making them suitable candidates as FRET donors, while GO worked as an efficient energy transfer acceptor. In the absence of analyte, the peptides on the UCNPs facilitated conjugation with GO via π π stacking and hydrophobic interactions inducing FRET process. The target antibody triggered peptide-UCNPs detachment from GO by forming peptide antibody complexes. In this case, the UCNPs drifted away from GO resulting in fluorescence recovery due to ceased FRET. The fluorescent signal was linear for a concentration range of 5 150 nM providing an LOD of 2 nM. Recently, Matsubara and colleagues employed divalent and tetravalent dendrimeric peptides on BDD electrode for highly sensitive recognition of HA of influenza virus [77]. The pentapeptide Ala-ArgLeu-Pro-Arg monomers were connected by a branched lysine core to obtain dendrimers. The biotin functional groups on the side chain of lysine were replaced with azide group to utilize the click chemistry for peptide immobilization on alkyne-terminated BDD electrode. The impedimetric measurements of tetramer peptide-BDD biosensor revealed LODs in the range of 0.13 2 6.7 PFU for different strains of influenza virus. A colorimetric sensor for H1N1 was fabricated by anchoring affinity peptides on polydiacetylene (PDA) nanoparticles by coupling the carboxylic acid group of the peptides and NHS ester on the PDA nanosensors [78]. The sequence of the peptide allowed specific recognition of HA of H1N1 virus, whereas the PDA facilitated a visual detection via disruption of the conjugated backbone due to steric repulsion during peptide-analyte binding leading to a shift from blue to red. The detection of H1N1 as low as 105 PFU was achieved with the proposed colorimetric sensor. Computational modeling is a helpful tool to rationally design affinity peptides for a specific target. Bao and colleagues followed this strategy to design an affinity peptide for HA of H5N3 virus by simulating docking efficiency of different peptides derived from P0 (KPNDAINF) [79]. The RMSD values and binding energy calculations revealed P2 with a sequence of KPNGAINF to be the most efficient ligand among others. Binding performances of the peptides were experimentally evaluated by

2. Biomedical applications

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3. Biosensors for virus detection

conjugating the candidate peptides with Europium nanoparticles via EDC-NHS coupling to perform fluorescent linked immunosorbent assay (FLISA) and fluorescent immunochromatographic test (FICT). Both P0 and P2 exhibited an LOD of 20 HAU mL21 for H5N3 in FICT; however, P2 showed better selectivity in FLISA tests. Furthermore, P2 revealed better performance than P0 in clinical sample detection, which is attributed to larger interaction surface with HA. In another study, computationally designed peptides were developed by targeting the glycosylation sites on Zika virus [80]. A peptide library was generated by incremental construction approach starting from a 160 K possible tetrapeptide. Among four peptide libraries (tetrapeptides, pentapeptides, hexapeptides, and heptapeptides), the two peptides from each library were experimented and three peptides (i.e., LRGHA, KRNATP, GSKANNG) revealed similar affinity with LODs 104.7, 104.8, 104.5 copies mL21. Peptide nucleic acids (PNAs) are nucleic acid analogs made of a peptide backbone with nucleobase side chains. Similar to DNA and RNA, PNAs can form stable structures with DNA sequences via H-bonding. The electric repulsion is prevented due to lack of phosphate groups in PNA leading to stronger PNA/DNA complexes. PNAs exhibit many advantages such as resistance to enzymatic digestion, improved thermal stability, and low sensitivity against ionic strength of medium [47]. Furthermore, they are integrated with other nanomaterials to enhance the sensitivity. For instance, an anthraquinone labeled PNA probe (AQ-PNA) was combined with a paper-based graphene-PAni modified electrode for enhanced electrochemical activity to detect human papilloma virus (HPV) [81]. The negatively charged amino group at the N-terminus of AQ-PNA allowed electrostatic immobilization on positively charged G-PAni surface while the anthraquinone label promoted the electron transfer between the G/ PAni electrode and electrolyte. However, the electrochemical signal was suppressed in the presence of HPV type 16 DNA since the electron transfer was hindered due to rigid DNA/AQ-PNA complex formation. The current values in voltammograms were in linear relation with the concentration for a range of 10 200 nM with an LOD of 2.3 nM. In another study, a colorimetric sensor for influenza A strains was developed by incorporating charge-neutral PNAs and citratestabilized AuNPs [82]. The PNAs in the absence of target RNA induced agglomeration of AuNPs by shielding the citrate anions on the surface leading to visible color change from red to purple/blue (Fig. 3.4). The AuNPs remained stable when the target RNA was present in the solution since the PNA/RNA complex formation eliminated the charge shielding. The proposed assay demonstrated a visual LOD of 2.3 ng and spectral LOD of 1.2 ng. Other examples of peptide-based biosensors are presented in Table 3.3.

2. Biomedical applications

3.5 Molecularly imprinted polymer-based biosensors for virus detection

69

AuNP PNA Complementary RNA Non-complementary RNA 12 pmol PNA + 1 µL Phosphate buffer

Control

1 µL RNA extracted from clinical sample

Incubation at 37ºC for 10 min followed by addition of 0.80 mM AuNPs

Complementary sequence Free PNA induces AuNPs agglomeration bound PNA could not induce AuNPs agglomeration

Due to non-complementary sequence, free PNA induces AuNPs agglomeration

FIGURE 3.4 Working principle of PNA-based colorimetric sensor in the presence and absence of targeted viral RNA sequence [82]. PNA, Peptide nucleic acid.

3.5 Molecularly imprinted polymer-based biosensors for virus detection Molecularly imprinted polymers (MIPs) are plastic antibodies specifically designed for a desired analyte. The molecular imprinting is performed by polymerizing functional monomers in the presence of the template, which is later removed to introduce cavities, complementary to the target in terms of size, shape, and chemical functionality to the polymer matrix. MIPs present many advantages over natural antibodies such as high chemical and physical stability, cost-effectiveness, and easy production due to their polymeric nature [95]. Tancharoen and colleagues

2. Biomedical applications

TABLE 3.3

Other examples of peptide-based biosensors for virus detection.

Sensor platform

Target

Detection method

Detection range 3

105 PFU

LOD

Ref.

3

[83]

10 PFU

SA-peptide/PDA/SOG nanopillar

Influenza A viruses

Optical

10

CdSe QDs-peptide-AuNP

Norovirus like particles

Optical

0.1 pg/mL 50 ng/mL 21

124 fg/mL

[84] 21

Peptide on Au electrode

Human norovirus

Electrochemical

10 107 copies mL

7.8 copies mL

[85]

PNA-AQ on Cht-SPCE

Human papilloma virus

Electrochemical

0.02 12.0 μM

4 nM

[86]

Peptide/MUA/Au electrode

Dengue virus NS1

Electrochemical

0.003 12.5 μg/mL

1.49 μg/mL

[87]

EDOT-peptide

H1N1

Electrochemical

12.5 100 μg/mL

12.5 μg/mL

[88]

Acpc PNA probe- AgNPs

MERS-CoV Human papilloma virus

Optical

20 1000 nM 20 2500 nM

1.53 nM 1.03 nM

[89]

Fluorogenic peptide

SARS-CoV-2

Optical

10 108 PFU/mL

9.7 PFU/mL

[90]

Peptide on fiber optic

SARS-CoV-2

Optical

3.3 pM 10 nM

1 pM

[91]

Peptide-based molecular beacon

H1N1

Optical

0 103 copies per well

4 copies

[92]

101.5 IU/mL

[93]

-

[94]

PNA/AuNPs/ITO

Hepatitis C virus

Electrochemical

5 3 10

PeSHHA

HIV antibody

Electrochemical

1 100 nM

3

20 3 10 IU/mL 3

Acpc PNA, Anthraquinone-labeled pyrrolidinyl peptide nucleic acid; AuNPs, gold nanoparticles; Cht-SPCE, chitosan modified screen-printed carbon electrode; EDOT, 3,4-ethylenedioxythiophene; HIV, human immunodeficiency virus; ITO, indium tin oxide electrode; MERS-CoV, middle East respiratory syndrome coronavirus; MUA, 11-mercaptoundecanoic acid; PDA, polydopamine; PeSHHA, peptide-mediated electrochemical steric hindrance hybridization assay; PNA, peptide nucleic acid; PNAAQ, anthraquinone labeled peptide nucleic acid; SA-peptide, sialic acid-mimic peptide; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SOG, spin-onglass; QDs, quantum dots.

3.5 Molecularly imprinted polymer-based biosensors for virus detection

71

fabricated an electrochemical sensor by surface imprinting Zika virus on a polymer-GO nanocomposite [96]. Prior to imprinting, prepolymer mixture consisting of acrylamide, methacrylic acid, methyl methacrylate, and N-vinylpyrrolidone was heated until gelation point, mixed with GO and spincoated on gold electrode. Subsequent to addition of virus particles as templates, the polymer was treated with UV and heated to complete polymerization. The cavities specific to Zika virus were formed following the template removal with acetic acid. The electrochemical measurements exhibited a linear increase in current for the virus concentration range of 1023 102 PFU/mL with an LOD of 2 3 1024 PFU/mL. Furthermore, the MIP-based sensing platform achieved an LOD of 5 3 1022 PFU/mL in 10% diluted serum. A similar approach of MIP synthesis was previously followed for impedimetric detection of Dengue virus within a linear range of 1 to 2 3 103 PFU/mL achieving a detection limit of 0.12 PFU/mL [97]. MIPs can be customized by introducing numerous nanomaterials into the polymer matrix during the synthesis to enable varying detection methods. Luo and coworkers utilized vinyl-grafted on green- and red-emitting SiO2-coated QDs for simultaneous detection of Hepatitis virus A and B, respectively [98]. The vinylic QD nanocomposites were polymerized together with other functional monomers (i.e., N-isorpylacrylamide and zinc acrylate) and template to produce green-MIPs for HAV and red-MIPs for HBV (Fig. 3.5). Upon the addition of analyte, the emission of the MIPs was decreased due to quenching effect resulting from charge-transfer mechanism between the virus shell and the MIP. A linear correlation was observed between the emission signal of green-MIPs and the HAV concentration in the range of 0.3 95 nM with an LOD of 3.4 pM. The red-MIPs showed similar behavior for a concentration range of 0.5 90 nM with an LOD of 5.3 pM for HBV. High selectivity is attributed to the combined usage of hydrophilic monomer and metal chelation effect. Similarly, metal-organic frameworks (MOFs) can also be incorporated in MIP-based sensor design. This strategy was followed to fabricate nanoprobes for Hepatitis A detection by imprinting the virus on a pHresponsive polymer shell accommodating a MOF core [99]. The MOF core provided high surface area for increased imprinting site while responsive polymer facilitated a pH triggered template removal process. The nanoprobes exhibited a linear light-scattering response for a concentration range of 0.02 2 nM with an LOD of 0.1 pM. MIP-based biosensors usually employ electropolymerization for rapid and cost-effective production. An electrochemical sensing platform was fabricated by electropolymerization of o-aminophenol (o-AP) with the templated food-and-mouth-disease virus (FMDV) for 40 cycles of cyclic voltammetry and a subsequent template removal step with citric acid [100]. The current signal increased linearly for a concentration range of 4 ng/mL 75 ng/mL achieving an LOD of 1.98 ng/mL within a testing

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3. Biosensors for virus detection

(A) TEOS

MPS

remove

NH3, H2O

EGDMA AIBN

TEOS MPS

remove

NH3, H2O

EGDMA AIBN

NIPAAm

HAV

HBV

other viruses

(B)

Metal chelation and six-membered ring formed between template and zinc acrylate. Represented by HAV, the same is true for HBV.

FIGURE 3.5 Schematic illustration of fluorescent MIP synthesis and visual detection of analyte viruses by fluorescent MIPs (A). Metal chelation effect between zinc acrylate and template (B). [98]. MIP, Molecularly imprinted polymers. Source: Adapted with permission from Luo, L., Zhang, F., Chen, C., Cai, C. Visual simultaneous detection of hepatitis A and B viruses based on a multifunctional molecularly imprinted fluorescence sensor. Anal. Chem. 2019, 91, 15748 15756. Copyright (2019) American Chemical Society.

time of 5 min. In another study, electropolymerization was performed in the presence of aptamer/antigen complex to fabricate a nanohybrid ligands onto MWCNTs-Chitosan modified electrode for selective recognition of Hepatitis C core antigen [101]. Subsequent to removal of antigens from polydopamine matrix, the aptamer-MIP nanohybrid cavities were formed on the electrode surface. While the aptamers increased the selectivity for the antigen, MWCNTs-chitosan nanocomposite improved the charge transfer rate and resulted in an increased surface area for aptamer immobilization. The electrochemical nanohybrid platform could recognize viral antigen in a concentration range of 5 fg/mL to 10 pg/mL with an LOD of 1.67 fg/mL. Moreover, the proposed sensor could achieve impressive recovery values in the 50 times diluted serum samples.

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3.6 Conclusion and remarks

TABLE 3.4

NanoMIP and antibody-based SPR sensors for adenovirus detection [103].

Assay type

Surface ligand

Investigation range

Detection limit

Surface regeneration

Dissociation constant (KD)

Direct

MIP

0.01a20 pM

0.02 pM

N/A

3.10 3 10211 M

Direct

Antibody

0.15a20 pM

0.3 pM

Yes

1.41 3 1029 M

Sandwich

Antibody

0.004a0.5 pM

0.008 pM

Yes

2.30 3 10212 M

Solid phase synthesis method is an efficient imprinting approach employing glass beads as solid support on which the template is immobilized covalently. Such a solid phase synthesis method was used by Altintas and colleagues to synthesize bacteriophage MS2 imprinted polymer nanoparticles (nanoMIPs) [102]. The virus templates were immobilized on glass beads using glutaraldehyde as a linker and possible unreacted sites on the beads were blocked by ethanolamine. The optimized polymerization mixture containing different functional monomers and cross-linkers were introduced to the automated reactor after templated beads were packed in it. In addition, a functional monomer with a primary amine group was included into the polymer matrix to allow for covalent attachment of the nanoMIPs onto gold SPR sensor chip by EDC-NHS coupling chemistry. Following the radical initiated polymerization, the cold wash was applied to remove unreacted monomer and low-affinity polymer particles. The high affinity nano-MIPs were then collected by elution with 60 C distilled water. Due to the two-step elution process, solid phase synthesis provided nanoMIPs with homogenous binding sites similar to monoclonal antibodies. The developed nanoMIP-based SPR sensor achieved an LOD of 5 3 106 PFU/mL for an investigation range of 0.33 27 pmol. In another study, Altintas et al. compared the performances of antibody- and nanoMIP-based SPR sensors for adenovirus detection, where direct assay strategy resulted in 15 times lower LOD with nano-MIPs. The highest affinity was obtained with antibody-based sandwich assay, followed by direct MIP and antibody assays, respectively. The important characteristics of these adenovirus sensors are summarized in Table 3.4 [103].

3.6 Conclusion and remarks In this chapter, recent examples on virus biosensors were reviewed by focusing on the affinity ligand, detection method, construction procedure, and sensitivity. In these works various transducing systems, sensing surfaces, and recognition units were investigated to achieve

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superior sensitivity, enhanced selectivity, fast measurement, and pointof-care applicability. Antibodies are utilized in most of the studies due to high sensitivity toward the targeted virus subunit, however, nucleic acid-based receptors are favored when it comes to detection of viral genes. The disadvantages of antibodies and nucleic acids can be addressed by employing other synthetic probes like MIPs and PNAs as they provide stability and versatility at the same time. In addition, nanomaterials (e.g., AuNPs, QDs, CNTs) and nanopatterned surfaces are extensively utilized as sensing platforms to improve sensitivity by increasing the surface area. Despite the fascinating improvement in the biosensing field, the future holds challenges for virus biosensors. Differentiation of similar strains is one of the hurdles that should be carefully addressed. Even if the desired sensitivity is achieved, the strain selectivity is problematic especially when the recognition is performed on capsid rather than genetic sample. Comprehensive computational simulations may help addressing this issue and designing the selective synthetic receptors according to molecular binding interactions prior to experimental research. In this regard, we expect the use of computational studies extensively for designing virus specific affinity materials in the future. Miniaturization and development of user-friendly point-of-care devices will be the other essential requirements in the field of virus biosensors. As opposed to current diagnostic tools, new-generation biosensors must be easily evolved to a portable, easy-to-use, smart devices that can be utilized by untrained people to provide large-scale testing in a viral pandemic.

Acknowledgments Z.A. thanks the German Research Foundation (DFG, Grant number: 428780268) as the principal investigator.

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C H A P T E R

4 Biosensors for bacteria detection Yuwei Pan, Wenliang Li, Qingxin Hui and Zhugen Yang Cranfield Water Science Institute, School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom

4.1 Introduction Bacteria are widespread in the environment and are closely related to human daily life. Various diseases caused by bacteria have been the main concerns of researchers in the fields of food safety, clinical diagnosis, and public health. In developing countries, these bacterial pathogen-related diseases are the main causes of diseases or death due to lacking basic medical facilities and effective treatment methods [1]. Numerous pathogenic bacteria present in the land, water, air, animals, and plants may spread foodborne, waterborne, or nosocomial diseases to humans through the food chain [2]. Although antibiotics have been widely used to treat related infections, millions of people still suffer from bacterial infections, mainly due to delayed or incorrect diagnosis. Therefore there is an urgent need to explore economical, rapid, sensitive, specific, and user-friendly detection methods for on-site detection of bacteria. Traditional methods for bacteria detection are based on centralized laboratories. A variety of analysis methods have been applied to detect bacteria in samples such as food, saliva, blood, or urine, including viable cell count [3], staining assay [4], ultraviolet detection [5], fluorescence detection [6], polymerase chain reaction (PCR) [7], and loopmediated isothermal amplification (LAMP) [8]. Although these methods based on precision instruments are very reliable, they still have some limitations [9]. For example, their sensitivity and specificity may not be sufficient for effective diagnosis, and they require long analysis time from days to weeks, central laboratories, expensive facilities, and highly

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qualified technicians, suggesting that they are not suitable for use in resource-limited regions. In the past few years, researchers have investigated various methods for rapid bacteria detection to conquer the inadequacies of these traditional techniques. Among them, biosensors are a prospective method for bacteria detection since biosensors can provide the possibility for real-time information through fast on-site tracking during the entire detection process and detect multiple analytes in complex samples with less sample pretreatment [10]. A biosensor consists of a biological component and a physicochemical transducer. The biological components usually include antibodies, aptamers, cells, enzymes, engineered proteins, and imprinted polymers. The bioreceptor and the physicochemical transducer can work together and convert the biochemical signal into light, electricity, heat, weight, or other detection signals [11]. Today, there is growing interest in whole-cell biosensors based on incorporate living cells as biorecognition elements, utilizing their cellular responses to detect the corresponding analytes [12]. Advanced nanomaterials have also been introduced to enhance the capability of biosensors for bacteria identification. Furthermore, biotechnology, microfluidics, and electronic technologies have been explored to develop integrated biosensing platforms for point-of-care (POC) applications. This chapter presents an overview of the biosensors for bacteria detection. First, whole-cell biosensors for bacteria detection are presented. Then the introduction of nanomaterials to biosensors for bacteria detection are discussed. The applications of various biosensors for bacteria detection are subsequently introduced, including optical, electrochemical, and mechanical biosensors. This chapter also focuses on integrated biosensing platforms for multiplexed bacteria detection, providing a discussion of the challenges and perspectives of the applications for food safety, clinical diagnosis, drug development, and environmental monitoring.

4.2 Whole-cell biosensors for bacteria detection Whole-cell biosensors, also known as cell-based biosensors, are defined as biosensors that incorporate living cells as biorecognition elements, utilizing their cellular responses to detect the corresponding analytes [12]. The whole-cell biosensors can be classified into two types. Conventionally, the collective responses of cell populations upon external stimuli will be detected, recorded, and used to analyze the cell-cell interactions [13]; while the single cell sensing systems allow detection of cellular responses to study single cells or tiny bioactive units, including ion channels or receptors [14].

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Benefitting from the living-organism nature (i.e., cells could generate an intrinsic optimal response to the analytes via various stable molecular sensor arrays), the whole-cell biosensor could be applied to a broader spectrum of targets than molecular-based approaches, and provide rapid and sensitive analyses of analytes via in situ cell monitoring. Therefore the whole-cell biosensors are regarded as ideal in studying the physiological effects and/or bioavailability of analytes [12]. Moreover, there is no need for the expensive and time-consuming purification steps for proteins (antibodies) as required in other molecular-based approaches, such as enzyme-linked immunosorbent assay (ELISA), providing a relatively low-cost approach [15]. The cell sources of whole-cell biosensors are primarily cell lines and primary cultured cells. While the former is able to proliferate in vitro, and therefore is less demanding in preparation and culture, but only limited to a few cell types, the latter, extracted directly from animals, offers advantages of containing various cell types [12]. Bacterial cells such as Escherichia coli (E. coli) are also widely employed as bioreporters, as they could be readily genetically engineered for desired properties [16]. Although whole-cell biosensors could offer a number of advantages, they suffer from several intrinsic drawbacks, such as the stability and life-time of cells might not meet the required standards and the selectivity of the whole-cell biosensors might need to be improved for a specific analyte [12]. Moreover, since whole-cell biosensors rely on intracellular response of cells to a stimulus for detection, the detection on analytes that cannot cross the cell membrane is limited [17]. Although there are whole-cell biosensors able to detect analytes extracellularly, they can only be applied to a small number of targets due to the mechanism of action [18]. The breakthroughs in other areas such as nanotechnology, cellular mimicking and sensing, and microfluidics are thought to contribute to improve these shortcomings [12]. As a result, whole-cell biosensors, characterized by their unreplaceable advantages and improvable challenges, have been extensively utilized in various domains (e.g., environmental monitoring and medical diagnosis). Reported analytes of whole-cell biosensors include metabolites, heavy metals, and bacteria. The most recent example of whole-cell biosensors for detecting bacteria is a paper-based microfluidic sensor for the determination of lipopolysaccharides (LPS) [19], commonly seen on the outer wall of Gram-negative bacteria and important in reflecting bacterial contamination in food. They utilized the release of nitric oxide when mouse macrophage cells are treated with LPS and develop an electrochemical cell sensor composed of a screen-printed paper-electrode, a 3D cells-in-gelsin-paper culture, and a conductive jacket device (Fig. 4.1) [19]. This

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FIGURE 4.1 Schematic composition of paper-based 3D cell-based electrochemical sensor. Source: Reprinted from Jiang, H., et al., Miniaturized paper-supported 3D cell-based electrochemical sensor for bacterial lipopolysaccharide detection. ACS Sens., 2020;5(5):1325 1335 with permission from American Chemical Society.

device could achieve a limit of detection (LOD) of 3.5 3 1023 ng/mL of Salmonella enterica serotype Enteritidis (SE) in fruit juice samples. There have also been various whole-cell biosensors developed throughout the years for bacteria detection. Struss et al. [20] developed a colorimetric whole-cell biosensor to detect N-acylhomoserine lactones (AHLs). This device reached a LOD of 1 3 1028 M and is able to detect AHLs in physiological samples (e.g., saliva). A portable bioluminescence cell-based biosensor was developed by Roda et al. [21]. They employed the interaction between the analyte and the immobilized cells for the expression of luciferase, which could be engineered to emit green- or red-light for detection. Androgenic compounds and lactose analog isoproply beta-d-1 thiogalactopyranoside could be detected using this device with high precision and accuracy. In 2017, Idil et al. [22] proposed an electrochemical capacitive biosensor for E. coli identification combining microcontact imprinting technique, which could discriminate E. coli from other bacterial strains of similar shape and achieve a LOD of 70 CFU/mL. Moreover, it could also recover E. coli in river water to a rate of 81% 97%. Sun et al. [23], in 2018, developed a cell-based fluorescent biosensor for LPS detection by employing the cellular response upon binding between LPS and TLR4. This biosensor could reach a low LOD of 0.075 μg/mL and could be used to detect bacterial contamination in food.

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Whole-cell biosensors are regarded as promising analytical tools for the detection of bacterial pathogens, especially owing to their high sensitivity, specificity, and compatibility with various read-out methods, including electrochemistry, fluorescence, and colorimetry. A number of studies have been carried out to further develop cell-based biosensors, and enormous collaborative efforts have been made to prompt their extensive applications.

4.3 Nanomaterials-based biosensors for bacteria detection The demand for highly sensitive, highly selective, portable, and rapid detection of bacteria has promoted the continuous development of biosensors, and there is one particular trend in which advanced nanomaterials have been employed in this field to facilitate the bacterial sensing device evolvement [24,25]. The reason for using novel nanomaterials to design biosensors may connect with their distinctive physical, mechanical, magnetic, optical, chemical, and catalytic properties, which are potential advantages to develop biosensors with improved specificity and sensitivity [26]. Particularly, the changes made in nanomaterials like size, shape, surface charge, or composition can simply change the characteristics of the material itself, and these proper variations can enhance the sensitivity of the bacterial sensor and increase its versatility [27,28]. Moreover, nanomaterials can be combined with biological receptors to expand the detection capacities of diagnostic sensors due to the high affinity of selected biological molecules, mainly bacteriophage or proteins, involving enzymes, antibodies, and aptamers [29 31]. Thanks to the merits of nanomaterials, many studies have extensively applied various types of nanomaterials either as the transducers or receptors in bacterial sensors, and the frequently used nanostructures can be classified as noble metal nanoparticles (MNPs), carbon-based nanomaterials, and semiconductor nanocrystals [26,32]. The application of each type of nanomaterial and how its unique properties display useful functions in biosensor-mediated bacteria detection will be further discussed.

4.3.1 Noble metal nanoparticles Noble metal nanoparticles are usually used in the transducer part of bacteria detection sensors on account of their large surface area and unique interface characteristics that facilitate transduction and show desirable biocompatibility [26,33]. The desirable biocompatibility of the MNP allows various receptors to be attached to the surface of the MNP, and the common loading receptors include enzymes, aptamers,

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antibodies, antimicrobial agents, and organic ligands, which are able to identify bacterial analytes via physical or electrochemical reactions [34,35]. The introduction of various types of surface carriers shows great potential for amplifying and converting signals in the sensing step, which is beneficial for the whole transduction process. In addition, the production steps of the noble MNP are relatively simple and do not require advanced operations, and this can be seen as an advantage for large-scale and industrial manufacturing of biosensors without loss of quality [33]. The most prevailing noble metals used in the nano-based detective biosensors are gold, platinum, and silver, which are chemically inert but show significant physicochemical properties that provide superior selectivity and sensitivity. Particularly, the silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) are always popular for bacteria detection in food, environment, and biology areas, and this is because they have a high surface-to-volume ratio and show a high adsorptive property that can fully adsorb and interact with the passing through the analyte [36,37]. After the adsorption and interaction with the analyte, the properties of the nanoparticles may change, thereby generating an electrochemical or optical signal that can be detected, and the signal can quantify the concentration or content of the analyte. One interesting trend in recent years is that most studies also take advantage of surface enhanced Raman spectroscopy (SERS) when metal nanoparticles are applied for bacterial biosensor identification, and the SERS is well known as one of the ultra-sensitive analysis methods to enhance detection sensitivity and expand detection capability [26]. The combination of these two advanced technologies is because the electromagnetic (EM) enhancement occurs at the surface of the noble MNP, and the EM wave is beneficial to amplify the generally weak Raman signals [38]. Accordingly, it is possible to realize bacterial pathogen recognition at a low concentration, and this detective method may replace the conventional optical biosensing method with ultra-sensitivity and high selectivity [39]. Both optical biosensing and SERS-mediated sensing examples that use noble MNPs as the platform for bacterial identification are given in Table 4.1.

4.3.2 Carbon-based nanomaterials Carbon, as a material with some remarkable properties, has always been a well-acceptable platform to be a biosensing interface, and the nanocarbon refers to carbon particles with nanoscale [26,46]. Because of the nanoscale, some physicochemical properties of carbon are more superior for analytical performance, and nanocarbon makes a great

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TABLE 4.1 Noble MNPs AgNPs

AuNPs

Summary of noble metal nanoparticles for bacteria detection. LOD (CFU/mL)

Ref.

Pseudomonas aeruginosa, Staphylococcus aureus

100

[40]

Silver nanorod array-based SERS platform

P. aeruginosa

5.3 6 1

[41]

Electrochemical detection

Escherichia coli

-

[42]

Molecular beacon-Au nanoparticles hybrid nanoprobes recognize DNAs targets

E. coli

100

[43]

Colorimetric detection through capture of gold nanoparticles by chimeric phages

E. coli, P. aeruginosa, Vibrio cholerae, Xanthomonas campestris

-

[44]

SERS detection of bacteria using Au-coated magnetic nanoparticles

S. aureus

10

[45]

Detection principle

Target bacteria

SERS-based detection using silver nanocubes as Raman enhancer

contribution to the evolution of biosensors [47]. Current studies are extensively concentrated on the advancement and fabrication of these advanced materials, and carbon-based nanomaterials, particularly carbon nanotubes (CNTs), graphene, and fullerenes, are widely applied in designing biosensor for bacterial analysis [48]. One key way to detect bacteria with carbon-based nanomaterials is electrochemical detection. A special advantage of nanocarbon materials is that they have high electrical conductivity and show high sensitivity to surface electrical conductivity changes [47]. The high sensitivity of nanocarbon materials provides a significant basis to design electrochemical biosensors, and the relevant biosensors always have satisfactory detection limits and selectivity [49,50]. The nanocarbon materials also have great mechanical strength and can store charges, which is an advantageous characteristic for electron transfer and signal amplification [51]. Additionally, the potential of nanocarbon materials allows them to be used in extreme conditions over a wider range of temperature or dynamic due to their chemical stability and great electron transferability [46,49]. Generally, there are two categories of bacterial identification when it comes to carbon-based electrochemical biosensors, which are potentiometric sensors and impedance sensors, and the specific examples of these sensors are listed in Table 4.2.

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TABLE 4.2 Summary of carbon-based nanomaterials for bacteria detection. Biosensor type

Detection method

Carbon material

Target bacteria

LOD

Ref.

Electrochemical biosensor

Differential pulse voltammetry (DPV)

Poly(3,4ethylenedioxythiophene) (PEDOT) doped with carbon nanotubes

Mycobacterium tuberculosis

0.5 6 0.2 fg/mL

[52]

DPV

Antibody-single walled carbon nanotube bioconjugates

Staphylococcus aureus

10 CFU/mL

[53]

Cyclic voltammetry (CV), Electrochemical impedance spectroscopy (EIS)

Amino-modified SSDNA immobilized on multiwalled carbon nanotubes

Salmonella enteritidis, Salmonella typhimurium

55 67 CFU/mL

[54]

EIS

Bridged rebar graphene

Escherichia coli

10 CFU/mL

[55]

Multicolor fluorescent imaging

Red/blue fluorescent carbon dot attached magnetic nanoparticle

Methicillin-resistant S. aureus (MRSA), Salmonella DT104 superbug

-

[56]

Lateral flow immunoassay

Functionalized reduced graphene oxide

E. coli

105 CFU/mL

[57]

Optical biosensor

4.3 Nanomaterials-based biosensors for bacteria detection

89

Apart from the electrochemical biosensor, it is also feasible to develop an optical sensing device for nanocarbon materials to analyze bacteria as exhibited in Table 4.2. One primary reason for increasingly using nanocarbons is that the optical properties of nanocarbon materials show outstanding performance in bioimaging, and it is easy to apply these remarkable properties to meet higher demand for detection [49]. Mainly, when photodegradation, photobleaching, and photoblinking happen, the nanocarbon materials always maintain great stability, which is an outstanding feature compared to other common fluorophores. Additionally, it has the capability of emitting a wide range of light, from blue to red and tunable light emission after the absorption of photons, which are significant benefits for designing optical sensors [58]. The colorimetric sensor is an attractive way to employ nanocarbon materials to identify bacteria since the analytes easily interact with fluorophores from the carbon itself, and the interaction can be captured through changes of fluorescence, color, and image.

4.3.3 Semiconductor nanocrystals Semiconductor nanocrystals are widely applied in pathogen diagnosis, and for bacteria detection, it is also a promising option to develop a sensing device. The initial reason is related to the great progress made in semiconductor technology and surface chemistry, which allows the surface of semiconductor nanocrystals to be a place for the conjugation between diverse bacterial biomarkers and crystals [26]. More importantly, semiconductor nanocrystals are able to emit light under the excitation light source, and the light emission is tunable through the variations made on nanoparticles [26,59]. Also, the semiconductor nanocrystals exhibit nonlinear optical properties, high stability to thermal and photochemical changes, and quantum confinement effects. Compared to conventional organic fluorescent dyes, these noticeable optical properties facilitate the development of semiconductor nanocrystal-mediated fluorescence detection, which offers a low limit of detection. However, to use the spectroscopic technique for bacterial determination, some limitations of semiconductor nanocrystals need to be addressed first. The first problem that needs to be noticed is the reactive nature of nanostructured semiconductor, which is caused by the nanosize leading to the high surface area-to-volume ratio [26]. Another limitation of the semiconductor nanocrystal relates to its unsaturated and nonstoichiometry bonds, which contribute to the presence of surface states and unstable luminescence. In addition to the spectroscopic technique, these nanocrystals can also adopt other analysis methods,

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TABLE 4.3 Summary of quantum dots for bacteria detection. LOD (CFU/mL)

Ref.

Escherichia coli, Salmonella typhimurium

16 28

[60]

Fluorescence intensity

Serratia marcescens

10

[61]

CdS nanoparticles

Fluorescence spectrophotometry

Sulfate-reducing bacteria

100

[62]

Chitosan modified CdS quantum dots

Spectroscopy methods

Pseudomonas aeruginosa, Staphylococcus aureus

150 200

[63]

CdSe/ZnS quantum dots (QD)/NH2-Apt bioconjugates

Optical assay

E. coli

100

[64]

CdSe/ZnS quantum dots nanoparticles

Fluorescence signal

Salmonella

1000

[65]

Quantum dots

Detection method

Target bacteria

CdTe quantum dots conjugated with antibodies

Fluorescence imaging

CdTe quantum dots conjugated with Concanavalin A

such as sulfuration, colony counting, and flow cytometry techniques to detect pathogens. The most frequently used semiconductor in the bacterial biosensor is quantum dots, the II VI, III V, and IV VI group semiconductor materials, and some research regarding the employment of quantum dots for bacterial sensing are summarized in Table 4.3.

4.4 Various biosensors for bacteria detection According to the methods of signal transduction, biosensors can be divided into optical, electrochemical, and mechanical biosensors. Many reviews have been dedicated to biosensors for bacteria detection, and some reviews have reported on biosensors for detecting waterborne pathogens to monitor water quality [66], and biosensors for detecting foodborne pathogens to analyze food safety, especially electrochemical biosensors [67]. Various nanomaterials integrated in biosensors have also been reviewed, with carbon nanomaterials for electrochemical biosensing of pathogens [68], and AuNPs for colorimetric biosensing of pathogens [69]. In this section, we provide a broad overview of various biosensors for bacteria detection.

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91

4.4.1 Optical biosensors The optical transducers can detect small changes in refractive index (RI) or thickness that occur when cells bind to receptors fixed on the surface of the transducer, which are especially suitable for direct bacteria detection [10]. An optical biosensor exploits the changes in the optical properties of the sensor surface caused by the binding of an analyte, and then transduces them to the detector [70]. With the characteristics of simple operation, rapid detection, high sensitivity, optical fibers, and integrated optical sensors [9], various optical biosensors have been developed for bacteria detection, including chemiluminescence biosensors, colorimetric biosensors, fluorescent biosensors, interferometric biosensors, and surface plasmon resonance biosensors, and some related applications are mentioned in Table 4.4. Chemiluminescence (CL) is a luminescence phenomenon produced by the energy released during a chemical reaction that excites luminescent substances, which can avoid excitation light sources, thereby avoiding interference from light scattering [92]. Since no optical devices or external light sources are required, CL biosensors show advantages including rapidity, sensitivity, and low equipment cost. For example, Zhang et al. [71] presented a magnetic bead (MB)-based CL immunoassay for E. coli O157:H7 determination, with a LOD of 1.2 3 103 CFU/mL. The samples were added to induce primary immune recognition, and then biotin-coupled antibodies were added to initiate the second immune recognition. In addition, several CL biosensors have been used for determination of DNA sequences from bacteria such as Vibrio cholerae and Shigella species [93], S. enterica, and Campylobacter jejuni [94]. For instance, a cloth-based CL DNA biosensor was proposed for Listeria monocytogenes hlyA gene detection in milk [72]. The biosensor introduces the hybrid chain reaction (HCR)-based sandwich protocol, where the HCR products hemin/G-quadruplex DNAzyme molecules can be used as signal tags to provide efficient catalytic ability and enhance CL signal effectively. Various reagents including nanoparticles, metal oxides, and metal ions have been used for CL signal amplification since the original CL signal without enhancement is very weak [95]. Among the nanomaterials for construction of CL biosensors, mesoporous silica nanoparticles (MSNs) are popular in view of their large surface area and good biocompatibility. Gu et al. [73] developed a CL biosensor for S1 nuclease, E. coli, and S. aureus determination in drinking water and skim milk. The authors prepared hemin-MSN@DNA as an ultrasensitive CL probe for bacteria detection. The CL tag molecule-heme was encapsulated in the MSN mesopores through a specific DNA gate. Due to the heme-mediated CL enhancement, the best match between the specially

2. Biomedical applications

TABLE 4.4 Applications of optical biosensors for bacteria detection.

Biosensor type

Target bacteria

Bioreceptor

Linear range (CFU/mL) 4.3 3 10

LOD (CFU/mL)

Detection time

1.2 3 10

,2 h

95.2% 105.6%

Recoveries

Relative standard deviation (RSD)

Sample

Ref.

1.63% 4.99%

Spring water, apple juice, and skim milk

[71]

Chemiluminescence biosensor

Escherichia coli O157:H7

Antibody

4.3 3 10

Chemiluminescence biosensor

Listeria monocytogenes

Capture DNA

102 107

50

-

-

4.00 4.68%

Milk

[72]

Chemiluminescence biosensor

E. coli O157:H7 Staphylococcus aureus

Aptamer

10 109

3 2.5

-

90.57% 98.20%

1.10% 13.5%

Drinking water and skim milk

[73]

Colormetric biosensor

E. coli S. aureus

PEI-AuNPs

106 108

10

2 3h

-

-

Drinking water

[74]

Colormetric biosensor

E. coli S. aureus

Fluorescent probe

-

10

1h

-

-

Hand

[75]

Colormetric biosensor

S. aureus

Aptamer

102 107

81

5.5 h

-

-

Milk

[76]

Colormetric biosensor

S. aureus

Aptamer

1 105

1

B15 min

97.5% 110%

3.1% 4.8%

Cerebrospinal fluid, urine, split and serum

[77]

Colormetric biosensor

Salmonella typhimurium

Antibody

10 104

11

2.5 h

109.8%

-

Chicken carcass

[78]

Fluorescent biosensor

Salmonella enteritidis

Fluorescent probe

1.5 3 102 3 3 103

1.5 3 102

2h

-

-

Milk

[79]

3

5

3

Fluorescent biosensor

S. typhimurium

Antibody

1.4 3 102 1.4 3 106

58

2h

Fluorescent biosensor

E. coli O157:H7

DNAzyme

10 103

1.57

1.5 h

Fluorescent biosensor

Pseudomonas aeruginosa

Dual aptamers

10 107

1

1.5 h

Fluorescent biosensor

Coliform bacteria

Specific probe

-

-

Interferometric biosensor

Francisella tularensis

Antibody

4 3 104 250 3 104

Interferometric biosensor

Bacillus cereus E. coli

Antibody

Interferometric biosensor

P. aeruginosa MRSA

SPR biosensor

E. coli

2.17% 6.38%

Apple juice

[80]

,2%

Drinking water and apple juice

[81]

91.4% 108%

#6.5%

Skim milk, orange juice, and popsicle

[82]

-

-

-

Water and domestic wastewater

[83]

104

17 min

-

-

Serum

[84]

70 7 3 105 40 4 3 104

12 4

12.5 min 25 min

-

-

Buffer medium Ascitic fluid

[85]

Antibody Aptamer

800 1.6 3 105

49 29

# 20 min

-

-

-

[86]

Antibody

-

1.5 3 103

-

-

-

-

[87]

10 min

-

-

Sodium chloride injection

[88]

2

6

2

84.82% 98.39%

SPR biosensor

E. coli

Small molecule

10

SPR biosensor

S. typhimurium

Antibody

4.7 3 105 9.5 3 106

4.7 3 105

-

-

-

Romaine lettuce

[89]

Aptamer

5 3 10

128

-

85% 123%

6.5% 8.3%

Chicken

[90]

Aptamer

4

4

-

-

-

Culture

[91]

SPR biosensor SPR biosensor

S. typhimurium

Lactobacillus acidophilus S. typhimurium P. aeruginosa

10

10

2

10

10 9

10

8

10

94

4. Biosensors for bacteria detection

structured DNA gate and the MSN cylindrical hole, the capped DNA can be specifically switched after exposure to the bacterial lysate, allowing the increased hemin release. Furthermore, the hemin release will bring about a significant increment in the CL signal, which can be used to quantify bacteria. Colorimetry is a popular optical method that can identify bacteria in samples by observing visual color changes without any expensive equipment. Therefore the colorimetric method is a direct, economical, and convenient detection method, which is conducive to the development of low-cost biosensors. Thiramanas et al. [74] established a platform for colorimetric detection of E. coli and S. aureus in drinking water, which is based on the competitive binding mechanism of enzyme-/bacteria-nanoparticles complex. Kang et al. [75] also designed a colorimetric assay for detecting E. coli and S. aureus on contaminated hand employing an enzymatic-sensitive bacteria biosensor. After target bacteria were introduced, endogenous alkaline phosphatase (ALP)-bacteria destroyed the 2-hydroxychalcone phosphate groups and activated the excitedstated intramolecular proton transfer (ESIPT) and aggregation-induced emission (AIE) processes, which caused the biological probe to emit from yellow to red. Recently, Yu et al. [76] reported a colorimetric biosensor for S. aureus determination, which can detect 96 samples simultaneously. In the presence of S. aureus, due to the stronger interaction between S. aureus and aptamer, the aptamer was dissociated from the capture probe-aptamer duplex. The subsequent single-stranded capture probe was hybridized with a three-way junction (TWJ) nanostructure composed of three detection probes. The TWJ DNA nanostructure and the complex play important roles in amplifying detection signal and shortening analysis time. However, it shows some drawbacks such as a long detection process (5.5 h). In another approach, S. aureus in clinical samples was determined by a colorimetric biosensor involving stochastic DNA dualwalkers, which can speed enzyme digestion reaction and reduce response time [77]. The biosensor displayed the color change within 15 min, which was significantly shorter than those of other colorimetric biosensors, revealing great promise for ultrafast colorimetric bacteria detection. Moreover, a colorimetric biosensor was developed for Salmonella typhimurium detection in chicken carcass, using a magnetic grid separation column with immunomagnetic particle chains to separate and concentrate S. typhimurium cells [78]. The biosensor successfully separated around 70% of S. typhimurium cells, demonstrating that the magnetic grid separation method showed high separation efficiency. The detection of fluorescent biosensors is usually based on a combination of optical transducers and fluorescent bioreceptors. This type of biosensor is simple, sensitive, easy to fabricate, and is widely used to

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95

identify extremely low concentrations of analytes. Fluorescence resonance energy transfer (FRET) is based on the energy transfer between fluorescent particles used as energy donors and quencher particles used as acceptors. If the fluorescence of these two kinds of particles is within a certain distance from each other (Foster distance), the fluorescence of energy donors can be quenched by acceptors. The change in fluorescence intensity during FRET can be used to quantify the bacterial concentration. Yang et al. [79] proposed a biosensor for Salmonella enteritidis detection based on oligonucleotide-specific hybridization and FRET. In this case, DNA nicking endonuclease is used owing to its high sensitivity to dsDNA and enables the FRET reaction performed at room temperature. The DNA nicking enzyme will recognize and cleave the dsDNA produced by the fluorescent probe and the target strand thus carbon nanoparticles (CNPs) dissociate from black hole quencher 1 (BHQ 1) and emit fluorescence. S. aureus was also detected from a FRET biosensor based on graphene quantum dots (GQDs) and AuNPs [96]. A microfluidic biosensor was designed for online S. typhimurium detection in apple juice using MNPs [80]. After the fluorescent bacteria were concentrated and injected into the microfluidic chip after magnetic separation, the fluorescent microscope is used for fluorescence excitation to monitor the fluorescence points, and the smartphone APP is used for real-time video processing to count the number of fluorescent bacteria (Fig. 4.2A). Nevertheless, some problematic disadvantages regarding video processing speed and image capture quality cannot be overlooked. In 2020, a magnetic DNAzyme-copper nanoclusters (CuNCs) fluorescent biosensor was reported for E. coli O157:H7

FIGURE 4.2 Examples of optical biosensors for bacteria detection, including (A) fluorescent biosensor and (B) SPR biosensor. Source: Reprinted from Wang, S.Y., et al., A microfluidic biosensor for online and sensitive detection of Salmonella typhimurium using fluorescence labeling and smartphone video processing. Biosens. Bioelectron., 2019;140:69 76 with permission from Elsevier and Xu, Y., et al., Omega-shaped fiber-optic probe-based localized surface plasmon resonance biosensor for real-time detection of Salmonella typhimurium. Anal. Chem., 2018;90(22):13640 13646 with permission from American Chemical Society.

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4. Biosensors for bacteria detection

detection, where triple signal amplification and dual output of signal were realized [81]. Although the biosensor exhibited a LOD of 1.57 CFU/mL, it was not specific for homologous bacteria identification. In the same year, fluorescent polydopamine-polyethyleneimine (PDA-PEI) copolymer dots were prepared, which were labeled with two aptamers as fluorescent probes for Pseudomonas aeruginosa identification [82]. Compared with a single aptamer, two aptamers can simultaneously bind to different parts of the cell surface, thereby improving the detection sensitivity, suggesting that multiple aptamers can extended for bacteria detection to achieve high sensitivity. More recently, simultaneous detection of four main types of coliform bacteria in domestic wastewater were achieved by a fluorescence in situ hybridization (FISH) method [83]. The FISH method is attractive to be implemented in simultaneous detection or identification of fecal and nonfecal coliforms in water. Interferometry is an optical method used to measure changes in RI caused by biochemical reactions or interactions (e.g., DNA hybridization, antibody/antigen, enzyme/substrate interaction) [97]. By appropriately selecting the sensing film, the interferometer can be employed for identification and quantification of biomolecules. Through proper selection of bioreceptors and calibration, the concentration of the bacteria can be measured by an interferometric biosensor. For example, Mechaly et al. [84] developed an automated, fluidics-free, and online monitoring Octet Red Biolayer interferometry (BLI) system for Francisella tularensis detection in serum. Compared to the detection limits of 105 107 CFU/mL reported in the former commercial tests for F. tularensis detection (within 15 40 min), the achieved LOD (104 CFU/mL) and analysis time (17 min) were both lower. In another study, Maldonado et al. [85] proposed a bimodal waveguide (BiMW) biosensor involving photon interference for determination of Bacillus cereus and E. coli in ascitic fluid. Light is initially coupled in a single-mode waveguide that propagates the fundamental mode. Light will be coupled to another waveguide that supports two modes after a certain distance. Both modes then interfere and propagate until exiting the output of the waveguide. When the target bacteria are captured by the antibody through covalent coupling, the surface RI will change. Compared with other label-free biosensors, the excellent sensitivity of the biosensor may be due to the long interaction length (15,000 mm) of light propagating in the waveguide sensor area. Recently, the same group [86] modified the silanization scheme to further improve the surface repellency characteristics of the biosensor surface and adapted the BiMW biosensor for P. aeruginosa and MRSA detection. However, the application of the developed biosensor for testing complex clinical samples remains to be explored. For surface plasmon resonance (SPR) biosensors, the analyte solution usually passes through the biomolecule recognition interface and

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97

interacts with the bioreceptors fixed on the biosensor surface, resulting in a change in the RI near the biosensor surface [98]. Even though SPR biosensors only react to the RI changes caused by molecular binding events without the need for extra reagents or complex sample preparation procedures, they still have some disadvantages. For example, SPRbased systems are usually large and expensive equipment, and the size of the pathogen can interfere with certain measurements, leading to high detection limits. Arcas et al. [87] established a U-shaped goldcoated plastic optical fiber (POF) biosensor for E. coli determination. The biosensor only achieved a LOD of 1.5 3 103 CFU/mL, so it remains challenging to enhance sensitivity by improving the immobilization of bacteria or developing a novel high-resolution photoelectric device. E. coli was also detected by a laser scanning confocal imaging-surface plasmon resonance (LSCI-SPR) system [88]. The biosensor provides a promising platform for quantitative determination of known gram-negative bacteria and identification of unknown gram-negative bacteria in 10 min. Moreover, S. typhimurium in romaine lettuce was determined by a SPR biosensor using antibodies to target Flagellin of S. typhimurium [89]. Compared to direct assay, the preincubation one-step sandwich assay improved the SPR response significantly, with an average increase of 220%, suggesting that a higher molecular mass combined with the biorecognition molecules on the biosensor surface will result in a higher RI change and a higher SPR response. For local surface plasmon resonance (LSPR) phenomenon, when an unmodified analyte is bound to the surface of a local dielectric, LSPR will show intensity changes or shifts of single sharp spectral extinction peak. The precious MNPs and incident photons oscillate coherently with each other, where the resonance is related to MNPs [99]. Due to the strong plasma effect near the boundary of dielectric materials, gold has been widely used for fabrication of LSPR biosensors. Xu et al. [90] presented a Ω-shaped fiber-optic localized surface plasmon resonance (FOLSPR) biosensor for S. typhimurium quantification in chicken (Fig. 4.2B). Compared with the straight-shaped and the U-shaped FOLSPR, the observed RI sensitivity of the Ω-shaped fiber-optic probe was increased by 14 times and 2.5 times, respectively. An aptamerimmobilized LSPR-based biosensor was also fabricated for the detection of Lactobacillus acidophilus, S. typhimurium, and P. aeruginosa [91]. The presence of target bacteria can be demonstrated by the changes in LSPR peaks, where the extinction intensity increased logarithmically relative to the bacteria concentration. It is noteworthy that the amount of sample needed is only 3 μL while other sensors usually require tens to hundreds of microliters. The possibility of improving the sensitivity of the biosensor remains to be explored, such as using aptamers with higher specificity and affinity.

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4. Biosensors for bacteria detection

4.4.2 Electrochemical biosensors Defined by the IUPAC in 1999, electrochemical biosensors are independent integrated devices that can use electrochemical transduction elements for quantitative or semiquantitative analysis of reactions catalyzed by biometrics. The current generated by oxidation and reduction reactions is associated with the concentration of current/ generated electroactive substances, which can be measured by electrochemical biosensors [66]. Compared with optical biosensors, electrochemical biosensors provide considerable detection sensitivity even in turbid media because there are specific identification elements in the biometric layers of the former ones [98]. Today, electrochemical biosensors characterized by low cost, high accuracy, integration, and miniaturization have been widely used to identify bacteria [9]. Depending on different electrical signals generated by targets, electrochemical biosensors can be classified into potentiometric biosensors, amperometric biosensors, impedimetric biosensors, electrochemiluminescent biosensors, and voltammetric biosensors. Examples of these biosensors are listed in Table 4.5. Potentiometric biosensors are based on the determination of potential difference between the working electrode and the reference electrode in the electrochemical cell. When the analyte concentration changes, the potential of the working electrode changes significantly while that of the reference electrode remains unchanged [66]. Furthermore, the analysis signal is logarithmically related to the analyte concentration. Potentiometric biosensors are very suitable for measuring lowconcentration analytes in very small samples. Single-walled carbon nanotubes (SWCNTs) are usually used in potentiometric analysis, which can trigger potential responses in the presence of target bacteria by using biosensors based on SWCNT/aptamer hybrid materials. These materials display remarkably high surfaceto-volume ratio of nanotubes and supports charge transfer between ions. For instance, Zelada-Guillen et al. [100] presented a potentiometric biosensor for E. coli CECT 675 detection using SWCNTs as ion-toelectron transducers. The biosensor can complete E. coli detection in complex matrices (e.g., milk and apple juice) within a few minutes. In addition to E. coli, S. aureus on contaminated pig skin was detected using a similar potentiometric biosensor developed by the same group (Fig. 4.3A) [101]. The performance of biosensors by using two strategies to functionalize carbon nanotubes with aptamers were compared, including noncovalent adsorption and covalent bond formation. The detection limit obtained by the covalent functionalized biosensor was five orders of magnitude lower than that of the noncovalent functionalized biosensor. The biosensor was successfully utilized for S. aureus

2. Biomedical applications

TABLE 4.5 Applications of electrochemical biosensors for bacteria detection.

Biosensor type

Target bacteria

Bioreceptor

Linear range (CFU/mL)

LOD (CFU/mL)

Detection time

Recoveries

Relative standard deviation (RSD)

4

10 20 min

80% 90%

Sample

Ref.

-

Milk and apple juice

[100]

Potentiometric biosensor

Escherichia coli

Aptamer

4 2.4 3 10

Potentiometric biosensor

Staphylococcus aureus

Aptamer

8 3 102 108

8 3 102

6 11 min

15%

-

Pig skin

[101]

Potentiometric biosensor

S. aureus

Aptamer

-

1

1 2 min

-

-

Culture

[102]

Potentiometric biosensor

Listeria monocytogenes

Aptamer

10 500

10

0.67 h

88% 95%

-

Coastal seawater

[103]

Amperometric biosensor

Bacillus cereus, Mycobacterium tuberculosis

Bacteriophage

-

10

8h

-

-

Culture

[104]

Amperometric biosensor

E. coli

Enzyme

20 1 3 105

10

4h

-

-

Culture

[105]

10

90 min

-

-

Source, pond, and sludge

[106]

4

7

Amperometric biosensor

Streptococcus agalactiae

Antibody

10 10

Amperometric biosensor

Vibrio parahaemolyticus

Antibody

2.2 2.2 3 108

2

-

93.7% 108.3%

4.92%

Fish

[107]

Impedimetric biosensor

E. coli

Bacteriophage

103 109

8 3 102

,15 min

-

-

Skim milk and lake water

[108]

Impedimetric biosensor

E. coli

Antibody

102 105

102

-

-

-

Culture

[109]

Impedimetric biosensor

SRB

Antibody

1.8 3 101 1.8 3 107

1.8 3 101

-

-

-

Culture

[110]

(Continued)

TABLE 4.5 (Continued)

Biosensor type Impedimetric biosensor Impedimetric biosensor

Target bacteria S. aureus L. monocytogenes

Bioreceptor Aptamer Peptide

Linear range (CFU/mL) 10 10 10

3 3

4

LOD (CFU/mL)

Detection time

Recoveries

Relative standard deviation (RSD)

10

10 min

-

-

Culture

[111]

-

-

-

Milk

[112]

70 min

75% 116.7%

,5.8%

Milk, lake water, human saliva, and human urine

[113]

10

6

3

10

10

9

3.1 3 10

2

Sample

Ref.

Electrochemiluminescent biosensor

S. aureus

IgG

10

Electrochemiluminescent biosensor

Pseudomonas aeruginosa

Phage

1.4 3 102 1.4 3 106

56

30 min

78.6% 114.3%

,7.3%

Milk, glucose injection, and human urine

[114]

Electrochemiluminescent biosensor

E. coli O157:H7

AMP

5.0 3 102 5.0 3 105

1.2 3 102

-

-

-

-

[115]

Electrochemiluminescent biosensor

E. coli O157:H7

SIP

10 107

8

-

99.8% 102%

1.39% 2.31%

Water

[116]

Voltammetric biosensor

L. monocytogenes, S. aureus

Peptide

10 107

93

1 min

-

-

-

[117]

Voltammetric biosensor

Salmonella typhimurium

Antibody

10 106

10

1h

94.2% 118.0%

1.4% 4.5%

Milk

[118]

Voltammetric biosensor

E. coli

Enzyme

-

5

18 h

-

-

Water

[119]

4.4 Various biosensors for bacteria detection

101

FIGURE 4.3 Examples of electrochemical biosensors for bacteria detection, including (A) impedimetric biosensor and (B) electrochemiluminescence biosensor. Source: Reprinted from Ref. Etayash, H., et al., Impedimetric detection of pathogenic Gram-positive bacteria using an antimicrobial peptide from class IIa bacteriocins. Anal. Chem., 2014;86(3):1693 1700 with permission from American Chemical Society and Yue, H., et al., Label-free electrochemiluminescent biosensor for rapid and sensitive detection of Pseudomonas aeruginosa using phage as highly specific recognition agent. Biosens. Bioelectron., 2017;94:429 432 with permission from Elsevier.

determination in contaminated pig skin, indicating great potential for S. aureus detection in clinical diagnostics. A potentiometric aptasensor was also developed for S. aureus detection with enhanced performance [102]. Two versions of aptasensors were constructed, using graphene oxide (GO) and reduced graphene oxide (RGO) as transduction layers, respectively. Although both aptasensors can achieve detection limits as low as 1 CFU/mL in 1 2 min, the noise level involving GO is higher than those involving RGO. These biosensors can provide ultra-low detection limits extremely quickly, which is envisioned to set the way for new microorganism analysis systems. Further efforts are required to investigate filtering schemes to remove interfering electroactive substances. Moreover, L. monocytogenes in coastal seawater was determined by a potentiometric biosensor using an aptamer as a recognition element and protamine as a transduction indicator [103]. By combining the biosensor with an online filtration system to continuously filter seawater, the bioassay can enrich bacteria and eliminate common charged species and exhibited good recovery and high precision for L. monocytogenes detection in coastal seawater, which was consistent with those achieved by standard plate counting. Amperometric biosensors are based on measuring current changes at a constant potential, and the concentrations of analytes in the solution are proportional to current changes. Compared with the logarithmic relationship in potentiometric biosensors, amperometric biosensors exhibit an obvious linear concentration dependence [98]. Noble metals, graphite, carbon paste, and conductive polymers are suitable electrode

2. Biomedical applications

102

4. Biosensors for bacteria detection

materials for fabrication of amperometric biosensors. Although amperometric biosensors are relatively simple and easy to miniaturize, they will be limited by the specificity of the potential, where redox active substances may interfere with the amperometric signal and cause inaccurate results. Bacteriophages have extremely high specificity to their hosts since phage will first attach to specific receptors on bacterial cells after phage infection. The lysis of the phage-infected bacteria will cause the cell contents to be released into the surrounding medium, thus specific inherent cellular compounds can be quantified. As an example, Yemini et al. [104] proposed a phage-sensed amperometric biosensor for the identification of B. cereus and M. tuberculosis, with a detection limit of 10 viable cells mL21. The experimental setup was based on online electrochemical monitoring of a mixture of culture medium and substrate, which could analyze up to eight different samples simultaneously. Based on Fe3O4 MNPs-coated CNTs nanocomposite, a tyrosinase (Tyr) biosensor was established for E. coli determination [105]. Integrated with a flow injection assay (FIA) system, the biosensor was based on quantification of phenol generated by enzymatic reaction involving E. coli. In another approach, Va´squez et al. [106] developed an amperometric biosensor for Streptococcus agalactiae detection in source, pond, and sludge. A single antibody was employed to immobilize bacteria on a screen-printed carbon electrode (SPCE) and was also connected to a streptavidin-conjugated horseradish peroxidase (HRP) reporter to amplify electrochemical signal (Fig. 4.3B). The biosensor was demonstrated for S. agalactiae detection in spiked lake water, with the current intensities lower than those in spiked buffered solutions. This phenomenon indicates that the matrix effects play significant roles in current intensities, which may be due to the presence of particles, sediment, and coexisting bacteria. In the same year, a heterosandwich amperometric biosensor was designed for Vibrio parahaemolyticus detection in porcupine fish [107]. Compared to other nanomaterial-based methods, the biosensor showed a comparable sensing performance and an extremely low LOD of 2 CFU/mL. The investigations on improving antibody modification schemes and simplifying electrode manufacturing procedures are currently underway in their laboratory. In the working process of an impedimetric biosensor, a low-voltage sinusoidal excitation potential is applied to the electrochemical system at different frequencies to generate currents, and then the impedance change is determined as a function of frequency, which is evaluated based on the equivalent circuit [66]. Impedance biosensors can measure target molecules without restriction, and do not need to use analytes as enzymatic substrates or form electroactive substances. However, impedance biosensors may have variable repeatability, high detection limits,

2. Biomedical applications

4.4 Various biosensors for bacteria detection

103

and nonspecific binding phenomenon. As previously described, bacteriophages are expected to be used for bacteria detection due to their ability to distinguish between dead and living cells and higher thermal stability than antibodies with comparable sensitivity and specificity. For example, Tlili et al. [108] fabricated an impedimetric biosensor for screening and viability assay of E. coli, using bacteriophage T4 as recognition element for living bacteria. An impedimetric biosensor was also presented for E. coli O157:H7 quantification, where the antibodies were covalently linked to a conducting Polyaniline (PANI) film surface for biorecognition, without the need of any secondary antibodies or labels [109]. Compared with other secondary antibody-based methods, the detection mechanism of the biosensor is very simple. Reduced graphene sheets (RGSs) are monomolecular layers of carbon atoms stacked into a dense honeycomb crystal structure. Wan et al. [110] prepared an impedimetric immunosensor for marine pathogenic sulfate-reducing bacteria (SRB) detection, with a detection limit of 1.8 3 101 CFU/mL. RGSs were used as electronic conductors to achieve sensitive, selective, and stable detection of pathogens. Faraday impedance spectroscopy was performed for SRB quantification, providing that the diameter of the Nyquist diagram is equal to the charge transfer resistance (Rct). An impedimetric aptasensor was also proposed for human pathogen S. aureus detection, using protein A-binding aptamer as recognition element [111]. The aptamer was immobilized on the electrode by self-assembly, and the target bacteria was detected using a three-electrode device. The biosensor could obtain results within 10 min, which was consistent with previous methods with a significantly short analysis time. Moreover, further efforts should be devoted to evaluating different S. aureus strains and their possible antibiotic resistance. Compared to antibodies and aptamers, antimicrobial peptides (AMPs) are more stable in severe environments and reveal a wide range of activities and affinity to Gram bacteria. Etayash et al. [112] established an impedimetric biosensor for Gram-positive bacteria detection using AMP leucocin A as selective probes. Through the interaction between the C-terminal carboxylic acid of the peptide and the free amine of the preattached thiolated linker, leucocin A was chemically synthesized and covalently immobilized on the microelectrode for L. monocytogenes detection (Fig. 4.2A). The biosensor showed a LOD of 103 CFU/mL, which was similar to other reports such as AMP-based fluorescence tests. Although the biosensor can detect L. monocytogenes with concentration as low as 103 CFU/mL in 10% milk, the result shows that the biosensor is not very suitable for bacteria determination in pure milk due to the nonspecific adsorption of carbohydrates, fats and proteins in milk. Different methods will be explored to improve selectivity

2. Biomedical applications

104

4. Biosensors for bacteria detection

and sensitivity by employing covalently immobilizing AMPs, multiple ligands, or other sensor platforms. Electrochemiluminescence (ECL) is based on the transfer of electrons on the surface of electrodes with suitable luminophores and coreactants. The combination of ECL biosensors and magnetic beads is attractive to be implemented in food testing, water monitoring, and clinical applications. Carboxyl graphene can be used as a substrate for bioreceptor adsorption because it possesses a large surface area and can strength the signal of the Luminol ECL system. For instance, Yue et al. [113] adopted an ECL biosensor for S. aureus detection, which was fabricated by depositing the carboxyl graphene/porcin immunoglobulin G (IgG) composite on the glassy carbon electrode surface. The recovery rates of the biosensor in actual samples were 75.0% 116.7%, demonstrating that it is an effective pathogen screening method for environmental monitoring, medical diagnosis, and food safety control. Similarly, an ECL biosensor was reported [114] for P. aeruginosa detection, where a phage (PaP1) isolated from hospital sewage was used as a bioreceptor (Fig. 4.2B). After the specific binding between PaP1 and P. aeruginosa, the formed nonconductive biological complex impeded the interface electron transfer and prevented the diffusion of ECL active molecules, thereby weakening the ECL signal of luminol. Compared with other bioreceptors (e.g., antibodies, aptamers, AMPs), phages can be easily prepared in microbiological laboratories with limited resources. E. coli O157:H7 was also determined by two ECL biosensors, where AMP Magainin I was used as a bioreceptor while the ruthenium complex (Ru1) was employed as ECL label [115]. The target bacteria were first captured by the modified Magainin I, and then combined with the Ru1-labeled peptide as a signal probe. The LOD of the biosensor (II) was lower than that of the biosensor (I) (1.2 3 102 vs 2.3 3 102 CFU/mL) due to its lower background signal. However, the sensitivity of the former one was lower than that of the latter one. In another case, the ECL properties of nitrogen-doped graphene quantum dots (N-GQDs) and high selectivity of surface imprinted polymer (SIP) were employed to develop an ECL biosensor for E. coli O157:H7 detection [116]. Dopamine and target bacteria were first electropolymerized on the electrode. After target bacteria were removed, the constructed SIP can specifically recognize and capture them. The specific binding between target bacteria and antibody generated ECL signals in the presence of K2S2O8. Voltammetric biosensors analyze target bacteria by applying varying voltages. Specifically, a potential is applied to the electrode surface, and the current change is measured by two or three electrodes [120]. Different voltammetric biosensors have been used to detect bacteria, including linear sweep voltammetry (LSV), potential step voltammetry (PSV), and square wave voltammetric (SWV) biosensors. For example,

2. Biomedical applications

4.4 Various biosensors for bacteria detection

105

Eissa et al. [117] presented a biosensor for simultaneous determination of common foodborne pathogens including L. monocytogenes and S. aureus. The cleavage of the peptides by the corresponding proteases will cause MNPs to dissociate from the electrode surface, triggering changes in the SWV peak currents that can be used for bacterial quantification. The detection limits for L. monocytogenes and S. aureus were much lower than those from recent electrochemical biosensors. In addition, compared to previous affinity-based biosensors, the detection can be completed within 1 min. However, due to the lack of knowledge about the specific proteases of other bacteria, it is still challenging to extend this method to effectively detect different bacteria. Recently, a semiautomatic biosensor was established for S. typhimurium detection [118]. The biosensor contained a magnetic grid separator containing high gradient magnetic field and spiral channel, and a fluidic detection chip involving silver/silver chloride (Ag/AgCl) reference electrodes. Self-assembled MNP chains were used to continuously isolate S. typhimurium from large-volume samples while urease-coated AuNPs were used to specifically label S. typhimurium and strengthen signal amplification. Using an Ag/AgCl reference electrode array, LSV was used to determine catalyst for target bacteria quantification. Furthermore, an automated biosensor was proposed for voltammetric detection of E. coli [119]. The water sample was filtered using a polycarbonate filter to capture E. coli, and then lysogeny broth (LB) medium was added to induce the production of β-D-glucuronidase enzyme (GUS). Since the medium already contained 8-hydroxyquinoline glucuronic (8-HQG), the substrate of GUS, the GUS produced by E. coli would cleave 8-HQG. The cleavage product 8-hydroxyquinoline (8-HQ) can be oxidized, resulting in a current signal for CV measurement. The biosensor detected E. coli with a LOD of 5 CFU/mL in 5 mL, which can be used as an independent instrument with online data transmission function for water quality monitoring. Future work will focus on the optimization of the biosensor, such as integrating the electrodes into the filter chamber, installing the reservoir on the filter, and combining the filter unit and the nutrient reservoir in a single-use assembly.

4.4.3 Mechanical biosensors Mechanical biosensors offer the advantages of fast analysis and high sensitivity, without the requirements for sample handling or additional reagents [70]. The deflection of the mass-sensitive sensor surface can be measured by mechanical biosensors due to the binding of the target analyte to the functionalized surface [9]. Mechanical biosensors are usually divided into quartz crystal microbalance (QCM) biosensors,

2. Biomedical applications

106

4. Biosensors for bacteria detection

cantilever biosensors, surface stress-based biosensors, and whisperinggallery microgravity (WGM) biosensors. The use of mechanical biosensors for bacteria detection has been reported to a lesser extent. Newly developed mechanical biosensors for bacterial identification are shown in Table 4.6. QCM biosensors are label-free piezoelectric biosensors that can detect changes in the resonance frequency caused by the increase in surface mass of the sensors due to the binding of analytes [70]. Specifically, the measurement is based on covering the surface of the quartz crystal with specific bioreceptors. The bacteria in the sample will bind to the bioreceptors on the quartz crystal, resulting in an increase in the mass of the quartz crystal and a proportional decline in the resonance frequency of the oscillation. However, this may require a long incubation time for bacteria, as well as many washing and drying steps. For example, Cai et al. [121] presented a 2 3 5 model QCM biosensor array for parallelized detection of pathogens that cause wound infections including Clostridium tetani, Clostridium perfringens, Streptococcus pneumoniae, P. aeruginosa, and E. coli using probes as bioreceptors, which was based on hybridization analysis of internal transcribed spacer (ITS). In this study, AuNPs were used to magnify the frequency-shifted signal, thereby reducing the LOD to 1.5 3 102 CFU/mL. The sensitivity and specificity for target detection in 50 clinical samples were 94.12% and 90.91%, which were not significantly different from those of the culture method, confirming that the biosensor is promising for parallel and quantitative determination of pathogens in clinical diagnosis (Table 4.7). In addition to probes, antibodies are also widely used as bioreceptors for bacterial recognition in QCM biosensors. Viable E. coli O157:H7 in blueberries was detected via a simultaneous enrichment and detection system using a QCM biosensor with antibody functionalized AuNPs [122]. A sample containing target bacteria was circulated through the QCM system with brain heart perfusion (BHI) broth. The use of BHI enrichment was demonstrated to reduce the detection limit from 4 log to 0 1 log CFU/mL, indicating that this system can be employed for simultaneously enriching and detecting live cells in food safety inspections. Another food pathogen C. jejuni was determined using a QCM immunosensor (Fig. 4.4A) [123]. Rabbit polyclonal antibodies and commercially available mouse monoclonal antibodies were studied to prepare direct, sandwich, and AuNPs-amplified sandwich analysis. It was proved that the immunosensor using rabbit polyclonal antibodies as capture antibodies and coupled with AuNPs as detection antibodies could provide the lowest detection limit. Moreover, aquatic pathogenic bacteria Aeromonas hydrophila detection in fish tissue extract was achieved by a QCM immunosensor [124]. Although the R2 value (0.9999) of the immunosensor was higher than that of the indirect

2. Biomedical applications

TABLE 4.6 Applications of mechanical biosensors for bacteria detection.

Biosensor type

Target bacteria

Bioreceptor

Linear range

LOD

Detection time

Recoveries

Relative standard deviation (RSD)

Sample

Ref.

QCM biosensor

Pathogenic bacteria (5)

Probe

1.5 3 10 1.5 3 10 CFU/mL

1.5 3 10 CFU/mL

5h

-

-

Clinical samples

[121]

QCM biosensor

Escherichia coli O157:H7

Antibody

-

0 1 log CFU/mL

24 h

-

-

Blueberries

[122]

QCM biosensor

Campylobacter jejuni

Antibody

-

1.5 3 102 CFU/mL

-

-

-

Culture

[123]

QCM biosensor

Aeromonas hydrophila

Antibody

6 3 106 108 CFU/mL

6 3 106 CFU/mL

5 min

-

-

Fish tissue extract

[124]

Cantilever biosensor

E. coli

BCECF-AM

1000 4000 cells mm22

1000 cells mm22

,1 h

-

-

Culture

[125]

Cantilever biosensor

Foodborne bacteria (6)

ssDNA probe

3 1.2 3 105 cells mL21

1 9 cells mL21

,1 h

-

-

Milk

[126]

Surface stressbased biosensor

Staphylococcus aureus

Mercaptoundecanoic acid

0.5 3 103 8 3 103 cells mL21

0.5 3 103 cells mL21

-

-

-

Culture

[127]

Surface stressbased biosensor

E. coli O157: H7

antibody

103 107 CFU/mL

43 CFU/mL

30 min

-

-

Culture

[128]

2

8

2

TABLE 4.7 Applications of integrated biosensing platforms for multiplexed bacteria detection. Linear range (CFU/mL)

LOD (CFU/ mL)

Detection time

Recoveries

Relative standard deviation (RSD)

Biosensor type

Target bacteria

Bioreceptor

Sample

Ref

Potentiometric biosensor

Salmonella typhi Escherichia coli O157:H7 Staphylococcus aureus

Aptamer

0.2a103 4a104 8 3 102a108

0.2 4 8 3 102

60a120 s 60a120 s 6a11 min

15%

-

Semiskimmed milk, apple juice, and pig skin

[129]

Multijunction Biosensor

E. coli K12 S. aureus

Antibody

102a105 102a105

102 102

2 min 2 min

-

-

Culture

[130]

Impedimetric biosensor

Saccharomyces cerevisiae E. coli Enterobacteriaceae cloaca Bacillus subtilis

Lectin

103a107 103a108 104a106 103a107

103 103 104 103

60 min 60 min 60 min 60 min

-

-

Culture

[131]

Nanoplasmonic biosensor

Chlamydia trachomatis Neisseria gonorrhoeae

Antibody

300a107 1500 107

300 1500

-

-

-

Urine

[132]

Fluorescent biosensor

E. coli Salmonella enteritidis

Antibody

10a105 10a105

5 3

1h 1h

91.8% 98.2%

-

Tap water and lake water

[133]

Colorimetric biosensor

E. coli O157:H7 E. coli BL21

Antibody

-

5 20

30 min 30 min

-

-

Milk and orange juice

[134]

Fluorescent biosensor

E. coli O157:H7 Listeria monocytogenes Salmonella typhimurium

Antibody

102a107 103a107 103a107

102 103 103

1h 1h 1h

88% 111% 89% 111% 88% 106%

-

Lettuce, shrimp, and ground beef

[135]

Impedimetric biosensor

E. coli O157:H7 S. aureus

Antibody

102a105 102a105

102 102

-

-

-

Culture

[136]

Colorimetric biosensor

AB

Aptamer

-

450

40 min

-

-

Culture

[137]

110

4. Biosensors for bacteria detection

FIGURE 4.4 Examples of mechanical biosensors for bacteria detection, including (A) cantilever biosensor and (B) surface stress-based biosensor. Source: Reprinted from Ref. Zheng, F.J., et al., Simultaneous and ultrasensitive detection of foodborne bacteria by gold nanoparticles-amplified microcantilever array biosensor. Front. Chem., 2019;7:12 with permission from Frontiers and Jian, A.Q., et al., A PDMS surface stress biosensor with optimized micro-membrane: fabrication and application. Sens. Actuators B-Chem., 2017;242:969 976 with permission from Elsevier.

ELISA (0.947), indicating that the standard curve was more linear, the amplified signal of the ELISA may allow lower detection limits. Cantilever biosensors generally include microcantilevers functionalized with bioreceptors that oscillate at a specific resonance frequency. The resonance frequency of the cantilever will change as a result of the mechanical bending caused by the increase in the surface mass of the biosensor [70]. However, it is usually necessary to operate the biosensor in the air rather than in a physiological medium, and there are few reports on the use of cantilever biosensors to detect bacteria in actual samples. For instance, a coupled electromechanical biosensor was prepared for E. coli detection in view of electric acceleration sampling in microelectrodes and electromechanical signal transduction on cantilever [138]. Within 30 min of the sampling time, the E. coli captured on the cantilever surface by means of AC electrokinetic effects (dielectrophoresis) caused a significant shift in resonance frequency compared with unassisted capture. Efforts are currently underway to improve the microelectrode design and the geometry of the electron beam to enhance mechanical detection. In contrast, viable E. coli cells were determined by a mass-change sensitive cantilever biosensor using acetoxymethyl ester of 20,70-bis(2-carboxyethyl)5,6-carboxyfluorescein (BCECF-AM) as an indicator of viability [125]. Once BCECF-AM diffuses into cells, it will be hydrolyzed into fluorescent BCECF by nonspecific esterases, which are known to exist only in live cells. Cantilever sensors immobilized with living cells could be extended for monitor environmental toxic pollutants since living cells could respond to toxins. A microcantilever biosensor was proposed for monitoring foodborne bacteria Lactobacillus plantarum by dynamic force microscopy [139]. A tap-mode atomic force microscope was exploited to determine the

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111

resonance frequency shift of a L. plantarum-modified cantilever. The cantilever operating in dynamic mode can achieve target quantification with a sensitivity of 383 6 3 pg/Hz and a selectivity of 400 cells within 4 h, indicating that this biosensor can be employed to determine foodrelated microorganisms for quality control of food industry. Recently, simultaneous determination of foodborne bacteria (e.g., V. parahaemolyticus, Salmonella, S. aureus, L. monocytogenes) was achieved by a AuNPsamplified microcantilever biosensor [126]. The developed six pairs of bacteria-specific ssDNA probes (ssDNA1 1 ssDNA2) were immobilized on the self-assembled monolayers (SAMs) of the cantilevers (Fig. 4.4A). The ssDNA2-AuNPs in the solution would capture target gene sequences to form AuNPs-ssDNA2-target complexes, which were then hybridized with ssNDA1 immobilized on the cantilever sensor beam, resulting in secondary cascade amplification. Compared with previous reports, the sensor exhibited a LOD of 1 9 cells mL21 within 1 h, indicating its great promise for multiplexed and ultrasensitive detection in food samples. The work of the surface stress-based biosensors is based on the deformation of the sensitive elements to sense the surface stress generated during the interaction between the bioreceptor and the target bacteria. Therefore the consistency and stability of the sensitive elements is of great significance for the performance. Regarding the application of surface stress-based biosensors for bacteria detection, only a few reports have been published to date. A polydimethylsiloxane (PDMS) micromembrane surface stress biosensor was adopted for S. aureus identification [127]. The sandwich structure of two circular electrodes with PDMS membrane was prepared, and a layer of 11 mercaptododecanoic acid (MUA: SH-(CH2)10-COOH) was covered on the biosensor (Fig. 4.4B) The linear relationship between the reverse capacitance and S. aureus concentration can be used to quantify S. aureus. Using double-layer stable gold nanostructures (D-AuNS-SSMB) as a stable sensitive element, a surface stress-based biosensor was designed for E. coli O157:H7 quantification [128]. Surface stress was generated on the biosensor, which played a decisive role in increasing the resistance of D-AuNSSSMB. Compared to microcantilever biosensors, the developed biosensor involving D-AuNS-SSMB showed a wider detection range and a much lower LOD. This is because the gold layer of the former is unstable, while D-AuNS-SSMB possesses a stable double-layer gold layer. Whispering gallery mode (WGM) microresonators have been employed for real-time, label-free determination of biomolecules. By using suitable bioreceptors to properly functionalize its surface to attach target biomolecules, WGM biosensors have been utilized for bacteria detection. A biosensor was reported for S. aureus detection using WGM microdisks functionalized with LysK phage protein [140]. The binding

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4. Biosensors for bacteria detection

event of S. aureus and Lysk caused the resonant peak of the microdisk resonator to shift to a longer wavelength, which can be employed to calculate the surface density of S. aureus on the resonator surface. A WGM biosensor was presented with a sensitivity of 1.2 3 102 E. coli mm22 as well [141]. It was demonstrated that attaching cylindrical bacteria to the high-Q optical microsphere sensor in a random horizontal direction would disturb the resonance frequency and line width of the sensor. The adsorption of E. coli not only caused a wide shift in the resonance frequency, but also caused an obvious rise in linewidth. The change in resonance frequency was linearly related to the bacteria concentration.

4.5 Integrated biosensing platforms for multiplexed bacteria detection Although biosensors for bacteria detection have made outstanding progress in sensitivity and specificity, the applications of these biosensors are still limited in actual use. Regarding bacteria detection, most of the biosensors that have been developed are in view of the identification of a single type of bacteria. However, because there may be multiple bacterial pathogens in food, environmental, or clinical samples, it is necessary to develop biosensors for multiplex detection of bacteria [135]. By improving biosensors for signal bacteria to biosensors that can simultaneously detect multiple bacteria, this helps to shorten the detection time, reduce the detection cost, and enhance the detection efficiency of large-scale samples. Zelada-Guille´n et al. [129] developed a potentiometric biosensor for multiplex detection, using SWCNTs as transducers and aptamers as bioreceptors. The stable amide bonds were formed between the carboxylic moieties on the SWCNTs and the primary amine spacers on the aptamers. The detection limit as low as 0.2 CFU/mL was reported for Salmonella typhi. However, the detection limit for S. aureus was much higher (8 3 102 CFU/mL), which may result from the thick polysaccharide layer on S. aureus surface and the low content of antigens exposed for biological recognition. Moreover, SWCNTs were also employed in a continuous flow multijunction biosensor for multiplex detection of E. coli K12 and S. aureus [130]. Gold-plated tungsten wires coated with SWCNTs and polyethyleneimine were used to fabricate junction biosensors, and each junction was functionalized with specific antibodies. Compared with stationary junction sensor, the improvised biosensor could be applied for multiplex analysis of samples with large volumes (over 1 mL vs 10 μL). The lectins can be deposited on the polyelectrolyte membrane through electrostatic interaction, thereby achieving controllable

2. Biomedical applications

4.5 Integrated biosensing platforms for multiplexed bacteria detection

113

production of lectin self-assembly monolayers (SAMs). The lectinpolyelectrolyte membrane can provide a suitable bionic interface for specific bacteria adhesion. Xi et al. [131] tested an impedimetric biosensor for multiplex detection of Saccharomyces cerevisiae, Bacillus subtilis, Enterobacter cloacae, and E. coli using lectin-based SAMs. The interaction between lectins and four bacteria were studied, demonstrating that there were different linear relationships between the logarithm of bacteria and the WRet values of the working electrode. The difference in the linear range and slope of the linear plot proves the difference in the strength of the binding affinity between bacteria and lectins, suggesting that this biosensor can be extended for sensitive discrimination of bacterial types by observing the difference. Microfluidics technology is employed for precise control and processing of microscale fluids [142]. The basic operation units (e.g., pretreatment, extraction, reaction, identification) can be integrated on the microchip. Through the micromachining process, micromillimeter fluid channels, valves, sensors, detectors, and other units can be fabricated on substrates of various materials [143]. Because microfluidic technology has the advantages of low price, portability, fast response, high precision, and low reagent consumption, it has been exploited to construct biosensors, thereby providing integrated biosensing platforms for bacteria detection. Soler et al. [132] developed a nanoplasmonic biosensor for multiplex determination of Chlamydia trachomatis and Neisseria gonorrhoeae in urine (Fig. 4.5A). By functionalizing nanohole sensor arrays in the microfluidic system with specific antibodies, the sensor can quantify target

FIGURE 4.5 Examples of integrated biosensing platforms for multiplexed bacteria detection, including (A) nanoplasmonic biosensor and (B) fluorescent biosensor. Source: Reprinted from Ref. Soler, M., et al., Multiplexed nanoplasmonic biosensor for one-step simultaneous detection of Chlamydia trachomatis and Neisseria gonorrhoeae in urine. Biosens. Bioelectron., 2017;94:560 567 with permission from Elsevier and Dogan, U., et al., Multiplex enumeration of Escherichia coli and Salmonella enteritidis in a passive capillary microfluidic chip. Anal. Methods, 2020;12(30):3788 3796 with permission from RSC Publishing.

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4. Biosensors for bacteria detection

bacteria. The biosensor provides a new strategy for on-site sexually transmitted infection (STI) diagnosis, which can be integrated into a portable device using LED light source and CMOS detector. In another study, Dogan et al. [133] developed a passive capillary microfluidic chip for multiplex quantification of E. coli and S. enteritidis in tap water and lake water (Fig. 4.5B). Two kinds of quantum dots would interact with MNP-target bacteria to form a sandwich complex, whose emitted fluorescence was consistent with bacteria concentration. The assay was performed on a capillary microfluidic chip only by pipetting reagents and samples, while fluorescence measurement was realized by a handheld fluorescence spectrophotometer. This microfluidic chip provided excellent LOD values (3 5 CFU/mL) and short analysis time (1 h). Furthermore, the microfluidic chip can be extended for on-site analysis of drinking water because it does not require complex off-chip equipment, and only needs a handheld fluorescence spectrometer to perform measurements. Recently, many researchers have introduced portable biosensors for bacteria detection. Hossain et al. [134] developed a lateral-flow colorimetric paper sensor for multiplex measurement of E. coli O157:H7 and E. coli BL21. In this study, the lysed sample flowed into the substrate area to initiate enzymatic hydrolysis of the substrate, thereby causing color changes. The formation of red-magenta on the paper indicated the presence of coliforms. On this premise, the formation of blue on the paper indicated E. coli BL21, while the absence of blue indicated E. coli O157:H7. Combined with magnetic preconcentration, the paper sensor showed the detection limits for target bacteria within 30 min. Xu et al. [135] also developed a portable fluorescent nanobiosensor for multiplex detection of foodborne pathogens. In this work, magnetic separation was similarly used for bacteria preconcentration from the sample solution. In lettuce, shrimp, and ground beef, the recoveries of three bacteria were 88% 111%, 89% 111%, and 88% 106%, respectively. However, specific calibration curves for pathogens in the corresponding food must be established to facilitate field testing. Furthermore, on-site blind detection of freshly collected real food samples has further proved the feasibility of the biosensor. Therefore the biosensor has large application prospects for identification of various foodborne pathogens in vegetables, seafood, or livestock meat, which is beneficial for on-site monitoring of foodborne diseases. Biotechnology, nanotechnology and biosensing, microfluidics, and electronic technologies are attractive to be implemented in developing integrated biosensing platforms. It is significant to integrate sample collection, separation, nucleic acid extraction, amplification, and detection into an automatic platform [144]. Many recent studies have shown that the integrated biosensing platforms can be employed for pathogen

2. Biomedical applications

4.6 Conclusion and perspectives

115

detection and disease diagnosis, which establishes new avenues for the development of POC applications. Tian et al. [136] adopted a polyethylene glycol (PEG)-based integrated microfluidic device for multiplex detection of E. coli O157:H7 and S. aureus. Antibodies were immobilized on two nanoporous alumina membranes, and then the functionalized membranes were integrated in the microfluidic device. When target bacteria were captured by the antibodies, some nanopores were blocked to prevent electrolyte from passing through these nanopores, resulting in an increase in impedance amplitude signal that can be used for bacterial quantification. The microfluidic device provides a portable platform for simultaneous detection of various foodborne pathogens for monitoring food safety. In another study, Wu et al. [137] tested a nitrocellulose-based integrated microfluidic system for Acinetobacter baumannii (AB) measurement. The designed electromagnetically driven microfluidic device included microovalves, micropumps, and micromixers comprised of magnetic composite membranes, which realized the automatic transportation and mixing of reagents and samples in the microfluidic chip. Because the integrated microfluidic system used electromagnets, each microfluidic component could be precisely controlled without the requirement of sophisticated external equipment. The dual aptamer colorimetric method based on nitrocellulose membrane was applied for bacterial quantification in the microfluidic chip. The entire process could be completed within 40 min, indicating that this system has broad prospects for rapid and point-of-care diagnosis. In order to realize the clinical application of the system, it is also necessary to explore methods to isolate bacteria from complex actual samples. For example, magnetic beads can be integrated into the microfluidic system to capture bacteria in clinical samples.

4.6 Conclusion and perspectives Recently, biosensors have been developed as a new tool for detection of bacteria. The current trends in optical, electrochemical, and mechanical biosensors for bacteria detection were discussed in this chapter. Particularly, whole-cell biosensors based on incorporate living cells as biorecognition elements were presented. Although numerous attempts have been dedicated to the progress of biosensors recently, there are only a few commercially available biosensors for bacteria detection. An ideal biosensor must demonstrate outstanding specificity, excellent sensitivity, and superior reproducibility for distinguishing and determining target bacteria in complex sample matrices, with simple construction process and cheap construction cost. Without sample pretreatment, it can perform rapid on-site detection and provide real-time analysis

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4. Biosensors for bacteria detection

results. However, it is still a significant challenge to construct ideal biosensors. For example, due to the interaction of compounds contained in the complex sample matrices (e.g., milk, serum, and wastewater), the specificity and sensitivity of actual sample detection are usually not as high as those of buffer detection. Moreover, SPR, QCM, and cantilever biosensors have the characteristics of large size and high cost, so it is difficult to make them portable and low cost. In order to commercialize laboratory-based biosensors, it is necessary for academia and industry to strengthen cooperation. Nanomaterials including noble MNPs, carbon-based nanomaterials, and semiconductor nanocrystals can be introduced to heighten the capability of biosensors for bacteria detection. In addition to the most widely used antibodies, the new bioreceptors with high specificity (e.g., DNAzyme, bacteriophage, and peptide) demonstrate excellent ability for bacteria detection in the complex sample matrices. Biotechnology, microfluidic, and electronic technologies can be further explored to develop integrated biosensing platforms for POC purposes. With the advantages of affordability, rapidness, sensitivity, specificity, simplicity, and portability, biosensors are attractive to be implemented for bacteria detection in food safety, clinical diagnosis, drug development, and environmental monitoring.

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C H A P T E R

5 Biosensors for drug of abuse detection Kheibar Dashtian, Fereshteh Amourizi, Neda Shahbazi, Aida Mousavi, Bahar Saboorizadeh, Sana Safari Astaraei and Rouholah Zare-Dorabei Research Laboratory of Spectrometry & Micro and Nano Extraction, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran

5.1 Introduction In the modern world, abuse of illicit drugs such as alcohol, cocaine, ecstasy, hallucinogens, heroin, inhalants, ketamine, marijuana, etc., has become a global concern and affects both human health and community stability. Social care providers need to protect humankind from this phenomenon [1 8]. Some institutes, such as the National Institute on Drug Abuse (NIDA), are grappling with illicit drug use and making a vital investment in drug abuse research due to the importance of this public hazard [9 11]. Biosensors have progressively become a widespread tool for helping scientists and governments address drug abuse [9,10,12]. Biosensors as an integrated autonomous device provide quantitative analytical data about drug targets. A biosensor often requires a bioreceptor for detecting target analytes combined with a transducer, which forms a physicochemical parameter such as optical, electrochemical, or spectroscopically and may fully comply with modern point-of-care testing for emergencies for a portable analytical device for controlling illicit drugs in various kinds of real samples [1,13 15]. Generally, biosensors can be categorized into two main groups: natural and synthetic based

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FIGURE 5.1 Schematic representation of different parts of biosensors applied for abused drugs.

on the origin of the bioreceptor used in development of the sensing assay [13,16,17]. Natural bioreceptors for most illicit drug research include enzymes, antibodies, and nucleic acids whereas synthetic bioreceptors mainly include molecularly imprinted polymers (MIPs) and aptamers [18 21]. With the aid of such receptors, the sensing systems gain desirable sensitivity, specificity, limit of detection (LOD), and limit of qualification (LOQ) for the detection of drugs of abuse [12,22,23]. A schematic presentation of different biosensors applied for abused drugs is provided in Fig. 5.1. In this chapter, a bottom-up approach is followed to describe biosensor development and application procedure for the sensitive detection of abused drugs using fluorescent, colorimetric, electrochemical, and real-time biosensing platforms. Several potential disciplines of biosensing technology including forensic drug analysis and biological evidence are highlighted to encourage researchers to focus on this emerging field.

5.2 Drug biosensing 5.2.1 Colorimetric approach A roboust sensor has a special strategy for distinguishing a single analyte in complex sample. In particular, colorimetric sensors have been applied widely for visualizing color changes when detecting the required analyte. Colorimetric biosensing could be the simplest

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FIGURE 5.2 Schematic representation of biorecognition elements, colorimetric elements, and response mechanisms used in the colorimetric biosensor design.

way for an untrained person among all noninvasive methods [24,25]. Numerous commercial brands are contributing to such sensing systems [26]. As shown in Fig. 5.2, the colorimetric biosensors coupled with a variety of recognition elements, such as aptamers, enzymes, and antibodies, can detect illicit drugs by the interaction of the biorecognition element with the signal transduction element as the colorimetric part of the biosensor. One of the main parts of colorimetric biosensors is nanoparticles with subdivision of noble metal nanoparticles that enable specific functionalization for detecting the expected drugs with high sensitivity and minimum sample preparation in every environment from hair to body fluid-based samples [27 29]. Rather than regular samples, drug abuse levels are often measured in epidemiology (WBE) as complex matrix, which a common and cost-effective way is colorimetric

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determinations in the general public [30]. To achieve high specificity without any interference in such measurement media, conjugation of nanomaterials with aptamers, enzymes, and antibodies is often a preferable strategy for developing analytical platforms [31 33]. 5.2.1.1 Enzymes in colorimetric approach Generally, enzyme-based biosensors, which have high accuracy and sensitivity and rapid response time, are considered as suitable candidates for detecting drug abuse [34,35]. One crucial advantage of enzymatic biosensors is determination of a variety of target analytes in diagnostics as well as in biological and biomedical research [36 39]. In contrast, disadvantages include critical operating conditions, poor stability as well as temperature and pH alteration has caused their widespread disuse [13]. Colorimetric enzymatic sensors have two different mechanism pathways for biorecognition of the selective interactions among specific elements and a target analyte. The first mechanism refers to a catalytic conversion pathway where analyte is metabolized by enzyme and consequently can be detected in a specific concentration in the proportion of LOD of the biosensor. The second pathway is based on the inhibitory effect of an analyte and reduces the enzyme activity by an inhibitor entering the system. In enzyme colorimetric biosensing, the enzyme immobilization process must be applied onto the defined surface of a transducer such as Nobel metal nanoparticles, 2D materials (Graphene, MXene, Metal chalcogenides) and transition metal oxides [15,35,36,39 41]. Among the immobilization methods, microencapsulation in organic polymers, hydrogels, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and other mesoporous materials play a key role in preserving the sensitive structure of the enzyme and facilitating mass transfer (MT) of the substrate in harsh sensing media [1 4]. Microencapsulation methods have been more successful in comparison to others since they provide versatile microenvironments for sensing reactions in nanoreactors to confine molecules in nanoscale dimensions [5]. Also, the enzyme selection as a significant step in the design of a colorimetric biosensing assay, which is based on its binding ability and catalytic activity to convert the substrate into a traceable product, is very important [20]. Enzyme-linked immunosorbent assay (ELISA) strategy is routinely applied for immobilizing the antibody/antigen on the related substrate and can be categorized as direct and indirect. In the direct method, the antibodies are immobilized on the substrate and labeled as well as unlabeled antigens compete for antibody binding (Fig. 5.3) [42], while in the indirect method uses the immobilized antigens on the related substrate in which the labeled as well as unlabeled antibodies are introduced to the ELISA plate to compete with each other [43 50].

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FIGURE 5.3 (A) Indirect sandwich ELISA and (B) direct competitive ELISA structures [51 53]. Source: Figure was adopted with permission from Refs. [42 44]. Copyright 2019, Elsevier.

FIGURE 5.4 Schematic illustration of cocaine determination by direct competitive mpEIA. Source: Figure was adopted with permission from [54].

Enzyme-based colorimetric biosensors can be used as an efficient option in diagnosis of abused drugs in real samples such as food, water, urine, blood, plasma, etc. Enzymatic catalytic reactions due to the high speed in the production of reactive oxygen species (ROS) create the oxidation power of colorimetric probes in the least possible time, which can be applied as a rapid, noninvasive, and viable cost-effective method for detection of different illicit drugs [6]. For example, Juan et al. fabricated a magnetic NPsconjugated enzyme-based immunoassay to detect cocaine in saliva and urea fluids. As shown in Fig. 5.4, the magnetic beads reduce the consumption of

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antibodies and conjugated enzymes. In this regard, cocaine antibodies were immobilized on protein-functionalized magnetic microbeads based on direct competitive immunoassays and HR-labeled cocaine competed with unlabeled ones for binding to antibodies. Eventually, to measure cocaine, 3,3’,5,5’-Tetramethylbenzidine (TMB) was added to the samples as a substrate with H2SO4 and it was given 20 min to complete the quenching reaction to measure the absorption of the samples at 450 nm [54]. Additionally, colorimetric biosensing devices that integrate with an enzyme recognition element are an excellent alternative for breath analysis due to the selectivity of the enzyme layer enabling selective detection of abused volatile organic compounds (VOCs). Andreescu et al. report a custom-made 3D-printed device to fabricate strip-type colorimetric ethanol (EtOH) biosensor containing cerium oxide nanoparticle (Ce-NPs) and alcohol oxidase (ALOx) enzyme affixed within a layer of poly(ethyleneimine) (PEI) and coated onto paper (Fig. 5.5A). This biosensor benefited from a combination of peroxidase-like mimetic material with chromogenic activity, allowing the user to blow on the sensor surface and obtain an immediate naked-eye reading of the alcohol level in the breath (Fig. 5.5B). 5.2.1.2 Aptamers in colorimetric approaches The colorimetric aptasensor mechanism for selective determination of drug abuse works based on the unique interaction between the target analyte and aptamers that causes a complexation between them. The conformational change and an oligonucleotide-assisted aggregations consequently cause color change depends on the colorimetric probe distances [55]. For example, the aggregation of Au-NPs as a colorimetric agent includes two separate parts: passive aggregation and active aggregation. In passive aggregation the absence of an analyte causes the color change from red to purple, while in active aggregation the absence of an analyte does not lead to a color change [56,57]. The mechanism of amplifying the signal in this kind of aggregation includes salt-induced self-assembly based on the negative charge of Au-NPs through salt addition that not only can neutralize the surface charge but also involves the self-aggregation phenomenon [58,59]. In the presence of aptamers, the interaction of Au-NPs with salt cannot cause aggregation after the bounding of the analyte to the aptamer and Au-NPs undergo aggregation, which causes the color change. Routinely, high-resolution transmission emission microscope image, UV vis spectra, and dynamic light scattering technique could confirm these aggregations. In most cases, methamphetamine and 3,4-methylenedioxy-N-methylamphetamine abused drugs are detected with this method [60]. Additionally, nonaggregated Au-based alloys NPs can apply to abused drugs such as methamphetamine and cocaine detection [8]. In the aggregated mechanism, phosphate buffer saline (PBS) was used as a routine saltinduced aggregator applied to Au-NPs in the presence of drugs of abuse

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FIGURE 5.5 (A) Schematic illustration of the design of the 3D-printed holder and biosensor containing “Sample In,” “Sensor In” and exhaust openings for EtOH breath (the immobilization of CeNPs and ALOx within PEI on paper and their enzymatically generated H2O2 occurring at the surface of the sensor is magnified). (B) Mmouthwash was first rinsed and breath was exhaled in a balloon and subsequently the balloon content was allowed to flow for a desired time in the device while the sensor was wetted and placed inside. The holes in the device allowed the air to come out and release the pressure. The color change was formed on the paper sensor, which was then dried and quantified by smartphone and image analysis software. PEI, Poly(ethyleneimine). Source: Figures were reproduced with permission from [7].

that interacted with the specific aptamer of each target. Introducing aptamers, which adopt different tertiary structures in the absence of their specific target, causes color changes of Au-NPs due to their surface plasmon resonance (SPR) (Fig. 5.6). Sanli et al. reported on cocaine determination by AuNP-aptamer complex under aggregating conditions with NaCl salt. Cocaine aptamers are linked with Au-NPs complexation via Van der Waals interactions and after adding cocaine target it bounded to aptamers and in the presence of NaCl a full aggregation takes place and color change can be obtained [61]. Similar to this method, recognizing antibiotics such as Kanamycin with salt-inducing and the color change could be implemented [62]. Additionally, the development of colorimetric aptasensors can

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FIGURE 5.6 Scheme for the visual methamphetamine detection based on the different interactions of the aptamer and the red to the purple color change of Au-NPs. Source: Figure was adopted with permission from [60].

be used for antibiotic abuse detection and are widely applied for animals, as they are found in animal-based foods that consequently affect human health [63]. In another type of colorimetric aptasensor, more than one noble metal NPs was applied for duplexed detection of drugs or two different illicit drugs in one assay with an aptamers-noble metals biosensor due to their different SPR wavelength for more specific detection [64]. In this strategy, which involves core-shell and composite structure, capabilities are at the same level but with different optical signals [11]. These NPs were immobilized with partially corresponding complementary strands of aptamers as a reporter probe (RP-DNA). In this regard, the detection of methamphetamine (METH) and cocaine in wastewater was reported by Yang et. al. In this case, at the same time, two magnetic beads were modified with capture DNA probes (CP-DNA): Au-NPs designed with cocaine reporter probe and Au@Ag-NPs labeled with METH reporter DNA probe. In the absence of analytes, the reporter probe and capture probe bound to the aptamer and Au@Ag DNA magnetic complex structures were formed and gathered with a magnet while after analytes addition aptamers joined to analytes and capture and reporter could not be joined together. The results confirmed that single analytes, dual analytes, and the absence of both analytes cause different colors (Fig. 5.7A) [11]. In another report, the detection of methamphetamine as an example of an illicit drug in a biological and environmental matrix was achieved using the same method

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FIGURE 5.7 (A) Schematic illustration of simultaneous colorimetric detection of METH and cocaine based on nonaggregated NPs. C-RP, Cocaine reporter probe; M-RP, methamphetamine reporter probe; M-CP, methamphetamine capture probe; C-CP, cocaine capture probe; NPs, nanoparticles. (B) Preparation of RP, CP, and colorimetric detection of METH and cocaine based on nonaggregation Au@Ag core-shell NPs. Source: Figure adopted with permission from K. Mao, J. Ma, X. Li, Z. Yang, Rapid duplexed detection of illicit drugs in wastewater using gold nanoparticle conjugated aptamer sensors, Sci. Total. Environ., 688 (2019) 771 779. (B) Figure adopted with permission from K. Mao, Z. Yang, J. Li, X. Zhou, X. Li, J. Hu, A novel colorimetric biosensor based on non-aggregated Au@ Ag core shell nanoparticles for methamphetamine and cocaine detection, Talanta, 175 (2017) 338 346.

[65]. This colorimetric aptasensor consists of a specific single-stranded DNA sequence coated with Au@Ag as a reporter probe (RP), a capture probe (CP) conjugated with superparamagnetic beads (MBs), and an illicit drug-binding DNA aptamer (Fig. 5.7B). The DNA aptamer could hybridize with both RP and CP and generate Au@Ag-DNA MBs sandwich structure. In this strategy, when an external magnetic field is applied, Au@Ag-DNA-MB is collected from the solution, leading to a reduction in the SPR intensity. However, Au@Ag-DNA-MB sandwich structure cannot be formed in the presence of the METH and cocaine abuse drugs, due to the higher affinity of the aptamer to abuse drugs than that to complementary ssDNA. Finally, the SPR intensity was applied to the quantification of METH and cocaine drug concentrations (Fig. 5.7B). In colorimetric aptasensors for directly isolating signal processing through SELEX, transduce binding events into a detectable change like absorbance via target-induced displacement of a small indicator molecule. Diethylthiatricarbocyyanine (Cy7) can stack into DNA three-way junctions (TWJs) that greatly alter the indicator absorbance spectrum [66]. Fig. 5.8

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FIGURE 5.8 Detection of MDPV with a label-free Cy7-displacement colorimetric assay. (A) Cy7 binds within the TWJ domain of MA (left). MDPV displaces Cy7 from the binding domain (right) to generate a change in the absorbance spectra of Cy7. (B) UV visible spectra of 3 M Cy7 premixed with 7 M MA in the presence of varying concentrations of MDPV (0, 0.16, 0.31, 0.63, 1.25, 2.5, 5, 10, 20, 40, 80, 160, 320, 640 M). The increasing concentrations (shown as black - red curves) resulted in decreased absorbance at 760 nm and increased absorbance at 670 nm. TWJ, Three-way junction. Source: Figure was adopted with permission from H. Yu, W. Yang, O. Alkhamis, J. Canoura, K.-A. Yang, Y. Xiao, In vitro isolation of small-molecule-binding aptamers with intrinsic dye-displacement functionality, Nucleic acids Res., 46 (2018) e43-e43.

represents a designed TWJ for 3,4-methylenedioxypyrovalerone (MPDV) as in the absence of analyte binding to Cy7, where in the presence of the analyte, displacement happens and results in an absorbance change. Functional DNAs have some subcategories such as DNAzyme and aptamers from which numerous researchers have benefited in biosensing of small molecules such as abuse druges (Table 5.1) or macromolecules [67,68]. This strategy is based on the combination of an aptamer as a biorecognition element and a peroxidase-mimicking DNAzyme as a signal recognition element within a hairpin structure as well as a nucleotide sequence base-pair and a double helix that ends with an unpaired loop to form a DNA probe. Generally, the activation of this probe is blocked via base pairing in the hairpin structure [69]. In such a way, the target aptamer complex opens the hairpin structure and releases the G-quadruplex sequence (nucleic acid structure rich in guanine), which can generate amplified colorimetric signals [70]. The Hairpin DNA probes (HDPs) is filled in the blank process category and detects the target abuse drugs. Additionally, a smart functional DNA hairpin includes aptamer as target recognition element and DNAzyme as signal readout element and their specific function are blocked by base pairing in a cage, then the presence of hemin, results in self-assembly of G-quadruplex sequence into active DNAzyme [71]. The DNAzyme catalyzes the colorimetric reaction of the colorimetric probe such as TMB, ortho-phenylenediamine, etc., in the presence of oxidizing agents such as persulfate, H2O2, etc., for triggering events [72]. The most important point of this kind of biosensor is how to design a kind of efficient DNA hairpin for implementation [73]. For example, Zhou et al. report cocaine-aptamer interaction as a model. In the proposed model, the key point for blocking the G-quadruplex sequence of DNAzyme is using the complementary base to pair with guanine. Both

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hemin and G-quadruplex sequence have peroxidase activity to form OH radicals and oxidize organic probes. Therefore results confirmed that in the presence of hairpin-C10 and hemin coloration process, colorimetric change with different concentrations of cocaine occurred (Fig. 5.9).

FIGURE 5.9 (A) Principle of smart functional DNA hairpin for target biosensing; (B) Secondary structure of hairpin for sensor design (hairpin-C10 for cocaine was shown as a model). Source: Figure was adopted with permission from J. Nie, D.-W. Zhang, C. Tie, Y.-L. Zhou, X.-X. Zhang, A label-free DNA hairpin biosensor for colorimetric detection of target with suitable functional DNA partners, Biosens. Bioelectron., 49 (2013) 236 242. TABLE 5.1

Colorimetric-based biosensors for abuse drug detection.

Linear range

Assay time (min)

Ref.

75

[3]

Analyte

Bioreceptor

Colorimetric agent

GHB

Enzyme

Au-NPs

1a16 mM

MA MDMA

Aptamer

Au-NPs

5a400 μM 5a400 μM

COC

Aptamer

Au-NPs

0.2 25 nM

60

[61]

Kanamycin

Aptamer

Au-NPs

0.05 0.6 μg/mL

20

[62]

METH COC

ssDNA

Au@Ag NPs Au-NPs

1.0 200 nM 10 nMa150 nM

110

[11]

METH COC

ssDNA

Au@Ag NPs

0.5 100 0 0.5a200

15

[65]

MDPV

Dyedisplacement

Cy7

0 to 40 μM

2

[66]

COC

DNA hairpin

Biomarker

0.1 μMa10 mM

10

[71]

COC

Aptamers

Au-NPs

5

[74]

Ketamine

Aptamer

TMB probe

6

[75]

Sulfonamides

Enzyme

4-nitrophenyl acetate probe

2.5a40.0 μM

15

[76]

MOR AMP COC

Enzyme

TMB probe

-

5 10

[77]

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[60]

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5. Biosensors for drug of abuse detection

5.2.2 Fluorescence approaches Sensing illicit drugs with fluorescent biosensors made of different biological materials such as aptamers, enzymes, nucleic acids, antibodies, and antigens as biorecognition elements that are immobilized or grow on the fluorescent probe materials has been reported in different studies [78 81]. Fluorescent biosensors include small scaffolds mounted on fluorescence probes with receptors, and this placement can be considered enzymatically, chemically, or genetically to detect the target and then transmit a traceable fluorescent signal [37]. Some recent reports have demonstrated that the most common types of bioreceptors in fluorescence abuse drug biosensing are aptamers and enzymes, which are discussed as follows. 5.2.2.1 Aptamers in fluorescence approaches Among the abovementioned biorecognition elements, aptamers, known as “chemical antibodies,” have many unique advantages, including ease of chemical synthesis, high chemical stability, low molecular weight, lack of immunogenicity, and ease of modification and manipulation compared to their protein counterparts, which made them widely applicable for drug abuse detection [30,82 88]. Different types of fluorescent nanostructures such as fluorophores, quantum dots, upconversion NPs (UCNPs), Au-NPs, graphene oxide (GO), etc., are in this category and make the detection of desired analyte achievable with the fluorescent method [89]. However, due to the limitation of living surroundings of bioactive materials (aptamers), the stability, repeatability, lifetime, and complexity of the consequent biosensors are the significant distances that remain between benchtop laboratory prototypes and their commercial applications. Moreover, the analytical capability of fluorescent biosensors in more complex matrices, such as oral fluid and urine, is usually not that desirable, and further development and validation are still required. There are two main methods of fluorescence-based biosensing assay: labeled and label-free systems (Fig. 5.10). Typically, a small number of aptamers do not have fluorescence automatically, so having a fluorescent label is very important to get a measurable signal [90]. Among label-free fluorescent biosensors, the SPR method is the most common, and in the label-based sensors, a combination of aptamers and optically active molecules is required. In this method, one end of the aptamer is modified by a fluorescence probe with a quencher in the other end, and eventually, a change in the aptamer configuration will increase or decrease the fluorescence intensity [91]. 5.2.2.1.1 Labeled aptamers in fluorescence approaches

The structure of aptamers before and after binding to analyte varies. The structure of aptamers can be changed in accordance with the

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137

FIGURE 5.10 General fluorescence biosensing different categories. Source: Figure was adopted with permission from K. Wandelt, Encyclopedia of Interfacial Chemistry: Surface Science and Electrochemistry, Elsevier 2018.

fluorophore or the Fo¨rster resonance energy transfer (FRET), which occur between two fluorophore molecules. Signal changes, as increases or on-mode and decreases or off-mode of the signal, reflect the connection process and allow quantitative analysis of the target concentration [92]. The fluorescence labels that mainly benefit from small fluorophore molecules can be referred to as quantum dots and other fluorescent NPs [93]. These small molecules have advantages such as hydrophilic nature, are less reactive with functional groups, and can be conjugated with aptamers [94 96]. Another labeled fluorescence strategy that has received more attention in the sensing field is the strand movement of aptamers in the presence of target molecules [97]. This involves using more than one single-labeled aptamer instead of a single-strand aptamer sequence to increase the affinity to the target molecule (see Fig. 5.11 as a typical example for cocaine sensing). It is characterized by a change in the fluorescence profile due to the change that takes place in the adaptive structures of the species [98]. Simple, rapid, and real-time monitoring of biomolecules in matrix samples makes this strategy noteworthy [1,99,100]. In the signal-on method, increasing or recovering the fluorescence intensity happens in the presence of the illicit drug so, in the absence of analyte, fluorescence intensity is not observed [89]. In the FRET strategy, one end of the aptamer is conjugated to a fluorophore as donor and the fluorescence signal is quenched which after adding an acceptor such as noble metal NPs the FRET shut-off mechanism has occurred and consequently the fluorescence signal is reconstructed for quantitative analyte measurement [101 103]. The change in fluorescence intensity is the result of a change in the rotational motion of the fluorophore, which plays a vital role in drug-aptamer binding in single fluorophore-labeled aptamer systems [104]. In general, aptamers act as a cross-linker between FRET donors and

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FIGURE 5.11 Schematic representation of cocaine sensing based on structure switching optical biosensing platform. (A) Structure switching of cocaine combining the two aptamer fragments and (B) hybridization and revival of the aptamer with optical fiber. Source: Figure was adopted with permission from E. Aydindogan, S. Balaban, S. Evran, H. Coskunol, S. Timur, A bottom-up approach for developing aptasensors for abused drugs: biosensors in forensics, Biosensors, 9 (2019) 118.

acceptors that are referred as secondary structures [105,106]. In many cases, the structural change of the aptamer on the target connection can significantly affect the fluorescence of a conjugated dye. Therefore fluorescence labeling is a fundamental method for target analysis and quantification [107]. One solution to achieve this goal is to replace or modify the illicit drug using advanced fluorescent dyes that respond to their local environment by changing fluorescence parameters such as altered polarity, hydration, flipping dynamics, and formation/breaking of hydrogen bonds [92,108]. In the labeled strategy fluorescence polarization (FP) is an intrinsic

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parameter of fluorophores that is reported based on the ratio of vertical and parallel intensities under the excitation of plane-polarized light (Fig. 5.11) [109,110]. When conditions such as fluorescence lifetime remain constant, FP is measured by the volume or molecular weight of the markfluorophores labeled recognition probe [111,112]. 5.2.2.1.2 Label-free aptamers in fluorescence approaches

Fluorescently labeled aptamer biosensors offer a wide variety of interesting applications. Labeling aptamers with fluorophores is costly and timeconsuming and fluorophore molecules can interfere with abused drug bonds [113]. Generally, the mechanism of function in label-free aptasensors depends on intelligent interactions between the aptamers and their complementary strands, target, or signal probes. This unique property of nucleic acids allows them to be appropriate for detecting various types of drugs [114]. Several labeled-free-fluorescent aptasensors that have been reported for abused drug detection based on switching on/off mechanism are summarized in Table 5.2. 5.2.2.1.3 Other strategies of aptamer-based fluorescence abuse drug biosensing

5.2.2.1.3.1 Messenger activation upon aptamer binding Using logic engineering, aptamer binding can be applied to activate specific sequences that may be utilized as messengers for extensive signaling. As an example, a system can be designed based on aptamer interactions in the fluorescence method. Target pointed out has been developed to detect cocaine with target-induced strand displacement. The work uses two probes (hairpin-probe and single-strand probe, ss-probe) that have TABLE 5.2 biosensors.

A charted synopsis of aptamer-based signal-off/signal-on fluorescent

Target analyte

Mechanism type

Labeling method

Biosensing mode

LOD

Ref.

AFB1

Competitive assay

Label-free

Signal-off

μg/L

[115]

COC

Repositioning of quencher

Label-free

Signal-off

-

[116 118]

COC

Fluorophore displacement

Label-free

Signal-off

nM

[119 121]

COC

Messenger activation

Label-free

Signal-on

nM

[122]

Neomycin

Enhancement of Fluorophore binding

Label-free

Signal-on

nM

[123,124]

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two cocaine aptamer detection sequences. In the presence of cocaine, both probes communicate with the target and form a 3D set. The hairpin probe, which undergoes a structural change, causes the hairpin rearrangement to open and hybridize and after forming a double-stranded form, the ss-probe and cocaine are moved to another hairpin-probe for bonding that creates a new amplifier cycle and fluorescence signal [122].

5.2.2.1.3.2 Fluorophore displacement upon aptamer binding An example of this mechanism is applying a double-stranded DNA (dsDNA) to detect cocaine. A ds-DNA was utilized as an effective model for CuNPs formation at low concentrations of CuSO4. The formed NPs have significant fluorescence properties to determine the applicability of the strategy. In this case, cocaine was tested with a 30-mer DNA aptamer. Cocaine binding aptamer (CBA) and its complementary strand formed a stable duplex that supported CuNPs formation. In the presence of cocaine, strand CBA was attached to cocaine, which caused ds-DNA to disappear. In this process, the intensity of fluorescence ds-DNA-CuNPs decreased with increasing cocaine concentration [119]. 5.2.2.1.3.3 Repositioning of quencher upon aptamer binding Repositioning of quencher is a method that is different from the abovementioned types in which aptamer conjugated donor and quencher are brought together upon target binding. For example, a deoxyribonucleotide aptamer was introduced to detect cocaine and instability in a bunch of three junctions made up the cocaine binding and led to the end labeling of the short bunch by fluorophore and quencher. In the absence of cocaine two groups are open while in the presence of cocaine three-way connections are closed. The result of these structural changes is the convergence of fluorophore and quencher, which indicates the presence of a ligand. Studies of cocaine detection based on this method are summarized in Table 5.3 [116]. 5.2.2.2 Enzymes in fluorescence approaches The application of antibodies in ELISA is focused on a diagnostic technique designed to identify cell surface antigens [129,130]. Schematic of a disposable cartridge-based competitive ELISA is depicted in Fig. 5.12, which represents the detection of drug abuse. Antibody was first coated on the inner surface of a capillary and the free drug and corresponding drug-HRP conjugate (competitor) were mixed and withdrawn into the capillary where they competed for limited antibodies on the surface and ELISA reaction occured for modulating signals for recording imaging process [129].

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TABLE 5.3 A summary of repositioning of quencher upon aptamer binding fluorescence sensor to cocaine detection. Sensing strategy

Mechanism

Labeling method

Linear range [µM]

LOD [nM]

Ref.

Mix-and-detect

Turn-off

Label-free

1 500

250

[125]

Strand-displacement polymerization/FRET

Turn-on

covalent

0.2 200

200

[126]

FRET

Turn-off

covalent

10 2500

1000

[116]

Target-induced conformational change

Turn-off

Label-free

0 8

20

[127]

ATMND displacement reaction

Turn-off

Label-free

0.10 10

56

[128]

FIGURE 5.12 Schematic of competitive ELISA antibody for fluorescent abused drug detection [131]. Source: Figure was adopted with permission from W. Xue, X. Tan, M.K. Khaing Oo, G. Kulkarni, M.A. Ilgen, X. Fan, Rapid and sensitive detection of drugs of abuse in sweat by multiplexed capillary based immuno-biosensors, Analyst, 145 (2020) 1346 1354; S.Y. Toh, M. Citartan, S.C. Gopinath, T.-H. Tang, Aptamers as a replacement for antibodies in enzyme-linked immunosorbent assay, Biosens. Bioelectron., 64 (2015) 392 403.

5.2.3 Electrochemical approaches The discovery, production, trafficking, and consumption of abused drugs have become major problems in all human societies and threaten human health in many different aspects [10,132]. Therefore electrochemical sensors as fast, sensitive, easy, and practical methods have been upgraded to be used alongside spectroscopic, piezoelectric, thermometric, and chromatographic methods for detecting processes in different matrices [9]. An electrochemical biosensor consists of three parts: The bio-recognition element, transducer (potentiometric, voltammetric, conductometric, etc.), and signal reader [133]. The interaction between the analyte and the recognition element is converted into a signal by the transducer system and signal analysis is performed [134]. Recognition

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elements include antibodies, aptamers, and artificial recognition agents including synthetic peptides and MIPs in electrochemical biosensors, which are the same transducers in different forms such as potentiometric, voltammetric, conductometric, etc. [9,133,134]. Generally, this system uses three electrodes, including a reference electrode (e.g., Ag/AgCl), a counter electrode (e.g., Pt), and a working electrode that acts as the most important electrode [135]. Depending on the drug type, various working electrode supports such as screen-printed electrodes (SPE), carbon paste electrodes (CPE) and glass-carbon electrodes (GCE), carbon cloth, integrated and array electrodes, fluorine-doped tin oxide (FTO), indium tin oxide (ITO), metal foil (e.g., Ti, Ni, Al, Mo), metal foams (e.g., Zn, Ni, Ti), and metal wire (e.g., Ag, Au) are applied for design and fabrication of an electrochemical aptasensor [135 137]. As mentioned above, recognition elements are immobilized on a working electrode as a transducer and consequently electrochemical measurements are conducted using two- or three-electrode systems. The electrochemical biosensor as a robust diagnostic strategy converts the information received from the elements through a transducer into an electrical signal by desirable electrodes [138]. 5.2.3.1 Antibodies in electrochemical approaches Immunoglobulins (Ig) or antibodies (Abs) are glycoproteins that are produced by plasma cells [139]. Antibodies are one of the most approachable recognition elements in small molecule monitoring due to their exceptional dissociation constant (Kd) values and exclusive capability to specifically recognize an antigen via an antigen-binding variable region (carboxylic functionalities) that contains paratopes to bind with epitopes of an antigen [21]. An Abs has two light chains and two different heavy chains in a light-heavy-heavy-light structure arrangement, which are held together by disulfide bridges. They have one Fc region containing amine functionalities that mediate biological functions (e.g., the binding capacity to cellular receptors) and a Fab region that contains the antigen-binding sites [140]. The abovementioned functional groups (FGs) facilitate the Abs immobilization on desirable electrode surfaces for biosensing of small molecules. There are several reports oon the Abs immobilization strategy showing both advantages and limitations. Conventionally, an electrode is submerged in an antibody solution and incubated under certain conditions (concentration, temperature, time, humidity, pressure, and pH), which is called the passive adsorption process [140,141]. This strategy suffer from weak dissociation energy of bonds with random alignment through an affinity loss that could occur by their denaturation, while they are easy, rapid and did not require any Abs modification [142].

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143

The most common Abs immobilization is stable covalent interaction via cross-linking to chemically activated surfaces with aldehydes (e.g., glutaraldehyde), epoxides (ether bond), or carbodiimides and succinimide ester (e.g., an EDC-NHS coupling mechanism) [142]. However, this strategy can not guarantee comprehensive orientation of the product due to the intense effect on the capacity of electrodes. For amplifying of electrochemical signals in this strategy, antibody orientation, and subsequently sensitivity several mechanisms have been developed: an avidin/streptavidin-biotin, Abs-ligand interaction (Protein A, Protein G, Protein A/G, secondary antibodies), and metal affinity coordination quantum dots (QDs). Although these mechanisms are often more complicated but a defined can improves the efficiency of sensors [143]. One of the major challenges of antibodies in abused drugs and complex matrix analysis is their limited working conditions [physiological pH (7.2 7.4) and T (37 C 38 C)]. In one study, Sanli et al. [143] designed cobalt oxide NPs and singlechain antibody fragments (scFvs) functionalized screen-printed electrode (SPE)-based electrochemical biosensor for cocaine detection (Fig. 5.13). The findings revealed a desirable selectivity for cocaine as well as a linear range of 5.0 250 ng/mL and an LOD of 3.6 ng/mL [143].

FIGURE 5.13 Schematic representation of the composition of the SPE-CoNTA-Ab biosensor for cocaine detection. Source: Figure was adopted with permission from O. Parkash, C.Y. Yean, R.H. Shueb, Screen printed carbon electrode based electrochemical immunosensor for the detection of dengue NS1 antigen, Diagnostics, 4 (2014) 165 180.

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5.2.3.2 Aptamers in electrochemical approaches Due to the abovementioned advantages for electrochemical sensors, aptamer-based biosensors as a biorecognition element in the diagnosis and measurement of abused drugs are widely used and have replaced traditional methods with many limitations [133]. Similar to antibodies, aptamers are attached to the functionalized and activated supported electrodes as the recognition element to detect the target abused drug [138]. To ensure reactivity, orientation, access to the aptamer connected to the surface, and to minimize the connection phenomena, it is vital to control the electrode surface (e.g., surface tension, surface energy, surface roughness, work function, topology, and morphology) [14,144]. The most common strategies include physical adsorption, chemisorption, avidin-biotin interaction, electrochemical grafting, using reactive ends capable of functional groups, covalent binding on activated electrodes via amide and thiol-gold binding, or cross-linking agents [145]. In the presence of the abused drug, the 3D configuration of an aptamer switches to a locked structure leading to considerable changes in electrochemical properties and signals [9]. To stabilize the recognition molecules on some of the as-mentioned supports, the support must be modified with different coating materials such as polymers or nanostructures. Additionally, for amplified electrochemical signals and better analytical performance, these supports can be decorated (e.g., deposited, loaded, coated, intercalated) with suitable electroactive materials [146,147]. In the last few years, researchers have studied the effects of electrodes modified with different nanoparticles, including nanobiomolecules, noble metal nanoparticles, metal oxides, metal chalcogenides, MOFs, and carbon-based materials (e.g., graphene, multivalued carbon nanotubes, graphitic carbon nitride) [30,148,149]. Bozokalfa et al. reported an aptamer-based biosensing strategy for cocaine monitoring as follows: Polythiophene bearing polyalanine homopeptide side chains (PT-Pala) were electrochemically coated onto the GCE surface and then cocaine aptamer was attached to the polymer via covalent conjugation chemistry (Fig. 5.14). The as-designed bioassay was used for cocaine detection in the concentration range of 2.5 10 nM [150]. 5.2.3.3 Molecularly imprinted polymers in electrochemical approaches Even though the as-mentioned antibodies and aptamers generally increase the sensitivity and selectivity of electrochemical sensors, their application is limited due to instability in harsh conditions (pH, T, and nature of the medium), lack of easy access to sourcing them, and the need for remarkable skill to immobilize them on the electrode surface [151]. Therefore to refine these limitations, MIPs as an alternative artificial

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145

FIGURE 5.14 Schematic representation of PT-Pala and further modification by the aptamer immobilization and electrochemical biosensing of cocaine as commonly abused drug. Source: Figure adopted with permission from G. Bozokalfa, H. Akbulut, B. Demir, E. Guler, Z.P. Gumus, D. Odaci Demirkol, et al., Polypeptide functional surface for the aptamer immobilization: electrochemical cocaine biosensing, Anal. Chem., 88 (2016) 4161 4167.

recognition element were developed [19,152 154]. MIPs are the complementary option for the specific recognition of the small molecules [18,155]. To design MIPs with selective ordered cavities a monomer is easily polymerized in the presence of the target molecule and a cross-linking agent producing the cross-linked functional monomer/template complex [156,157]. In comparison with natural aptamers and antibodies, MIPs have lower Kd values, which makes them a preferred receptor for small molecule detection in complex matrices and environments where natural receptors can decompose, denature, or lose their applicability and affinity [158]. MIPs can be synthesized in powder form and then deposited on the desirable electrode, or their synthesis can be directly performed on the electrode surface via electro-polymerization, hydro & solvothermal, drop-coating of a prepolymerization mixture, lithography, or in situ polymerization [159]. Electropolymerization and in situ polymerization have been found to be preferable to all other forms of biosensing due to their appreciable adherence to a transducer surface and simple and accelerated mechanisms [159,160]. When MIP particles are synthesized in powder form, the deposition step is performed via an extra step of covalent or electrostatic binding or impregnation of the MIPs particles in casting polymers such as gelatin, chitosan, guar gum, or PVDF and poly-tyramine [161]. Incorporation in a polymeric membrane is a relatively easy process and does not require FGs at the MIP surface, but can lead to a decrease in the availability of the binding sites and a higher variability between measurements [9]. Generally, the covalent interaction is favored over the electrostatic interactions. Hence the chemical grafting of MIPs via a cross-linking agent to the electrode surface due to the higher stability and regularity is more important. Floera et al. [162]

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FIGURE 5.15 Schematic representation of the electrochemical fabrication of the MIP sensor for cocaine detection. MIP, Molecularly imprinted polymer. Source: Figure adopted with permission from A. Florea, T. Cowen, S. Piletsky, K. De Wael, Electrochemical sensing of cocaine in real samples based on electrodeposited biomimetic affinity ligands, Analyst, 144 (2019) 4639 4646.

developed an electro-polymerizing MIP onto graphene-modified electrodes as a selective electrochemical biosensor for the direct detection of cocaine (Fig. 5.15) [162]. They integrated Pd-NPs in the sensing layer for increasing the interaction between the imprinted sites and the electrode surface and for overcoming their homogeneous distribution [162]. The MIP was synthesized by cyclic voltammetry (CV) using paraaminobenzoic acid as a high-affinity monomer selected by computational modeling and cocaine as a template molecule. The results revealed the proposed biosensor can detect cocaine in the range of 100 500 μM with an LOD of 50 μM. Some other electrochemical strategies for abused drug detection are summarized in Table 5.4.

5.2.4 Real-time analysis of abused drugs Point-of-care (POC) diagnostics devices are becoming an attractive multidisciplinary topic in analytical, clinicalm and medical sciences since they provide an excellent opportunity to achieve quick diagnostic results in a nonlaboratory setting for patient bedside care [179]. Moreover, POC devices as one-step, scalable, fast, smart, and cost affordable systems have been developed to facilitate the detection of specific targets in a complex matrix [180]. They are robust and promising strategies for preliminary

2. Biomedical applications

TABLE 5.4 A summary of sensors designed based on aptamers to detect abused drug levels. Electrode support

Abused drugs

Aptasensor type

Linear range (nM)

LOD (nM)

Sample

Electrochemical methods

Ref.

AgNPs/MWCNTs-IL-Chit/ GCE

Cocaine

Functionalized Aptamer

2 2500

0.15

Human serum

DVP

[163]

SWNTs/Au

Cocaine

Functionalized Aptamer

0.1 10

0.105

Rat serum

DVP

[164]

Gold electrode

Cocaine

Supramolecular aptamer, RCA and combined with multiplex binding of the biotin-streptavidin

2 500

1.3

Urine

DVP

[165]

Gold electrode

Cocaine

DDIAS-based aptasensor

10 5000

10

Sewage

EIS

[166]

GCE/PT-Pala

Cocaine

Aptamer

2.5 10

1.5

Urine and Saliva

DVP

[150]

AgNPs/Apta/CdTe QD/ GCE

Cocaine

Aptamer

0.05 6000

0.005

Human blood serum

DVP

[167]

AuNP/ PAMAM/GA/ CHIT/SPCE

Codeine

Aptamer

0.001 0.01

0.0003

Human serum

DVP

[168]

ZnS modified β Cyclodextrin/DNA

Codeine

Aptamer

0.073 73

0.037

Protein

DVP

[169]

PbS modified Cyclodextrin/ DNA

Codeine

Aptamer

0.0073 7.3

0.0057

Protein

DVP

[170]

SA DNA/Au-NPs-modified chitosan/nucleic acid

Methamphetamine

Aptamer

1 10

0.7

Human serum

CV

[171]

Polytyramine/sol-gel/ fMWCNT@Au-NPs MIP/PGE

Ketamine

MIP

1 1000

0.7

Human plasma

CV

[172]

(Continued)

TABLE 5.4 (Continued) Electrode support

Abused drugs

Aptasensor type

Linear range (nM)

LOD (nM)

Sample

Electrochemical methods

Ref.

Au-NPs/MIP/f-MWCNT/ PGE

Morphine

MIP

8 5000

2.9

Human plasma

SWV

[173]

SPCE-MIP

Ecstasy

MIP

42.37 73

3.9

Blood serum

SWV

[174]

SPCE-MIP

Ecstasy

MIP

100.9 160

6.4

Urine

SWV

[174]

GNS@Ag NPs/TRA-MIP NPs/[BMP]Tf2N RTIL)modified CPE

Tramadol

MIP

3.50 107

2.04

Pharmaceutical and biological real samples

CV

[175]

MIP@ Cube-Ag/C@Fe3O4 modified PGE

Tramadol

MIP

33 9999

9.3

Pharmaceutical and biological real samples

SWSV

[176]

Polypyrrole@Sol-Gel MIP/f-MWCNT/GCE

Tramadol

MIP

0.2 20

0.03

Pharmaceutical and biological samples.

CV

[177]

Au-SPE/(PANI 1 AgNPs)/ MIP

Tramadol

MIP

0.0029 29.98

-

Human saliva and urine samples

CV

[178]

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disease identification and immediate clinical or medical treatment [179]. This equipment is commonly utilized in clinical departments, physicians’ offices, and homes without central laboratory equipment or skilled staff [181]. Real-time tests based on biorecognition elements have been developed for documenting both quantitative and qualitative outcomes obtained from the selective interaction between targets and corresponding recognition elements. As mentioned before, conventional biorecognition elements include aptamers, antibodies, molecularly imprinted biopolymers, microalgae, and enzymes in different shapes and structures [1]. The detections methods based on POC are electrochemical, physical (T, resistance, etc.), and optical (fluorescence, colorimetric, SPR, etc.) methods that can be utilized to reveal the real-time interaction of targets and related recognition elements [1,182]. In other words, POC bioassays are based on competitive binding immunoassays to detect te small and macromolecule targets with no requirement for multipart sample preparation or sophisticated instrumentation [4]. POC-based analysis methods provide quick and robust assays for abuse drug monitoring. Generally, specific POC testing for drugs of abuse could be desirable in many settings such as police stations, forensics labs, emergency units, drug treatment clinics, detoxification units, and pain management clinics. Real-time POC testing (POCT) has attracted great attention in recent studies kusing in situ analysis to develop screening and treatment tools to facilitate interventions to decrease mortality and morbidity due to drug abuse [2,183]. The differences between POC devices and conventional abused drug analysis methods are summarized in Fig. 5.16. Some of the popular POC devices that are commercially available to monitor drugs of abuse include Triage drugs of abuse panels and Triage TOX drug screens (Biosite, Inc., San Diego, California); On Trak Test card 9 (Varian, Inc., Palo Alto, California); Rapid drug screen (American Bio Medica, Kinderhook, New York); Intercept (OraSure Technologies, Inc., Bethlehem, Pennsylvania); Profile-II (MEDTOX diagnostics, Burlington, North Carolina); and Status DS (LifeSign LLC, Somerset, New Jersey) [183]. Compliance and management are important aspects of POCT, and the analytical performance of these devices contributes to the total quality of the results and thus their medical application. Three common analytical methods in POC sensing include immunochromatographic, electrochemical, and spectroscopic test strips, discussed in the following sections. 5.2.4.1 Immunochromatographic test strips based on real-time analysis of abused drugs POCT device-based immunoassays utilize antigen-antibody reactions, chromogenic antibodies, chromogenic drug conjugates, and fluorescent

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FIGURE 5.16 Laboratory analysis procedures versus. POC analysis. POC, Point-of-care.

antibodies or drug conjugates [183]. Generally, antibodies interact with a particular drug of abuse, an intermediate or end product of metabolism, and a type of compound [184]. Qualitative outcomes are obtained based on a particular predetermined calibrator concentration in which the positive results reveal a higher concentration than the calibrator cutoff, while negative results report concentrations below the cut-off value and provide binary results of the type yes/no [183,184]. The key advantages of immunoassays over other techniques are their fast procedure time, ease of assay operation, wide availability of platforms, and capability to detect multiple drugs within similar categories. The main drawback of these methods is their limited specificity and sensitivity, leading to false-positive or negative outcomes [184]. In the late 1900s, lateral flow-based immunoassay (LFA) or immunochromatographic test strips (ICTS) were first developed by Unipath as the most widespread diagnostic mode of POC testing in medical and clinical diagnosis, drug-of-abuse analysis, therapeutic observing, healthcare investigation, environmental detection, food safety, and law enforcement [179]. Immunochromatography is commonly utilized as a

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quick and nonlabor-POC technique, in which all sensing steps take place without human intervention or automated systems because of the specific interaction between the test strip and a sample. However, produced immunochromatographic tests are robust devices for recognizing some materials while the their great challenge is finding many reagent lines having unlike characteristics on the test strip surface. By promoting test strips from 1D to 2D geometry of test zones, a noticeable growth in the number of simultaneously controlled parameters is achieved. These binding zones with different characteristics create a systematic array of spots that can be displayed in several rows both in the lateral and vertical directions [185]. Most commercial ICTS structures are based on plasmonic nanoparticles like colloidal Au-NPs since their remarkable absorbance in the visible range allows them to create prominent colors that are easily seen by the naked eye. Generally, the signal reporting elements have a noteworthy effect on the recognition sensitivity so the selection of a desirable substance holds vast importance in creating ICTS with acceptable reliability and accuracy. Today, most Au-NPs-based colorimetric ICTSs are designed in which the particular colors originating from Au-NPs (generally rubyred) in the detection region can be instantaneously observed by the naked eye [186,187]. The noted color-based ICTS is excellent for both semiquantitative and qualitative recognition while suffering from some limitations for more precise quantitative analysis because the minor variations in target concentration cannot be recognized from the shades of the color signal-producing chemicals. As a result, many studies have been devoted to solving the as-mentioned limitation via the recommendation of new reagents to improve the colorimetric ICTS detection limit. These innovative kinds of reporting components generally hold fluorescent agents such as organic fluorophores, inorganic QDs, upconverting NPs, carbon-based nanodots, and dye-embedded nanostructures. Fluorometry-based ICTS has higher sensing contrast, lower background interference, and advanced sensitivity in comparison with colorimetrybased ICTS; consequently, they can perform quantitative recognition of trace amounts of targets [179,186,188]. The LFAs offer rapid, low-cost, portable, and modifiable tools in POC diagnostics and are mostly made of a sample pad, analytical membrane, conjugate pad, absorbent pad, and a backing pad [189]. Firstly, the sample (containing an abused drug) is introduced on the sample pad, moves along the conjugate pad, carried the target and subsequently attached to modified labeling particles passes through the analytical membrane in the direction of the absorbent pad. The flow of the sample facilitated by the capillary force originating from the absorbent pad and the sample passes from the test zone which contains the immobilized capturing particles so the target interacts with the recognition element to form a

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colored test line. Generally, LFAs are designed in sandwich and competitive forms. In the sandwich form that is applied for targeting multiple epitopes, the recognition fragments tagged with a color agent interact with the specific target and the resulting conjugate is attached to the matching receptor element’s test line. The recognition elements selectively interact with the capturing agents in the test zone and the intended target competes with the recognition element for binding. This setup is suitable for detecting small molecule targets or those with only one single specific antigen [1]. For drug of abuse detection, the LFA system is precoated with the target drug (or its metabolite) on the test line section and the conjugate pad is composed of a specific monoclonal antibody and Au-NPs colloids that are located at the beginning part of the membrane. When the free drug sample is injected into the system the colored antibody-colloid Au conjugate passes with the sample through the membrane by capillary action. After that, the reported complex faces a drug conjugate immobilized in the test line area in which the specific interaction between antibody-Au conjugate and the precoated drug conjugate happens and creates a red colored line due to the creation of a complex between the antibodies and the drug conjugate. Consequently, the appearance of a colored line on the test line section can reveal that the sample does not contain the target or its concentration is below the LOD of the system. In the drug-containing sample, the competition between the drug antigen and the drug conjugate immobilized on the test line part can be happened to interact with the antibody sites of the antibodyAu conjugate. When an adequate drug is found in the sample, the drug interacts with colored antibody-Au conjugate and no color appears on the test line. Generally, to check the validation of the performance of LFA tools, a control line with a different antigen/antibody reaction is added at a control site on the membrane strip [190]. Yu et al. reported an LFA platform for direct visual determination and quantitation of amphetamine (AMP), cocaine and morphine (MOR) in body fluids. For this reason, a PDMS plate with straight channels was placed on polycarbonate chips (PC) with activated carboxylic FGs for individual drug detection [77]. For preparing the probe lane, a solution of antigen-BSA conjugated was injected to the channels and incubated for 8 h. In the next step, the PDMS plate was removed and a mixture of the drug and its biotin-labeled antibody was delivered in the perpendicular direction of the PDMS plate. After that streptavidin nanosilver, ALP, and HRP were delivered into channels for staining different drugs in different channels. The labeled antibodies bond the analyte molecule in solution and preimmobilized probes and surface-bound antibodies cause the colorimetric quantitative detection (Fig. 5.17). Much effort has been put forth to made to improve the reproducibility, selectivity, sensitivity, quantifying, and multiple detecting properties of

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FIGURE 5.17 Schematic illustration of simultaneous detection of MOR, AMP, and COC using a microfluidic immuno-microarray: (A) quantitative detection of MOR based on the silver staining approach and (B) direct visualization of the presence of three drugs based on multicolor staining methods. AMP, amphetamine; MOR, morphine. Source: Figure was adopted with permission from L. Zhang, X. Li, Y. Li, H.-Z. Yu, A colorimetric immuno-microarray for the quantitation and direct visualization of illicit drugs in body fluids, Analyst, 146 (2021) 538 546.

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LFAs. In these sensing tools, antibodies are selected as recognition elements while aptamers are very rarely used for abuse drug recognition. In this regard, Liu et al. applied an aptamer in four common varieties of membranes LFA system as recognition molecules to detect cocaine. Cocaine aptamer-NPs in aggregate form were marked on the conjugation pad and test line, and streptavidin protein was utilized on the analytical membrane. This test works based on the assumption that the aggregate NPs are large and capable of flowing throughout the membrane, while the dispersed NPs can move to the end of the stick. In sample solution and the absence of the target, aggregated NPs can be rehydrated and move along the strip eventually stopping at the end due to their large size. In the presence of the target, the aggregated form is broken due to the selective interactions between the target and the specific aptamer. Thus the asdispersed NPs can migrate along the membrane and provide the specific line on the test zone that correlates with the concentration of the target in the buffer solution. The practicality of this proposed method in spiked serum samples has been successfully monitored [1]. It should be noted that the practical applications of aptasensors in legal samples are incredibly dependent on the selective and specific function of aptamers. Additionally, a portable miniaturized image-based fluorescence LFA system instead of the conventional colorimetric detection method for cocaine monitoring in sweat media has been developed. Desirable analytical responses have been achieved for fluorescence as a sign of test outcomes instead of simple color detection [190]. There is a great need for methods for the simultaneous one-step detection of several low molecular-weight compounds such as drugs of abuse. To refine this problem, a promotion in a lateral flow microarray has occurred in which the test zone of the strip contains several tens of regularly arranged spots which holding different immobilized immunoreagents. Recently, this method was applied for the simultaneous detection of extensively used and internationally controlled drugs of abuse, such as morphine, amphetamine, methamphetamine, and benzoylecgonine (the major cocaine metabolite) [185]. A combination of lateral flow and chip methodologies equipped with oligonucleotides as capture elements were developed as another LFA-based technique. In this regard, a collection of oligonucleotide derivatives with different capturing specificities was designed on the membrane, and detectable complexes were formed in these regions as liquid flowed along the test strip. The nitrocellulosesupported test zone contains a microarray spotted with up to 32 antigens that can selectively interact with labeled Au-antibodies after lateral flow. The assay function is rapid (10 min) and has revealed a low relative standard deviation (9%) and appropriate recoveries (95% 114%). The detection limits (2 20 ng/mL for drugs of abuse) of the mentioned assay are comparable to common single-analyte strip methods [185].

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5.2.4.2 Electrochemical-based real-time analysis of abused drugs Recently, electrochemical biosensors in POC testing have been developed to conduct in situ monitoring of pharmaceutics and other sensing applications. POC-based potentiometric sensors are at the center of interest in these applications in part because they have effectively overcome the time-consuming disadvantage of the usual laboratory studies. Ion-selective electrode (ISE)-based potentiometric assays with their features such as portability, low cost, miniature size, and energy savings make them suitable tools for POC applications. Conventional ISEs have been replaced by solid-contact ISEs and have eliminated internal filling solutions; they have other advantages such as mass production robustness and miniaturization. Solid-contact ISE activity is limited due to having unstable electrode potential. To address this problem, an ion-to-electron transducer layer has been embedded between the electrode surface and ion-sensing membrane to promote stability. Various types of nanomaterials like carbon nanotubes, conducting polymers, selfassembled monolayers, and graphene and their nanocomposites have been employed as ion-to-electron transducers [2]. For example, a portable, oneuse, economical, and reliable POC solid-state potentiometric sensor was developed to sense diazepam (DZP) in the presence of both metabolite (oxazepam) and its degradation product (2-amino-5-chlorobenzophenone). To fabricate a sensing tool, a Cu microfabricated support was modified with poly (3-octylthiophene) (POT) conducting polymer and Calix [4] arene (CX4) ionophore as a selective agent as well as reduce any incorrect positive results. The surface characterization of the Cu before and after modification with POT was investigated via electrochemical impedance spectroscopy (EIS) [2]. Wearable drug-screening structures were developed to meet the need for real-time, noninvasive, or minimally invasive drug nursing and realtime screening of drugs of abuse. Electrochemical wearable devices are mostly attracting attention for drug measurements because of their unique advantages such as selective performance toward important electroactive drugs, small size, low power requirements, low price, high speed, and highly scalable production via the use of printing equipment [2,191]. Recent research has revealed that developing wearable sensing devices for drug abuse detection has limitations due to the variations in vital signs. Some of them are a wristband that uses changes in three factors of heart rate, skin temperature, and conductance to screen for abused drugs, as well as smart footwear that incorporates pressure sensors in shoes to observe the corresponding variations in the manner of walking to sense alcohol consumption. These wearable sensors have attracted great attention, but they do not directly quantify the target drugs and their response depends on the study of physical factors that can be affected by conditions

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like stress and anxiety. To overcome these drawbacks, several studies have been conducted to develop wearable devices to observe the drug concentration directly rather than side effects or physical parameters [191]. Recent studies have focused on wearable devices based on different electrochemical techniques with the aim of developing innovative bodyworn drug monitoring tools. These kinds of wearable sensors are categorized as glove-based wearable devices for external drug screening and wearable sensors for observing dynamically varying drug amounts in different bodily fluids [191]. Wearable tools are important in numerous fields such as food safety, environmental monitoring, and forensic and security analysis as they simplify rapid decision procedures. Additionally, biochemical identification at the fingertips via electrochemical-based glove sensing tools offers innovative opportunities in the screening of target substances. Utilizing this smart glove has been the stimulus of researchers’ minds to find a more rapid, easy, and cost-effective strategy for on-site and real-time drug monitoring. While the glove-based wearable devices often need innovative materials and bioelectronic arrangements to carry out desirable conditions. Although this method has advantages, there are disadvantages to consider such as the need for a low-cost screen-printing technique for mass manufacture of the disposable smart gloves, the perfect combination of a small and comfortable wireless reader, and teaching users how to use the device in the field [191]. Currently existing sensing platforms for recognizing drugs of abuse are facing practical challenges. In this regard, an integrated competitive volumetric-bar-chart chip (CV-Chip) as a POCT was developed to analyze multiple drug targets, which incorporates the functional capabilities to fully overcome practical challenges. It demonstrated visual positive or negative bar-chart results based on the direct comparison between the gas generated by the sample and the internal control. The results showed that this platform could have a reasonable recovery rate for cocaine and amphetamine in spiked whole blood samples, which is an ideal biospecimen for the POC test. This sensing tool offers a sensitive, precise, fast, portable, and minimally invasive procedure for drug of abuse valuation in serum, urine, and whole blood samples [5]. 5.2.4.3 Spectroscopic based real-time analysis of abused drugs Spectroscopic techniques show promise in describing complex matrices and offer advantages of easy and nondestructive use without the demand for any pretreatment procedures as well as can be utilized as an appropriate method in POCT devices [192]. One of the refractive-indexbased biosensing techniques is fiber-optic particle plasmon resonance (FOPPR), which has revealed important results in real-time observing of selective interaction between the target and biorecognition element on the fiber core surface. The FOPPR assay procedure is based on the principle

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of absorption of fiber-optic evanescent wave via Au-NPs, gold nanospheres, silver nanoparticles [193] and gold nanodots at the fiber core interface, utilizing a signal enrichment pattern through several total internal reflections (TIRs) of light in an optical fiber [194]. Particle plasmon resonance (PPR) or localized surface plasmon resonance (LSPR), is a result of interaction between light and Au-NPs. The PPR appears when the frequency of incident light and the oscillation frequency of the hot electrons in the NPs are the same; this interaction results in a wide visible absorbance spectrum of the NPs. Both extinction cross-section and peak location are significantly dependent on the minor changes in the local refractive index of the NPs surrounding medium. When the light in the fiber core is amplified by the multiple total internal reflections (TIRs) feature, the evanescent wave on the surface of the fiber core stimulates the PPRs of immobilized Au-NPs, thus the transmitted light is reduced through the fiber by interacting with the Au-NPs. The FOPPR immunosensor for on situ nursing of molecular interactions operates on the variation of localized evanescent wave absorption by the Au-NPs upon molecular binding, causing a measurable reduction in transmission intensity monitored at the distal end of the optical fiber. This technique offers an appropriately sensitive way to monitor molecular interaction in realtime with no necessity of labeling procedures. These biosensors have been applied in different chemical and biological fields such as environmental protection, medical diagnostics, cytokine and sepsis biomarker detection, food safety and agricultural monitoring [195]. The competitive immunoreaction in the FOPPR biosensor provides a method to observe low-molecular-weight molecules like illicit drugs, utilizing a labeled compound that has a structure similar to the target analyte, to compete for the interaction to the selective capture part on the fiber surface. In a recent study to detect methamphetamine (MA), a competitive surface immunoassay that was composed of unlabeled free MA molecules in solution in competition with immobilized bovine serum albumin (BSA) MA conjugates (located at the fiber core surface) to selectively bind with free anti-MA antibodies in solution. Consequently, in the absence of free MA molecules in sensing media, the largest amount of free anti-MA antibody molecules binded selectively with the immobilized BSA MA, causing a significant reduction in transmitted light intensity in the optical fiber. In the presence of free MA in the sample, MA molecules and the surface-immobilized BSA MA qwew competing for binding anti-MA antibodies. The anti-MA antibody molecules interacted with the surface-immobilized BSA MA and its concentration was decreased, resulting in a result minor decrease in transmitted light intensity. The intensity reduction had a linear correlation with the quantitated amount of free MA molecules, which revealed a detection limit of 0.16 ng/mL. These MA-FOPPR biosensing tools can be applied in diluted human

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urine samples with no need for interference adsorption pretreatment procedures for MA monitoring. With these positive outcomes, future development of FOPPR biosensor for recognition of other illicit drugs in urine and oral fluid is possible [195]. In a novel spectroscopic design, an analytical sensing platform based on microNIR spectroscopy connected to chemometric analysis was reported to detect amphetamines in nonpretreated oral fluids. The main advantage of this compact system is its small portable spectrometer in the nearinfrared area, which due to its geometry and optical resolution allows you to obtain comparable results. Partial least square-discriminant analysis (PLS-DA) and PLS regression (PLS) were applied to validate the amphetamine (AMP) measurement data. Results proved that this platform can recognize AMP abuse in simulated oral fluid samples with the same accuracy and sensitivity as the reported certified reference approaches. This method would simplify AMP drug analysis and may be helpful at first-aid points. This spectroscopic AMP analysis in oral fluids offers a system that is nondestructive, solvent-less, and fast. In addition, the combination of microNIR and chemometric confirmed the automation of the sensing platform that was developed to simplify law enforcement processes and assist in providing rapid results [192]. Next-generation colorimetric biosensing is integrating these analytical systems with portable devices such as microfluidic devices or paper devices [196]. In this regard, paper materials due to an abundance of advantages such as simplicity, economics, and portability play a vital role as supporting material for microfluidic devices [197]. Colorimetric biosensor-based paper microfluidic devices coupled with noble metal colorimetric probes for abused drug detection have been reported [74]. The mechanism of these sensors is color change by active and inactive aggregation of noble metal NPs that is induced by salt in the presence of a target on a paper strip [198]. For example, cocaine determination by this method was conducted by Yang et al., which showed that aggregation occurs by two single-stranded DNA (ss-DNA) fragments (anticocaine aptamers, ACA-1, and ACA-2) and salt solution moving to the aptamer channel as well as cocaine in the sample interact with aptamers so there are not free aptamers to prevent the aggregation of Au-NPs that bound strongly to the paper matrix in the further channel when faced with salt (see details in Fig. 5.18). To confirm portability visual and RGB measurements were applied for data curation and analysis. In another study, atmospheric pressure ionization technologies including direct analysis in real-time (DART) ion source coupled with a time-offlight mass spectrometer (DART-TOF-Ms) and dopant-assisted positive photoionization ion mobility spectrometry (DAPP-IMS) with no radioactivity were applied as a novel analytical tool for the fast monitoring and recognition of 53 abused drugs. The detection limits (20 2000 ng/mL and

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FIGURE 5.18 Schematic of the microfluidic colorimetric detection of cocaine using Au-NPs/aptamer complexation. Au-NPs are placed at the end of the terminal channel while aptamers and salt are located in the center of the channel (the sample and moving solution are placed in a vial). The presence of cocaine and salt permits the aggregation of the NPs resulting in black color. Source: Figure was adopted with permission from L. Wang, G. Musile, B.R. McCord, An aptamer-based paper microfluidic device for the colorimetric determination of cocaine, Electrophoresis, 39 (2018) 470 475.

20 2000 μg/mL) obtained by applying DART-TOF-Ms and DAPP-IMS, respectively, met the actual demand in the medical science laboratory. Fast response time, specificity toward analyte, high-throughput potential, and simple sample preparation were the advantages of this method. This study suggests that a method combining DART-TOF-Ms and DAPP-IMS is suitable for the rapid observation and identification of abused drugs in the absence of chromatography methods. The results of the analysis showed reduced mobility (K0) documented from DAPP-IMS and precursor ions and fragments with high mass accuracy operating at the in-source collision-induced dissociation (CID) mode of TOF-Ms to avoid false-positive results. Therefore this monitoring procedure can rapidly analyze a large number of samples to detect drugs of abuse. The main drawbacks of this technique are that this screening tool might not be able to identify all constituents of the mixtures or recognize very analogous compounds such as epimers. Conversely, remarkable properties such as fast response time, ease of the procedure, and high-functional potential for several compounds make this method appropriate as an alternative in a forensic science laboratory [6]. In summary, most immunoassay detecting procedures suffer from interference when antibodies are selected as recognition elements. In this case, the capturing element interacts with metabolites of the drug

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TABLE 5.5 advantages and limitations of point-of-care devices. Advantages

Limitations

Faster result time

Error in assay report

On-site testing eliminates transportation delays to the laboratory references

Deficient quality control

Small sample volume

Sample collected without applying an appropriate protocol

Instrumental readers give an objective result

The results that are read visually are subjective

Device readers automate test times

Device settings are usually manual and may cause errors

Device readers automate test times

Device settings are usually manual and may cause errors

Common drug classes are available

Limited test cutoffs

Simplified sample identifications (ease of use)

Lack of comparability to lab results and potential for having interferences effect to have false-positive results

Small and portable

The test might be done in an inappropriate condition (high or low temperature)

instead of the target drug. To solve this limitation, the application of novel recognition elements such as aptamers have been explored. The main advantages and limitations of POC devices are summarized in Table 5.5.

5.3 Conclusion and remarks Illicit drugs include synthetic chemical substances and natural ones such as alcohol, amphetamines, barbiturates, benzodiazepines, cannabis, cocaine, hallucinogens, methaqualone, and opioids. Abuse of these drugs has major consequences for humankind. The dangerous effects are not only related to the abused drug but also relate to chemical dependency and addiction. There are three major opportunities (colorimetric, electrochemical, and fluorescence) for the detection of abused drugs with biosensors along with molecular receptors to decrease drug abuse and aid forensic labs with their high sensitivity, specificity, and efficiency. The emerging biosensor applications reviewed in this chapter have paved the way for detecting illicit drugs and improving the public health.

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Further reading F. Amourizi, K. Dashtian, M. Ghaedi, B. Hosseinzadeh, An asymmetric Schiff basefunctionalized gold nanoparticle-based colorimetric sensor for Hg 2 1 ion determination: experimental and DFT studies, Anal. Methods (2021). W.R. de Araujo, T.M. Cardoso, R.G. da Rocha, M.H. Santana, R.A. Munoz, E.M. Richter, et al., Portable analytical platforms for forensic chemistry: a review, Anal. Chim. Acta 1034 (2018) 1 21. R. Fishel, Mismatch repair: choreographing accurate strand excision, Curr. Biol. 31 (2021) R293 R296. X. Han, F. Sheng, D. Kong, Y. Wang, Y. Pan, M. Chen, et al., Broad-spectrum monoclonal antibody and a sensitive multi-residue indirect competitive enzyme-linked immunosorbent assay for the antibacterial synergists in samples of animal origin, Food Chem. 280 (2019) 20 26. J. Kaur, P.K. Singh, Enzyme-based optical biosensors for organophosphate class of pesticide detection, Phys. Chem. Chem. Phys. 22 (2020) 15105 15119. A. Lantam, W. Limbut, A. Thiagchanya, A. Phonchai, A portable optical colorimetric sensor for the determination of promethazine in lean cocktail and pharmaceutical doses, Microchem. J. 159 (2020) 105519. Y. Li, R. Su, H. Li, J. Guo, N. Hildebrandt, C. Sun, Fluorescent aptasensors: design strategies and applications in analyzing chemical contamination of food, Anal. Chem. (2021). K. Ma, X. Li, B. Xu, W. Tian, Label-free bioassay with graphene oxide-based fluorescent aptasensors: a review, Anal. Chim. Acta (2021) 338859. L.R. Schoukroun-Barnes, F.C. Macazo, B. Gutierrez, J. Lottermoser, J. Liu, R.J. White, Reagentless, structure-switching, electrochemical aptamer-based sensors, Annu. Rev. Anal. Chem. 9 (2016) 163 181. S. Soylemez, S. Kurbanoglu, Enzyme-based electrochemical nanobiosensors using quantum dots, Electroanalytical Applications of Quantum Dot-Based Biosensors, Elsevier, 2021, pp. 307 339. M. Thanaraj, R. Rathanasamy, S.K. Jaganathan, Advancements in ultra-sensitive nanoelectronic biosensors for medical applications, Curr. Nanosci. 17 (2021) 679 693.

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C H A P T E R

6 Biosensors for nucleic acid detection Mehrdad Forough, Ecenaz Bilgen and ¨ zgu¨l Persil C O ¸ etinkol Department of Chemistry, Middle East Technical University, C ¸ ankaya, Ankara, Turkey

6.1 Introduction Nucleic acids (NAs) have always been the center of research concerning life: its origins, endurance, and inheritance through generations. NAs are classified into mainly two groups based on the difference in sugar units and the heterocyclic nitrogenous bases. Deoxyribonucleic acid (DNA) contains 20 -deoxyribose and adenine, thymine, guanine, and cytosine bases while ribonucleic acid (RNA) has the sugar group ribose and uracil instead of the thymine base. DNA is the carrier of the genetic information in eukaryotic cells, and the information carried within the DNA molecules has to be expressed for several purposes including protein synthesis. RNA takes part in the transcription and translation processes, helping to locate the necessary information from the nucleus to cytosol for protein synthesis. In addition, noncoding RNAs (ncRNAs) are known to play key roles in several molecular mechanisms including RNA splicing, epigenetic memory, chromosome maintenance, and segregation [13]. Hence, these two classes of NAs are responsible for the viability of the cell, and their stability, function, composition, and structure have been extensively investigated over the past decades [4]. Apart from identifying their structural features and understanding their biological relevance, many efforts have been directed toward developing simple, smart, rapid, and highly specific methods for their detection.

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NA detection is an important aspect of several fields including clinical diagnostics, gene therapy, forensic analyses, and environmental and food monitoring. They have been especially considered as noteworthy diagnostic indicators/biomarkers to track the presence and progression of various diseases [57]. Consequently, over the past two decades, the development of novel NA detection platforms especially for point-ofcare (POC) diagnostics and portable-device applications has attracted significant attention [8]. There have been remarkable publications with key findings in the field. The general strategy for the biosensing of target NAs frequently relies on the same principle; the hybridization of a known single-stranded (ss) NA (as probe) with its complementary strand [9] resulting in the formation of the double-stranded (ds) NA structure. Thus the detection of the complementary strand-DNA or RNA can be straightforwardly attained by transducing the signal of hybridization through a mechanism such ash optical, electrochemical (EC), electromechanical, magnetic, or thermometric [10,11]. Numerous reports include nanomaterial-based, EC, and optical methods to sense the presence of NA structures with high specificity and sensitivity [6]. Moreover, due to its highly sensitive and straightforward outcomes, the use of the CRISPR/Cas technology for NA detection evolved as a promising alternative to replace the conventional techniques [12,13]. Recently, the CRISPR/Cas systems have become the nextgeneration NA biosensing technology due to their remarkable ability to detect NA biomarkers from important pathogens [14]. The findings of excellent biosensors for NA detection may contribute to resolving ongoing challenges in diagnostics, environmental safety, and global health issues. NA biosensors are promising candidates to remove the need to access highly qualified laboratory equipment and personnel, which is especially difficult for developing nations where healthcare systems are inadequate. In 2003, the World Health Organization (WHO) developed the term ASSURED to define the criteria for the ideal testing of infectious diseases which stands for Affordable, Sensitive, Specific, User-Friendly, Robust or Rapid, Equipment-free, and Deliverable to those in need [15]. It is of great interest that the current testing techniques are being raised to these standards for the well-being of humanity. It is estimated that current practical biosensor applications, which may lead to the production of widely available POC diagnostic tests, can save millions of lives annually [16,17]. The effort to innovate excellent NA biosensors continues with accelerating speed and advanced NA biosensors are on the edge of becoming preferred alternatives over the well-established techniques that are used on a daily basis [18]. In this chapter, the recent progresses in NA biosensors and the trends in the development, design, application, and characterization of optical (with a focus on fluorescence resonance energy transfer (FRET)-based

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probes, EC biosensing strategies, and CRISP/Cas-assisted NA detection, as well as the role/impact of nanostructures to improve the sensitivity of the detection platforms for NAs) are summarized along with the advantages, shortcomings, and current challenges.

6.2 Optical nucleic acid biosensors: principles and feasibilities To date, numerous optical NA biosensors have been developed to offer advantages over electronic NA biosensors. Besides their low sensitivity to electronic interference, their low cost, label-free detection, thermal and mechanical stabilities, miniaturization abilities, and resistance to corrosion [19] make them ideal and versatile candidates, especially for multiplex sensing applications. Among various optical approaches, surface plasmon resonance-based (SPR, as the basis of several advanced biosensing techniques such as microarray, lab-on-chip array technology, and biochip genotyping), surface-enhanced Raman spectroscopy (SERS)-based and fluorescence (Fo¨rster) resonance energy transfer (FRET)-based platforms are the most commonly investigated ones and are reviewed in this chapter. Since the main emphasis of this chapter is the general principles of NA biosensing, detailed discussions of the history, technology, and applied enhancement mechanisms of optical methods are not addressed here. Readers who need further details on the history, broader aspects, and mechanism of the optical methods are referred to Refs. [2030].

6.2.1 Surface plasmon resonance-based nucleic acid biosensors SPR phenomenon is a charge-density oscillation [11] that occurs at a two media interface with the dielectric constants of opposite signs such as a medium of interaction (like buffer and water) and a thin metalcoated surface (mostly Au and Ag) [31,32]. SPR monitors the variations in refractive index at the interface between the metal surface and interaction medium upon immobilization/adsorption of recognition moieties (DNA target or ligand such as DNA probe) onto the metal-coated surface and enables label-free detection and analysis of bimolecular interactions [6,3335]. The alterations in reflectivity give rise to a signal that is proportional to the mass of the biomolecule on the surface [11]. Due to its unique advantages including short response/analysis time [36], elevated specificity and versatility, desirable reproducibility/repeatability, good precision, facile sample preparation, and relatively low cost [28], SPR has made an enormous contribution to single and multiple biosensing of NAs like DNA, RNA, and aptamers as well as to their real-time monitoring interactions with proteins, enzymes, and various small

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molecules [3740]. For instance, SPR biosensors have been employed to study protein-NA interactions involved in several diseases including HIV [41]. In HIV infection, the most widely investigated protein-NA interactions include nucleocapside protein, reverse transcriptase, and HIV integrase [27]. In SPR-based NA biosensors, the probe is typically a specific sequence of an oligodeoxyribonucleotide or oligoribonucleotide with hybridization/binding ability to a certain target [27]. The analytical efficiency of SPR-based NA biosensors substantially depends on several factors such as sample composition, sensor platform, functionalization method, detection methodology, specific adsorption of target molecules, sample complexity, and the biorecognition properties of the probe [27]. Consequently, both the design and immobilization steps of probes on the surface of a given sensor are of great importance in SPR NA biosensors [9,42,43]. There are two major issues concerning the aforementioned steps (especially for the immobilization step) including the reproducible and robust grafting/attachment of probes to well-oriented and accessible positions on the surface along with a low-fouling background [43]. The most frequently applied approaches to tackling these issues are the adsorption of NA via a thiol linker [44], the covalent binding of NA to a functionalized surface, and the use of tetrameric biotinbinding (e.g., avidin (streptavidin)) interactions. These give rise to an ensemble of appropriately oriented probes attached from just one endpoint, enabling the efficient immobilization of probes and tuning of the grafting density, which in turn influence the hybridization efficiency to achieve lower/improved limit of detection (LOD) [27]. In recent years, the application of SPR technique in biosensing of various NAs has been explored greatly and much effort has been dedicated to the development, design, and improvement of SPR-based NA biosensors for diverse applications [4565], such as the detection of microRNAs in medical diagnostics [51], foodborne pathogens (food safety application) [66], genetically modified organisms [67,68], biomarkers of pathogenic bacteria [69], mutations in human genomes related to various disorders [50,70,71], and environmental monitoring applications [72,73]. For instance, a relatively fast and highly sensitive SPR method (30 min) for the quantitative detection of a microRNA was reported by Ding et al. [74] with a LOD of 9 pM. In their platform, the target miRNA was captured by DNA probes on the surface of a chip, followed by a signal amplification strategy using streptavidin binding with biotin on the long DNA supersandwich assemblies. Also, a highly sensitive, fast, and portable multiangle scanning SPR sensor for the detection of NA hybridization on a gold film chip, modified with bovine serum albumin, was designed by Huang et al. [59]. With only one stepping motor to rotate a belt, the SPR angle scanning mode that retains the incident angle and reflected angle equally was achieved. The sensor was reported to have an angle scanning accuracy of 0.002 degrees

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and response sensitivity of 3.72 3 1026 RIU (refractive index unit). The developed sensor was able to detect the hybridization of target DNAs in a 0.01 μmol/mL solution. One of the recent ultrasensitive SPR-based labelfree bioanalytical platforms for microRNA-21 quantification was reported by Mujica and coworkers [75]. This biosensiyng platform relies on the self-assembling bilayers of poly(diallyldimethylammonium chloride) and graphene oxide (GO) at a gold surface modified with 3-mercaptopropane sulfonate, followed by the covalent grafting of the DNA probe. GO was used to increase the sensitivity of the biosensing event and to anchor the probe DNA. The biosensor was reported to have linear dynamic range (LDR) between 1.0 fM and 10 nM, sensitivity of 5.1 6 0.1 m M21, and LOD of 0.3 fM. Recently, a SPR-based sensor was developed for detecting nucleocapsid antibodies, which were specific against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in undiluted human serum samples [76]. This rapid SPR biosensor coated with a peptide monolayer and functionalized with SARS-CoV-2 nucleocapsid’s recombinant protein was able to detect anti-SARS-CoV-2 antibodies with sensitivity in the nM range within 15 min of sample/sensor exposure. Even though SPR offers some remarkable advantages in biosensing applications of NAs, compared to the EC or chemiluminescence sensing strategies, this technique suffers from some drawbacks including relative insensitivity and susceptibility to interferences from nonspecific binding (especially from complex biological matrices, which forms a finite background signal) [19]. Such disadvantages restrict SPR’s practical applications due to its inability to sense extremely small variations in refractive indices in the case of the compounds with low molecular weights such as short-chain NA oligonucleotides (e.g., miRNAs) involved in the binding step, particularly when the packing density of the film/surface is very small [9]. In that case, SPR is no longer sensitive enough to monitor/identify the binding events accurately. Thus, there is a need to incorporate efficient secondary amplification strategies [7782]. For example, fluorescence tagging of biomolecules [9,83] can be used to monitor interfacial interactions as an alternative approach to increase/promote the sensitivity and specificity of SPR technique greatly [57]. Until now, diverse DNA/RNA probes have been proposed to enhance the target recognition and improve the sensitivity of SPR biosensors for NA detection including synthetic DNA analogs such as peptide NAs (PNAs) [84], their derivatives [85], and locked NAs (LNAs) [86]. PNAs provide an attractive platform since they hybridize in a sequence-dependent manner according to Watson-Crick base-pairing rules [87]. Moreover, due to the decreased electrostatic repulsion between the probe and the target NA (relevant to replacing the negatively charge ribose-phosphate backbone of DNA with a pseudopeptide analog), PNA probes offer an increased affinity to NA targets [27]. In conclusion, it is worth noting that in order

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to allow SPR technique to be a routine bioanalytical tool especially in the complex biological milieu for NA biosensing applications, further research and development are needed [27].

6.2.2 Localized surface plasmon resonance-based nucleic acid biosensors Localized surface plasmon resonance (LSPR) is another potential candidate to develop plasmonic NA biosensors with good sensitivity that occurs on metallic nanoparticles/nanostructures (NPs/NSs) when an incident electromagnetic field is introduced to nanostructures with sizes smaller than or comparable to the wavelength of the incident light [88]. The restriction of the movement of electrons via the internal metal framework induces collective electron charge oscillations, leading to the absorbance of light within the UV-vis band and resulting in the appearance of extinction bands [8992]. Hence, the biosensing events in LSPR sensors are tracked by monitoring the shifts/changes in the extinction bands. The nature of LSPR detection strongly depends on the composition, shape, size and interparticle distance of the NPs/NSs [93]. The length scale of the plasmonic materials, achievability, ease of use, and low reagent consumption are generally considered as the most notable differences between LSPR and classic SPR [94,95]. LSPR sensors avoid the need for complex instrumentation required by traditional SPR sensors and provide a straightforward and economical alternative for biomolecule optical sensing. Furthermore, relative to SPR, LSPR provides higher surface area for immobilization of the sensing probes, a miniaturized platform to obtain compact devices and compatibility with several other techniques such as fluorescence (FL), Raman, and infrared (IR) spectroscopy [96,97]. However, the large-scale synthesis of NPs/NSs, their size and shape control, and colloidal stability in aqueous solutions are the most challenging issues that need to be overcome for better performance and reproducibility [89,98,99]. Moreover, since almost all of the LSPR-based biosensing measurements detect variations in the refractive index at the NP/NS surface, higher concentrations of small molecule/biomolecule analytes are required to coat the surface and remain intricate in terms of achieving satisfactory LODs [100]. Generally, LSPR-based sensing strategies using noble metal NPs can be categorized into three main platforms: colloidal homogeneous sensing, surface-confined heterogeneous array sensing, and surface-confined heterogeneous single NP sensing (single plasmon biosensors) [98,101]. Compared to each other, these platforms have their own advantages including simplicity and fast detection procedure (for colloidal methods) and low LODs and susceptibility to study at the molecular level (for single plasmon biosensors), which make the LSPR-based biosensing

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assays ideal candidates for the detection of a wide range of biomolecules. In recent years, a great number of reports concerning the application of LSPR of metallic NPs, especially AuNPs [102], and also LSPR imaging technique in NA sensing have been published [102111], which provide favorable sensitivities and performances compared to other recently suggested approaches (i.e., EC methods, etc.) [112,113]. A comprehensive comparison among different LSPR-based NA sensing assays along with the routine and novel strategies toward heightened sensitivity of this technique are given in two excellent review articles [90,96]. Some of the recently reported LSPR applications for NA detection and investigation of DNA-enzyme interactions are highlighted as follows. One of the first surface AuNP/polymer nanocomposites-based LSPR sensors for the label-free detection of a 20 base pair long DNA (from the parasite Giardia lamblia) was reported by Lednicky´ and Bonya´r [110]. The AuNPs used were synthesized by solid-state dewetting on nanobowled aluminum templates. The nanocomposite sensor with a LOD of 5 nM was reported to have a red-shift of 6.6 nm in LSPR absorbance upon 2 h hybridization with 1 μM target DNA. Another recently published LSPRbased label-free quantitative platform for probing archaea in complex mixtures utilized arrays of metallic nanostructures on the end facets of optical fibers [111]. The developed fiber-optic nanoprobe offers real-time and low-sample-volume quantification of ssDNA in aqueous medium with high sensitivity (LOD 5 10 fM) and selectivity, demonstrating the feasibility of LSPR-based probes as compact and cost-effective biosensors for ssDNA detection. Roether et al. [109] also developed a LSPR-based label-free microfluidic biosensor for real-time monitoring of DNA-DNA polymerase interaction. The nanoplasmonic LSPR probe was functionalized with ssDNA template (T30), spaced with hexanedithiol in a 1:1 molar ratio. Next, P8 as the used DNA primer was attached to T30, and the second strand was elongated by DNA polymerase. The nanoplasmonic structures possessed a sensitivity of 6 546 nm/RIU. This platform represents a benchmark in developing microfluidic LSPR chips for DNAenzyme interactions in biosensing technologies. Another recently published LSPR platform for the detection of avian influenza virus (AIV H5N1) using a multifunctional DNA three-way junction (3WJ) uses the advantage of hollow Au spike-like nanoparticles (hAuSNs) [114], which provide target recognition by aptamer binding, connection with the substrate, and signal enhancement. To achieve multifunctionality, each piece of the DNA 3WJ has been tailored to a hemagglutinin binding aptamer, fluorescein amidite (FAM) dye, and thiol group. Also, a new proof-ofconcept study [115] was reported for the clinical COVID-19 diagnosis by combination of the plasmonic photothermal effect and LSPR sensing transduction. Two-dimensional gold nanoislands (AuNIs) functionalized

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with complementary DNA receptors were exploited to develop a sensitive detection platform for SARS-CoV-2 through NA hybridization. This dual-functional LSPR biosensor revealed a high sensitivity toward the selected SARS-CoV-2 sequences with a very low LOD of 0.22 pM in a complex multigene mixture. Readers interested in more discussions concerning LSPR-based NA biosensors are strongly encouraged to review the respective journal articles for additional details [98,102,116121]. Due to its simplicity, satisfactory analytical merits, and short analysis time as well as affordable cost, we believe that the LSPR-based biosensing techniques will continue to be an important research topic in the field of NA biosensors in the forthcoming years. However, several challenges (two of the main challenges are noted below) need to be overcome for the daily utilization of LSPR-based techniques. Most of the current plasmatic NA sensing approaches are only based on synthetic oligonucleotides (short ssDNA targets). Thus, rigorous effort should be made in the detection of endogenous NAs in complex biological matrices decorated with high background and competing targets. In other words, from a practical point of view, the new LSPR-based approaches should be tested with clinically extracted DNA and RNA structures instead of just synthetic oligonucleotides to prove the applicability of the proposed sensing strategy in complex biological media. Another important factor that should be addressed is the stability issues of the used NPs in harsh biological environments. It is extremely important that the NPs used in detection of NAs have adequate physical stability through the purification steps of DNA/RNA samples prior to detection. Otherwise, this will inevitably impose additional requirements before detection and will increase the total analysis time/cost, making the sensing strategy inapplicable for POC sensing scenarios. To gain more details concerning the challenges of the LSPR-based techniques for biosensing applications of NAs, readers are referred to a review article by Fong et al. [98] and the excellent references cited therein. There are also several factors that affect the NA sensing assays’ efficiency in LSPR-based approaches such as the types of plasmonic NPs and NAs, the recognition length, and the surrounding medium. Sensitivity can be improved by proper design of the nanostructures, which can be exploited to promote different resonance modes [122]. The developed methodologies to address the problems of LSPR-based NA biosensors have also been reviewed/ investigated elsewhere [90,98,99,123,124].

6.2.3 Surface-enhanced Raman scattering nucleic acid biosensors SERS, as a Raman spectroscopy-based powerful and promising technique, utilizes the increased efficiency of Raman scattering generated by

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plasmonic nanoresonators. This pioneering biosensing technique combines the intrinsic structural specificity and experimental flexibility of Raman spectroscopy with plasmon-mediated amplification of Raman scattering. In this technique, the adsorbed molecules in the near vicinity of metallic nanostructures’ (noble metals such as Au and Ag colloids) surfaces exhibit significantly amplified Raman signals upon laser excitation [90] providing detailed information regarding the identity of biomolecules of interest [125,126]. By providing immense advances in nanofabrication techniques and spectroscopic apparatus, it enables to achieve sensitivities down to single-molecule detection level [127129]. SERS has proved itself as an ultrasensitive, very selective, and efficient analytical tool in the field of diagnostic applications [130132] and has been extensively applied in detection of disease-related NAs, especially miRNAs, demonstrating the potential of this technique for addressing the complex challenges of biomedical research [133] and overcoming key drawbacks of traditional approaches [134,135]. SERS is a nanoscale-using optical approach with a typical signal enhancement factor up to 1011 orders of magnitude relative to “nonSERS” Raman signal [6,11,136,137]. The substantial signal improvement mechanism in SERS is greatly attributed to the combination of two important processes: (I) electromagnetic and (II) chemical enhancements [138141]. An elaborate description regarding the major contributing mechanism, electromagnetic enhancement, is given in the following contributions [24,142]. There are two main SERS-based design approaches: direct and indirect [142]. The direct approach, one of the most straightforward detection platforms, involves the direct acquiring of the intrinsic SERS spectrum of the target analyte. Conversely, the indirect approach involves monitoring of the extrinsic SERS signal from Raman reporters (molecular labels) where the signal is indirectly correlated to the number of recognition events involving a specific target. SERS-based biosensing of NAs mostly rely on the indirect approach mainly due to its effectiveness in biological matrices and complex media [143,144]. Compared to the direct approach, in the indirect approach the spectral interference from the sample matrix is relatively low. On the other hand, the direct approach is simpler, and a considerable amount of research has focused on addressing the key problems of the direct approach including reproducibility and sensitivity [144,145]. To optimize the detection efficiency of indirect SERS technique for NA detection using metal NPs as SERS generators, various parameters should be taken into consideration. The selection of a suitable Raman-active dye, selection of the appropriate excitation wavelengths for the irradiating lasers, and the assembly of metal NPs to create NP junctions on the substrates (usually known as hot spots) in a predictable and reproducible manner are of great importance [146148]. Some studies also highlight

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the need for chemical additives as aggregating agents and phosphate backbone neutralizers to facilitate the approach of negatively charged dyes to the negatively charged NA backbone [149]. SERS-based biosensors offer a broad range of unrivaled benefits for NA biosensing/detection including ultrasensitive and rapid detection, easy sample preparation, no signal interference from the analyte matrix (which is usually aqueous medium), high throughput performance, less sensitivity to photobleaching [150], intrinsic structural characterization and operation in complicated environments as well as selective fingerprint signals. These advantages promote SERS as a practical and outstanding tool for clinical diagnosis applications, high-throughput screening systems, biological research, and simultaneous detection of NAs with potential for multiplexed detections [24,135,151]. A large number of original reports and comprehensive review papers with different perspectives including diverse detection means, various amplification technologies, and using multifunctional nanomaterials have been published for the advanced applications of SERS in NA biosensing [9,135,144,152170]. Also, a number of new classes of SERS-based assays have been suggested for the single and simultaneous detection of NAs [171175]. For example, Al nanocrystal aggregates as a plasmonic substrate with near-infrared SERS enhancement were recently used for the label-free detection of 9-mer ssDNA oligomers as well as for quantitation of two different 20-mer ssDNA oligomers [176]. In that study, the preferential affinity of the ssDNA phosphate backbone to the oxide layer of the Al nanocrystal preserves, the spectral features, and NA cross-sections prohibited nonspecific adsorption on the substrate surface and enable the quantitative analysis of ssDNA. In another study, a SERS sensor was designed using an Au/Ag bimetallic SERS reporter coupled with the complementary dsDNA-aided target-catalyzed hairpin assembly, which achieved sensitive detection of acute myocardial infarction-related miRNA (miR-133a) with a LOD of 0.306 fM [177]. A high throughput SERS biosensing assay based on freezing driven DNA binding using DNA-conjugated gold NPs with precisely manipulated DNA conjugation densities was reported by He et al. [178] for sensitive detection of miRNA structures with a LOD of 0.12 3 10212 M. A novel SERS-based miRNA-21 detection approach was proposed by Liang and coworkers [179] that involves the coupling of SERS technology with the cascade amplification of biotin-streptavidin system. In their study, the SERS-based sandwich structure is composed of Si@Ag@antidigoxin/ digoxin-DNA substrates, miRNA-21, and a Ag@4MBA@DNA-biotin probe. By using the multiorder amplification detection system, the LOD of the probe was decreased to 38.02 fM with a wide LDR from 100 fM to 100 nM. Moreover, the first unequivocal recognition of a triplex DNA structure by SERS spectroscopy was reported by Guerrini and

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Alvarez-Puebla [180]. The structural discrimination of a triplex structure against its duplex and tertiary strand counterparts has been revealed by recognizing key markers bands in the intrinsic SERS fingerprint using positively charged spermine coated-AgNPs as the plasmonic substrate. The obtained results highlight the applicability of direct and label-free analysis of NAs through SERS spectroscopy beyond the ssDNA and canonical dsDNA forms. A new highly sensitive and robust dual-mode SERS/SPR biosensor was reported by Song et al. [181] for the detection of NAs using a special DNA supersandwich signal amplification strategy (which led to about 10 and 4 times the signal enhancements compared to only SPR and SERS sensing, respectively) based on a silver nanorod-covered silver nanohole array. The scientific logic behind the use of this dual-mode sensing strategy was based on their complementary performance (i.e., the higher the specificity of SPR, the higher the sensitivity of SERS, which had improved the robustness of the biosensor in the complex human serum matrix). The obtained results clearly demonstrated that the dual-mode biosensor has a great potential for the detection of trace disease-related NA biomarkers and can be considered as a powerful high-performance sensing tool for early-stage diagnosis applications. Despite the aforementioned superiorities, the extensive application of SERS technique remains a challenge for bioassays and molecular diagnoses arising from the lack of highly active, stable, and reproducible SERS-active substrates [169]. The reporting of novel NA SERS biosensors is consistent at academic scale. However, their effectiveness and applicability in real-life applications are still challenging. There are several factors that restrict their implementation such as the optimization of nanofabrication protocols for large-scale production of reliable and efficient SERS substrates, the commercial availability of both in-house synthesized molecular elements, and the need for more economical and user-friendly instrumentation [142].

6.2.4 Fluorescence-based nucleic acid detection methods Fluorescence biosensors are based on the detection of the change in the frequency of electromagnetic emissions upon exposure of a fluorescent dye to radiation [11]. A broad number of fluorescent biosensors that target NAs as bioreceptors have been implemented so far. In general, the fluorometric biosensing of NAs are based on transducing the analyte-bioreceptor interactions to an analytical signal, which can be acquired by the binding/attachment of one or more fluorophores to an oligonucleotide sequence. The analytical signal is often the change(s) observed in the FL characteristics (i.e., lifetime, FL quantum efficiency/yield, or other FL characteristics) of the fluorophore.

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The target-triggered conformational changes of the NA of interest alter the FL features of the binding fluorophores. FL biosensors are among the most preferred platforms for NA detection and sequencing due to their sensitive and highly specific superior performance, allowing rapid diagnoses [182]. A variety of FL biosensing platforms employ this pioneering signaling tool in the design of various NA biosensors/assays including cell biology-oriented assays (like fluorescent in situ hybridization), fiber optic-based biosensors, and NA microarrays [9,26,92]. FL-based techniques have also been widely exploited to investigate ligand-NA, protein-NA, and aptamer-substrate interactions [183]. In most of these platforms, fluorescent NA probes are usually designed by modifying oligonucleotides via covalently attached fluorophores and/or quenchers [184]. Modified oligonucleotides themselves also sometimes can act as fluorophores. Having appropriate optical properties, displaying excellent photostability and fluorescent efficiency (possessing a high FL quantum yield and long FL lifetime), as well as capability to perform over a wide range of excitation and emission wavelengths are always considered as the main requirements for an ideal fluorophore. Though several fluorophores have been developed to detect various target analytes with high activity in cellular environment, only a few fluorophores have displayed high performance in modified NA systems [184]. Several mechanisms including FRET, photo-induced electron transfer, metal-enhanced and aggregation-induced emission as well as excimermonomer switching have been employed as the well-established signalemission strategies/mechanisms in the design of FL biosensors. Among those, FRET sensors are still considered as the most robust and favorable tools [185,186]. A large fluorophore library is available for FRET-based sensing platforms with potential applicability in a variety of biological systems [187189]. Many detailed reviews describing the physical mechanisms of FRET are present in the literature [190194]. Here, only a brief outline of its basic principle is described. The FRET phenomenon (Fig. 6.1A) is an electromagnetic radiationless energy transfer from a donor to an acceptor fluorophore when they are in close proximity. The excited donor fluorophore leads to the emission of an acceptor fluorophore, due to the overlap between the emission spectrum of the donor (Fig. 6.1B) and excitation wavelength of the acceptor [195,198,199]. The FRET process is incorporated into the biosensing strategies through two general concepts. In the first one, the donor and acceptor pairs that are initially far enough to undergo FRET can be brought to close proximity upon a conformational change in the recognition moiety induced by the binding of the target analyte. In the second one, the donor and acceptor pairs are incorporated/labeled on the target analyte and the recognition moiety, respectively, where the binding event brings the two fluorophores close enough to undergo

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FIGURE 6.1 (A) Jablonski diagram elucidating excitation and emission of the donor fluorophore, FRET between the donor and acceptor, and transitions between their energy states. The released energy from the relaxation of the donor fluorophore is emitted by an appropriate acceptor in close proximity, leading to the excitation of one of its electrons, and further to the emission of a photon by the acceptor rather than the donor. (B) Schematic principles of FRET process; R0 represents the distance between the donor and acceptor [195]. The FL spectra indicate typical shapes of excitation and emission FL patterns of donors and acceptors [196,197]. Source: Reprinted with permission from S.A. Hussain, S. Chakraborty, Organoclay hybrid films with improved functionality, Clay-Polymer Nanocomposites, Elsevier, 2017, pp. 273305; B. Martinac, Single-molecule FRET studies of ion channels, Prog. Biophys. Mol. Biol. 130 (2017) 192197, respectively.

FRET [11]. It is well known that the efficiency of the energy transfer depends on the distance between the donor/acceptor pairs [200]. Indeed, for FRET to occur, the donor and acceptor pairs must be within 110 nm (generally ,10 nm) of each other and the difference of vibrational levels between the ground state and the first electronically excited

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state must correspond to both donor and acceptor (the transition dipole orientations of the donor and acceptor must be approximately parallel to each other) [196,201205]. When the FRET acceptor is also a fluorophore, FRET leads to its excitation and subsequent FL emission. Alternatively, the FL quenching occurs in the presence of molecules/ compounds known as quenchers whose FL quantum yield is Bzero (such as metal NPs, especially gold NPs [206]). In this case, the probe detects the target analyte only by the change in the FL of the donor without the need of the separation of the donor and acceptor and specific spectral overlap, meaning that these dark transfer reactions are based on contact quenching [207]. However, if the acceptor is also a fluorescent emitter, then the FL of the acceptor will be displayed [208]. There are two major groups of fluorescent donor molecules being used in FRET assays [209]. These include traditional fluorophores (a huge group of molecules such as organic fluorescent dyes with properties such as low cost, small size, and possibility for modifications and fluorescent proteins) [210] and NP-based materials [211,212] (such as photoluminescent quantum dots (QDs) and two-dimensional (2D) nanomaterials/nanosheets like graphene) [213,214]. FRET-based NA biosensors have a few leading superiorities such as nontoxicity, simple design, acceptable biocompatibility as well as accessibility to cellular content [186]. Basic design of FRET-based NA probes relies generally on fluorophore-quencher FRET, fluorophore-fluorophore FRET, multiacceptor or multidonor FRET, and cascaded FRET configurations. The fluorophore-quencher FRET itself includes two main general approaches: the use of organic dye/quenching molecules (molecular beacons (MB)) [215], DNAzyme probes [216], “Yin-Yang” probes [217]) and organic dye/quenching nanomaterials [218] or fluorescent nanomaterial/quenching nanomaterials [219,220]. The properties of commercially available FRET dyes attached to DNA structures and characterized in NA environments have been reviewed elsewhere [221]. Fluorophore-fluorophore FRET systems are commonly designed using organic organic dye and fluorescent nanomaterial-organic dye probes. One of the most typical examples of such NA probes are binary probes [222]. Recently, great effort has also been dedicated to the development of dyes with less uncertainty in position and orientation relative to the NA [223]. Moreover, the combination of two donors and one acceptor has also been reported [224]. The properties of commercially available FRET dyes attached to DNA structures and characterized in NA environments have been reviewed elsewhere [221]. Also, for further understanding of the application of various FRET mechanisms in biosensing and bioimaging of diverse NA structures, we recommend these outstanding review articles [186,195,221,225229]. A brief update/examples concerning the design of FRET-based probes for

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quantitative biosensing of NAs in the concepts of MB, binary probes, hybrid NA probes, and nanomaterial-based NA hybridization probes is given below. The development of some innovative FRET-based nanobiosensors have been promoted with recent advances in nanotechnology. These include novel biosensors for i-motifs, G-quadruplex structures (G4), miRNA, and so forth [230233]. Recently, Wang et al. [234] reported a new signal amplification strategy assisted by RNase H for the detection of DNA targets with an incredibly low LOD of 23 fM. In their study, a tailormade RNA MB conjugated with a FRET pair (fluorophore and quencher) was designed and combined with the employment of RNase H. It is claimed that this new method can solve some potential issues regarding the use of other nucleases, such as poor specificity, limitation in the selection of target sequence, and high background signal and may be beneficial in diagnosis and therapeutic applications. A binary oligonucleotide probe for selective DNA/RNA detection based on FRET from QD (CdSe/ZnS core shell)-DNA conjugates to an organic dye (cyanine-5)-DNA conjugates was reported by Peng et al. [235]. In their design, the selective hybridization of the donor/acceptor DNA conjugates to target DNA enabled FRET with a signal to background (S/B) ratio of 9 in FL signature, demonstrating the applicability of QD-based binary probes for DNA and RNA analysis. An interesting, low-cost, and ultrasensitive FRET-based platform for the simultaneous detection of multiple microRNAs (miR-21 and miR-221) in human cancer cells was proposed by Hu and coworkers [236]. In this study for the first time, the integration of hyperbranched rolling circle amplification (HRCA) with QD-based FRET strategy was demonstrated using a single-color QD as the donor and two fluorescent dyes as the acceptors (Cy3 for the detection of miR-21 and Texas Red for the sensing of miR-221). It was revealed that the HRCA reaction can be accomplished under isothermal conditions with the same reverse primer for the target miRNAs, and the products of the amplification reaction for both target analytes can specifically hybridize with the same capture probes. The obtained LODs were as low as 0.072 fM for miR-21 and 0.016 fM for miR221, proving great potential for the proposed technique in biomedical and clinical diagnosis applications. In another study, Li et al. [237] reported a FL “on-off-on” switching sensor for simultaneous monitoring of dualtarget DNAs; the promoter region of cauliflower mosaic virus 35 s (P35s) and terminator region of nopaline synthase from transgenic soybean (TNOS) to form dsDNA through specific hybridization between target DNAs and ssDNA probes. The platform was based on CdTe QDs coupled with multiwalled carbon nanotubes@graphene oxide nanoribbons (MWCNTs@GONRs) and consisted of two steps (on and off). The FRET mechanism as a result of strong ππ stacking interactions between ssDNA probes and MWCNTs@GONRs has been shown to lead to quenching (turning off) of FL emission of the probe followed by the specific

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hybridization between target DNAs and ssDNA probes to form dsDNA probe, which cause the dual-fluorescent probes to generate strong fluorescent emissions (turning on) as a result of releasing the dsDNA probe from the surface of MWCNTs@GONRs. This multiplex monitoring biosensor exhibits low LODs (0.5 nM for P35s and 0.35 nM for TNOS) and provides a feasible screening tool for genetically modified organisms and other versatile NA sensing applications. Some additional research/remarks for the application of FRET-based strategies in the NA biosensing platforms are also introduced in the upcoming sections of this chapter. The FRET-based methods for NA biosensing have been noticeably increased and have attracted the attention of several researchers worldwide in the last decades. However, there are still several challenges concerning FRET-based methods and their applications. For instance, despite the wide ranges of materials being in use as donors and acceptors in addition to the number of methods to label biomolecules, the intrinsic deficiencies of organic dyes and their weak transfer efficiencies and poor photobleaching resistances are still viewed as shortcomings. Developing novel fluorophores to address the drawbacks of organic dyes as well as looking for new strategies to improve FRET efficiency will be the two main concerns in the future. In the case of the application of nanomaterials as alternative compounds due to their unique optical features, great quenching efficiencies, higher stability, and excellent FL lifetimes, their cytotoxicity, biodegradability, purity, and bulky size remain challenging [186].

6.3 Electrochemical nucleic acid biosensors EC sensors are among the most frequently used detection platforms, and their application is expanding in various routes, especially in detection of a broad spectrum of biomolecules for POC and portable-device applications [238241]. Over the past decade, EC-based biosensing techniques have been widely reported for the detection of NAs as an attractive and reliable alternative to conventional, optical, and other common quantification/sensing methodologies. Circumvention of optical pathways/elements (e.g., light sources, filters, lenses, and mirrors) can be considered as the most prominent advantage of EC biosensors over optical biosensing approaches [242]. In EC biosensors, generally a ssDNA (as the most preferred bioreceptor) is immobilized onto an electrode (as the electrically active surface, which electrical signals can be directly generated on), and the changes in the interfacial and EC properties/parameters of the electrode layer (e.g., the variations in current, potential, conductance, impedance, or other electrical properties of the solution) caused by the hybridization reactions are measured [243].

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Offering high sensitivity and simplicity in operation/sample preparation, ability to be miniaturized, capability to couple with modern microfabrication technologies, compatibility with nanotechnology, portability, cost effectiveness, competitiveness, and elimination of the cleaning and cross-contamination issues are among the main advantages of EC biosensors. However, relative to other label-free systems, sensitivity can sometimes be low and EC devices can be susceptible to nonspecific interferences/interactions/adsorption [244,245]. A signal amplification strategy is usually required to improve the detectability/sensitivity of EC NA biosensors. A vast number of studies have been published previously with a focus on EC NA sensors, which provide a basic overview of the different aspects of EC sensing principles (from fundamentals to fabrication and miniaturization, diverse applications and opportunities) of NAs and some of the most advances in this regard [8,113,246255]. Moreover, basic concepts, current status of knowledge, recent advances in EC analysis, and comparison of different strategies for the development of highly sensitive EC NA biosensors as well as a detailed survey on commercially available or close-to-market-entry EC-based platforms have been reviewed in other reports [242,256259], providing great details about EC biosensing assays for NAs. The special focus of this section is to introduce the general principle of EC NA biosensors and their particular advantages as well as to highlight the most important parameters in their design, originality, and transduction mechanism. Basically, there are four fundamental components for the construction of an EC NA biosensor: the selection of a sequence-specific NA recognition and capture element/probes, choosing a suitable substrate electrode and a proper probe grafting strategy, choosing the configuration of an EC signal-generation or amplification strategy, and selecting the sensing technique (predominantly voltammetry, amperometry, linear sweep voltammetry, cyclic voltammetry, differential pulse voltammetry, square-wave voltammetry (SWV), EC impedance spectroscopy, and many more) [246]. All four components have a significant impact on the fabrication, detection, and analysis of target NAs. To ensure high sensitivity, the maximum appropriate amount of the target NA needs to be captured by the capture probe. The excessive binding of the capture probe can saturate the biosensors’ surface and lead to an insufficient area for NA interaction [246]. Several substrate electrodes ranging from traditional electrodes (such as gold and glassy carbon) to modified electrodes and disposable mechanical pencil electrodes have been broadly used in EC biosensing of NAs. There are diverse modification strategies for substrate electrodes with the aid of several nanostructures like NPs, nanotubes, and nanowires. Since the hybridization efficiency and minimization of nonspecific adsorption play important roles in the achievement of good selectivity and high sensitivity, it is vital to control the

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surface chemistry and coverage of the electrodes to achieve stable capture probe layers on the substrate electrodes. There are a few dominantly used immobilization strategies to introduce and graft the capture probes. These include: (1) the self-assembly of thiolated capture probes on substrate electrodes (mostly gold/noble metal NP) as the routinely used strategy to improve the hybridization efficiency and minimize the nonspecific interactions [245,260]; (2) the attachment of biotinylated capture probes through the biotin-avidin interactions [261]; and (3) the direct covalent attachment or indirect attachment by an anchoring layer onto the electrode surface [246]. Signal amplification strategies, required to improve the detectability/ sensitivity of EC NA biosensors, are discussed in detail in a number of comprehensive reviews with a specific focus on prospects for POC diagnostics using especially nanostructures [262266]. The use of nanostructures is one of the most broadly applied amplification approaches, and includes two important strategies: enhancing the loading of electroactive species prior to EC detection (the detection of the target analyte can later be performed via EC techniques), and serving as catalysts for the electrolysis of large amounts of substrate molecules (it means that the EC response of the substrate molecules is serving for the detection of trace amounts of NAs). These strategies rely on the conductivity of NPs, which can be exploited to design EC detection platforms [267]. On the basis of the role of the nanomaterials, the amplification strategies are categorized in three main classes: nanocatalysts, nanoreporters, and nanocarriers. In the nanocatalysts category, nanomaterials act as catalysts to promote the generation of electroactive species, whereas the nanomaterials themselves act as redox active species and cargos loaded with reporter molecules in the nanoreporters and nanocarriers, respectively. Fig. 6.2 shows a schematic overview of the function of nanomaterials in EC signal amplification based on a simplified sandwich assay for detection of NAs. Ordinarily, NPs act as labels for the captured NA via both specific interactions (such as DNA hybridization or aptamer recognition) and nonspecific interactions (such as electrostatic or ππ interactions) [268]. Most EC biosensing configurations for NA detection are based on the inherent redox activity along with hybridization as the main response strategy [251,252]. However, a number of different strategies such as electric-field-assisted discrimination of various DNA sequences [269], hybridization detection in carbon nanotube fieldeffect transistors [270], channel EC sensor array chips [271], and bio-bar-code amplification platforms [272] have also been reported for NA detection. Fig. 6.3 depicts a schematic representation of EC concepts for NA biosensors. As can be seen from Fig. 6.3A, direct

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FIGURE 6.2

Schematic representation of the role of nanomaterials in EC signal amplification for NAs biosensing. Source: Reprinted with permission from L. Bezinge, A. Suea-Ngam, A.J. deMello, C.-J. Shih, Nanomaterials for molecular signal amplification in electrochemical nucleic acid biosensing: recent advances and future prospects for point-of-care diagnostics, Mol. Syst. Des. Eng. 5 (2020) 4966.

oxidation of NA bases such as guanine (G) or adenine (in this case guanine) can easily be used for NA detection. Fig. 6.3B illustrates a typical redox-labeled NA detection with conformationally switchable DNA probes, exemplified by the hairpin structure. The redox label in its folded state is in the electrode vicinity, whereas it is removed from the electrode surface upon hybridization. In Fig. 6.3C, NA biosensing with soluble redox indicators is displayed. It is clear that the EC transformation performance is different on ssDNA and dsDNA layer. NA biosensing via the DNA-mediated electron transfer between the electrode and a redox indicator (capable of intercalation exclusively into dsDNA) is presented in Fig. 6.3D. The detection of single nucleotide polymorphisms in dsDNA duplexes through the catalytically amplified reaction is simulated in Fig. 6.3E. It should be noted that the DNA-mediated electron transfer is less efficient in the presence of single nucleotide polymorphism. Fig. 6.3F displays a typical NA sandwich assay with a biocatalytic label mediated by electroactive species (M) and transformation of enzymatic substrate (S) to the product (P) [258]. For EC NA hybridization detection, generally one of the four different pathways is preferred. These include: (1) monitoring a decrease/ increase in the oxidation/reduction peak current of the label, which selectively binds to NA structure (e.g., dsDNA/ssDNA); (2) monitoring the decrease/increase in the oxidation/reduction peak current of the electroactive NA bases such as guanine or adenine; (3) monitoring the EC signal of the substrate after hybridization with an enzyme-tagged probe; and (4) monitoring the EC signal of a metal NP probe attached after hybridization with target NAs.

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G

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FIGURE 6.3 Schematic concepts for NA EC detection. (A) Represents the direct oxidation of NA bases such as guanine (G) for NA detection; (B) a typical redox-labeled NA detection with conformationally switchable DNA probes, exemplified by the hairpin structure, demonstrating that the EC transformation performance is different on ssDNA and dsDNA layers; (C) biosensing of NA with soluble redox indicators (in the top view: I to I* represents a ferricyanide reduction reaction and the bottom view: red dots represents the cationic Ru hexamine complex bound to the sugar-phosphate backbone of DNA, which are reduced); (D) represents DNA-mediated electron transfer between the electrode and a redox indicator with exclusive capability of intercalation into dsDNA for NA biosensing; (E) represents the catalytically amplified reaction (such as ferricyanide reduction (I to I*) catalyzed by dsDNA-intercalated redox indicator for the detection of single nucleotide polymorphisms in dsDNA); (F) displays a typical NA sandwich assay with a biocatalytic label mediated by electroactive species (M) and transformation of enzymatic substrate (S) to the product (P). Source: Reprinted with permission from E.E. Ferapontova, Basic concepts and recent advances in electrochemical analysis of nucleic acids, Curr. Opin. Electrochem. 5 (2017) 218225.

There are several drawbacks of EC techniques for NA biosensing that hinder their practical real-life applications. Two of the major drawbacks of EC biosensors are reproducibility and reusability. Classic EC sensors are often based on a single signal and usually provide poor robustness, reliability, and reproducibility arising from the alterations in electrode

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areas and the degradation of nontarget-induced reagents [273]. In addition, due to the biomolecule adsorption, interface biofouling is another drawback for EC sensing platforms [274,275]. To overcome these shortcomings, ratiometric assays have emerged lately as an alternative, due to their good stability and selectivity [276]. The principle of ratiometric EC platforms relies on the simultaneous application of two independent redox components in such way that one component acts as the signal reporter while the other acts as an internal control [277]. In other words, the ratio of the two redox current signals (not the absolute value) produced by two or more electroactive substances at different potentials is employed as a signal output to detect the target analytes. One main advantage of this technique is its ability to provide an internal selfcalibration and therefore to reduce the false-positive/negative signal and background interference, and also to improve the specificity of the detection [278]. This dual-signaling response strategy is relatively reliable and improves the accuracy/stability, reproducibility, and credibility of detection in complex matrices/samples [279]. A detailed survey of literature clearly demonstrates that a number of ratiometric EC biosensing platforms have emerged for NA analysis in recent years [276,277,280283]. Additionally, it is of significant importance to develop EC sensing platforms resistant to nonspecific adsorption. For this purpose, biosensing platforms have been integrated into lab-on-achip devices recently [284286]. In such microfluidic techniques, sample matrices are simplified by a separation unit before being directed towards the electrodes, increasing the capability of multiplex analysis of NAs [265,287]. In summary, the field of NA biosensing has witnessed a huge addition of EC-based platforms and impressive progress has been dedicated to the development of NA biosensing (Table 6.1). The promising advantages of EC sensing techniques such as ease of operation, cost effectiveness, portable equipment, rapidity, and high sensitivity fits perfectly to the needs of biosensing assays [301]. Accordingly, EC NA biosensors holds great promise, and widespread applications/breakthroughs are expected in the near future.

6.4 Strategies for improving the sensitivity of nucleic acid biosensors Recent developments in NA detection technology have the potential to replace common diagnostic techniques such as the polymerase chain reaction (PCR), which suffers from relatively complicated experimental procedures. However, NA biosensors still need to become as specific as PCR and current advances in nanotechnology may provide tremendous

2. Biomedical applications

TABLE 6.1 Transduction mechanism, characterization, and LOD of some recently published EC platforms for NA biosensing. Transduction mechanism/EC technique

Amplification strategy/detection assay

Electrode material/ modification

Target NAs

Capture probe

LOD

Ref.

DPV

Strep-alkaline phosphatase

Gold

Biotin-ssDNA

HS-ssDNA

4.7 nM

[288]

Chronoamperometry

Antidigoxigenin coupled HRP

Gold

ssDNA of Zika virus

Biotin-ssDNA

0.7 pM

[289]

SWV

Nanoparticles as carrier DNA-Au@MNPs

Gold foil

ctDNA

MB-DNA

5 fM

[290]

Amperometry

Invertase-Fe3O4-Au

Glucose meter

ssDNA (HIV DNA)

SH-ssDNA

0.5 pM

[291]

DPV

RNA polymerase and strep-alkaline phosphatase

Gold

gDNA

SH-ssDNA

0.97 fM

[292]

Amperometry

Strep-HRP

Gold

ssDNA and genomic DNA

ssDNA (polydA SAM)

10 fM

[293]

Impedance

Nanoparticles as carrier

Nanoporous polycarbonateAuNTs

ssDNA

HS-ssDNA

1 fM

[294]

SWV

CdS QDs as reporter

Glassy carbon

ssDNA

Biotin-ssDNA

0.22 fM

[295]

CA

HDA and antifluorescein-POD Fab

Carbon

gDNA

Biotin-PCR product from target

0.5 aM

[296]

DPV

Exonuclease III and strep-alkaline phosphatase

Gold

ssDNA and HAV cDNA

HS-ssDNA

8.7 fM

[297]

SWV

Alkaline phosphatase-labeled streptavidin

Kapton HN (Goodfellow)

SARS-CoV (30-bp of genome)

HS-ssDNA

6 pM

[298]

CV and DPV

HCR

Gold

Survivin mRNA

Thiolated oligonucleotide

3 fM

[299]

DPV

Supersandwich-type recognition strategy without NA amplification and reverse transcription

SPCE

RNA of SARS-CoV-2

CP

200 copies/ mL

[300]

DPV

PANI nanowires

GCE

COVID-19 N-gene. (nucleocapsid phosphoprotein gene)

N-gene

3.5 fM

[301]

DPV, Differential pulse voltammetry; HRP, horseradish peroxidase; HCR, hybridization chain reaction; ctDNA, circulating tumor DNA; MB-DNA, methylene blue (MB)labeled probe DNA; SWV, square-wave voltammetry; MNPs, magnetic nanoparticles; CA, chronoamperometry; CV, cyclic voltammetry; SPCE, screen printed carbon electrode; CP, ACCTTTCCACATACCGCAGACG-(CH2)6-SH; PANI, polyaniline.

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opportunities to propose efficient, rapid, and sensitive NA detection platforms that can be used for POC diagnostics [9,61]. The use of nanomaterials has been one of the frequently studied aspects of biosensing applications due to their cost-effective, facile, and highly sensitive characteristics. The varying properties of nanomaterials such as shape, size, morphology, composition, and spectral features make them adoptable for target analytes with practical modifications. The recent applications of nanostructure-based NA biosensors are represented in Table 6.2. One of the commonly applied methods for NA sensing is the use of metallic NPs due their simplicity in data acquisition and enhanced sensitivity. Namely, gold nanoparticles (AuNPs) attract great attention with the rise of colorimetric detection methods, which takes into account the changing color ability of AuNPs upon aggregation in the presence of target DNA (tDNA) [326,327]. AuNPs exhibit an absorption maximum around 520 nm, which is dependent on the size of the individual NPs. Any alterations in the average size of AuNPs caused by the presence of target analytes may be measured via both visual and spectroscopic techniques. Mirkin group have described many colorimetric applications for colorimetric NA detection based on AuNPs [328,329]. Recently, a femtomolar detection limit was reported for tDNA sensing with a oligonucleotide conjugated, poly ethylene glycol (PEG) functionalized AuNP platform [302]. Dynamic light scattering (DLS) method was also used to detect the presence of NAs by measuring the change in average diameter of AuNP aggregates upon addition of the target DNA [303]. Another widely used metallic NP for the detection of NA structures is the silver nanoparticles (AgNPs) due to their high extinction coefficient, biocompatible nature, and large surface area that can readily be modified for specific analytes [330]. One such example is the use of oligonucleotide-silver nanoparticle (OSN) conjugates for the colorimetric detection of tDNA strands with improved sensitivity due to the greater extinction coefficient values of AgNPs [120]. Recently, SERS detection of DNA by the DNA-mediated growth of AgNPs was described by Gao et al., which was based on the usage of PNA immobilized on a glass slide [304]. Another OSN approach was reported by Liu et al., where silica-coated AgNPs were used for fast, diagnostic DNA detection [305]. Composite nanostructures based on metallic NPs were also proposed for NA sensing (e.g., the silver-coated magnetic-NPs described by Xu and coworkers [306]). Carbon-based nanomaterials offer great advantages over other nanostructures since they are cost-effective and environmentally benign. Consequently, several different carbon-based nanomaterial platforms have been reported for NA sensing in recent years. A carbon nanotube nanoelectrode was designed by Li et al. to develop an EC, ultrasensitive NA detection platform, which resulted in a few attomolar LOD for the

2. Biomedical applications

TABLE 6.2

Previously reported nanostructure-based NA biosensors.

Nucleic acid structure

Sensing strategy

Sensing platform/element

Detection technique

Sensitivity, LOD

References

ssDNA

tDNA hybridization

DNA-conjugated PEGAuNPs

UV-vis

2.54 fm

[302]

ssDNA

tDNA hybridization/aggregation

DNA-conjugated AuNPs

DLS

1 pM

[303]

ssDNA

tDNA hybridization/aggregation

OSN

UV-vis

NP

[120]

ssDNA

AgNPs-DNA interaction

PNA-modified glass slide

SERS

3.4 pM

[304]

ssDNA

tDNA hybridization/aggregation

Silica-coated AgNPS

UV-vis

100 pM

[305]

ssDNA

tDNA hybridization

Silver-coated nanoparticles

Naked eye/Absorbance device

10 fM

[306]

ssDNA

Oligonucleotide hybridization

Carbon nanotube nanoelectrode

Electrochemical

1 aM

[307]

ssDNA

tDNA hybridization/FRET

GO

Fluorescence

0.12 μM

[308]

ssDNA

tDNA hybridization

DNA stabilized silver nanocluster/GO

Fluorescence

1.18 nM

[309]

ssDNA

tDNA hybridization/FRET

GO

Fluorescence

5 pM

[310]

ssDNA

tDNA hybridization/FRET

QD-DNA conjugate

Fluorescence

1 nM

[311]

ssDNA

tDNA hybridization

Silica-coated QD-molecular beacon

Fluorescence

0.1 nM

[312]

ssDNA

HBV-DNA hybridizing with the pDNA

Graphene QDs

Electrochemical

1 nM

[313]

p53 DNA

Noncovalent interactions between cp53 ssDNA and the GQDs/ECL-ERET

Graphene QD/AuNPs

Electrochemiluminescence

13 nM

[314]

(Continued)

TABLE 6.2 (Continued) Nucleic acid structure

Sensing strategy

Sensing platform/element

Detection technique

Sensitivity, LOD

References

dsDNA

MTX-dsDNA interactions

CdTe-CDs-MTX

Fluorescence

1.0 nM

[315]

ssDNA

tDNA hybridization

Carboxylic CQD

Fluorescence

17.4 nM

[316]

G4 DNA

Hemin-G4 interactions

Mushroom-derived CQDs

Fluorescence

189 nM

[317]

ssDNA

tDNA hybridization

QD-fullerene-molecular beacon

Fluorescence

100 fM

[318]

dsDNA

TFO probe-dsDNA hybridization

MOF-FAM labeled TFO probe

Fluorescence

1.3 nM

[319]

ssDNA

tDNA hybridization

Amine-functionalized MOF

Fluorescence

NP

[320]

ssDNA

tDNA hybridization

MOF(MIL-101)

Fluorescence

73 pM

[321]

ssDNA

tDNA hybridization

Iron-based MOF nanorods

Fluorescence

10 pM

[322]

ssDNA

tDNA hybridization

2D-MOF nanosheets

Fluorescence

0.2 pM

[323]

ssDNA

tDNA hybridization

MOF-based nanoprobe

Fluorescence

20 fM

[324]

G4 DNA/H-telo

Hemin-G4 interactions

Ce-MOF

Fluorescence

665 pM

[325]

tDNA, target DNA; PEG, poly ethylene glycol; DLS, dynamic light scattering; OSN, oligonucleotide-silver nanoparticle conjugates; NP, not provided; HBV, hepatitis B virus; MTX, mitoxantrone; TFO, triplex-forming oligonucleotide; ECL-ERET, electrochemiluminescence resonance energy transfer; p53 DNA, 50 -TTG AGG TGC GTG TTT GTG CC-30 .

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199

tDNA [307]. Graphene is another carbon-based nanomaterial that is often used for designing EC and fluorometric NA biosensors. The 2Dcrystal structured graphene oxide (GO) is considered an even better alternative than graphene for biosensing applications since it exhibits better quenching properties and miscellaneous bioconjugation potential [331]. Single-molecule FRET studies previously revealed that GO can successfully form covalent interactions with DNA for proper NA sensing [332]. However, in the FRET-based GO sensor both the tDNA and the GO probe are required to be labeled with FRET donor and acceptor groups [308]. Zhang et al. also demonstrated a DNAstabilized silver nanocluster/GO biosensor for DNA detection based on the hybridization chain reaction [309]. A highly sensitive GO platform was described by Alonso-Cristobal and coworkers, which is represented in Fig. 6.4 [310]. Their biosensor employed GO and DNA-functionalized, SiO2-coated upconversion nanoparticles (UCNPs), which are lanthanide-doped inorganic structures [333]. UCNPs are considered as great alternatives for organic dyes in biological applications due to their near-infrared (NIR) excitation wavelength (980 nm), low toxicity, stability, and unique optical

FIGURE 6.4 Schematic representations of the GO-based detection platform for the sensing of tDNA. The scheme depicts the response of the sensor platform in presence of the noncomplementary DNA and the complementary tDNA strands. Source: Reprinted with permission from P. Alonso-Cristobal, P. Vilela, A. El-Sagheer, E. Lopez-Cabarcos, T. Brown, O.L. Muskens, et al., Highly sensitive DNA sensor based on upconversion nanoparticles and graphene oxide, ACS Appl. Mater. Interfaces 7 (2015), 1242212429.

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properties [334]. The DNA-functionalized, SiO2-coated NaYF4:Yb, Er UCNPs were present as FRET donors. In the presence of noncomplementary DNA (nontarget DNA) strands, the NPs were adsorbed on the GO surfaces, which resulted in the FRET quenching. On the other hand, in the presence of the complementary strand (tDNA), the NPs were not capable of being adsorbed on the GO surface, which results in FL emission upon excitation at 980 nm (Fig. 6.4). QDs are nanoscale-semiconductor particles that possess unique optical properties due to the quantum confinement effect. They are usually found more advantageous over fluorescent dye molecules because of their higher molar extinction coefficients, photostability, and tunability. QD technology has been connected to biosensor applications since QDs can be engineered for specific purposes with diverse synthetic routes and surface modifications [335]. Zhou et al. reported a QD-DNA conjugate for the label-free detection of DNA, based on the FRET between the dye-labeled DNA strands and the fluorescent QDs [311]. Wu et al. was able to achieve the detection of the target DNA in 0.1 nM concentration using silica-coated QD-MB [312]. Another sensing strategy is the EC method developed by Lu and coworkers, which was based on graphene QDs and AuNPs to improve the sensitivity of the DNA damage detection [313,314]. A different NA detection platform was reported by Liu and coworkers, which was based on a QD-fullerene-molecular beacon probe (Fig. 6.5) [318]. The method took advantage of the quenching ability of fullerene (C60) molecules in the presence of the fluorescent polymer-coated carboxyl CdSe/ZnS QDs. As depicted in Fig. 6.5, the

FIGURE 6.5 Sensing strategy of the C60-QD molecular beacon probe in the presence of the tDNA strand on a magnetic nanoparticle (MNP). The bottom photographs represent the microscopic bright field and fluorescence images of the magnetically concentrated nanosensor before and after the addition of tDNA strands, respectively. Source: Reprinted with permission from Liu, Y., Kannegulla, A., Wu, B., Cheng, L.J., Quantum dot fullerene-based molecular beacon nanosensors for rapid, highly sensitive nucleic acid detection, ACS Appl. Mater. Interfaces 10 (2018) 1852418531.

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201

QDs were quenched by C60 molecules that were attached to MB. When the tDNA was introduced to the platform the FL was regained since C60 groups became distant to QDs due to the interaction between the molecular beacon probe and the tDNA. Carbon quantum dots (CQDs) were also studied often for biosensing since they can be synthesized via green synthetic routes and offer great potential to be used in in vivo imaging [336]. Previously, acid-coated cadmium telluride QDs (CdTe QDs) and CQDs were described for the ratiometric dsDNA detection based on the FL alterations caused by the anthraquinone drug Mitoxantrone (MTX) upon addition of tDNA [315]. A carboxylic CQD platform was used to detect ssDNA via the interactions between a FAM-labeled DNA probe, CQDs, and tDNA [316]. Furthermore, Kumari et al. reported mushroomderived CQDs for visual detection of DNA G-quadruplexes [317]. Metal-organic framework (MOFs)-based sensors are another popular platform used for sensitive NA detection. Ever since their discovery, MOFs were involved in various biosensing applications, including NAs [319]. MOFs are rigid, porous crystalline inorganic materials, which are prepared by integration of metal containing secondary building blocks (SBUs) and using organic linkers. A MOF-based biosensor for specific recognition was reported in 2013, where the fluorescein amidite (FAM) labeled triplex forming oligonucleotide (TFO) probes were strongly absorbed on the MOF structure and were used to recognize specific dsDNA due to the FL response caused by the dsDNA-TFO interaction that releases FAM from its quenched state [319]. An amine-functionalized MOF platform also employed a FAM-labeled ssDNA probe for specific recognition of the tDNA [320]. Fang et al. reported the label-free detection of DNA targets with MOF MIL-101 and claimed that their method had improved the detection by increasing signal-to-background ratio [321]. Additionally, Tian et al. reported the iron-based MOF nanorods for NA detection and described the effects of SBU-linker associations [322]. An intracellular 2D-MOF nanosheet probe was designed to sense DNA and microRNA strands based on the FL response gained upon hybridization of tDNA to the probe DNA on the MOF nanosheets, with a low LOD of 0.2 pM [323]. A multicolor MOF FL sensor (Fig. 6.6) was designed by Wu and coworkers for the detection of tDNA with enhanced sensitivity, where the porous structure was loaded with fluorescein dye and DNA probes [324]. The proposed sensing mechanism of the probe was based on the interaction between MOF probe that conjugated with hairpin DNA strands and the tDNAs, as shown in Fig. 6.6. When tDNA strands interacted with the MOF probe, the fluorescent dye encapsulated inside the MOF structure were released and the corresponding FL response led to the detection of NA structures. One other application was recently reported by Javan Kouzegaran et al., which is the hemin conjugated Cerium-based MOFs (Ce-MOFs) for highly sensitive detection of human

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6. Biosensors for nucleic acid detection

FIGURE 6.6 Schematic representations of the (A) the fabrication process of MOF-based nanoprobes and (B) multicolor detection of tDNA strands. Source: Reprinted with permission from S. Wu, C. Li, H. Shi, Y. Huang, G. Li, Design of metal-organic framework-based nanoprobes for multicolor detection of DNA targets with improved sensitivity, Anal. Chem. 90 (2018) 99299935.

telomeric (H-telo) G4 DNA [325]. This was the first report on the development of a MOF probe specific for the recognition of a certain G4 structure. As the Hemin molecules incorporated to Ce-MOF structure, the intrinsic FL of the Ce-MOF was lost whereas the addition of H-telo caused the regain of the FL due to Hemin/H-telo G4 interactions. The Ce-MOFs-Hemin probe exhibited excellent sensitivity (665 pM) toward the target G4 structure with a satisfactory linear range (1.6a39.7 nM) demonstrating the potential of MOFs in diagnostic, therapeutic, and sensing applications of G4 DNAs. One main advantage of the Ce-MOF probe was its high selectivity to H-telo compared to other G4 structures such as C-myc and BCL-2.

6.5 CRISPR/Cas-assisted biosensing platforms for nucleic acid detection Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) systems have been a massive improvement for gene editing technology and have been intensely investigated for their applications in NA biosensing [337,338]. CRISPR/ Cas technology has become a promising alternative in biosensing applications due its highly specific, sensitive, and rapid nature that may

2. Biomedical applications

6.5 CRISPR/Cas-assisted biosensing platforms for nucleic acid detection

203

open up new horizons for POC NA detection [339]. The architecture of CRISPR/Cas biosensors can readily be modified for selective and sensitive NA detection as they can be designed for specific targets without modifying the Cas effectors. The CRISPR systems can be categorized into two classes depending on the effector integration. Class 1 CRISPR systems rely on multiple effectors where each protein performs a particular duty, while Class 2 systems function with a single effector that is responsible from several operations in the CRISPR system. Therefore, Class 2 CRISPR systems are of great interest in biosensing applications because of their simpler practice and better efficiency. The main operating principle of Class 2 systems is the association of the multifunctional effector protein to an RNA sequence, which is called the guide RNA sequence (gRNA), to form the ribonucleotide protein complex. gRNA has a customizable part called the crRNA that determines the specificity of the system toward the tDNA sequences, and a noncoding RNA component that adopts 2D structures, which facilitates the association between gRNA and effector proteins through hydrogen bonding and aromatic stacking interactions [340]. The gRNA-effector protein interactions induce structural changes to activate the effector protein and cause the formation of the RNA surveillance complex, which then targets NA complementary to the crRNA for the enzymatic degradation process. The Class 2 systems are mostly used for NA biosensors and may be subdivided into three types, Type II, Type V, and Type VI, and each type consists of signature Cas proteins [339]. One of the Type II effectors are the Cas9 proteins, which target dsDNA through crRNA, and the DNA target can be changed by introducing custom crRNA sequences to the system. Several biosensing applications have been reported for the CRISPR/Cas9 systems, which embody the use of AuNPs and the colorimetric detection approach [341,342]. Type V effector proteins such as Cas12 and Cas14 exhibit an additional enzymatic process to the target sequence cleavage observed in Type II effectors, which is the untargeted collateral cleavage of ssDNA sequences [343,344]. This means that Cas12 and Cas14 can recognize both dsDNA and ssDNA. There are Cas12a and Cas12b subtype proteins, and one of the unique methods reported for the Cas12b is the HOLMESv2 approach [345]. Li et al. reported the Cas12b-assisted HOLMESv2 method for the detection target RNA, DNA, and quantitation of target DNA methylation, which have shown that HOLMESv2 holds great potential for diagnostics applications [345]. Furthermore, Wang and coworkers described a rapid and visual detection platform for NAs, based on the CRISPR/Cas12a systems, that is called the Cas12aVDet method [346]. One other example is the DETECTR method, which also embodies the Cas12a protein, and it has been recently used to detect SARS-CoV-2 in 1-h time interval, with a better LOD than the conventional PCR assay [347]. Another recent

2. Biomedical applications

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6. Biosensors for nucleic acid detection

report was described by Jiang et al. for the detection of SARS-CoV-2 with a CRISPR/Cas-12a-assisted, magnetic pull-down-colorimetric assay, based on AuNPs (M-CDC assay) [348]. The workflow of the M-CDC assay is depicted in Fig. 6.7. The principle of the M-CDC assay was the colorimetric detection of the target RNA sequence extracted from SARS-CoV-2 patients, with CRISPR/Cas12a activity and visual response of the DNA-AuNPs probes upon distraction of the magnetic pull-down of AuNPs. The assay proposed a rapid, visual, and convenient alternative to the common diagnosis of SARS-CoV-2 since the MCDC method was clinically tested to be 95.12% consistent with the approved reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis [348]. The Covid-19 pandemic outbreak has shown the world the importance of quick and reliable assays that can be used for diagnostics of diseases since the common techniques such as PCR

FIGURE 6.7 (A) Schematics of clinical diagnostics of SARS-CoV-2 with the M-CDC assay, which can be completed within 50 min after RNA preparation and RT-RPA. Detection results can be observed by the naked eye. (B) The main laboratory equipment needed for the M-CDC assay that includes a rotational mixer, a mini dry bath, and a magnetic rack. (C) Assessment of clinical sample detection (n 5 41) where all 41 clinical RNA samples were first diagnosed with clinical RT-qPCR kits. Among these, 1 2 21 are positive, and 22 2 41 are negative. Results from the N/O-gene line represent a mixed detection of both N and O genes in a single test tube. The N-gene line and O-gene line represent the single-gene analysis result and NTC represents no-template control. (D) Heat map analysis of dual-gene and single-gene detection results based on the absorbance results. Source: Reprinted with permission from Y. Jiang, M. Hu, A.A. Liu, Y. Lin, L. Liu, B. Yu, et al., Detection of SARS-CoV-2 by CRISPR/Cas12a-enhanced colorimetry, ACS Sens. (2021).

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205

lack good accuracy and adequate accessibility due to limited healthcare resources in underdeveloped regions. The Cas14a, Cas14b, and Cas14c subtypes are also employed for biosensing techniques, and the Cas14-DETECTR method has been proposed as a highly specific detection platform for diagnosis of ssDNA pathogens [343,349]. Unlike Type II and Type V proteins, Type VI effectors specifically target RNA sequences and the Type VI subtype proteins Cas13a and Cas13b have been involved in biosensing technology. One such example is the SHERLOCK method described by Gootenberg and coworkers, which is a lateral flow assay-based technique, and it relies on the Cas13a protein [350]. A further enhanced version the SHERLOCK assay was the SHERLOCKv2, and it was recently involved in a protocol for the diagnosis of Covid-19 [18]. Overall, the entering of CRISPR/Cas systems to the field of NA biosensors has led to the development of highly specific, sensitive, efficient, and convenient methods that hold significant potential to be integrated into clinical procedures for POC diagnostics [337]. The rapid advances in CRISPR/Cas-assisted NA biosensing suggests that the future of detecting target NA sequences may rely on these inexpensive and feasible assays.

6.6 Biosensor applications based on the nucleic acid structure RNA and DNA, the two main NAs, are both vital for all living beings and developing biosensors for NAs is growing in importance each day. NA biosensors have been found applications in many diverse areas as clinical monitoring, in vitro diagnostics, food industry, and environmental monitoring. The developed biosensors mostly rely on the structure/ conformation of the NAs and the hybridization specificity between different NA strands. Both DNA and RNA can adopt many conformations, which include hairpins, cruciform, triple helices, and the four-stranded G-quadruplex structures. Since the structure of NAs determine their function in cellular processes, structural transitions between different conformations within certain sequence elements can be crucial for biological function [47]. ssDNA is an important intermediate in various cellular processes such as transcription, gene regulation, and protein recognition. Several biosensors have been developed for selective detection of ssDNA strands for enhancing sequencing techniques commonly used for NA characterization. Murphy et al. described a single-molecule FRET-based approach to sense the behavior of ssDNA, poly dT, for the determination of its structural flexibility [351]. Some groups utilized nanostructures such as NPs, nanopores, and graphene to improve the sensitivity and selectivity of ssDNA detection platforms [352354]. For example,

2. Biomedical applications

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Yu et al. described a sensitive surface-enhanced Raman scattering detection method based on both gold-coated magnetic and silver NPs and obtained the quantitative detection of specific ssDNA in 1100 fmol range [352]. EC detection techniques were also employed in ssDNA biosensing applications [355,356]. For instance, Cinti and coworkers developed a paper-based EC detection platform for sensing single- and double-stranded DNA structures, which is graphically demonstrated in Fig. 6.8 [357]. The method was based on the EC sensing of the hybridization of the target strand to the TFO probe, which was labeled with the redox target methylene blue and immobilized onto AuNPs-coated disposable screenprinted electrodes (Fig. 6.8). The use of AuNPs was for the enhancement of the performance of the device and the proper immobilization of the TFOs to the surface of screen-printed electodes (SPEs) through Au-S interactions. It was described that SWV signal change led to the detection of ssDNA and more importantly the paper-based device can be printed on conventional office paper or filter paper, which notably decreases the cost of the NA biosensor. The paper-based strips were capable of sensing target DNAs rapidly, with 3 nM LOD and without the need for costly conventional laboratory equipment. This recent paper-based device reported by Cinti et al. was based on a previously reported EC sensor designed by Ricci and coworkers to sense doublestranded DNA (dsDNA) [358].

Blank + Target

e-

+ Target

Multi-8 analysis Paper-based SPE AuNPs TFO probe Methylene Blue Target (ssDNA or dsDNA)

FIGURE 6.8 Schematic representation of the paper-based E-DNA platform for the detection of single- and double-stranded tDNA. Source: Reprinted with permission from S. Cinti, E. Proietti, F. Casotto, D. Moscone, F. Arduini, Paper-based strips for the electrochemical detection of single and double stranded DNA. Anal. Chem. 90 (2018) 1368013686.

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207

NA biosensors have also been developed for specific recognition of dsDNA as it is the predominating structure of the human genome. In general, the dsDNA sensors are widely dependent on the hybridization of the individual complementary strands [328,359]. Ricci and coworkers introduced the TFO probes, which facilitated the formation of a triplestranded intermediate and resulted in the detection of dsDNA. Their system consisted of immobilization of the methylene blue labeled-TFO probes on Au electrode surface and the sensing mechanism was governed by the Watson-Crick and Hoogsteen base pairings between the TFO probe and target DNA, as shown in Fig. 6.9. When the target DNA strand was introduced to the system, the formation of Watson-Crick base pairing followed by an additional Hoogsteen base pairing resulted in the formation of the unique triplex structure and brought redox target methylene blue to the electrode surface to create an alteration in the signal (Fig. 6.9). The presence of both base-pairings between the TFO probe and the target DNA provided a double check for the sensor and improved the sensitivity of the EC device. The TFO probe was also used to fluorometrically detect dsDNA as the formation of the triplex intermediate results in FL response if the TFO is designed accordingly [360]. Another recent FL detection platform was reported by Li et al. where they reached the sequence specific recognition of dsDNA in solution without the complementary strand hybridization approach [361]. They employed a novel α-azidoether quenched (Q-STAR) probe, which consisted of a quencher (Dabsyl) and a fluorescein group, as well as Triphenyl phosphine (TPP) probes. When both the Q-STAR and TPP DNA

FIGURE 6.9 (A) Schematic representation of the Clamp-switch E-DNA sensor for the detection of single- and double-stranded tDNAs. (B) The sensing strategy of the Clampswitch E-DNA sensor. Upon addition of the tDNA, the electron transfer efficiency increases due to the closeness of the redox label to the electrode surface and an increase is observed in the measured current. Source: Reprinted with permission from A. Idili, A. Amodio, M. Vidonis, J. Feinberg-Somerson, M. Castronovo, F. Ricci. Folding-upon-binding and signal-on electrochemical DNA sensor with high affinity and specificity, Anal. Chem. 86 (2014) 90139019.

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probes bonded to dsDNA via Hoogsteen base pairing and created a triplex hybrid structure, the quencher Dabsyl was released from the QSTAR probe, which caused a FL turn-on signal. Their findings were promising for in vivo imaging of DNA as the probe was able to detect the NA without altering the double-stranded structure. Other than playing a mediocre role for ssDNA and dsDNA detection, triplex DNA structures are found in the human genome and they are known to interfere with gene expression [362,363]. The triplex DNA structures arise from the association of a single strand with a dsDNA and are observed especially in mirror repeat regions and long polypurine-polypyrimidine stretches [364,365]. Although DNA triplexes are noteworthy for DNA nanotechnology and biosensor development, fewer biosensors have been reported for their specific detection [366]. Some examples include the microarray detection of DNA triplexes with DNA-functionalized AuNPs and the fluorometric monitoring of intramolecular triple strand formation by 2-aminopurine and 6-methylisoxanthopterin [367,368]. Over the past years, G-quadruplexes (G4s) have become one of the extensively investigated DNA secondary structures. They are formed in NAs that are rich in guanine base and found in genomic DNA sequences that hold biological importance [369]. It was anticipated that due to their regulatory roles, G4s may be present in the promoter regions of important genes that are strictly controlled for cell vitality [370]. Many G4forming promoter regions have been found to be tightly associated with cancer and various small molecules targeting G4s have been investigated in the context of proposing more effective cancer therapeutics [371]. Targeting G4s have also become an intensely studied area for biosensor applications since revealing their prevalence and structural dynamics is crucial for enlightening their involvement in cellular functions. They also offer great conformational diversity. A vast majority of the proposed G4biosensors are based on FL detection by novel fluorescent dyes, which selectively target G4s or unique nanostructures such as, NPs, MOFs, CDs, and functionalized QDs [317,325,372]. An off-on-off Azacyanine-AuNP conjugated probe was recently described by Bilgen et al. for selective, fluorometric detection of G4 structures formed in the promoter regions of vascular endothelial growth factor (VEGF) [372]. The selective G4 sensing was achieved by the FRET mechanism taking place between the fluorescent dye Azacyanine 5 (Aza5) and AuNPs (Fig. 6.10). At the initial step, Aza5 molecules lost their FL due to the presence of AuNPs (off-state). The addition of L-cysteine restores the FL (on-state) by replacing Aza5 molecules from the surface of AuNPs via strong Au-S interactions. When the G4 structures were introduced to the “on state” where Aza5, AuNPs, and L-cysteine molecules were present, a significant decrease in the FL was observed, which brought the system to the “off-state” once again. The observed decrease in the FL at the final “off-stage” was selective to

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FIGURE 6.10 Proposed sensing mechanism of the AuNPs-Aza5 conjugated probe for selective detection of G4s. Source: Reprinted with permission from E. Bilgen, M. Forough, O¨. Persil C ¸ etinkol. A conjugated gold nanoparticle-azacyanine off-on-off fluorescence probe for sensitive and selective detection of G-quadruplexes. Talanta 217 (2020) 121076.

the G4 structures since no similar response was obtained for single-, double-, and triple-stranded DNAs. Other cyanine dyes, such as thiazole orange, that target G4 structures were also reported as alternative detection methods and many groups directed their efforts to understand how the structural modifications in dye molecules may improve the selectivity [373377]. Other groups reported conjugated biosensors for G4 detection where they combined dye molecules and nanostructures. As an instance, a thiazole orangemodified carbon dot sensor was described by Jin et al. for the ratiometric detection of G4 and dsDNA [378]. Many other dye molecules were suggested for the selective detection of the G4s such as N-methyl meso-porphyrin IX, crystal violet and thioflavin T, as well as some newly synthesized novel molecules such as 1,3-imidazolidine-2-thione and 6-(2-(anthracen-9-ylmethylene) hydrazinyl)- N2, N4-diphenyl-1,3,5triazine-2,4-diamine (9CI) [379382]. Recently, Antonio et al. reported a novel biosensor, synthesized by incorporating silicone rhodamine (SiR) and an analog of the previously known G4 ligand pyrostadin (PyPDS), that can be used to detect G4 formation in live cells [383]. Their sensor was used to screen G4 DNA in live cells by single-molecule FL imaging and the role of G4s during cellular process such as replication and transcription was visually demonstrated. RNA biosensors hold immense significance since they can be used for clinical diagnostics of especially deadly viruses. The PCR is accepted as the primal method for diagnosing viral and bacterial species; however, PCR protocols require well-trained specialists for proper execution since RNAs are extremely sensitive to external chemical environment.

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Therefore, rapid and sensitive alternative routes have been investigated for RNA sensing. Biosensors based on nanostructures for RNA detection in living cells have been widely studied. MB can be designed for highly specific and sensitive RNA-based gene detection [384]. MB are hairpin NAs labeled with a FRET donor at one end of the strand and the acceptor group at the other end. The hybridization of the molecular beacon to the target gene sequence leads to target RNA detection due to the changing positions of the fluorophore and quencher groups attached to the beacon probe. One of the recent reports described the pH-induced colorimetric detection of human telomerase RNA since telomerase upregulation or reactivation is an important biomarker in cancer cells [385]. In addition to that, an isothermal method based on one-step RNA amplification and detection principle (iROAD) was described by Koo et al. for diagnostics of respiratory viral infections and a DNA-silver nanocluster probe was designed by Shen et al. to detect Norovirus RNA [386,387]. Moreover, Jeong et al. reported a fluorometric detection method based on a DNA-modified GO platform that is able to sense the subtypes of Influenza RNA and introduced a rapid and alternative biosensing for the diagnosis of the widespread, seasonal disease [388]. Another approach was the use of QD and PNA conjugates for the detection of RNA [389]. Epigenetic mechanisms of the human genome implement molecular changes in the chromosome regions and affect important processes such as gene expression, gene silencing, and DNA-protein interactions [390]. DNA methylation is one of the most studied epigenetic mechanisms, which occurs via the covalent modification by addition of CH3 group to the fifth carbon of cytosines [391]. DNA methylation machinery is associated with numerous diseases such as Alzheimer’s and cancer, therefore the development of biosensors for methylation detection has been an active research area [392394]. The commonly employed techniques for methylation screening are methylation-specific restriction enzyme PCR/southern blot analyses and bisulfate-based assays, which require time-consuming sample manipulation stages [395]. Recently, alternative techniques have been developed for rapid DNA methylation screening that were governed by the use of nanotechniques, colorimetric detection, and EC applications [396]. The use of QDs for fluorometric DNA methylation detection was advantageous over fluorescent dyes due to their spectroscopic features and photochemical stabilities [209]. A QDbased FRET method was reported by Ma and coworkers that was able to detect DNA methylation in cancer tissues [397]. The method was based on the separation of methylated and unmethylated DNAs by methylation-selective restriction enzymes and quantitative detection of methylation levels from QDs to Alexa Fluor-647 (A647) fluorophore by FRET. The method enabled the simple and low-cost detection of

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methylation levels via A647-labeled triphosphate nucleotides and provided a nondestructive alternative for cancer diagnosis. Methylationspecific QD fluorescence resonance energy transfer (Ms-qFRET) was designed by Bailey and coworkers for both qualitative and quantitative detection of DNA methylation [398]. The working principle of the MsqFRET probe for the detection of methylated DNA is shown in Fig. 6.11. The genomic DNAs were subjected to bisulfite conversion and amplified by PCR where the reverse primer was labeled with an organic dye (Cy5) and the forward primer was biotinylated. Afterwards, QDs were

FIGURE 6.11 Principle of Ms-qFRET for detection of DNA methylation. Source: Reprinted with permission from V.J. Bailey, H. Easwaran, Y. Zhang, E. Griffiths, S.A. Belinsky, J. G. Herman, et al., MS-qFRET: a quantum dot-based method for analysis of DNA methylation. Genome Res. 19 (2009) 14551461.

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introduced to the labeled primers where the QDs served as donors and the Cy5 served as acceptor, allowing the detection of DNA methylation by screening the emission of Cy5 and the quenching of QDs. Another enzymatic detection approach by QDs was also reported by Bailey and coworkers [399]. After the Cy5- and biotin-labeled DNA strands undergo bisulfite treatment and enzymatic amplification, the amplified NAs can attach to the streptavidin-functionalized QDs. Optical detection of the methylated DNAs was achieved either with the conventional spectrometer or confocal FL spectroscope applicable for single-molecule FL. Furthermore, Keeley et al. demonstrated an improved, highly sensitive DNA methylation technique consisting of FRET linker probes and QDs, which aimed to decrease the background noise caused by PCR amplification [400]. Rafiei et al. developed a method based on graphene quantum dots (GQDs), which were able to recognize the difference between the A-form and B-form DNA structures while serving as a detection platform for methylation content [401]. A nanobiosensor was also reported for the fluorometric detection of methylated DNA where the Fe@Au NPs were used to immobilize the DNA single strands and the fluorophore dipyridamole was used to detect and differentiate the methylated and unmethylated DNA samples based on their different FL response [402]. The FL enhancement of dipyridamole was correlated with increasing methylation levels and the LOD of the method was reported as 3.1 3 10216 M. Wang et al. studied the detection of DNA methylation with tetramethylammonium-filled nanopores since the nanopore technology attracts interest for DNA biosensors due to its simplicity, rapidness, and label-free nature [403]. A colorimetric detection method was developed for methylated DNA detection purposes, based on the enrichment of magnetic microspheres with AuNPs [404]. EC biosensors for the detection of DNA methylation also hold great potential to knock out the classical bisulfate treatment by providing further simplification in biosensor applications [405,406].

6.7 Conclusion and outlook The detection of NAs as important biomarkers play a key role in plenty of areas ranging from diagnostics, prognosis, and clinical applications to food and environmental analyses. Over the last three decades, impressive progress has been made in the development of NA biosensors. Yet, many research groups worldwide have been motivated to develop/design new or improved sensing platforms to meet clinical, medical, and bioanalytical needs. Significant interest has been focused on achieving selectivity/specificity with impressively low LODs specially for POC diagnostics along with

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the ease of commercialization. The sensitivity and selectivity of traditional optical, EC, and electromechanical detection methods have been increased extraordinarily over the years. However, mainly due to the drawbacks of these traditional techniques, such as being time-consuming, costly, cumbersome, and susceptible to matrix effects, scientists are already on the search for next-generation sensing platforms. So far, CRISPR/Cas-assisted platforms are thought to be one of the most promising candidates for such futuristic biosensor applications, especially in pathogen-specific NA detection. Nevertheless, their applicability in diverse and complex systems needs to be confirmed before the realization of their practical use. Furthermore, especially the design of miniaturized, cost-effective, simple, straightforward, rapid, and high-throughput sensing platforms to achieve complex and in vivo target detection will continue to be an important and required aspect of new NA biosensor platforms. Many challenges need to be overcome in the coming years, but the outlook of NA biosensors is positive and breakthroughs are realizable in the future.

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C H A P T E R

7 Biosensors for glucose detection Ekin Sehit1,2 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

7.1 Introduction Diabetes mellitus is global health problem for millions of people. This disease causes elevated blood glucose levels and leads to severe complications such as kidney failure, stroke, heart attack, and amputation if not treated timely [1]. Although there is no permanent cure for diabetes, progression of the disease can be managed by monitoring the blood glucose levels regularly. In addition to diabetes, patients with other glucose metabolism disorders (e.g., hypoglycemia, impaired glucose tolerance) should track the blood glucose levels as a preventative measure [2]. The necessity for frequent blood glucose measurements has led to an enormous demand in the biosensor industry for rapid, cost-effective, and sensitive glucose sensors. The demand was addressed by excess research on glucose sensors employing various transducing systems, namely electrochemical, acoustic, thermal, optical, and magnetic. Electrochemical sensors in particular have been studied excessively since the first generation of glucose sensors due to many advantages including simplicity, accuracy, and low cost [3]. However, recent advancements in nanotechnology and optics have resulted in an increased amount of research on optical glucose sensing devices as well. Enzymes are used as recognition units in various glucose sensors for their ability to selectively catalyze glucose oxidation reaction. Such an enzymatic oxidation reaction produces hydrogen peroxide and D-glucono-1,5 lactone, which is then hydrolyzed to gluconic acid (Fig. 7.1). Enzymatic glucose sensors have constantly been investigated

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© 2023 Elsevier Inc. All rights reserved.

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7. Biosensors for glucose detection

+ O2

β - D glucose

Glucose oxidase

+ H2O2

D – glucono – δ - lactone

H2O

Gluconic acid

FIGURE 7.1 Glucose oxidation by glucose oxidase enzyme [4].

since the first glucose biosensor was developed by Clark and Lyons in 1962. This first enzyme electrode, in which glucose oxidase (GOx) was incorporated over an oxygen electrode via a dialysis membrane, could detect the oxygen consumed in the GOx catalyzed oxidation of glucose to measure blood glucose levels [5]. The first glucose biosensor suffered from variation of oxygen in the background, which decreased the accuracy of the sensor. Updike and Hicks solved the issue by implementing another electrode without the enzyme to be used as a reference for background oxygen levels [6]. In addition to oxygen dependence, the first generation of glucose sensors lacked sensitivity due to interference caused by other electroactive species such as uric acid, ascorbic acid, and drugs [2]. Therefore the second generation of glucose sensors used artificial mediators (e.g., ferrocene derivatives, ferricyanide, conductive salts) instead of oxygen as an electron acceptor to enhance electron transfer process between the active site of the enzyme and the electrode [5]. In the third generation of glucose biosensors, the artificial mediator is aimed to be removed completely to obtain direct electron transfer between the enzyme and the electrode. This is achieved by immobilizing the enzyme directly on the electrode, which is modified with nanomaterials for an enhanced electron transfer process [7]. Although the enzyme as a recognition unit provides high sensitivity and selectivity for glucose detection in complex mediums, it is expensive and prone to denaturation in harsh conditions. Therefore in recent studies noble metal and metal-oxide based nanomaterials as well as lectin protein are

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7.2 Electrochemical glucose biosensors

employed for glucose oxidation to replace the enzyme as the ligand. Today, novel glucose monitoring devices are developed for continuous and noninvasive monitoring of blood sugar levels. The following sections of this chapter elaborate on these techniques to provide an indepth overview to readers about the recent trends in glucose sensors.

7.2 Electrochemical glucose biosensors Electrochemistry-based transducers follow the electron flow created by a chemical reaction. In glucose biosensing, the current is usually originated from glucose oxidation facilitated by enzyme or a metalcentered electrocatalyst. Enzymatic catalysis of glucose oxidase is shown in Fig. 7.1, where the reaction takes place in two steps in the presence of a flavin molecule. However, glucose oxidation via synthetic electrocatalysts is explained by two models: activated chemisorption model and incipient hydrous oxide adatom mediator model (IHOAM). The activated chemisorption model suggests that the oxidation process is initiated by adsorption of the glucose molecule onto the catalyst (electrode) surface via bond formation. Simultaneously, the hydrogen atom of hemiacetal carbon is extracted and binds to the electrode (Fig. 7.2) [7]. Following the adsorption of the glucose on the metal, it is oxidized to glucono-δ-lactone, which is further converted to gluconic acid [8]. The activated chemisorption model falls short to explain electroactivity of gold electrodes in basic solutions and catalytic activity of Pt on certain oxygen insertion reactions leading to formulation of IHOAM [9]. This model claims the need for an incipient hydrous oxide formation at adatom sites, which are metastable atoms with high energy on the defect sites. Such adatoms are oxidized in lower voltages than thermodynamically expected potentials. The hydrous oxides formed at the adatom sites have lower coordination number and act as mediators in electrocatalytic oxidation reactions such as glucose oxidation (Fig. 7.3) [9]. Both of these models can be considered for catalytic reaction on noble metal electrodes, but they are not sufficient to elucidate glucose oxidation

H

METAL

METAL

FIGURE 7.2 The activated chemisorption model [8].

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METAL

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Glucose

M[OH]ads n e-

Gluconolactone

ORED n e-

M*

OOX

FIGURE 7.3 The incipient hydrous oxide adatom mediator model. The reactive hydrous oxide (OHads) accelerates the electrooxidation of glucose [10]. Used under Creative Commons CC BY 4.0. https://creativecommons.org/licenses/by/4.0/.

on transition metals or metal oxides. However, glucose oxidation on transition metals (e.g., Ni, Co, Cu) can be clarified by redox reaction of the transition metal centers [8]. Under anodic bias, metal oxide with lower oxidation number is oxidized to a higher oxide, which can form adsorbed hydroxide anions (OHads) bound to metal surface. Such OHads radicals are able to oxidize organic species in close proximity to the electrode. Creation of these oxidative OHads radicals is favored in alkaline conditions leading to requirement of high pH values for metaloxide-based glucose biosensors [8].

7.2.1 Enzymatic electrochemical glucose biosensors Enzymatic glucose recognition has been employed since the first glucose biosensor was developed by Clark and Lyons. However, thanks to current knowledge on nanotechnology, the electrodes are enhanced significantly by incorporation of various nanomaterials in sensor design increasing surface area and thus sensor performance. For instance, a nanocomposite made of multiwalled carbon nanotubes MWCNTs and cobalt(II) sulfide NPs was employed on glassy carbon electrode (GCE) for GOx immobilization to assist direct electron transfer between the enzyme and the electrode surface [11]. The nanocomposite of MWCNTs and CoS NPs provided a large specific surface area and suitable environment for direct electron transfer. The proposed sensing platform achieved an LOD of 5 μM in the linear range of 8 μM
1.5 mM. Similarly, ZnO nanostructures were employed on fluorinated tin-oxide electrode together with polyvinyl alcohol (PVA) to immobilize GOx with cyanuric chloride as the linker [12]. I
V measurements of the sensor exhibited a detection limit of 2 μM within a wide detection range of 0.2
20 mM. ´ Lipinska et al. designed a nanocomposite electrode by combining the structured titanium foil with AuNPs for enzyme immobilization (Fig. 7.4) [13]. The bimetallic nanocomposite electrode facilitated the rapid electron transfer leading to enhanced sensitivity. The electrochemical measurements revealed two distinct linear ranges (40 μM
15.05 mM and 15.05
40 mM) with an LOD of 1.75 μM. Furthermore, the sensor

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239

FIGURE 7.4 The fabrication procedure of structured titanium electrode modified with AuNPs, chitosan, and GOx [13]. AuNPs, Gold nanoparticles; GOx, glucose oxidase.

was tested for glucose detection in sweat, saliva, and serum samples revealing acceptable recovery values. In recent studies, electrochemical enzyme-based glucose sensors are developed for wearable, noninvasive, user-friendly glucose detection applications. Therefore electrodes with high flexibility and elasticity came into prominence for the next-generation glucose biosensors. A flexible glucose sensor based on a gold/MoS2/gold nanofilm was fabricated by sandwiching MoS2 NPs between gold-sputtered layers on a commercial polyimide film [14]. Following the immobilization of GOx enzyme on the flexible electrode with the aid of a thiol-based chemical linker, the sensor was investigated for glucose detection by amperometry. The elastic sensor achieved an LOD of 10 nM for a linear detection range of 10
500 nM. Another flexible electrode was designed by incorporating MWCNTs and Pt microspheres onto carbonized silk fabric (CSF), which is then modified with GOx via Nafion solution as a binder [15]. The MWCNTs, Pt, and CSF provided flexibility and conductivity at the same time while the enzyme enhanced the sensitivity and selectivity for glucose detection. Amperometric measurements performed in physiological pH revealed good linearity within a concentration range of 0
5 mM with an LOD of 0.05 mM. Sweat is a commonly studied medium in glucose biosensing providing a user-friendly noninvasive detection method. For instance, a laser-induced graphene (LIG) electrode decorated with PtNPs and chitosan-GOx composite was utilized for glucose detection in sweat samples [16]. Relatively low electrochemical activity of LIG was improved by acetic acid treatment prior to PtNPs and GOx modification to improve

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conductivity of the electrode. Following the acid treatment, PtNPs were electrodeposited by cyclic voltammetry (CV) leading to enhances electrical properties. GOx/PtNP/acetic acid-treated LIG electrode could detect glucose as low as 0.3 μM within a linear range of 0.3 μM
2.1 mM. The proposed sensor was utilized to test glucose levels before and after meal in human sweat revealing a potential for commercial applications. In another study, a 3D paper-based microfluidic screen printed electrode (SPE) was constructed for glucose detection in human sweat and blood [17]. After screen printing of working, counter, and reference electrodes on the paper-based device, the working electrode area was further modified with Prussian blue (PB) carrying reduced graphene oxidetetraethylene pentamine (rGO-TEPA/PB) nanocomposite. Meanwhile, GOx enzyme was immobilized on counter and reference electrodes to produce H2O2 in the presence of glucose, which was then transferred through the paper into the working electrode. H2O2 was reduced in the working electrode due to electrocatalytic activity of PB resulting in an electrochemical signal formation while rGO-TEPA enhances the sensitivity by large surface area and conductivity. Such a sensor could determine glucose in a linear range of 0.1
25 mM with an LOD of 25 μM. Furthermore, the paper-based device was tested in glucose-spiked sweat samples showing acceptable recovery values. A microfluidic device equipped with GOx immobilized 3D porous graphene aerogel was developed for glucose detection using small volumes of sample [18]. Freeze-dried graphene aerogel provided high conductivity and large surface area for the increased amount of enzyme immobilization. H2O2 produced from the enzymatic glucose oxidation was measured by amperometry and a linear relation could be observed for glucose in a concentration range of 1
18 mM with an LOD of 0.87 mM. The sensor was further utilized for glucose detection in human serum samples revealing good recoveries. Vargas and colleagues fabricated a sensor chip for simultaneous detection of two crucial biomarkers, namely glucose and insulin [19]. For glucose detection, GOx was immobilized on chitosan film then coated with redox mediator tetrathiafulvalene (TTF) while the insulin measurement was achieved by an antibody-based sandwich assay. The calibration curves for both analytes were obtained in PBS revealing an LOD of 0.2 mM for glucose within a working range of 0
20 mM. The dual biomarker sensing chip was also tested using whole blood and saliva samples showing promising performance for simultaneous detection of glucose and insulin. Continuous glucose monitoring (CGM) is another crucial application of glucose biosensors in which the glucose levels of the patient is observed throughout the day. A noninvasive self-powered enzymatic biofuel cell (EBFC)-based glucose sensor was developed to address such a need by entrapment of laccase and GOx in MOF to produce cathode

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241

and anode, respectively [20]. The enzyme encapsulated MOF structures were in situ grown on cellulose acetate nanofiber membranes and further modification with MWCNTs and AuNPs was performed to improve electrochemical performance. The self-powered biosensor could detect glucose in a concentration range of 1a20 mM with an LOD of 5.347 μM. Moreover, the biosensor could preserve its sensitivity to glucose for 15 h. In another study, GOx was utilized together with Pt and Ag wires integrated with silk/D-sorbitol pyramidal microneedles for continuous glucose monitoring [21]. The Pt and Ag wires in the microneedles served as reference, working, and counter electrodes while the silk/D-sorbitol composite on the working electrode provided a stable and biocompatible platform for enzyme immobilization. The proposed biosensor exhibited a linear detection range of 1.7
10.4 mM during in vitro measurements. Further examples of enzymatic electrochemical glucose biosensors are listed in Table 7.1.

7.2.2 Nonenzymatic electrochemical glucose biosensors Enzymatic biosensors suffer from many disadvantages due to their proteinaceous nature. Their performance strongly relies on the environmental conditions such as pH, temperature, and humidity limiting their applications. Therefore nonenzymatic glucose sensors are now commonly studied to provide artificial recognition units as opposed to naturally restricted bioreceptors. Metals, alloys, and metal oxides are highly utilized as nanocatalysts in electrochemical glucose sensing due to their electrocatalytic activity. Furthermore, they are frequently combined with other nanomaterials such as graphene and derivatives, carbon nanotubes, and conductive polymers to enhance the sensitivity. Noble metals (e.g., Au, Pt, Pd) are of benefit in glucose biosensing due to their high electrocatalytic activity as well as better stability compared to enzymes [2]. For instance, gold can be used in the form of AuNPs with MWCNTs for glucose detection [35]. Here MWCNTs provide electrical conductivity and large surface area as a support material for AuNPs while the anionic functionalities on MWCNTs surface repel the anionic interferents. Selectivity is further improved by Nafion film application on the surface resulting in a linear range of 0.1
25 mM with an LOD of 10 μM. In another study, bimetallic core-shell structures of noble metals were fabricated for an enhanced catalytic performance [36]. Nanoparticles composed of Au core and Pt dendritic shells were modified with additional Au layer for increased sensitivity. The core-shell sensor was tested in a wide concentration range of 0.5 μM
10 mm showing an LOD of 445.7 ( 6 10.3) nM. The real sample analysis carried out with five times diluted whole blood samples were in good agreement

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TABLE 7.1 Electrochemical enzymatic glucose biosensors. Sensing platform

Measurement technique

Detection limit

Linear range

Measurement medium

Storage stability

Ref.

GOx/Fe3O4/PPy@ZIF-8

Amperometry

0.333 μM

1 μM
2 mM

PBS, serum

10 days

[22]

PANI-TT-GOx

Amperometry

1 μM

5 μM
5 mM

PBS, urine

15 days

[23]

CF-PEDOT-GOx

Amperometry

-

0.5
15 mM

PBS, wine

60 days

[24]

Cu-nanoflower@AuNPs-GOx NFs

Voltammetry

18 nM

0
0.01 mM, 0.03
0.1 mM

PBS, fetal bovine serum

20 days

[25]

GOx-GO-SH-Au-SPE

Voltammetry

0.3194 mM

3
9 mM

PBS




[26]

GR/PPD/(AuNP)PPCA-GOx

Amperometry

0.08 mM

0.20
150 mM

PBS-KCl, human serum, pharmaceutical preparations

14 days

[27]

3D-PMED

Amperometry

5 μM

0
1.9 mM

PBS, sweat




[28]

(CS/GLM)-GCE

Voltammetry

1.31 μM

0.01
10 mM

PBS, juice

7 days

[29]

BPCNF900/GOx/GCE

Amperometry

23 nM

10 nM
3.8 mM

PBS, serum

7 days

[30]

4-ATP/PVA/PEI/AuNPs/ GOx NFs

Impedimetry

0.9 μM

10
200 μM

PBS

4 days

[31]

GOx/SBPThi/GCE

Voltammetry

0.66 μM

1.97 μM
4.0 mM

PBS, serum

2 weeks

[32]

GR/PPy/AuNPs(AuCl4-) GOx

Voltammetry

0.1 mM

0.1
0.7 mM

PBS, serum

19 days

[33]

GR/PANI:rGO/GOx

Amperometry

0.089 mM

0.5
50 mM

PBS, serum

8 days

[34]

3D, Three-dimensional; 4-ATP, 4-aminothiophenol; AuNPs, gold nanoparticles; BPCNF, bacterial cellulose porous carbon nanofibers; CF, carbon fiber; CS/GLM, chitosan and GOx encapsulated liposome microreactor; GCE, glassy carbon electrode; GOx, glucose oxidase; GR, graphite rod; NFs, nanofibers; PANI, polyaniline; PEDOT, poly (3,4ethylenedioxythiophene); PEI, polyethyleneimine; PMED, paper-based microfluidic electrochemical integrated device; PPCA, poly(pyrrole-2-carboxylic acid); PPD, poly(1,10phenanthroline-5,6-dione); Ppy, poly(pyrrole); PVA, poly(vinyl alcohol); rGO, reduced graphene oxide; SBP, Schiff-base polymer; SH, thiol functionality; TT, thermally treated; ZIF-8, zeolitic imidazolate framework.

7.2 Electrochemical glucose biosensors

243

with the commercial glucometer. However, although noble metals provide strong electrocatalytic activity and stability, they suffer from poor selectivity, slow kinetics, and high cost. Metal oxides are stable, easily modified, and low-cost alternatives for nonenzymatic oxidation of glucose. Ahmad and coworkers employed hierarchical CuO nanoleaves for electro-oxidation of glucose in alkaline environment [37]. By voltage application the Cu(II) of the nanoleaves is oxidized in NaOH solution forming Cu(III) species, which catalyzes the glucose oxidation to gluconolactone. Amperometry measurements showed a linear increase for a concentration range of 5 μM
5.89 mM with a detection limit of 12 nM. The hierarchically designed sensor exhibited acceptable recoveries for diluted serum samples. Similarly, NiO nanopetals were grown hydrothermally on FTO-coated glass for nonenzymatic glucose detection [38]. During cyclic voltammetry Ni12 species are oxidized to form Ni13, which behaves as a catalyst for glucose oxidation while it is reduced to Ni12. Following the immediate transformation of gluconolactone to gluconic acid, it reacts with water producing gluconate and hydronium. The NiO nanopetals revealed a linear range of 0.1
1.2 mM with an LOD of 1 μM. Oxides of different metals can be combined to improve sensing performance. For instance, NiO was combined with defect-induced TiO2 nanoparticles to improve selectivity and durability of NiO on electrode surface [39]. Such a mixed oxide-based sensor achieved a detection limit of 0.7 µM within a linear range of 2 μM
2 mM. Metallic electrocatalysts can be combined with other nanostructures such as CNTs, polymers, and graphene derivatives. Nguyen and colleagues utilized PtNPs-MWCNT nanocomposite as a support material for the electrodeposition of Au-Ru NPs catalysts to ensure efficient distribution and utilization [40]. Following the printing of nanocomposite on liquid crystal polymer substrate to produce sensing units, Au-RuNPs were electrodeposited on the working electrode of sensors. The printable sensor exhibited a linear detection range of 1a10 mM with an LOD of 68 μM. Coordination polymers allow to integrate metal-based catalytic centers and bridging ligands in an ordered manner. Meng and coworkers employed Co coordination polymer spheres (Co CPSs) loaded on 3D microporous carbon (MPC) support to simultaneously benefit from the electrocatalytic activity and electrical conductivity [41]. Such an electrode design achieved a wide linear detection range of 0.5 μM
1.58 mM with an LOD of 84 nM. Furthermore, the Co CPSs/ MPC-GCE sensor demonstrated acceptable recoveries for glucose testing in human serum (Fig. 7.5). Molecularly imprinted polymers are a class of synthetic recognition units where the cavities matching the analyte in terms of shape, size, and chemical functionality are formed within a polymeric matrix.

2. Biomedical applications

244

7. Biosensors for glucose detection

Pyrolysis

Assemble

Etching Polymerization

MPC

SiO2

Co CPs

Co CPs/MPCs

Co CPs/MPCs

Co CPs/MPC-GCE

FIGURE 7.5 Fabrication procedure of Co CPSs/MPC-GCE sensor and glucose detection [41].

Imprinted polymers can target large biostructures such as protein, bacteria, virus as well as small molecules like drugs, endotoxins, and pharmaceuticals. Glucose imprinted polymers have been utilized for sensitive and selective detection of glucose in various media. Sehit and colleagues fabricated an electrochemically synthesized glucose imprinted polymer with AuNPs for glucose detection in blood [42]. The one-pot synthesis was realized by electropolymerization of o-phenylenediamine in the presence of glucose and AuNPs on gold substrate by multistep amperometry method. The templated glucose molecules were removed by incubating the sensor in NaOH solution overnight resulting in formation of imprinted sites complimentary to glucose molecule. The voltammetric measurements showed that AuNPs-MIP sensor could detect glucose as low as 1.25 nM within a working range of 1.25
320 nM. Such a sensitivity was achieved due to AuNPs incorporation within the sensing surface as MIP sensor without the AuNPs could not quantify glucose even at much higher concentrations. We have summarized some other examples of the recent nonenzymatic electrochemical sensors in Table 7.2 along with their important features.

7.3 Optical glucose biosensors Optical glucose biosensors usually exploit the alteration in fluorescence signal, refractive index, or transmission characteristics upon the glucose recognition by natural or synthetic receptors on the sensing unit.

2. Biomedical applications

TABLE 7.2 Electrochemical nonenzymatic glucose biosensors. Sensing platform

Measurement technique

Detection limit

Linear range

Measurement medium

Storage stability

Ref.

Cu NPs-LIG

Amperometry

0.39 μM

1 μM
6.0 mM

0.1 M NaOH, serum

3 weeks

[43]

Ni(TPA)-SWCNT-CS/GCE

Amperometry

4.6 μM

20 μM
4.4 mM

0.1 M NaOH, serum

30 days

[44]

Au@Cu2O/Nafion/GCE

Voltammetry

18 μM

0.05
2.0 mM

0.05 M NaOH, pharmaceutical injection

4 weeks

[45]

Nafion/CuO/ZnO-DSDSHNM/GCE

Amperometry

357.5 nM

500 nM
100 mM

0.5 M NaOH, serum

15 days

[46]

Ag-PANI/rGO

Amperometry

0.79 μM

0.1
50 μM

0.01 M PBS, juices, milk, coke

4 weeks

[47]

VCo-Co(OH)2

Amperometry

295 nM

0.4 μM
8.23 mM

0.1 M NaOH, serum




[48]

Au foam

Amperometry

0.14 μM

0.5 μM
12 mM

0.3 M NaOH, serum

30 days

[49]

Co3(BTC)2 MOFs/GCE

Amperometry

0.33 μM

1 μM
0.33 mM0.33 mM
1.38 mM

0.1 M NaOH, serum




[50]

Pt-CuO/GPE

Amperometry

0.1 μM

3.125
25 mM

0.15 M NaOH, serum




[51]

Fe3O4 nanospheres

Amperometry

33 μM

0.1
1.1 mM

0.5 M NaOH

30 days

[52]

CuS NSs/Cu2O/CuO NWAs/Cu foil

Amperometry

0.89 μM

0.002
4.1 mM

0.1 M NaOH, serum

3 weeks

[53]

CuO/NiO-C/cello tape

Amperometry

37 nM

100 nM
4.5 mM

0.1 M NaOH, serum

56 days

[54]

Hollow Mn 2 Cu 2 Al oxide nanocomposite /Ni foam

Amperometry

0.43 μM

5 μM
2.5 mM

0.5 M NaOH, serum

30 days

[55]

BTC, 1,3,5-benzene tricarboxylic acid; CS, chitosan; DSDSHNM, dumbbell-shaped double-shelled hollow nanoporous microstructures; E-Co3(BTC)2 MOF: GCE, Glassy carbon electrode; GPE, graphite pencil electrode; LIG, laser induced graphene; MOF, metal organic framework; Ni(TPA), nickel(II)-terephthalic acid; NPs, nanoparticles; NSs, nanosheets; NWAs, nanowire arrays; PANI, polyaniline; rGO, reduced graphene oxide; SWCNT, single-walled carbon nanotubes; VCo-Co(OH)2, cobalt hydroxide nanosheets with cobalt vacancies.

246

7. Biosensors for glucose detection

FIGURE 7.6 Schematic illustration of fiber-optic biosensors [57].

Fiber optics are commonly utilized as sensing platforms in optical detection tools due to their compact size, high sensitivity, rapid response, and immunity to electromagnetic waves [56]. Optical fibers are glass materials allowing total internal reflection (TIR) of the incident light through the core part due to the refractive index difference compared to cladding (Fig. 7.6) [57]. In many sensing applications, the evanescent wave leaking to the cladding is monitored as it can provide information on binding events on the surface of the optical fiber. Since the evanescent wave exponentially decays, the cladding is often removed completely or partially to enhance the interaction of the evanescent field with the surrounding environment leading to a boost in sensitivity [58,59]. Fiber-optic sensors-based on surface plasmon resonance (SPR) detection is another class of commonly employed optical recognition tools. Surface plasmon waves (SPW) are charge oscillations on the interface of a metal and dielectric, which are sensitive against the refractive index change of the dielectric medium [59]. SPWs are excited when the propagation constant of the SPW matches with that of the evanescent field. Any alteration in the refractive index on the dielectric medium directly affects the coupling conditions, which is then realized in the resulting transmission spectrum. In addition to the spectral interrogation, change in the intensity or phase difference in the output signal can also be monitored for detection [60]. Furthermore, tapering the fiber or applying grating on the detection area can improve the sensitivity of the optical sensor.

7.3.1 Enzymatic optical glucose biosensors Optical fibers are commonly employed in biosensing technologies due to their high sensitivity, simplicity, versatility, miniaturization, and remote applicability [57]. Yu and colleagues utilized an optical fiber modified with a nanocomposite film comprised of fluorescent carbon

2. Biomedical applications

247

7.3 Optical glucose biosensors

quantum dots (CQDs), GOx, and cellulose acetate (CA) [61]. Fluorescent signal of QDs-based sensing surface decreased linearly for a concentration range of 10
100 nM due to quenching effect of H2O2 released upon the enzymatic oxidation of glucose. Such a fiber-optic sensor revealed a detection limit of 25.79 nM and applicability in glucose detection in real samples. In another study, tapered single mode optical fiber was decorated with graphene oxide (GO), AuNPs, and GOx enzyme for sensitive glucose detection [62]. The transmitted light was red-shifted as the glucose concentration is increased due to the gradually increasing refractive index of the sensing medium. The tapered optic fiber sensor demonstrated an LOD of 2.6 mM and a linear detection range of 0
11 mM. Xu and coworkers employed a fiber-optic sensing platform modified with long period fiber grating (LPFG) to obtain an increased sensitivity against the refractive index surrounding [63]. LPFG surface was further functionalized with GO to improve hydrophilicity of the sensing platform leading to excellent conditions for GOx immobilization (Fig. 7.7). A shift to longer wavelengths was observed in the transmission spectra for increased glucose concentrations in the range of 0
1.2 mg/dL. The LPFG fiber-optic sensor was also utilized in glucose detection in artificial body fluids achieving satisfactory recovery values. A ratiometric fluorescent biosensor was developed both as an aqueous solution and a hydrogel film using carbon dots (CDs), rhodamine 6G (Rh6G), GOx, and HRP [64]. The fluorescent emission of CDs in aqueous solution was quenched by the OH radicals released during the HRP catalyzed conversion of H2O2 to H2O, while the fluorescence effect of Rh6G remained unaffected by the enzymatic reactions. As the glucose in the CD/Rh6G/GOx/HRP biosensor solution was increased, a distinct color change was observed from blue to green due to the stepwise

Core

LPFG

Cladding

Graphene oxide

EDC/NHS

GOD

FIGURE 7.7 Stepwise functionalization of LPFG fiber-optic sensor [63]. LPFG, long period fiber grating.

2. Biomedical applications

248

7. Biosensors for glucose detection

decrease in the blue fluorescence emission of CDs and unchanged fluorescence emission of Rh6G. A linear range of 0.1
500 μM with LOD of 0.04 μM was observed for CD/Rh6G/GOx/HRP aqueous solution. In the same study, the hydrogel matrix of polyacrylic acid cross-linked with diacrylated polyethylene glycol was decorated with Rh6G, acrylated CDs, GOx, and HRP to develop a glucose biosensor in a stable solid film form. The hydrogel film revealed a linear detection range of 0.5
500 μM with an LOD of 0.08 μM. Furthermore, both of the biosensors were tested in glucose-spiked diluted human serum showing good recoveries. A hydrogel-based biosensor was also employed for enzymatic glucose sensing in another work, where GOx was incorporated into the partially oxidized dextran (PO-Dex) and chitosan film via the Schiff bond formation between the amino groups of GOx and aldehyde groups of PO-Dex during the layer-by-layer (LbL) assembly [65]. The swelling behavior of sensing hydrogel changed in the presence of glucose leading to a linear increase of the optical path length for a glucose concentration range of 0
20 mM. Moreover, the hydrogel film preserved its sensitivity for up to 7 weeks of storage and exhibited good selectivity against possible interferents such as fructose, galactose, ascorbic acid, and urea. A lab-on-a-chip (LOC)-based optical biosensor was fabricated for noninvasive glucose detection in saliva [66]. Such a device allows a premixing of the saliva sample and GOx-HRP mixture, which were injected from different inlets. The H2O2 released from the enzymatic reaction in the saliva was further mixed with N,N’-diethyl-p-phenylenediamine (DEPDA), 4-chloro-1-naphthol (4CN), and horseradish peroxidase (HRP) resulting in the blue color formation in the saliva sample whose absorption was then detected in the measurement part. The LOC device could detect glucose in a concentration range of 1
10 mg/dL. Similarly, a noninvasive biosensor coupled with smartphone application was fabricated for glucose recognition in saliva samples [67]. The device was constructed on a paper filter-based strip by immobilizing the GOx together with bromocresol purple as a pH responsive dye. Following the color change as a response to decreased pH during the enzymatic glucose oxidation, the app was utilized to estimate the glucose concentration in saliva by evaluating the RGB pixel intensities during the color change. The smartphone-based sensor showed a linear range of 50
240 mg/dL with a detection limit of 24.6 mg/dL in spiked saliva samples. Hussain et al. employed the photonic interpenetrating polymer network (IPN) structure consisting of photonic solid-state cholesteric liquid crystals (CLCsolid) and a cationic polyelectrolyte poly(2-dimethylaminoethyl methacrylate) (PDMAEMA) for glucose detection [68]. GOx was immobilized within the polymeric network by copolymerizing acrylic acid and further coupling reaction with EDC/NHS. The gluconic acid released by

2. Biomedical applications

7.3 Optical glucose biosensors

249

the enzymatic glucose oxidation triggers the swelling of the polyelectrolyte as a response to local pH increase. Such a swelling causes an increased pitch of CLCsolid, which is observed as a redshift in the color. The proposed sensor showed linearly increasing red-shift for increased concentrations of glucose in aqueous solutions for a range of 0.7
12 mM with a detection limit of 21 μM. CGM devices are useful for diabetic patients to provide better insight on the course of the disease. Furthermore, such tools can be employed in biotechnology to observe the glucose levels of cell cultures. Tric and colleagues employed a commercially available fiber-optic oxygen sensor and modified it with GOx and hydrophilic diffusion membrane to develop an optical biosensor for continuous glucose monitoring in cell cultures [69]. The CGM device revealed a dynamic range of 0
20 mM, LOD of 0.45 mM, and a stability of more than 52 days.

7.3.2 Nonenzymatic optical glucose biosensors The limitations of enzymes are also prominent in optical enzymebased sensors, which drive the researchers to develop nonenzymatic optical biosensors. Most of the nonenzymatic optical sensors have employed metal-based synthetic catalyst for electrooxidation of glucose. For instance, Cu21-incorporated PANI coated optode was utilized for glucose sensing [70]. The in situ synthesized hybrid film was also coupled with o-dianisidine dye as an indicator. The absorbance showed a linear relation with glucose concentration range of 50
350 mg/dL with an LOD of 2 ppm. The Cu21/PANI-based optical biosensor was tested for analysis of human serum samples showing results in good agreement with a commercial glucose sensor. In another study, ZnO nanorods were utilized with UV irradiation for photoelectronic oxidation of glucose [71]. The H2O2 released during the photo-oxidation reaction of glucose quenched the photoluminescence emission of ZnO linearly in the concentration range of 0.5
30 mM. Boronic acid derivatives demonstrate pH-dependent binding affinity of saccharides; therefore they are commonly employed in glucose sensing applications [72]. In a recent study, 4-mercaptophenylboronic acid (4-MPBA) was utilized on AgNP-decorated PEG hydrogel microparticles for SERS-based recognition of glucose [73]. The thiol species of 4-MPBA provided efficient immobilization on AgNP-decorated surface while its benzene moiety was easily identifiable in SERS spectra. The obtained signal increased in proportion to glucose concentration from 1 pg/mL to 1 μg/mL. Tam and colleagues utilized phenylboronic acid (PBA) with aniline-functionalized graphene quantum dots (a-GQDs) to fabricate a paper-based printed sensor as well as a hydrogel-based

2. Biomedical applications

250

7. Biosensors for glucose detection

composite film [74]. PBA and a-GQDs were electrostatically attracted to each other, which enabled a π
π stacking interaction between the two moieties. Such an interaction allowed an electron transfer between PBA and a-GQDs leading to a quenched fluorescent signal of a-GQDs in the absence of glucose. As the glucose concentration in the solution was increased gradually, the fluorescent emission was recovered revealing a linear correlation between the relative intensity and the glucose concentration in the range of 0
40 mM. The paper-based sensor was fabricated by modifying paper substrate with a-GQDs and PBA, which demonstrated good applicability in human serum and tear samples. The hydrogel composite of a-GQDs and PBA was immobilized on optical fiber. Such a biosensor achieved a linear range of 0.05
20 mM with a detection limit of 2.1 μM. Sun and colleagues employed polyPBA grown on tapered fiber probe for optical glucose detection [75]. Prior to polymer film formation, hydroxyl groups were introduced onto the sensing part of the fiber, which was later treated with silane coupling agent. The peak reflectance of polyPBA-coated fiber linearly increased with glucose for a concentration range of 0
60 mM. Furthermore, the proposed sensor was tested for glucose detection in human serum showing results in agreement with the hospital measurements. In addition to the phenyl boronic acid, pyrene boronic acid can also be utilized for glucose detection. Yu et al. employed a D-shaped optical fiber-based SPR sensor with pyrene boronic acid and graphene-MoS2 nanocomposite for glucose detection [76]. The D-shaped obtained by polishing only one side of the fiber provided a more suitable surface for 2D nanomaterial deposition while the high absorption coefficient of MoS2 increased the sensitivity of the SPR sensor. The shift in the resonance wavelength was monitored for a glucose concentration range of 0
300 mg/dL. Concanavalin A (Con A) is protein that can bind to carbohydrates with monosaccharide specificity. Lobry and coworkers immobilized Con A on Au-sputtered and polydopamine (PDA)-coated tilted fiber Bragg grating (TFBG)-based optical sensor for glucose detection [77]. The quinone and cathecol functionalities of PDA assisted the strong adhesion of Con A on the fiber surface by covalently binding to the amine groups of the protein (Fig. 7.8). Furthermore, the sensitivity toward the refractive index of the surroundings was enhanced by exploiting the surface plasmon resonance effect of the gold. The PDA layer thickness as well as the glucose binding events were observed by following the shift of the plasmonic signal toward higher wavelengths due to the alteration in the refractive index of the surrounding medium. SPR-TFBG revealed a detection limit of 1027 M while the highest sensitivity was achieved for the concentration range of 1026
1024 M. Likewise, Li and colleagues utilized Con A entrapped in a hydrogel matrix on Si substrate for optical

2. Biomedical applications

251

7.4 Other glucose biosensors

TFBG

Dopamine Au sputtering Au layer

Au + PDA

In Tris buffer Con A

In PBS + MnCl2 + CaCl2

Glucose

Au + PDA + Con A + D-glucose

Au + PDA + Con A In PBS

FIGURE 7.8 Fabrication steps and sensing principle of SPR-TFBG glucose biosensor [77].

glucose detection. The LbL-assembled thin film demonstrated oscillations in the reflection spectra (Fabry-Perot fringes) whose properties depend on the thickness of hydrogel film. Addition of glucose into the measurement medium induced a higher degree of swelling due to the decreased cross-linking density in the hydrogel matrix. The alteration in the swelling behavior was observed as a shift in the Fabry-Perot fringes in the reflection spectra as well as the increase in optical path length, which was linearly correlated with the glucose concentration for a range of 0
70 mM with LOD of 0.716 mM.

7.4 Other glucose biosensors Many current examples of glucose biosensors exploit electrochemical or optical detection methods. However, there are also studies utilizing different technologies such as photoelectrochemistry, piezoelectricity, and thermal microsensing. Photoelectrochemical (PEC) biosensors possess the advantages of both electrochemical and optical biosensors. In PEC sensors the active species on the electrode is excited by light and the resultant response is measured as the signal [78]. TiO2 is extensively utilized in PEC biosensing due to photoelectrochemical activity, stability, low toxicity, and high catalytic activity. For instance, Yang and colleagues employed TiO2 as 3D hollow-out nanowire clusters (NWc) together with GOx enzyme for PEC-based detection of glucose [79]. The 3D network of TiO2 NWc provided rapid reaction kinetics, enhanced diffusion ability, and excellent catalytic activity. The photocurrent of 3D hollow-out TiO2 NWc/GOx biosensor increased linearly as the glucose

2. Biomedical applications

252

7. Biosensors for glucose detection

concentration was increased from 0 to 2 mM due to reaction of photogenerated holes in the valence band of TiO2 and the H2O2 was released during the enzymatic oxidation of glucose. Such a biosensor showed a detection limit of 8.7 μM. In another study, TiO2 nanorods were coated with PDA to fabricate core-shell nanoarrays for PEC glucose sensing [80]. The PDA enhanced the adsorption and stability of the GOx enzyme on the photoelectrode surface while facilitating the separation of the photogenerated charge carriers leading to enhanced photosensitivity of the TiO2/PDA core-shell composite. The TiO2/PDA/GOx-modified sensor exhibited two linear ranges (0.2
1.0 mM and 1.0
6.0 mM) with an LOD of 28.5 μM. Quartz crystal microbalance (QCM)-based sensing platforms are extensively used in biosensing technologies due to rapid and real-time measurements, superior sensitivity, and portability. When there is an analyte binding on the QCM sensor, the weight on the sensor changes leads to a shifted resonance frequency of the piezoelectric microbalance, which can be simultaneously observed in the sensogram. Since the quantification relies on the mass change, the detection of small molecules on QCM is a challenging procedure. However, Dou and coworkers achieved a record-LOD of 3 mg/L with polyPBA hydrogel coated on QCM electrode surface [81]. They initially introduced a double-bond functionality onto QCM electrode. Afterward, a prepolymerization mixture was pressed in between the QCM electrode and a quartz surface. Following the cross-linking of prepolymerization mixture by UV irradiation, the quartz surface was removed leaving the hydrogel coating on the surface of QCM electrode. Such a polymerization technique resulted in a compact and uniform film formation with abundant glucose binding sites. A linear decrease in frequency shift was observed for a glucose concentration range of 0
160 mg/L. The QCM sensor also demonstrated good applicability in artificial saliva samples. Thermal microdevices can detect changes in the temperature resulting from biocatalytic reactions. Inomata et al. developed a thermal microsensor using a vanadium oxide thermistor for glucose detection [82]. The device consists of a detection and a reference sensor to minimize the effect of background noise. Additionally, Si3N4 membrane was utilized for thermal and electrical insulation of the system. Following the initial voltage application and the enzymatic reaction, the output voltage of each sensor was measured as an indicator of temperature change. A linear dependence was observed between the slope of the increasing output voltage and the glucose concentration up to 1000 mg/dL. In another study, a closed-loop enclosed split ring resonator was operated to investigate the permittivity change of the blood depending on different blood glucose levels [83]. The structure of the sensing platform aided the confinement of the electromagnetic field for improved sensitivity.

2. Biomedical applications

TABLE 7.3 Other examples of optical glucose biosensors. Sensing platform

Measurement technique

Detection limit

Linear range

Measurement medium

Ref.

Amine@POSS-APBA/AuNPs/OF

LSPR

25 μM

0.1 mM
1 M

Buffer (pH 5 5)

[84]

GdVO4 core/shell UCNPs

Fluorescence spectroscopy

-

1
100 mM

PBS

[85]

PBA-GQDs

Photoluminescence

3.0 mM

4
40 mM

Aqueous solution, human serum

[86]

GOx/AuNPs/Tapered fiber

LSPR

322 μM

0
10 mM

PBS

[87]

GOx/PANI/OF

Optical power and MPD

10 nM

Up to B 100 μM

PBS

[56]

ZnO nanotubes on PCB

Fluorescence spectroscopy

70 μM

0.1
15 mM

Aqueous solution, human serum

[88]

GOx/ PS@PtP*Si@C6 membrane

Fluorescence spectroscopy

31 μM

0.1
2 mM

Artificial serum

[89]

CaCO3-PDA@DOX-GOx

Fluorescence spectroscopy

6 μM

0.01
1 mM

PBS, human serum

[90]

NPS-AuFON

Raman spectroscopy

11.1 mM

0
1 M

NaOH solution

[91]

GOx on NECL

UV-Vis spectroscopy

-

0
2.4 mM

Artificial tear, mice tear (in vivo)

[92]

GOx-HRP-H2O2 -TMB

Ambient light sensing with phone

0.005 mg mL21

0.039
10 mg mL21

Urine

[93]

APBA, Aminophenylboronic acid; AuNPs, gold nanoparticles; DOX, doxorubicin; GOx, glucose oxidase; GQDs, graphene quantum dots; HRP, horseradish peroxidase; LSPR, localized surface plasmon; MPD, modal power distribution; NECL, nanoparticle embedded contact lens; NPS-AuFON, nature-patterned substrate with gold films over nanostructures; OF, optical fiber; PANI, polyaniline; PBA, phenylboronic acid; PBS, phosphate buffered saline; PCB, printed circuit board; PDA, polydopamine; POSS, polyoligomeric silsesquioxane; PS@PtP*Si@C6 membrane, Pt(II) meso-tetra (pentafluophenyl) porphyrin doped Polystyrene beads and Coumarin-6-captured silica particles decorated membrane; TMB, 3,30 ,5,50 -tetramethylbenzidine; UCNP, upconversion nanoparticles; UV, ultraviolet.

254

7. Biosensors for glucose detection

The measured resonance frequency shifted linearly as the glucose concentration is increased from 2.7 mM to 22.2 mM. Furthermore, in vivo tests were performed on volunteers revealing good applicability and sensitivity. Further examples of miscellaneous glucose biosensors are listed in Table 7.3.

7.5 Conclusion and remarks Several research works on glucose detection were evaluated in this chapter focusing on the type of transducer and the presence of the enzyme. Although electrochemical detection of glucose has been highly preferred due to simplicity and rapidness, there is a growing research on optical glucose sensors that employ optical fibers. In addition to electrochemical and optical glucose detection tools, a number of glucose biosensors with novel detection principles have also been reported in the literature. Enzymatic glucose recognition is adopted in the majority of studies for glucose detection due to its eminent selectivity. However, nonenzymatic sensing platforms are improving each day as new specific and sensitive nanomaterials are being developed. These nanomaterials such as AuNPs, QDs, CNTs, and polymeric particles are employed in both electrochemical and optical recognition systems due their enhanced electrochemical and optical properties. A recent challenge of the glucose biosensing field is to develop and commercialize rapid, accurate, noninvasive, and continuous glucose monitoring devices. CGM methods provide more information about the status of the diabetic patients, and are widely favored by the medical community for diagnosis of the diabetes. Additionally, the current glucometers, which require painful and tedious process of finger pricking, cause discomfort to the patients who need to perform detection multiple times a day for years. Therefore traditional glucose measurement tools are going to be replaced with uninterrupted, painless, and user-friendly glucose biosensors that allow detection in various body fluids such saliva, sweat, and tear.

Acknowledgments Z.A. thanks the German Research Foundation (DFG, Grant number: 428780268) for the financial support as the principal investigator.

References [1] World Health Organization, Glob. Rep. Diabetes (2016).

2. Biomedical applications

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C H A P T E R

8 Recent advances in biosensing technologies for detecting hormones Kakali Ghoshal Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, Nashville, TN, United States

8.1 Introduction Maintenance of homeostasis requires coordination and communication between different organs and neighboring cells and tissues. There are specific chemical molecules called “hormones” that regulate metabolism, development, growth, and reproduction, thus maintaining system homeostasis. Hormones are messenger molecules produced by the endocrine glands that exert their actions in distant organs. In the target cells, the hormone binds to its receptors (cell surface or intracellular) to trigger cascades of biochemical events that eventually modify the activity/function of the cell [1]. Hormones are classified into four groups based on their chemical structures and mode of actions, namely as peptides and proteins, amino acid derivatives, steroids, and fatty acid derivatives. Peptide and protein hormones are made up with chains of amino acids of various lengths. These hormones are predominantly secreted by the hypothalamus, pituitary gland, and pancreas. In some instances, they have inactive precursors or prohormones that cleave into active hormones. Their receptors are either present on cell surface or transmembrane (e.g., insulin receptor) [1]. Amino acid derivative hormones are in general modified amino acids (such as tyrosine and tryptophan). They are mostly released from thyroid glands (T3 or T4) or adrenal medulla (catecholamines) [1]. Their receptors can be both cell surface and intracellular. Steroid hormones are derived from cholesterol

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regulating many physiologic processes, including the development and function of the reproductive system [1]. They are synthesized in the adrenal cortex, the gonads, and the placenta. They can enter the cells and thus their receptors are intracellular or nuclear. Fatty acid derivative hormones are known as eicosanoids. They are derived from lipids or fatty acids such as arachidonic acid, lipoxins, and prostaglandins [2]. In biological systems hormones circulate at very low concentrations (1 nM or less) and a minute change can indicate a disease. Therefore ultrasensitive methods of detection are required. There are many conventional diagnosis methods for hormone detection such as highperformance liquid chromatography (HPLC), high-performance liquid chromatography/mass spectrometry (HPLC
Ms), and gas chromatography/mass spectrometry (GC
Ms), which involve complicated operations, high cost, and lengthy processes. Biosensors are analytical devices that can provide fast and real-time detection of chemical substances through a biorecognition element and transducer [3,4]. Biosensors are highly sensitive, selective, cheap, convenient, and easy to operate. A biosensor is a simple device comprised of a biorecognition element, transducer, and an output system comprised of an amplifier, processor, and display. Fig. 8.1 shows a schematic representation of a typical biosensor. Biorecognition elements are target molecules, and they can be enzyme, antigen, or nucleic acids. Calibration characteristics of biosensors include sensitivity, selectivity, linear concentration range, response time, regeneration, lifetime, etc. [5,6]. Transducers can translate the signal generated by analyte-bioreceptor interaction and convert them into measurable units. Based on the characteristics of the transducers, biosensors mostly fall under electrochemical or optical categories. Biorecognition element Analyte

Amplifier

Processor

Display

Transducer Detector

Output System

FIGURE 8.1 Schematic diagram of biosensor. A biosensor is typically comprised of detector, transducer, and the output system.

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8.2 Biosensor types based on biorecognition elements

263

In this chapter we describe the recent works on hormone biosensors primarily focusing on electrochemical ones. A brief description of hormones is given along with the immobilization methods and analytical performance of these electrochemical biosensors. At the end of the chapter optical and other cutting-edge technological advances such as microbial screening are also briefly discussed.

8.2 Biosensor types based on biorecognition elements Biorecognition elements are an integral part of a biosensor with high specificity along with strong and selective affinity toward bioanalytes [7]. Biorecognition elements may be of natural origin or synthetic constructs. Throughout the chapter we discuss various biorecognition elements that are used to detect hormones. Here we give a brief classification of different available biorecognition elements.

8.2.1 Antibody Antibodies are naturally occurring with “Y” shaped 3D conformation, comprised of light and heavy chains. Antibodies have highly specific binding domains and form “antigen-antibody immuncomplexes” upon binding with bioanalytes. Antibody hiorecognition elements are affinity-based and are immobilized via covalent linkage to sensor surface (Fig. 8.2A).

8.2.2 Enzymes Enzymes have their binding sites buried within their 3D conformational structures. Enzymes are specific toward their analytes, and they use hydrogen-bonding, electrostatics, and other noncovalent interactions to form recognition patterns. Enzymatic biosensors are catalytic, and they convert their analytes to a measurable end product to be monitored via amperometric or electrochemical transduction methods (Fig. 8.2B).

8.2.3 Nucleic acid and aptamers Nucleic acid biosensors achieve bioanalyte specificity through complementary binding motif of DNA. A DNA target can be artificially synthesized and immobilized at the sensor surface. Aptamer biorecognition elements and other pseudonatural modalities provide a much wider range of biosensor applications with their abilities to target various bioanalytes. Aptamers are single-stranded oligonucleotides and are designed through a combinational selection process, known as Systemic Evolution

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(A)

(B)

(D)

(C) Initial Library (DNA or RNA) Pool generation

Target

Molecularly Imprinted polymers

EVALUATION

Regeneration

Binding

THE SELEX Cycle Amplification

Washing

Template Removal

Elution

FIGURE 8.2 Different classes of biorecognition element used in hormone detection. (A) Antibody: the red circled area is the binding domain; (B) Enzymes: at the catalytic domain target analyte binds to it to form enzyme-analyte complex, and then converts the target analyte to measurable product; (C) Nucleic acid and Aptamers: SELEX cycle starts with selecting the bioanalytes with oligonucleotide where the unbound aptamers are removed. The bound aptamer sequences were amplified with PCR to regenerate the oligonucleotide library and the cycle is restarted again; (D) Molecularly Imprinted Polymers are designed to encapsulate the target bioanalyte, effectively forming synthetic recognition patterns between the bioanalyte and polymer matrix. Source: Courtesy, Morales et al., Bioconjug. Chem. 29 (10) (2018) 3231
3239, [75]

of Ligands by Exponential Enrichment (SELEX). The SELEX cycle starts with incubating the target bioanalytes with all various aptamers present in the oligonucleotide library. Unbound aptamers are washed off. The bound aptamers with target analytes are amplified with polymerase chain reactions (PCR) to regenerate the oligonucleotide library for the next SELEX cycle (Fig. 8.2C).

8.2.4 Molecularly imprinted polymers Molecularly imprinted polymers (MIPs) are synthetically fabricated biorecognition elements. MIPs use a templated matrix to achieve analyte specificity through patterns of noncovalent bonding, electrostatic interactions, or size inclusion/exclusion (Fig. 8.2D).

8.3 Biosensors based on transducers in hormone detection The transducer is an integral part of a biosensor that converts one form of energy into another form in a way that it generates a measurable signal [8]. Based on the characteristics of a transducer, biosensors

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can be classified in various ways, such as electrochemical or optical, as most transducers generate either electrochemical or optical signals proportional to the analyte or hormones added to the bioreceptor. Recent biosensor technology also includes microbial screening techniques to detect hormones [9,10]. In the following sections we discuss the various biosensing techniques that have been developed to detect hormones in natural and synthetic sources. The information is summarized in Fig. 8.3 and Table 8.1.

8.3.1 Electrochemical biosensors for hormone detection The functioning of an electrochemical biosensor involves a chemical reaction between immobilized biomolecule and target analyte producing or consuming ions or electrons, which affects measurable electrical properties of the solution/electrode interface in terms of electric current or potential [8,52]. The device generally consists of three electrodes (working, auxiliary, and reference) confined in a space filled with the biological species of interest to be tested in a suitable electrolyte. The

FIGURE 8.3 Schematic diagram representing different classes of biosensors based on transducers in detecting hormones. Source: Courtesy E.B. Bahadir, M.K. Sezginturk, Electrochemical biosensors for hormone analyses. Biosens. Bioelectron. 68 (2015) 62
71, Epub 2015/01/07 [3]

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TABLE 8.1 Classification of hormone biosensors based on transducers. Transducer

Characteristics

Biorecognition element

Hormone

Electrochemical It involves a chemical reaction between immobilized biomolecule and target analyte producing or consuming ions or electrons, which affects measurable electrical properties of the solution/electrode interface in terms of electric current or potential. Amperometric

Potentiometric

The analyte initiates a redox reaction changing the current between working and reference electrode. The change is measured.

The potential or charges between an ion selective electrode and a reference electrode is measured.

Antibody

Human growth hormone (hGH) [12].

"

Human serum chorionic gonadotropin (hCG) [15
17].

"

Insulin [20
22]

Adiponectin transmembrane receptors (AdipoR1 and AdipoR2)

Adiponectin [63]

Enzyme

Epinephrine [23]

"

Norepinephrine [24]

Antibody

Thyroxine (T4) [25]

"

Testosterone [28]

"

Progesterone [29,30]

"

Estradiol [31]

Electroanalytical activity

17β-estradiol [32,82]

Antibody

17β-estradiol [84]

Antibody

Adrenal cortical hormone (ACH) [33]

"

Testosterone [34]

Impedimetric

Conductometric

Biorecognition changes the resistivity or conductivity of the medium that is measured.

It measures the change in the electrical conductivity of the medium due to entry of analytes that changes the ionic strength/species.

Antibody

ACTH [37]

"

Human growth hormone (hGH) [38]

Antibody

Parathyroid hormone (PTH) [40
42]

"

Human chorionic gonadotropin (hCG) [43,44]

"

IGF-1 [45]

"

Insulin [46,47]

DNA aptamer

Insulin [48]

Antibody

T3 [50]

DNA aptamer

Thyroxine (T4) [39]

"

Progesterone [51]

Antibody

Cortisol [52,53]

Antibody

Follicle stimulating hormone (FSH) [59]

Optical Optical biosensors emit a detectable optical signal directly proportional to analyte concentration. Fo¨rster Resonance Energy Transfer (FRET)

In FRET based biosensors the distance is measured for nonradiative energy transfer from an excited donor to an acceptor.

Insulin aptamer

Insulin [67]

(Continued)

TABLE 8.1 (Continued) Transducer

Characteristics

Biorecognition element

Hormone

Surface plasmon resonance

When biomolecules bind to the sensor surface, it increases the refractive index, inducing a shift of the SPR angle. This shift is directly proportional to the amount of the analytes.

Complex of resin and human nuclear estrogen receptor ligand binding domain

Estrogen [79]

Total internal reflectance fluorescence (TIRF)

Optical thickness of the transducer changes during binding events onto the surface, measuring the directly proportional analytes quantity.

Antibody

Progesterone [81]

"

Testosterone [86]

Microbial Screening Techniques This protocol is relatively recent which employs the microbes such as bacteria or fungus reservoirs as biosensing agents. Bacterial allosteric transcription factors (aTFs) were used to dissociate certain steroids, and this dissociation is measured.

Progesterone [69]

Human estrogen receptors were constructed on a fungus Aspergillus nidulans and gene expression is controlled by β-galactosidase on a measurable unit.

17β-estradiol [70]

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electrodes have an external electrical connection that contains components that supply potential across the electrodes and measure the electrochemical output, which is directly related to the interaction of the biomolecules on the surface of the working electrode. Sometimes an additional chemical species is also introduced that translates the extent of the interaction of the biomolecules to a more prominent redox reaction, called the redox probe. Redox probes also help in enhancing the signal to-noise ratio. The reaction may generate measurable current (amperometric) at a particular potential sensitive to the interaction of the hormone/biomolecule with the electrode, a measurable potential or charge accumulation (potentiometric), or measurably alter the conductive properties of the medium (conductometric) or the impedance (ohmic or charge transfer resistance) [52]. The direct measurement of the electrical response in terms of current/potential/impedance makes electrochemical sensors simpler and compact compared to the complex transduction component of nonelectrochemical biosensors. 8.3.1.1 Amperometric biosensors Amperometric biosensors are one of the simplest and oldest electrochemical sensors [53]. An amperometric biosensor consists of an electrode augmented with a suitable bioreceptor layer where the biological system of interest (analyte) undergoes redox reaction. The electrode is subjected to a suitable potential (with respect to a biocompatible reference electrode) that is aligned with the redox potential of the analyte species to be tested. Depending on the change in the oxidation state, electron is exchanged between the analyte and the electrode, which creates current signal. The electron flux (or the current) monitored is directly proportional to the number of species electrochemically transformed on the electrode. The current response in amperometric biosensors is measured through chronoamperometry (CA), cyclic voltammetry (CV), differential pulse voltammetry (DPV), or square-wave voltammetry (SWV). While CA displays the current response with respect to time at a particular applied potential, CV, DPV, and SWV show the measurement of current when the potential is cycled back and forth. Some of the commonly tested hormones and their amperometric detection techniques are described below. The major advantages of the amperometric biosensor are its simplicity, cost-effectiveness, and portability. However, it is time consuming and sometimes too sensitive to the surrounding environment. It often also needs redox elements to enhance the current production [54]. Adrenocorticotropic hormone (ACTH) is a polypeptide class of hormone secreted by the anterior pituitary gland and an integral part of the hypothalamic-pituitary-adrenal axis. It is often released by stress response and its principal function is to release cortisol. Moreno-Guzma´n et al. [55] developed an ACTH detector that involves immobilization of anti-ACTH

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antibodies by amino phenylboronic acid onto screen-printed carbon-modified electrode surfaces. Alkaline phosphatase-labeled streptavidin was employed for electroanalytical response, where 1-naphtyl phosphate acted as the enzyme substrate. The electrochemical oxidation of 1-naphtyl to 1-naphtol was measured at different ACTH concentrations through DPV over 20.15 to 0.60 V potential range to monitor the affinity reaction. ACTH level in biological samples was detected with a detection limit as low as 18 pg/L [55]. Human growth hormone (hGH) is a peptide hormone that stimulates growth, reproduction, and cell regeneration in humans. It also stimulates production of insulin-like growth factor 1 (IGF-1) and increases the concentration of glucose and free fatty acids. Prolactin is a peptide hormone secreted from the pituitary gland, enabling mammals to produce milk. Serafı´n et al. [18] developed a label-free dual electrochemical immunosensor for the multiplexed determination of hGH and prolactin. The immunosensor employed carbon nanotube
screen-printed carbon electrodes (CNT/SPCEs) modified with poly(ethylene-dioxythiophene) (PEDOT) and gold nanoparticles, on which the corresponding hGH and prolactin antibodies were immobilized. Dopamine was used as a redox probe and the affinity reactions were monitored by measuring the decrease in the differential pulse voltammetric oxidation response [18]. This immunosensor showed wide ranges of linearity and low detection limits of 4.4 and 0.22 pg/mL, respectively. Ramanaviciene et al. [12] developed a biosensor to detect hGH where anti-hGH was immobilized on the self-assembled monolayer (SAM)modified surface plasmon resonance (SPR) chip. The detection of specific anti-hGH antibody interactions with human growth hormone immobilized on the SPR-chip was performed and compared through three different techniques: SPR, electrochemical [PA (pulsed amperometry) and CV], and electrochemical chemiluminescence (ECL) methods. The electrochemical methods were performed using modified SPR chip as the working electrode, Pt wire as the auxiliary electrode, and Ag/AgCl as the reference electrode. Experiments were performed in room temperature in 0.05 M phosphate buffer solution (with 0.1 KCl, pH 5 7.4). Horseradish peroxidase (HRP)-labeled secondary antibodies, specifically interacting with the formed immune complexes, were used for enhancement of signal reception and sensitivity. This immune complex interacted with 0.25 mM of TMB (3,30 ,5,50 -tetramethylbenzidine) and 0.1 mM of H2O2 (hydrogen peroxide). TMB functions as a redox mediator, which is converted to a highly reactive cation radical TMBox0 by the HRP aided by the presence of H2O2. The quick oxidation of TMBox0 to a more stable TMBoxv is the final step transducing the electrochemical signal. Both concentrations of TMB and H2O2 influence the ECL signals. PA was performed at 200 mV whereas CV was done between 275 and 650 mV (scan rate 5 50 mV/s).

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Compared to the SPR and ECL techniques, the electrochemical method demonstrated much lower detection limit of 0.027 nM as well as a wider linearity range of 0.098
1.11 nM [12]. Procalcitonin is a peptide precursor of the hormone calcitonin, the latter being involved in calcium homeostasis. Blood procalcitonin level in healthy individuals is below the limit of detection (LOD) (0.01 μg/L). However, after bacterial infection, the procalcitonin level rises. Fang et al. [13] developed a sandwich-based electrochemical immunosensor combining simple immunosensor array as well as an effectively designed trace tag. The immunosensor was fabricated by layer-by-layer coating of graphene (GC), carbon nanotubes (MWCNTs), chitosan (CS), and glutaraldehyde composite on the working electrode. The trace tag was prepared by loading high-content signal HRP-labeled secondary procalcitonin antibody with AuNPs, which were coated with mesoporous silica nanoparticles (MCM-41) through thionine linking [13]. This system can detect procalcitonin level as low as 0.5 pg/mL. B-type natriuretic peptide (BNP) is a potent cardiac neurohormone exhibiting diuretic, natriuretic, and vasodilatory properties, and thus it maintains cardiovascular homeostasis. At normal condition blood BNP levels are extremely low (B20 ng/L), but they rise to B2 μg/L for patients with severe congestive heart failure. Matsuura et al. [14] detected BNP by using AChE-labeled anti-BNP-modified gold nanoparticles where the AChE activity was measured based on the chemisorption/electrochemical reductive desorption process of a thiocholine compound on a silver electrode [14]. Human serum chorionic gonadotropin (hCG), a glycoprotein hormone, is measured to detect pregnancy as well as testicular and ovarian cancers. Numerous immunoassay techniques such as enzyme-linked immunoassay (ELISA), fluoroimmunoassay, and radioimmunoassay have been employed to detect hCG. However, these techniques are expensive, complicated, and at times have lower precision. Wei et al. [15] developed an ultrasensitive immunosensor for the detection of hCG employing a new type of label based on HRP and platinum nanoparticle-modified MSN (mesoporous silica nanoparticles) [15]. A sandwich-type protocol was employed to prepare the immunosensor with the primary antibody (Ab1) immobilized onto thionine (TH) and graphene-modified glassy carbon electrode (GCE). The detection of hCG was demonstrated through chronoamperometric reduction of H2O2 (1 mM) at 20.2 V versus SCE (saturated calomel electrode reference). The amperometric response was shown to scale linearly with the hCG content in the concentration range of 0.01
12 ng/mL with a detection limit of 7.5 pg/mL. The concentrations of Ab2, HRP, and Pt were shown to influence the reduction of H2O2, thus leading to better control on sensitivity.

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Li et al. [16] extended this work and developed label-free immunosensors for the detection of hCG employing nanoporous gold (NPG) foils and graphene sheet-modified GCE [16]. Hydroquinone (HQ) was used as a redox species to obtain correlation of current responses and hCG content. The high surface area and the porous networks of the NPG help in enhancing immobilization of anti-hCG. A linear current response was observed for an hCG concentration of 0.5
40 ng/mL with a LOD of 0.034 ng/mL. Ortiz et al. [17] developed supramolecular strategies to achieve a potentially reagent less biosensor architecture employing a trifunctionalized carboxymethylcellulose (CMC) polymer housed via host-guest interactions with a gold-electrode immobilized cyclodextrin monolayer [17]. The CMC carrier was synthesized and trifunctionalized with anti-βhCG antibody, HRP, and ferrocene (Fc) moieties. The amperometric response was based on the detection of H2O2 via the HRP-Fc enzyme-mediator couple. IGF-1 is a protein hormone molecularly similar to insulin that plays an important role in childhood growth and anabolism in adults. IGF-1 production is stimulated by growth hormone and it is primarily produced in liver. Serafı´n et al. [18] developed an amperometric sensor with multiwalled carbon nanotubes that enabled direct covalent binding of anti-IGF1 monoclonal antibody due to the presence of high content of surface with confined carboxyl groups. A sandwich immunoassay was arranged with peroxidase-labeled polyclonal antibody, H2O2 as the enzyme substrate, and catechol as redox mediator to monitor the response of H2O2 at 250 mV. IGF-1 was measured in human serum and the detection limit was evaluated as 0.25 pg/mL, more than 100 times lower than the lowest values reported for the ELISA [18]. Insulin, a peptide hormone, secreted by the β cells of the pancreatic islets maintains normal blood glucose levels by facilitating cellular glucose uptake, regulating carbohydrate, lipid, and protein metabolism, and promoting cell division and growth through its mitogenic effects [19]. Thus accurate detection of blood insulin is crucial in some disease condition such as diabetes, dyslipidemia, cancers, etc. Viswanathan et al. [20] described a dual biosensor for both glucose and insulin, based on enzymatic reaction and immunoassay with utilization of ferrocene microcapsules, respectively. The layer-by-layer (LbL) films on the ferrocene microcrystal followed by antiinsulin antibody sensitization were utilized for the biolabled ferrocene microcapsule production. These microcapsules acted as a probe in the proposed system. The LOD for insulin was 10 pg/mL of 100 μL sample, which is equivalent to 10212 g of insulin [20] Glucose was detected through amperometric response of H2O2 at 0.5 V. Li et al. [21] described a method of insulin detection through CA and SWV response of H2O2, where zinc silicate spheres loaded with

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palladium nanoparticles (Zn2SiO4-PdNPs) acted as dual-function labels. The matrix was comprised of electrodeposited gold where the icosahedral gold nanocrystals were coated with the primary antibodies and formed a 3D mode for the abundant insulin. The system allowed to detect a concentration range of 0.1 pg/mL
50 ng/mL of insulin [21]. Wang et al. [22] developed a novel and ultrasensitive signal-off immunosensor for insulin detection using CuS-SiO2 composites as dualsignal amplifier, proving a detection limit as low as 0.03 pg/mL [22]. To immobilize the primary antibody, they employed n-type semiconductor CdS sensitized carbon-doped titanium dioxide (C-TiO2/CdS). Adiponectin, an adipose tissue-derived hormone, plays protective roles against diabetes, dyslipidemia, obesity, and other metabolic complications [56,57]. A lower level of plasma adiponectin may indicate serious cardiometabolic complications and insulin resistance [56]. Brazaca et al. [58] developed an electrochemical biosensor to detect adiponectin based on their transmembrane receptors, namely AdipoR1 and AdipoR2 [58]. The gold electrodes were modified by coating with 3-MPA, EDC, and NHS, using a SAM technique to harbor adiponectin receptors. This cyclic-voltammetry-based biosensor displayed linearity (R2 5 0.992) in a wide range of concentrations range (0.025
0.75 μmol/L) under optimized conditions. It had a detection limit as low as 7.0 nmol/L. They also optimized it in human serum samples. Epinephrine and norepinephrine belong to the catecholamine group and act both as hormone and neurotransmitter. Epinephrine or adrenaline is synthesized both by the adrenal glands and neurons in the medulla oblongata. Epinephrine is given to treat anaphylaxis, cardiac arrest, asthma, and superficial bleeding. Biologically it plays an important role in the “fight-or-flight” response. Norepinephrine or noradrenaline, another catecholamine group of hormones, is associated with stress, blood pressure regulation, heart rate, immune responses, and glycogen metabolism. In blood, abnormal norepinephrine levels are indicative of many diseases such as hypothyroidism, congestive heart failure, arrhythmias, and idiopathic postural hypotension. Therefore measuring their concentrations in biological fluids has great importance in pharmaceutical and medical science. Apetrei et al. [23] developed an amperometric biosensor for detection of epinephrine, where immobilized tyrosinase on single-walled carbon nanotube (SWCNT) screen-printed electrodes were used to measure the adrenaline concentration. Tyrosinase facilitated the oxidation of adrenaline to adrenaline quinone, which was electrochemically reduced at 20.07 V (vs Ag/AgCl) on the sensor [23]. Dai et al. [24] developed a biosensor using the enzyme phenylethanolamine N-methyl transferase and cofactor S-(50 -Adenosyl)-L-methionine chloride dihydrochloride with disposable screen-printed mesoporous carbon electrodes to detect norepinephrine in biological samples [24].

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The amperometric response was measured at 20.2 V. The mesoporous architecture (3
5 nm) of the biosensor along with the micropores (1 nm) resulted in high surface area that resulted in high sensitivity and a low detection limit (100 pg/mL). Thyroxine (T4) is a tyrosine-based hormone secreted by the follicular cells of thyroid gland, which are primarily responsible for regulation of metabolism. It contains four iodine atoms, and it is the predominant thyroid hormone. Abnormal T4 levels cause many endocrine disorders, therefore accurate measurement is of utmost importance. Zhang et al. [25] developed an enzyme-free sandwich electrochemical immunosensor for the detection of free fraction of T4. They immobilized the primary antibody on three-dimensional microporous CS
Au nanoparticle hybrid (3DOM CS
AuNPs) film electrode and magnetic multiwall carbon nanotubes (MMWCNTs) were used as label of secondary antibody. Under optimal condition this sensor had a lower detection limit of 0.20 fg/mL [25]. 19-Nortestosterone, a C18 anabolic steroid hormone, differs from testosterone in that it does not possess a C-19 methyl group. 19Nortestosterone has been used as a growth-promoting agent to accelerate weight gain in animals. However, until 1984, the presence of nortestosterone in the urine of both humans and animals was proof of illegal administration. Conneely et al. [26] developed a cheap, disposable immunosensor with screen-printed electrode to detect nortestosterone. Detection was carried out through chronoamperometric behavior of TMB/H2O2 redox system at 0.1 V, with HRP as the enzyme label. The detection level of this sensor in urine was found to be 10.5 pg/mL [26]. Androsterone is an intermediate product in the synthesis of androgens in humans. Mundaca et al. [27] developed a 3α-hydrosteroid biosensor for androsterone determination by immobilizing the enzyme 3α-hydroxysteroid dehydrogenase in a composite electrode platform comprised of a mixture of MWCNTs, octylpyridinium hexafluorophosphate ionic liquid, and NAD1 cofactor. The detection was performed through amperometric analysis of reduced nicotinamide adenine dinucleotide ( 1 hydrogen) at 0.4 V. It demonstrated a LOD of 0.15 μM. This biosensor provided good results in the determination of androsterone in spiked human serum samples [27]. Testosterone, a steroid hormone under androgen group, plays important roles in male sexual differentiation, protein synthesis, and physical performance. Eguı´laz et al. [28] developed a disposable electrochemical immunosensor with SPCEs and magnetic beads where antitestosterone was immobilized. HRP was used as a label and amperometry and was done at 20.2 V with H2O2 and HQ as the redox mediator. The detection limit was 1.7 pg/mL [28]. Progesterone, a steroid hormone, is secreted by phase luteal ovarian cycle and during pregnancy. Progesterone regulates menstrual cycle

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preparing the uterus for implantation of the blastocyst and also maintains pregnancy. Thus progesterone determination in plasma or in milk is important in application areas ranging from clinical fertility measurement to pregnancy diagnosis in veterinary and farming practice. Hart et al. [29] developed a SPCE as the base transducer for a disposable amperometric progesterone biosensor. Monoclonal sheep antiprogesterone antibody was immobilized onto the transducer by interaction with a layer of rabbit IgG previously coated onto the SPCE. CA was performed for phenol detection at 0.7 V [29]. Monerris et al. [30] developed an integrated immunosensor to detect progesterone by directly attaching antiprogesterone monoclonal antibody on a modified gold disk electrode with gold nanoparticles lodged on a cysteamine SAM. The immunosensor was based on a competitive assay involving HRP-labeled progesterone, which oxidized pyrocatechol (H2Q) in the presence of H2O2 to benzoquinone (Q). They reported a detection level as low as 0.08 ng/mL [30]. Estrogen is a sex hormone responsible for the development and regulation of the female reproductive system and secondary sex characteristics. Estradiol is a natural and commonly available estrogen. Ojeda et al. [31] developed a sensor based on the surface modification of a screenprinted carbon electrode with grafted p-aminobenzoic acid followed by covalent binding of streptavidin and immobilization of biotinylated antiestradiol. Estradiol was detected by applying a competitive immunoassay with peroxidase-labeled estradiol and measurement of the amperometric response at 2200 mV using HQ as redox mediator. The detection limit achieved was 0.77 pg/mL and urine and serum samples were utilized [31]. Estradiol or 17β-estradiol, a natural estrogen, shows the highest estrogenic activity with increasing milk yield, weight gain, and reproductive capacities in mammals [59]. Thus it is widely used in the agricultural and livestock industry. However, it is identified as a potential endocrine-disrupting chemical by the US Environmental protection Agency (EPA) [59,60]. Therefore it is of utmost importance to quantify this in meat and milk to limit its health hazard. Ozcan et al. developed a modified carbon paste electrode to determine 17β-estradiol in milk and pharmaceutical samples. The modification was performed using cysteamine self-assembled gold nanoparticle-modified fumed silicadecorated graphene nanoribbon nanocomposite giving enhanced oxidation peak in interaction with 17β-estradiol. The characterization of the electrode was done by CV, scanning electron microscopy, and Raman spectroscopy [59]. Moreira et al. [32] reported a biosensor based on carbon paste electrode modified with magnetite nanoparticles and the ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate in the electroanalytical determination of 17β-estradiol and estriol. They reported

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detection limits of 50 and 300 nmol/L for 17β-estradiol and estriol, respectively [32]. Dai et al. [24] developed a 17β-estradiol biosensor using an α-estrogen antibody as biorecognition element and K3Fe (CN)6/K4Fe(CN)6-based transducer. The redox reaction was measured by DPV. The range of detection was 2.25 and 2250 pg/mL. The 17βestradiol was detected in tap water and stimulated urine [61]. Moreira et al. [62] developed a modified carbon paste electrode with Fe3O4 nanoparticles and the ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate for the electroanalytical determination of E1 by SWV. Under optimized conditions, the calibration curve showed two linear ranges from 4.0 to 9.0 μmol/L and from 9.0 to 100.0 μmol/L. The LOD and quantification were found to be 0.47 and 4.0 μmol/L, respectively. They detected the presence of E1 in pork meat samples [62]. Xenoestrogens are molecules that mimic the structure of biological estrogen and bind to estrogen receptors. They have potential health hazardous outcomes including cancers and infertility. Xenoestrogens are present in plastics, pesticides, chemicals, and water systems and thus advanced sensor technology is required to detect them in soil and water. Pupinyo et al. [63] developed a label-free, real-time, impedancebased E-screen cell biosensor in which the MCF-7 cells were cultured on electrodes [63]. Upon estrogen treatment the cell proliferated, thus increasing the impedance. This change in impedance was monitored and was directly proportional to the 17β-estradiol concentration. The unknown estrogen concentration was measured by comparing the impedance of hormone-free medium with the impedance records of the test sample. They measured the known xenoestrogenic compound bisphenol-A with this system [63]. 8.3.1.2 Potentiometric biosensors While amperometric sensors are based on the measurement of current response of the redox active species through controlling the potential applied on the biosensor electrodes, potentiometric sensors are based on measurement of change of potential induced as a result of immobilization of antibodies on the electrode. Antibodies or antigens possess net electrical charge polarity in aqueous solutions, which depends on both isoelectric points of the concerned species and the solution composition. After introduction of antigen in the vicinity of the electrode sensor, the interaction with the immobilized antibodies results in a change of the surface charge, which can be measured against a reference electrode. The measurement of change in potential (or the potential shift) before and after the antigen-antibody interaction forms the basis of detection of analytes in a potentiometric sensor. Potentiometric-based biosensors provide advantages such as real-time detection along with the possibility of continuous analysis on different

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analytes [54]. However, these biosensors are time consuming and sensitive to temperature and surrounding environment [54]. Tang et al. [33] developed a potentiometric-based biosensor for the detection of adrenal cortical hormone (ACH). The sensor was achieved by self-assembling immobilization of adrenal cortical hormone antibody (anti-ACH) on a gold electrode-modified gold nanoparticle (Au) and a thiol-containing sol-gel network. The detection was based on the change in the potentiometric response before and after the antigen-antibody reaction. The detection limit was obtained as 5.2 ng/mL. The extent of protein immobilization was shown to be influenced by the size of gold nanoparticles with 24 nm nanoparticles showing the highest sensitivity on potentiometric response. Regeneration of the used probe was done by dipping it into 0.2 M glycine-hydrochloric acid (Gly-HCl) buffer solution (pH 2.8) for few minutes to break antigen
antibody linkage followed by cleaning with a phosphate buffer solution (pH 7.0) [33]. Liang et al. [34] developed a label-free electrochemical biosensor for testosterone where the antibodies were encapsulated into polyvinyl butyral sol-gel film doped with gold nanowires. Gold nanowires conjugated testosterone antibody onto the probe surface and provided a biocompatible microenvironment enhancing the immobilization of biomolecules. The linear response of the biosensor was observed for the testosterone concentration range of 1.2
83.5 ng/mL with a detection limit of 0.1 ng/mL [34]. 8.3.1.3 Impedimetric biosensors Impedimetric biosensors are based on analysis of the impedance of a suitable electrode surface that can vary based on the concentrations and interactions of antibody and antigen adhered on the surface. The impedance spectroscopic measurements (EIS) can be done with or without the presence of a redox probe at an appropriate potential [64,65]. For sensors without redox probes, the impedance behavior is solely based on the growth, amount, and morphological properties of the adherent species on the electrode surface. However, the majority of the impedimetric biosensors require a redox probe (usually few millimolars of [Fe (CN)6]32/42) that triggers a distinct electrochemical reaction at a particular applied potential based on the antibody-antigen interaction. EIS measurements yield both trends of charge transfer resistances Rct (through Nyquist plots) and phase change (through Bode plots) with respect to the input frequency range. The behavior of the extent of antibody-antigen interaction can be correlated with the change in Rct or the phase. The majority of the impedimetric biosensors are based on analysis of Rct values that are typically obtained from the Randles circuit fitting [35] of a Nyquist plot. The value of the Rct specific to the condition of the antigen-antibody interaction is typically obtained as the diameter of the semicircle in a Nyquist plot portrayed on the real axis

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(x axis). A longer diameter indicates a higher Rct that suggests a slower reaction of the redox probe influenced by the state of the antibody/antigen and vice versa. Impedimetric biosensors offer simple real-time detection. However, they can also be sensitive to the surrounding environment. They often require bulky devices and theoretical stimulation for data analysis [54]. Corticotropin-releasing hormone (CRH) is a peptide hormone that activates the synthesis and release of ACTH from the pituitary gland. Duran et al. [37] developed an impedimetric immunosensor for CRH using halfantibody fragments that covalently immobilized on the gold nanoparticlebased surface. EIS measurements were done over a frequency range of 100 kHz
0.01 Hz potentiostatically at 0.23 V (vs Ag/AgCl). The potential of EIS measurements had been deduced by observing the redox behavior (FeCN623/24 in KCl 0.1 mol/L serves as the redox couple) in the CV run over a potential range of 0
0.6 V (vs Ag/AgCl). The Rct (charge transfer resistance) of the faradaic behavior of the redox couple (FeCN623/24) was obtained from the Nyquist plot from a fitted equivalent Randles circuit. The Rct values were shown to scale linearly with the CRH concentration in the range of 10.0
80.0 μg/mL, with a LOD of 2.7 μg/mL [37]. Li et al. [36] developed a miniaturized immunosensor the highly sensitive detection of ACTH using EIS in connection with disposable screenprinted gold electrodes. The immunosensor has the potential to detect ACTH in clinical samples. EIS measurements were recorded using 10 mM Fe(CN)632/42 as the redox probe in 50 mM phosphate-buffered saline with 100 mM NaCl (pH 7.4), potentiostatically at 0.3 V. Frequency range was 10 kHz
1 Hz. The detection limit for ACTH was found to be 100 fg/mL [36]. Rezaei et al. [38] described a selective impedance immunosensor to detect human growth hormone (hGH) based on immobilization of a hGH antibody on Au nanoparticle-modified gold electrode. CV and EIS measurements were performed using 1 mM K3[Fe(CN)6]/K4[Fe(CN)6] (acting as redox probe) in 0.1 M PB solution (pH 5 4.4). CV was performed between 20.2 and 0.8 V to determine the appropriate potential for EIS. Rct values were obtained from the Nyquist plots of the EIS performed potentiostatically at 0.17 V with a frequency range of 100 kHz
0.1 Hz. The final output signal of the sensor was based on relative impedance defined as [Rct(i) 2 Rct(0)]/Rct(0) where Rct(0) was the value of the charge transfer resistance as antibody was immobilized on the electrode surface, and Rct(i) was the value of the charge transfer resistance after the antigen binded to the antibody. A linear relationship of relative impedance with respect to antigen concentration was observed in the concentration range of 3
100 pg/mL. The LOD observed was 0.64 pg/mL [38]. Parathyroid hormone (PTH) is a polypeptide-based hormone secreted by the chief cells of parathyroid glands that regulates calcium

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¨ zcan et al. [39] developed two impedance-based biosenhomeostasis. O sors to detect PTH. The first biosensor was based on the gold electrode modified by 12-mercapto dodecanoic (12MDDA) where antiparathyroid hormone (anti-PTH) was covalently immobilized. PTH was detected within a linear range of 10
60 fg/mL [39]. The second one was based on the gold electrode modified by mercaptohexanol (6-MHL) and having a ¨ zcan et al. [42] developed linear detection range of 0.1
0.6 pg/mL [41]. O a biosensor for PTH detection on the gold electrode modified by cysteine SAMs where anti-PTH was covalently immobilized onto cysteine layer by using an EDC/NHS couple. PTH was detected within a linear range of 10
60 fg/mL [42]. EIS measurements in all three reports were done potentiostatically at an appropriate potential determined from CV analysis using K3[Fe(CN)6]/K4[Fe(CN)6] as the redox couple. The electrochemical signal of the biosensor was evaluated based on the differential impedance termed as ΔRct 5 Rct(PTH)
Rct(anti-PTH), where Rct(PTH) and Rct(anti-PTH) were the electron transfer resistances of the electrode surface before and after anti-PTH is coupled with PTH, respectively. In all three studies PTH was detected in artificial serum samples. Human chorionic gonadotropin (hCG) is a glycoprotein hormone produced by trophoblast cells that surrounds a growing embryo, and eventually forms the placenta. Also, many cancerous tumors produce hCG. Therefore measurement of hCG level is very crucial in both detecting pregnancy and some cancers. Truong et al. [43]developed a sensitive label-free impedimetric hCG immunosensor by using a commercial screen-printed carbon ink electrode. The carbon ink electrode of disposable electrochemical printed chip was modified by deposition of polypyrrole-pyrole-2-carboxylic acid copolymer and then immobilization of hCG antibody. EIS measurements were done potentiostatically at the open circuit potential in a frequency range of 100 kHz
50 mHz using 5 mM K3[Fe(CN)6]/K4[Fe(CN)6] as the redox couple. The biosensor response was based on the Rct values that showed a linear relationship with the logarithm of the antigen concentration in the range of 100 pg/mL
1 ng/mL. The detection limit for α-hCG was 2.3 pg/mL [43]. Teixeira et al. [44]developed a device by introducing a polyaniline (PANI) onto a screen-printed graphene support for hCG detection. The PANI-coated graphene acted as the working electrode of a threeterminal electrochemical sensor, functionalized with anti-hCG, by means of a process that enabled oriented antibody binding to the PANI layer [44]. Rezaei et al. [45] described an impedimetric immunosensor for IGF-1 based on immobilization of monoclonal antibody on gold nanoparticle (dual-)-modified gold electrode. EIS was measured potentiostatically at 0.23 V within the frequency range of 0.01 Hz
100 kHz using 1 mM K3[Fe(CN)6]/K4[Fe(CN)6] as the redox couple. The relative impedance

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was based on [Rct(i) 2 Rct(0)]/Rct(0) where Rct(0) and Rct(i) were the charge transfer resistances before and after antigen binding to the antibody, respectively, was plotted versus the IGF concentration and used as the biosensor response. The relative impedance showed a linear relationship with IGF concentration in the concentration range of 1.0
180.0 pg/mL with a detection limit of 0.15 pg/mL [45]. Xu et al. [46] reported an impedimetric label-free biosensor for detecting serum insulin. The transducing surface was antibody-modified and based on polyethylene glycol monolayer-modified polycrystalline gold. EIS measurements were performed using 1 mM K3[Fe(CN)6]/K4[Fe (CN)6] as the redox couple, potentiostatically at 0.22 V within a frequency range of 10
0.01 kHz. The final biosensor response was demonstrated in terms of relative impedance plotted with respect to the logarithm of insulin concentrations. The relative impedance showed a linear relationship with insulin content in the concentration range of 5 pM
50 nM, with detection limit of 1.2 pM [46]. Luo et al. [47] developed a highly sensitive and selective electrochemical insulin biosensor based on a chemisorbed zwittorionic polymer support and a novel reagent-less sensing technique based on phase monitoring electrochemical impedance spectroscopy. The EIS measurements were performed in the nonfaradaic potential region (related to double-layer capacitance) at around 0 V. The phase changes (obtained from bode plots) rather than impedance were shown to be more sensitive to the insulin concentration and displayed a linear relationship with the logarithm of insulin content within a concentration range of 0.1
200 pM with a LOD of 42.6 fM. They claimed this system could detect insulin blood serum in a linear range from 0.1 to 200 pM [47]. Ensafi et al. [48] described a method to detect plasma insulin where a poly-orthophenylene diamine substrate was decorated with gold nanoparticles and single-stranded DNA (ss-DNA) aptamer was immobilized on the surface of a pencil graphite electrode (PGE). Electrochemical impedance spectroscopy and CV was employed to characterize the biosensor, and the detection of insulin was demonstrated based on the charge transfer resistance of the biosensor after interaction with insulin. The detection limit was found to be 0.27 nmol/L where the linear detection range was 1.0
1000.0 nmol/L [48]. Leptin is a peptide hormone, predominantly produced by adipose ¨ zcan tissue, that diminishes adipocyte fat storage by reducing hunger. O et al. [49] developed a graphite paper-based impedimetric immunosensor to detect leptin in clinical serum samples. This biosensor had a very low detection level for leptin (0.00813 pg/mL) with high reproducibility and sensitivity [49]. Nguyen et al. [50] developed a label-free electrochemical impedimetric immunosensor for the detection of triiodothyronine (T3), a thyroid hormone. The gold nanoparticle layer on the gold electrode was

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generated by CA method and antitriiodothyronine antibody was immobilized on electrode surface. A dynamic linear range of detection of 0.01
100 ng/mL was found [50]. Park et al. [40] developed a label-free electrochemical biosensor to detect T4 comprised of multifunctional DNA structure/rhodium nanoplates heterolayer. The multifunctional DNA was immobilized on porous rhodium nanoplates (pRhNPs)-heterolayer-modified Au microgap electrode. T4 and the T4 DNA aptamer binding was affirmed by enzyme-linked aptamer assays and filtration experiments. The lower LOD for clinical samples was evaluated as 11.41 pM [40]. Jime´nez et al. [51] reported the selection and characterization of single-stranded DNA aptamers that bind progesterone and their application in a biosensing platform. EIS measurements were performed potentiostatically at 0.23 V with 5 mM of [Fe(CN)6]32/42 acting as the redox probe to obtain Rct before and after the binding of progesterone (P4). The calibration curve of the aptasensor was constructed by plotting the linear relationship of relative Rct change with respect to P4 concentration. It exhibited a linear range from 10 to 60 ng/mL with a detection limit of 0.90 ng/mL for progesterone [51]. Kinnamon et al. [66] developed a label-free biosensor for cortisol using MoS2 nanosheets integrated into a nanoporous flexible electrode system. The MoS2 nanosheets were surface functionalized with cortisolspecific antibodies and sensing was achieved by measuring impedance changes associated with cortisol binding. This sensor was sensitive toward clinical samples such as human sweat, with the lower detection limit being 1 ng/mL [66]. Arya et al. [67] described a cortisol biosensor, where cortisol-specific monoclonal antibody was covalently immobilized onto the surface of a PPAuNP/Au electrode using N-ethyl-N0 -(3dimethylaminopropyl) carbodiimide and N-hydroxysuccinimide (EDC/ NHS) chemistry [67]. Despite the simple operation of the impedimetric biosensors, EIS analysis is not straightforward and involves several complexities and assumptions. For example, comparisons among different sensors with varying electrode surface areas would only be meaningful if the charge transfer resistance (Rct) was normalized by the electrode surface area [35,68
71]. The morphology-dependent extent of antibody-antigen interaction on the electrode can significantly alter the electrochemically active surface area, which in turn can add further complexity. The Rct is also not completely independent of the ohmic resistance, which is a combination of electrode internal resistance, external circuit resistance, and the solution resistance, which is a strong function of the electrolyte composition and the distances between the working, reference, and counter electrodes [35,64,72]. Thus complete description of system specifications and area normalization of Rct is essential for universality of the calibration

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8. Recent advances in biosensing technologies for detecting hormones

curves based on EIS analysis. This is something researchers and readers interested in impedimetric biosensors should pay attention to. 8.3.1.4 Conductometric biosensors for hormones Conductometric sensors are based on the fact that the interaction of the analyte of interest with the electrode surface can trigger consumption or generation of charged species, which can result in an overall change in the ionic composition (and hence the conductance) of the electrolyte. The change in the conductance is translated to an electrical signal, which can be correlated with the concentration of the defined species on the electrode surface. Muda et al. [73] developed a conductometric sensor to quantitate human follicle stimulating hormone (FSH) from urine samples. FSH is a peptide hormone secreted by the gonadotropic cells of the anterior pituitary gland regulating growth, development, and reproduction. In a microtiter well containing antibody-antigen complex a self-fabricated gold coated electrode was dipped. Substrate was added to this system to initiate a secondary reaction, thus producing electroactive species that change the conductivity of the solution. This change was measured as it was proportional with the concentration of the hormone present. The lowest detection limit obtained was 0.75 mIU/mL, which was well under the physiological range of the hormone [73]. However, we are unable to find recent studies on conductometric biosensors indicating it to be an obsolete technique to detect hormones. Depending on the mode of operation of the transduction signal, electrochemical sensors can also be broadly classified into faradaic and nonfaradaic sensors. The former is the predominant type of sensors, which are typically based on a faradaic reaction of a redox probe that translates the biomolecule interaction into a more prominent signal. However, nonspecific binding or parasitic reactions can also trigger the redox reaction, which can compromise both sensitivity and selectivity. Also, introduction of redox probe sometimes increases the operational cost and reduces the portability. On the other hand, nonfaradaic sensors are based on the measurement of the capacitive signal because of change in charge distribution and conductance. This is measured through EIS or other methods like DPV or SWV. However, working with nonfaradaic sensors involves challenges of complications related to the Debye double-layer can mask the excess charges brought by the transducer to the electrode/electrolyte interface. Further, the capture of target molecules from the bulk can be altered because of screening of bulk by the double layer at high ionic strength. Also, calculation of calibration data of hormones based on capacitance as an electrochemical signature is less robust and sometimes lacks repeatability as opposed to redox reaction-based information, which is based on sharp and repeatable electrochemical signals [74].

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Thorough understanding of applicability of electrochemical sensors for detection of hormones and other biological species would require consideration of repeatability of the electrochemical signals and knowing the detailed relationships of interaction of the biomolecules/hormones with the electrochemical signals they produce.

8.3.2 Optical biosensors for hormones Optical biosensors allow quick, real-time, label-free, and accurate detection of several bioanalytes [75]. Optical biosensors are comprised of a compact analytical device having a biorecognition element integrated with a transducer system [76]. Optical biosensors emit a detectable optical signal directly proportional to analyte concentration [76]. The optical transducer can rely on fluorescence, luminescence, internal reflection, SPR, absorption, or light scattering spectroscopy [77]. Wang et al. [78] developed a FRET model-based insulin biosensor and detected insulin in human plasma. The model was constructed using near-infrared quantum-dots (NIR-QDs) and oxidized carbon nanoparticles (OCNPs) as the energy donor and acceptor. They anchored an insulin aptamer as a selected linker onto the surface of NIR-QDs, which was effectively bound to OCNPs through the π
π interaction. When insulin was subjected to the sensor, the QD
aptamer
OCNP complex was decomposed because of the weakened π
π interaction. In the process FRET was inhibited and the fluorescence of NIR-QDs was measured to quantitate insulin. The sensor could detect insulin as low as 0.6 pM [78]. Zhang et al. [79] developed an electrochemiluminescencent (ECL) biosensor for detecting insulin using a novel ECL resonance energy transfer (ECL-RET) strategy. Here, carboxyl poly(9,9-dioctyfluorenyl-2,7-diyl) dots (PFO dots) were utilized as ECL donor, 3,4,9,10-perylenetetracar-boxylic acid (PTCA) as ECL acceptor, and H2O2 as the coreactant. In this system they incorporated primary insulin antibodies, which are selective toward insulin and the detection level exhibited was 3.0 3 1026 ng/mL [79]. Liu et al. [80] demonstrated a simple plasmonic optical fiber biosensing platform to achieve an improved light
matter interaction and advanced surface chemistry for ultrasensitive detection of environmental estrogens [80]. They developed a gold film-coated tilted fiber Bragg grating (TFBG)-based SPR biosensor for the ultrasensitive screening of environmental estrogens. The specific interaction of nuclear estrogen receptors (nERs) and environmental estrogens were converted into detectable signals through the covalent surface modification of plasmonic fiber optic [80]. The subsequent detection limit for environmental estrogens was found to be 1.5 3 1023 ng/mL of estradiol equivalent, which is significantly lower than the defined maximal xenoestrogen level in drinking water set by the Japanese government.

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Tschmelak et al. [81] developed a total internal reflectance fluorescence (TIRF)-based biosensor to detect progesterone in water and milk [81]. The biosensor was comprised of an immobilized progesterone derivative and a commercially available monoclonal antibody to progesterone as biorecognition element. They reported an LOD between 45.5 and 56.1 pg/mL depending on milk type [81]. Tschmelak et al. [82] developed a similar TIRF-based biosensor to detect testosterone in environmental samples such as water using antitestosterone antibody as biorecognition element [82]. Optical biosensors allow real-time detection. They are reliable with high sensitivity. Despite being rapid, highly selective, and sensitive, the application of optical biosensors in healthcare is still under development. The latest advances in optical sensors involve the incorporation of nanostructure substrates to the optical system. However, nanomaterials are expensive, along with sometimes offering lower stability and requiring complicated developmental strategies, which hinder commercialization of many optical sensors [54,75]. They are also often sensitive toward the surrounding environment [54,74].

8.3.3 Microbial screening technique for hormone detection Microbial screening using bacteria or fungus is a cutting-edge technology and recently some studies were conducted to detect hormones. Bacteria responds to a wide range of stimuli, and they are an enormous and largely untapped reservoir of biosensing proteins [9]. Grazon et al. [9] identified and isolated progesterone-sensing bacterial allosteric transcription factors (aTFs) for use in biosensor devices [9]. Zutz et al. [10] developed a fungal-based estrogen biosensor utilizing genetically modified filamentous fungus Aspergillus nidulans. This fungus contains the human estrogen receptor alpha and a reporter construct, where β-galactosidase gene expression is controlled by an estrogen-responsive-element. The estrogen response of this fungus was validated in various samples like blood and urine, and the detection level of 17β-estradiol was 1 ng/L [10]. However, the use of bacterial cells as sensors is limited by response times, biosafety concerns, practical limitations of using a cellular host, and vast unknown unsequenced genomic content [9].

8.3.4 Wearable sensors for hormone detection In the future development of comfortable, noninvasive wearable biosensors to detect hormones in real time will accelarate. However, so

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far not many studies are available in this area. Lee et al. [83] demonstrated a wearable lab-on-a-patch (LOP) platform comprised of a stretchable, label-free, impedimetric biosensor and a stretchable microfluidic device for on-body detection of the stress hormone cortisol. This device was comprised of a 3D nanostructured Au working electrode and thus was highly sensitive and could detect cortisol in sweat in pM levels [83].

8.3.5 Other biosensors for hormone There are certain transducers and biosensing technologies that does not fall into any of the categories discussed above. Kartal et al. [84] prepared insulin imprinted poly(hydroxyethyl methacrylate)-N-methacryloyl-(L)-histidine methyl ester-based quartz crystal microbalance sensor for insulin determination. In this system the best-fitted model to explain the interactions between molecular imprinted chip and insulin molecules was the Langmuir adsorption isotherm. The detection limit found was 0.00158 ng/mL [84]. Balakrishnan et al. [85]used standard semiconducting materials with silicon as the substrate and polysilicon (PS) as the sensing electrode to detect hCG [85]. The hCG antibodies were immobilized on a polysilicon nanogap (PSNG) electrodes. The linear range and detection limit of hCG were 8
230,000 mIU/mL and 0.28 mIU/mL, respectively. Salivary cortisol is a stress predictor. Tlili et al. [86] developed a label-free immunosensor based on a single-walled, carbon nanotubebased chemiresistive transducer. Carbon nanotubes were functionalized with a cortisol analog where a monoclonal anticortisol antibody was ligated. Both artificial saliva with varying cortisol concentrations and control phosphate buffer were measured in this system and corresponding decreases in the resistance/conductance of the nanotubebiomolecule hybrid could detect the cortisol concentration. This immunosensor had excellent binding selectivity for cortisol even in the presence of structurally similar steroids and a detection limit of 1 pg/mL [86]. Synthetic auxins such as 1-naphthalene acetic acid (NAA) and 2,4-dichlorophenoxyacetic acid (2,4-D) were extensively used in agriculture for their stability and potency. Chun-Yi Ang et al. [87] developed a tool for rapid detection of these compounds in plant samples. The tool was based on the concept of corona phase molecular recognition (CoPhMoRe). This was achieved by designing a library of cationic polymers wrapped around SWCNTs with general affinity for chemical moieties displayed on auxins and its derivatives [87].

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8. Recent advances in biosensing technologies for detecting hormones

8.4 Discussion and conclusion This chapter summarized the recent technological advances of biosensors in hormone detection. Most of the studies described electrochemical biosensing technologies in detecting hormones, thus this was discussed predominantly in this chapter. However, other biosensing technologies such as optical or microbial screening or “mixed” sensors that do not fall under any particular category were also discussed indicating the hormone they detect. The concentration range and detection limit were also given. Most hormones are important biomolecules, and a minute change may indicate serious illness including diabetes, cancer, or cardiovascular disorders. Also, they are crucial factors in determining reproductive health and growth. Since reproductive hormones can increase growth, milk production, and fertility in animals, they are often used in the food and agricultural industry. However, they behave as endocrine disruptors and potentiate significant health hazards upon consumption [59,60]. Some synthetic compounds also mimic natural hormones and thus can block the hormone receptor. For example, xenoestrogens are compounds mimicking estrogens that bind with estrogen receptors [63]. A common source of xenoestrogens are plastics, pesticides, and chemicals, and they are prevalent in soil and drinking water. Hence, for the sake of accurate medical diagnosis, healthy foods, and sustainable environment, precise tools to detect hormones are of significance. Traditional chromatographic techniques are lengthy, often inaccurate, complex, and expensive. Biosensors are alternative approaches to traditional detection techniques, and they are groundbreaking in modern healthcare, precision medicine, drug discovery, and pharmacogenomics since they are simple, reliable, rapid, and inexpensive. They can be modified into portable small devices that provide rapid point-of-care (POC) detection and are proven to be efficient to detect bioanalytes in body fluids such as blood, saliva, sweat, or urine. Examples of commercialization of portable biosensors include rapid blood glucose, pregnancy, and ovulation detecting kits [3]. The latter uses urine to detect hCH and luteinizing hormone concentrations to identify pregnancy or ovulation [3]. The most promising application of biosensors in medical diagnosis involves the development of cell phone-based POC technologies [88]. Over the last two decades, microfluidic technologies and biosensor chips have also heavily been utilized in mainstream laboratory medicine [88]. A recent study depicted a wearable biosensor to detect the stress hormone cortisol [83]. These developments accelerate the innovation of new therapeutic targets including drug discovery and delivery. In Fig. 8.4 and

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287

FIGURE 8.4 Applications of hormone biosensors in various sectors including medicine, healthcare, and agriculture.

Table 8.2 applications in detecting hormones by various biosensing technologies are summarized. However beneficial biosensors are in detecting hormones in real-time manner, they also have some disadvantages. Not all biosensors are inexpensive and simple. Most of them are still in developmental phase and may not meet regulations, and thus may not commercialized. Some of the reasons include poor reproducibility, poor regeneration, low efficacy, time consuming, and unreliable detection. Despite of these challenges, biosensors are prevailing and rapidly growing as new age techniques to detect hormones in biofluids, and thus being an integrative part in modern healthcare.

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TABLE 8.2 Applications of hormone biosensors in diverse sectors. Medicine and healthcare Hormone

Types

Involvement

Detecting biosensors

Human growth hormone (hGH)

Peptide

Growth, reproduction, and cell regeneration

Amperometric electrochemical [11,71] Impedimetric electrochemical [32]

Procalcitonin

“

Calcium homeostasis

Amperometric electrochemical [72]

Insulin

Protein

Metabolic diseases like type 2 diabetes mellitus (T2DM), dyslipidemia, cardiovascular disorders, obesity

Amperometric electrochemical [15
17] Impedimetric electrochemical [39
41] Optical biosensor FRET-based, ECL resonance energy transfer (ECL-RET)-based [81] Quartz crystal microbalance sensor [62]

Adiponectin

“

“

Amperometric electrochemical [18]

Leptin

“

“

Impedimetric electrochemical [84]

Human serum chorionic gonadotropin (hCG)

“

Cancers, pregnancy detection

Amperometric electrochemical [12]

T

Amino acid derivatives

Growth and development

Impedimetric electrochemical [42]

Thyroxine (T )

“

“

Impedimetric electrochemical [43]

Epinephrine

“

Response to stress

Amperometric electrochemical [19]

Norepinephrine

“

“

Amperometric electrochemical [20]

Cortisol

Steroid

“

Impedimetric electrochemical [45,46] Label free chemiresistive transducer [64]

Testosterone

“

Reproduction

Amperometric electrochemical [22] Potentiometric electrochemical [30]

Estrogen

“

“

Amperometric electrochemical [25]

3 4

Progesterone

“

“

Amperometric electrochemical [23,24] Impedimetric electrochemical [44] Microbial screening [53]

17β-estradiol

Steroid

Given to animals for increasing their size and fertility. Detected in drinking milk, blood and urine.

Amperometric electrochemical [26,27] Microbial screening [55]

Estrone (E1)

“

Given to animals for weight gain. Detected in pork meat.

Amperometric electrochemical [77]

Progesterone

“

“

Optical biosensor TIRF-based [50]

1-Naphthalene acetic acid (NAA) 2,4-Dichlorophenoxyacetic acid (2,4-D)

Synthetic auxins

Used as herbicide, plant fertilizer, and to ripen fruits.

Corona phase molecular recognition [82]

Bisphenol-A

Xenoestrogen

Water

Amperometric electrochemical [78]

17β-estradiol

Steroid

“

Amperometric electrochemical [28]

Testosterone

“

“

Optical biosensor TIRF-based [51]

Estrogen

“

“

Optical surface plasmon resonance (SPR) [49]

Food industry and agriculture

Environment

290

8. Recent advances in biosensing technologies for detecting hormones

Acknowledgment I would like to thank Dr. Swarnendu Chatterjee, Arizona State University, for reviewing and refining the electrochemical biosensor section in this chapter. I would like to thank https://pixabay.com/ for providing free images, which were used to make figures in this chapter. I am indebted to my father late Bijoy Kumar Ghoshal for his constant support in creating this chapter. I lost him in December 2021, and this work is a tribute to his loving memory.

Conflicts of interest The author declares no conflict of interest.

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C H A P T E R

9 Biosensors for cancer biomarker detection Muqsit Pirzada1,2 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

9.1 Introduction Annual cancer fatalities average 10 million globally making it the second most common cause of death [1]. One out of every six deaths can be attributed to cancer. It can manifest in hundreds of distinct varieties including breast, lung, colorectum, prostate, liver, cervix uteri, esophagus, thyroid, bladder, pancreatic, kidney, corpus uteri, ovary, brain, pharyngeal, gall bladder, testicular, salivary glandular, vulva, penile, vaginal cancers as well as Hodgkin and Non-Hodgkin lymphoma, leukemia, melanoma, myeloma, sarcoma, and mesothelioma (Fig. 9.1). Although the cause of every cancer is not understood, most known risk factors can be categorized as environmental or genetic reasons. Environmental risk factors comprise several factors that include alcohol consumption and abuse, chemicals, tobacco smoke, radiation, pollution, ultraviolet light, infectious pathogens, and indoor emissions. Cancer risk and complications due to alcohol abuse are often severely underappreciated, although alcohol has contributed to more than 5% of all cancer-related deaths [3]. Certain chemicals such as aflatoxins, formaldehyde, benzene, asbestos, ethylene oxide, respirable crystalline silica, and endocrine disruptors (pesticidal as well as industrial) are capable of transforming healthy cells into cancerous cells [4]. Tobacco smoke is rich in more than 60 different carcinogens and lung cancer is, therefore, one of the most prevalent and deadliest forms of cancer [5].

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00009-1

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© 2023 Elsevier Inc. All rights reserved.

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Esophagus

Leukemia MMy NHL

Lx Brain/ CNS CoU

MoS OP Mt HL Th SG NP HP Vu

GB

Penis KS Vagina Testis

FIGURE 9.1 Tree map representing the contribution of different types of cancer to the overall mortalities due to the disease [2]. CNS, central nervous system; CoU, Corpus Uteri; GB, gall bladder; HL, Hodgkin’s lymphoma; KS, Kaposi’s Sarcoma; Lx, Larynx; MMy, multiple myeloma; MoS, melanoma of skin; Mt, mesothelioma; NHL, non-Hodgkin’s lymphoma; NP, nasopharynx; OP, oropharynx; SG, salivary glands. Statistics sourced from WHO GLOBOCAN. https://gco.iarc.fr/today/online-analysis-multi-bars?v 5 2020&mode 5 cancer&mode_population 5 countries&population 5 900&populations 5 900&key 5 total&sex 5 0&cancer 5 39&type 5 0&statistic 5 5&prevalence 5 0&population_group 5 0&ages_group%5B%5D 5 0&ages_group% 5B%5D 5 17&nb_items 5 5, 2020.

Radioactive emissions are capable of generating radicals and can cause mutations in the deoxyribonucleic acid (DNA), which corrupts the cells’ cancer defense machinery. Pathogens, especially viruses such as Epstein-Barr virus, human papillomavirus (HPV), hepatitis viruses, human T-cell lymphotropic virus, and human immunodeficiency viruses are well-known for their oncogenic behavior. Such pathogens can infiltrate the cell and stay in a dormant stage for years before transforming the previously healthy cell into an uncontrollably dividing immortal mass [6]. Genetic causes of cancer include autoimmune dysfunction and mutations. Autoimmune dysfunctions can not only cause cancer but also hinder cancer therapy and treatment. Genetic mutations cripple the cells’ tumor regulation pathway by restricting the expression or regulation of tumor-suppressing genes [7]. The US Department of Health and Human Services recently reported that overall, the 5-year cancer survival has improved by 20.6% from 48.9% to 69.5% [8]. However, this number does not reflect the survival in the case of cancers of the lungs, liver, pancreas, etc. (Table 9.1) which have not shown a proportional improvement or have occasionally shown a considerable decrease. Biosensors that exhibit the potential to diagnose cancer at an early stage may prove instrumental in

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TABLE 9.1 Some extremely lethal cancer types, their percentage 5-year relative survival by year of diagnosis, and the change in survival statistics from 1957 77 to 2010 16. Cancer type

1975 77 (%)

2010 16 (%)

Change (%)

Mesothelioma

9.7

10.7

1.0

SCLC

3.6

7.0

3.4

Liver and IBD

3.4

20.8

17.4

Pancreas

2.5

10.5

8.0

Brain and CNS

22.5

33.4

10.9

Cervix uteri

69.1

69.7

0.6

Corpus uteri

86.9

82.9

24.0

Esophagus

5.0

21.3

16.3

Kaposi’s sarcoma

80.7

76.3

24.4

Larynx

66.0

62.1

23.9

Abbreviations: CNS, Central nervous system; IBD, Intrahepatic bile duct; SCLC, Small cell lung cancer. Data sourced from SEER Cancer Statistics Review N. Howlader, A.M. Noone, M. Krapcho, D. Miller, A. Brest, M. Yu, et al. SEER. Cancer Statistic Rev. (2020) 1975 2017 [8].

formulating an efficacious therapy, especially for those cancers which are often diagnosed at advanced stages when most treatments are ineffective. In this way, biosensors can enhance the probability of survival. In clinical settings, biosensors function by recognizing disease-specific biomolecules, also known as biomarkers, in their ambient atmosphere. In the case of cancer, biomarkers are synthesized either by the tumor cells themselves or by other nontumorous cells as a response to the tumor. The occurrence of cancer from conception to the advanced stages is accompanied by changes in the concentration of these biomarkers and therefore such molecules can be considered as efficient indicators for screening and diagnosing cancer as well as monitoring the effectiveness of several treatment methods. These biomarkers can also aid in identifying at-risk groups in the population as well as people who might show recalcitrance to a particular therapy. Most of the methods in practice currently are based on microscopic and staining techniques that require invasive biopsies to identify cancerous cells based on their morphology [9]. Since these tests are not conclusive on their own, centralized testing facilities in hospitals must perform additional tests such as enzyme-linked immunosorbent assays (ELISAs) as well as molecular tools like proteomics and genomics for confirmatory tests. However, all

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of these methods are expensive, time-consuming, and can only be performed by highly skilled staff.

9.2 Cancer progress and biomarkers 9.2.1 Molecular biology of cancer occurrence and progress Cancer can be defined as a bunch of diseases that cause uninhibited growth and multiplication of cells with the ability to invade and spread to other regions in the body. Since cancer is not one but a collection of several diseases, its discriminating feature is the tissue wherein it originated. Cancers can be derived from epithelial (carcinoma) or mesodermal (sarcoma) cells. Cancers that arise from glandular tissues such as the breast are usually classified as adenocarcinomas [10]. Cancer of one area differs widely from cancer of any other area and the respective causes similarly vary. For example, skin cancers may arise from ultraviolet irradiation whereas most lung cancers stem from smoking tobacco. Such carcinogens are generally able to alter or mutate the DNA sequence. These alterations can range from a single base-pair missense mutation to aberrations at the chromosomal scale. Cancer shows a greater prevalence with age since the proliferation of such mutations is a gradual multistep process. In a healthy organism, damage to the genome results in cell death. However, if the damage is not lethal, it becomes the clonal origin that triggers a chain reaction resulting in cancer [11]. Such cells defy the apoptotic and regulatory defenses of the organism. The ensuing tumorigenesis can manifest via two pathways: (1) Oncogene activation and (2) tumor suppressor gene inactivation. Proto-oncogenes such as K-Ras are normally present in human cells to stimulate growth. Proto-oncogenes can be activated to produce oncogenes by multiple mechanisms such as mutations, chromosomal translocations, gene amplification, and the insertion of tumor promoters or enhancers by oncoviruses. In fact, point mutations in K-Ras have been observed in 20% 30% of all malignant tumors [12]. Oncogene activation disrupts the cell growth cycle and promotes cell division, which results in tumor formation. Growth factor receptors such as human epidermal growth factor receptors and vascular endothelial growth factor receptor are the most common oncogenic biomarkers investigated for cancer diagnosis and staging. Tumor suppressor genes or antioncogenes exhibit inhibitory features allowing the suppression of cell growth and division. Contrary to oncogenes, tumor suppressor genes undergo a loss of function resulting in dysregulation of proliferation or growth. For instance, tp53 is a tumor suppressor gene involved in DNA repair, apoptosis, cell cycle arrest, cellular metabolism as well as senescence and is usually referred to as

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“guardian of the genome.” In 50% of human cancers, this tp53 gene is mutated. Retinoblastoma protein (Rb) is another such biomarker that is integral to cell division and deletions, as well as mutations in the Rb gene, which can cause cancer. The fidelity and the emergence of mutations in the DNA are verified by the BRCA1 enzyme prior to cell division and mutations in the BRCA1 gene have been reported in more than 50% of all breast cancer patients [13] and nearly 90% in the case of ovarian cancer patients [14]. Mutations in tumor-suppressing genes may be observed in germ cells or somatic cells. Therefore unlike oncogenes, mutated suppressor genes can be inherited. Monitoring the levels and mutations in p53, Rb and BRCA1 are therefore indispensable in evaluating cancer susceptibility and prognosis (Fig. 9.2). Mutations in oncogenes and tumor suppressors follow different pathways and most cancers involve mutations in a minimum of 5 6 genes. The tumorous cells are subsequently selected for rapid growth and their capacity to invade as well as their competence to metastasize. This phenomenon is called tumor progression. Additional mutations in tumor cells can take place over time granting them the ability to invade different regions of the body and metastasize. Genes like β-catenin, BRAF, K-Ras, etc., which participate in the cellular signaling events, are also involved in carcinogenesis. Hanahan and Weinberg proposed six hallmarks of cancer in 2000: (1) Cancer cells evade cell death mechanisms such as apoptosis and autophagy; (2) they exhibit self-sufficient and sustained signaling for cell proliferation; (3) they evade suppressors and are insensitive to antigrowth signals; (4) they exhibit altered metabolic

FIGURE 9.2 A simplified scheme of cancer initiation due to DNA alterations caused by oncoviruses or carcinogens followed by progression to a vascularized tumor (prior to invasion and metastasis).

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programming, which facilitates invasion and metastasis; (5) they achieve immortality with respect to their replication potential; (6) and they stimulate and sustain angiogenesis [15]. Genetic mutations and instability as well as tumor-promoting inflammations are considered the enabling characteristics of carcinogenesis. In cancer, cellular energetics are dysregulated and cancer cells evade destruction by the immune system [16].

9.2.2 Cancer biomarkers Biomarkers are molecules from the tissue, blood, or other fluids that can be quantified to indicate a particular biological condition or state. Tumor markers, which are present in elevated amounts in bodily fluids and tissues, are broadly classified into proteinaceous or genetic biomarkers. Tumor marker concentrations not only assist in early-stage cancer screening and diagnosis but also help select the most favorable therapy for treatment. Additionally, regular monitoring of these biomarkers helps evaluate the effectiveness of therapy and enable the assessment of risk for relapse [17]. The science on gene expression has advanced significantly in the last few decades, with the capability to estimate the protein or gene concentration in individual cells. 9.2.2.1 Protein biomarkers Several proteins have been associated with diverse types of cancers. The histological types of cancer alter the presence and concentration of protein markers making biomarker selection challenging. For instance, some HER2-positive breast cancers show an excess of HER2 whereas HER2-negative cancers show normal levels of the protein. However, HER2-negative cancers may show an abundance of estrogen or progesterone receptors. Triple-negative breast cancers, on the contrary show normal levels of all three receptors and may instead overexpress phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit, alpha (PIK3CA), or VEGFRs, which are involved in neovascularization [18]. Therefore, it is essential to assess the levels of multiple different biomarkers to determine carcinogenesis. Carcinoembryonic antigen (CEA) is a glycoprotein that is often investigated as a tumor biomarker, especially in adenocarcinomas. It belongs to the immunoglobulin superfamily and is responsible for cell adhesion during fetal development. Although CEA concentration in healthy individuals is very low (2 4 ng/mL) [19], it can be elevated (up to B10 ng/mL) due to smoking or the presence of benign diseases like hypothyroidism. Higher CEA concentrations usually correspond to carcinomas of the lung, pancreas, breast, colon, or rectum, and levels beyond 20 ng/mL are

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indicative of metastasis [20]. CEA levels are also useful for discriminating between certain types of lung cancer. For example, lung adenocarcinoma is CEA positive whereas mesothelioma is CEA negative. The main limitation of CEA for cancer screening is high cross-reactivity of the ligands, which is even observed for monoclonal antibodies. Therefore, most CEA screenings are supplemented with immunohistochemical analysis of other tumor markers like Wilm’s protein, calretinin, and BerEp4 [21]. In addition to CEA and HER2, other important breast cancer biomarkers include the BRCA enzymes 1 and 2, as well as CA 15 3 and CA27.29, which are the antigen products of the mucin 1 (MUC1) gene. CA15 3 levels beyond 30 U/mL are suggestive of possible malignancy. The protein is also useful in the staging of breast cancer since its levels increase by 10%, 20%, 40%, and 75% in stages I, II, II, and IV, respectively. CA15 3 concentration is essential in predicting survival rates, disease recurrence, and the extent of metastasis [22,23]. Nonmalignant factors for increased CA15 3 levels include lactation, endometriosis, pregnancy, hepatitis, and inflammation of the pelvic floor. In comparison to CA15 3, CA27.29 shows superior sensitivity and specificity in breast cancers. CA27.29 levels below 100 U/mL are considered normal, and these levels are enhanced in 70% of breast cancer patients in the advanced stage. One of the earliest tumor markers to be recognized and applied for clinical purposes is prostate-specific antigen (PSA). While the PSA concentration in healthy patients does not exceed 4.0 ng/mL, 30% of prostate cancer patients show PSA concentrations above 4.1 ng/mL [24]. While PSA is not directly overexpressed as a result of mutations, the elevated levels during cancer result from distortions in the prostatic cell membrane as well as lymph angiogenesis. Analogous to the case of HER2, PSA concentrations cannot be considered as a conclusive indicator of prostate cancer as elevated PSA levels are also observed in the case of nonfatal tumors, prostatic inflammation, or hyperplasia. PSA screening is not definitive, and treatment of small, slow-growing tumors is expensive. Nevertheless, patients subjected to radical prostatectomy show noticeably lower morbidity and tumor progression than watchful waiting [25]. In conventional screening methods, false positives are frequent and modern biosensors are expected to eliminate this ambiguity [26]. Ovarian cancer is often linked to enhanced levels of cancer antigen (CA) 125. Cancers of the cervix, uterus, breast, lung, colon, liver, and digestive tract as well as nonpathological states such as pregnancy or menstruation are also accompanied by similar CA125 overexpression [27]. Increased levels of CA125 after surgical intervention and initiation of chemotherapy are indicative of treatment failure or relapse. Similarly, an increase is also evident when benign cells progress to a malignant stage. Monitoring the

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concentrations of CA125 along with alpha-fetoprotein (AFP), human chorionic gonadotropic (HCG), and lactate dehydrogenase is not only favorable for cancer detection but also progression and therapy. AFP levels are routinely screened for diagnosing hepatocellular carcinomas. Unlike most proteinaceous tumor markers, cancer-testis antigens are present in the serum, which is much easier to extract in comparison to tissue biopsies. These antigens are unfortunately not tumor-specific though and several of these types of these proteins are expressed by most tumors [28]. The overexpression of receptor binding antigen expressed in SiSo cells is observed in 98.4% of patients suffering from gastric carcinoma [29]. Since it is implicated in the prognosis of other cancers, it can be useful as one target in a multiple analyte assay just like cancer-testis antigens. 9.2.2.2 Genetic biomarkers As discussed in Section 9.1, the occurrence of cancer is a result of exposure to various genes from the somatic and germ cells. Such mutations are observed in not only oncogenes and tumor suppressors but also in other genes, which encode proteins involved in cell death, DNA repair, cell cycle regulation, and terminal transferase activity. Genetic mutations responsible for carcinogenesis vary from nonsense, missense, deletions, insertions, methylations, splicing, amplifications, and translocations [17]. Genetic tumor markers are often assorted by their penetrance, which estimates if the presence of a particular genotype will correlate with the occurrence of cancer. Mutations with high penetrance correspond to higher cancer risks [30]. The assessment of genetic biomarkers from bodily fluids such as serum, sputum, or urine is advantageous since it helps circumvent invasive and uncomfortable procedures such as biopsy. One of the most important biomarkers for carcinogenesis is the tp53 gene, which encodes the p53 protein. The p53 protein has a short halflife and its availability is regulated by the ubiquitin ligase MDM2. Mutations in the tp53 gene translate into stable mutant p53 proteins displaying an oncogenic “gain-of-function” [10]. Unlike most tumor suppressor genes, which undergo frameshift or nonsense mutations generating inactive truncated proteins, tp53 exhibits missense mutations. Mutant p53 proteins multiply to detectable concentrations and their extent of overexpression varies across the histological subtypes of cancers. Apart from the tp53 gene, mutations in genes like BAX (Bcl-2associated X) and CHK2 (checkpoint kinase 2), which are involved in the pathways that cause p53 protein response in cells may also generate mutant p53. p16 is another important tumor suppressor gene responsible for the regulation of the cell cycle. The translational product, p16 protein,

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suppresses tumors, and mutations of p16 promoter regions by methylation are common in lung cancers. Deletions of the p16 inhibitor are frequently involved in pancreatic carcinoma and mesothelioma. Non-small cell lung cancer patients exhibit a 54% 100% loss of heterozygosity at the p16 gene. p16 gene is overexpressed in cancers caused by HPV. Exposure to plutonium, radon, or tobacco smoke is often implicated in p16 gene mutations [11]. Ras genes participate in the pathway responsible for conveying extracellular signals to the cell nucleus. Since they are proto-oncogenic, they are activated by mutations to transmit incorrect signals to the nucleus resulting in cell growth and proliferation. Such mutations are observed in up to 90% of specific malignant tumors [31]. The codon 12 of the K-Ras gene is implicated in 60% of all Ras mutations [32]. K-Ras is a particularly interesting marker for biosensor development since mutant genes are detectable in blood, serum, sputum, and many other bodily fluids [17]. Telomeres are regions of duplicative nucleic acid sequences, which are responsible for protecting the extremities of linear chromosomes from digestion by nuclear enzymes. Mutations in telomerase relate genes such as telomerase reverse transcriptase, telomeric repeat-binding factor 1, and protection of telomeres protein 1 induce a shortening of the telomere length (Fig. 9.3). Cells register the unfolding of damaged telomeres as DNA aberrations and may either undergo senescence or apoptosis as a result. Cancer risk due to shorter telomeres varies across age, gender, and type of tumor. Those possessing the shortest telomeres are at a 1.5- to threefold higher risk of developing cancer than those with the longest telomeres [33,34]. Microribonucleic acids (miRNA) contain B22 nucleotides and do not encode any proteins. They regulate gene expression by reducing the translation or stability of messenger RNA. Misregulation of miRNA expression exhibits a causative function in cancer pathogenesis. On one hand, oncogenic miRNAs such as miRNA-17 92 (colon, breast, lung, and pancreatic cancers), miRNA-155 (lymphoma and lung cancer), and

FIGURE 9.3 The difference in telomere activity between healthy and cancerous cells.

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miRNA-21 (breast and lung cancer) are overexpressed in cancer tissues. On the other hand, miRNAs capable of tumor inhibition or suppression like miRNA-15 as well as miRNA-16 (lymphocytic leukemia), let-7 (ovarian and lung cancer), and miRNA-34 are underexpressed in these tissues. Circulating nucleic acids such as miRNA can be collected from patient serum or plasma and are promising biomarkers for noninvasive cancer sensing [17].

9.3 Electrochemical biosensors for cancer biomarker detection The oldest type of biosensors ever developed were electrochemical in nature. Electrochemical biosensors show higher specificity and sensitivity than optical biosensors although the latter is more common in the detection of cancer biomarkers. Such sensors are facile in operation, portable, affordable, and small making them ideal for decentralized and point-ofcare testing (POCT) [35 38]. Electrochemical biosensors consist of a biological recognition element, an electrical transducer, and a sensing system that converts the transduced signal into measurable output. The selection of the recognition element or the receptor is substantial in determining the sensitivity and specificity of the sensor. Immunosensors consist of antibody receptors that specifically bind to the targeted CA [39] whereas genosensors are immobilized with probe oligonucleotides that hybridize with the complimentary nucleotides from the target sample [40]. In enzymatic biosensors, enzyme receptors bind to the substrate at a fixed pH and temperature [41]. In recent years, more robust and lucrative receptors such as aptamers [37], molecularly imprinted polymers (MIPs) [42] as well as bacteriophages [43] have been increasingly incorporated in biosensors. Electrochemical sensing is performed by voltammetric, potentiometric, amperometric, impedimetric, or conductivity methods [44]. Voltammetric biosensors are based on the detection of an analyte using cyclic voltammetry (CV), linear sweep voltammetry, stripping voltammetry, and square wave voltammetry (SWV), which is a special type of differential pulse voltammetry (DPV). Most voltammetric techniques require low response times and are accomplished within a few seconds to a few minutes. Voltammetric biosensors for cancer generally exhibit sensitivities in the range of aM nM and ultrasensitivity can be achieved by integrating nanomaterials into the sensing matrix. For instance, in a sensor for the detection of neuron-specific enolase, which is a biomarker for small cell lung cancer, the sensitivity was enhanced eightfold following the addition of gold nanoparticles (AuNP) [42]. Similarly, results have also been observed in the detection of endogenous miRNAs in human serum using AuNP-modified screen-printed carbon electrodes (Fig. 9.4). The SWV signals exhibited a linear response over a wide range of miRNA

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Thiol-modified complimentary probe for miRNA-21 miRNA-21 p19 protein Complementary probe for miRNA-200 miRNA-200

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FIGURE 9.4 Voltammetric sensor for the detection of microRNA based on signal amplification with gold nanoparticle decoration [45]. Reprinted with permission from J. Am. Chem. Soc. 135 (8) (2013) 3027 3038. Copyright (2013) American Chemical Society.

concentrations (10 aM 1 μM) [45]. AuNPs are also used in nanocomposite-based voltammetric biosensors. AuNP nanohybrids with graphene and graphene derivatives allow femtomolar detection of cancer genes [46] and miRNAs [47,48] using CV and DPV, respectively. The synergistic effect of graphene and AuNP generates a large number of highenergy valance electrons with a controllable band gap that amplifies the current response during voltammetry. Other nanomaterials such as graphene foams [49], platinum nanoparticles [50], TiO2 nanofibers [49], copper nanoparticles [51], carbon nanotubes [52], and magnetic nanobeads [53] have also been harnessed for the electrochemical sensing of cancer biomarkers. Synthetic receptors such as MIPs are promising alternatives to antibodies due to their durability, ease of manufacturing and handling, and low cost [54]. MIPs can be used to develop fully electrochemical sensors, especially for protein biomarkers. Imprinting of the surface peptide of protein biomarkers addresses the complexity and high cost involved in imprinting whole proteins. It has been recently observed that imprinting multiple epitopes [42] within the same MIPs can increase the sensitivity by as much as two- to fivefold in comparison to single epitope imprints

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(Fig. 9.4) [55,56]. Another critical parameter for generating MIPs for cancer biomarkers is the choice of monomer. CA 15 3 MIPs synthesized with an intrinsically conducting polymer network [57] reported a wider detection range and lower limit of detection (LOD) in comparison to a nonconductive network [58]. This is because, in the former case, the functional monomer Toluidine Blue undergoes monomolecular selfassembly prior to polymerization by orienting itself to act as a linker between the electrode and the polymer matrix. However, the selfassembly takes significantly more time than the entire fabrication process for the nonconductive MIP made from 2-aminophenol. Aptamers are another type of high-affinity receptor that shows enormous potential for cancer detection. Aptamers are short sequences of nucleotide or peptides and are selected by a method known as systematic evolution of ligands by exponential enrichment (SELEX). In SELEX, aptamers are chosen from a large pool of nucleotides in multiple cycles until a candidate of desired binding specificity is obtained. Aptamers are therefore highly specific and ultrasensitive to the target. For example, a DPVbased aptasensor on a reduced graphene oxide platform was reported to detect as low as 80 ag/mL of CEA [59]. CA125 aptasensors show more than eightfold higher sensitivity and 28 times the detection range in the case of sandwich assays [60] in comparison to direct assays [61]. In amperometric detectors, a potential is applied to the sensor and the resultant current is quantified. In amperometric immunoassays for tumor markers, the first step involves the electroactive labeling of antibodies. This can be achieved using nanoparticles as well as enzymes. The next step is the primary antibody-mediated complex formation between the labeled antibody and the target protein. In the final state, the potential is applied, and the current response is measured. Amperometric biosensors are one of the most common electrochemical platforms for cancer biomarker recognition. For instance, interleukin-8 and PSA have been simultaneously detected using an amperometric sandwich immunoassay with the help of multiwalled CNTs (Fig. 9.5) [62]. The LOD was 5 pg/mL for PSA and 8 pg/mL for interleukin. In this assay, horseradish peroxidase (HRP) was used as enzyme labels. Such assays are amenable for biosensing in serum, cell lysates as well as cancer cells [63], Amperometric platforms are also capable of detecting low analyte concentrations in the attomolar range [64]. Gold aperture sensor arrays have also been fabricated for the amperometric detection of circulating prostate tumor cells [65]. Similar nanoprobes can be used for the assessment of drug-resistant cancer cells and they are more sensitive (LOD: 28 6 2 cells/mL) than conventional cytosensors based on ELISA (LOD: 158 6 8 cells/mL) [66]. Amperometric aptamers-sandwich assay for tumor cells has also been developed but the sensitivity is lower in comparison to most immunosensors. Chronoamperometry is a special type of amperometry in which the

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MWCNT

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IL-8 pAb

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FIGURE 9.5 Simultaneous detection of cancer biomarkers using an amperometric biosensor [62]. Ab2, secondary antibody; EDC, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide; HRP, horse radish peroxidase; IL-8, interleukin-8; mAb, monoclonal antibody; MWCNT, multiwalled carbon nanotube; NHS, N-hydroxysuccinimide; pAb, polyclonal antibody; PSA, prostate-specific antigen.

working electrode is subjected to a square-wave potential and the current response is plotted against time. It is a promising alternative strategy to evaluate the viability of breast cancer cells [67]. The technique shows high precision (8%) for the detection of tumor necrosis factor—α (TNF-α) in artificial saliva [68]. Impedimetric strategies have also gained prominence as auspicious techniques for sensing carcinogenesis markers due to their rapidity, low excitation voltage, and excellent sensitivity. They are durable for a long time and are capable of providing on-site detection signals in real-time [36]. Electrochemical impedance spectroscopy (EIS) is the most common type of impedimetric technique used in biosensing. It requires only 5 10 mV of excitation voltage, which is much lower than that required by most voltammetric techniques (B200 600 mV) [69]. This makes EIS the least damaging electrochemical detection method. EIS is performed by plotting the current response as a function of the applied alternating current voltage while the direct current bias is kept constant. In this technique, the changes in surface capacitance due to the immobilization of

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biomolecules as well as alterations in the electron transfer at the material interface are investigated. Although EIS is suitable for sensing proteins, genes as well as cells, it is not applicable for detecting volatile organic compounds that are emitted as a byproduct of cancer cell metabolism. EIS-based PSA aptasensors have shown five times’ broader detection ranges than their voltammetric counterparts [70]. However, the choice of transducers and nanomaterials plays a critical role since EIS is highly sensitive to the topography of the sensor surface and therefore a trend between the two techniques cannot be generalized. For example, the sensitivity reduces one- to fivefold from DPV to EIS for vascular endothelial growth factor (VEGF) sensing since the sensing due to the surfaceinduced mass transfer limitations to the redox marker [71]. EIS demonstrates excellent sensing performance for the specific detection of HER3 in spiked artificial serum with high recovery (105.5% 109%) [72]. In a recent study, an immobilization-free strategy was reported for miRNA-21 detection [73]. The impedimetric sensor relied on magnetic beads and was recalcitrant to hybridization with nonspecific nucleotides with single base-pair mutations. Bioimpedimetric studies can also be harmonized with growth dynamics to distinguish between tumor cell aggressiveness [74]. Aggressive cells proliferate at a higher rate and resist cell death, which ensures their growth in space and nutrient-deficient conditions. Non-faradaic EIS systems such as capacitive biosensors can be easily miniaturized since they do not require redox mediators or reference electrodes. The dielectric changes in the electrodes of such sensors are monitored by the charge/discharge cycles of the double-layer capacitance [75]. Research on capacitive sensors for cancer biomarkers is still in the nascent stage. In 2018, a capacitive aptasensor was reported for HER2 sensing in undiluted serum with the help of interdigitated electrodes [76]. The detection range was in the order of five orders of magnitude and the LOD was 1 pM. In potentiometric sensors, the electric potential gradient amongst two electrodes is computed in the absence of any current in the electrolytic cell. The voltage and the analyte concentration follow the Nernst relation. Since subnanomolar sensitivities are easily achieved with potentiometric sensors, it is useful for cancer biomarker detection. Light-addressable potentiometric biosensors have been developed using phage-display receptors for human phosphatase of regenerating liver-3, a liver cancer biomarker, as well as mammary adenocarcinoma cells [77]. Similar sensors have been developed with monoclonal antibodies of human epithelial cell adhesion molecules for detecting circulatory prostate cancer cells [78]. CEAimprinted self-assembled monolayer (SAM) can be used to fabricate potentiometric sensors with a linear range of 2.5 250 ng/mL [79]. These types of sensors are also suitable for rapid and label-free detection of protein biomarkers [80] as well as exosomal miRNA [81]. Tumor cells alter the pH of

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the adjacent microenvironment by secreting lactate resulting in a change in the electrochemistry of the local media, which can be investigated with potentiometric biosensors [82]. Chronopotentiometric stripping, a subtype of potentiometry in which square wave current is applied, can be used for glycoprofiling PSA with the help of sialic acid-specific lectins [83]. Other electrochemical methods such as field-effect transistors [84], conductometry [85 87], and electrochemiluminescence [88 90] have also been employed for cancer biomarker detection albeit to a relatively lesser extent.

9.4 Optical biosensors for cancer biomarker detection Optical detection involves harnessing the electrochemical properties of light such as shifts in polarization, absorbance, refractive index, luminescence, and fluorescence with the aid of photodetectors [91]. In these sensors, the excitation, emission, and illumination of light can be exploited for studying cancer cells and their interactions in the proximal microenvironment. Optical sensors are the most common biosensors since they facilitate label-free, affordable, fast, and real-time detection. They allow the control of output precision, assay time, maintenance requirements, and reusability depending on the qualitative and quantitative features of the cancer analyte. Analyte recognition usually takes place through catalytic or affinity-based mechanisms. Catalytic biosensing involves the conversion of an analyte to a product following a biochemical reaction catalyzed by the receptor. The driving force behind analyte detection for affinity-based sensing is the specificity of the recognition element. Popular optical phenomena of interest in biosensing include surface plasmon resonance (SPR), evanescent-wave fluorescence, surface-enhanced Raman spectroscopy, elliptical polarization, reflectometric interference, and bioluminescence [92]. SPR-biosensors are the most popular variety of optical biosensors in use. The SPR phenomenon proceeds in five steps: (1) Valence electrons from the sensor element (usually noble metal nanoparticles) exhibit high energy oscillations as a result of the electromagnetic field generated by metal-dielectric interfacial interactions. (2) The excitation events oscillate the electron charge density generating surface waves called surface plasmon polaritons. (3) The electric field arising from these polaritons decays in an exponential fashion within the surrounding environment whose depth of penetration is in the order of hundreds of nanometers. (4) The consequent evanescent field exhibits ultrasensitivity to refractive-index fluctuations in its adjacent medium. (5) Analyte-induced refractive index variations during resonance cause a shift in the excitation peak of the signal spectrum, which is itself arising from a light beam with a specific

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FIGURE 9.6 (A) Localized surface plasmon-based immunoassay along with its (B) working principle; wavelength shift in resonance peak observed due to analyte binding on the sensor surface [91].

incident angle or wavelength [91]. Noble metal nanomaterials are often employed for fabricating these sensors and they are therefore called plasmonic nanomaterials. SPR sensors are classified into propagating SPR (PSPR) arising from metallic nanofilms and localized SPR (LSPR) from metallic nanoparticles (Fig. 9.6). For instance, crown ether SAM with a roughness of 1.6 nm was conjugated with an oriented peptide for the PSPR detection of breast cancer cells (MCF-7) without the requirement of signal amplification [93]. A similar sensor was reported for the colorectal cancer biomarker endothelin-1 using mercaptoundecanoic acid SAM as a linker between the gold substrate and the monoclonal capture antibodies [94]. It demonstrated superior sensitivity of 2.18 mo/pg mL and the system was also compatible with electrochemical sensing techniques. Analogous PSPRs have been reported using Ti3C2-MXene nanosheets [95] and cysteamine SAM [96] for CEA and CA125, respectively. Comparative studies reveal a narrower detection range (0.1 300 U/mL) and lower sensitivity (0.01 U/mL) in PSPR CA125 assays than electrochemical methods (linear range: 0.01 500 U/mL; LOD: 0.01 U/mL) [96]. MIP assays using PSPR show broader linear ranges in comparison to immunoassays for CA125 and this trend can also be observed when the analyte is spiked in serum [97,98]. The sensitivity of immunoassays can be improved by switching from the direct to the sandwich format and further enhancement can be achieved by introducing plasmonic nanoparticles (i.e., AuNPs, as enhancers) [99]. Such nanomaterial amplified systems have been reported for gene biomarkers such as pentraxin-related protein (PTX3) [100]. SPR genosensors are capable of discriminating against single codon mutant variants of tp53 [101].

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In LSPR biosensors, the nanoparticles display quantum confinement effects since the particle size is much lower than the wavelength of incident light, which is used for excitation. Integration of nanomaterials to LSPR sensors makes them amenable to miniaturization and multiplexing. Electrons in the conductions band of metallic nanoparticles are plenty and incident photons excite them to oscillate collectively. The composition, shape, as well as size of the plasmonic nanomaterial, determine the optical properties of the sensor. Although silver and aluminum nanoparticles show the highest efficiency of scattering, their vulnerability to atmospheric oxidation proves detrimental for LSPR, which is why silver nanomaterials are sparingly employed [102,103]. LSPR immunoassays using gold nanodisk arrays on optical fibers are affordable and can be used for in vivo as well as remote sensing. Such optical-fiber LSPR devices can be combined with an endoscope or laparoscope for monitoring PSA [104,105] and interferon-γ (IFN-γ) [106] in real-time. Rayleigh scattering responses of AuNP-based devices enable attomolar sensitivity for PSA in buffer [107], which reduces to femtomolar levels in serum [108]. LSPR immunoassays can be included in microfluidic platforms for AFP detection in 50% diluted serum to generate label-free lab-on-a-chip sensors [109]. Multiplex detection of CEA, AFP, and PSA in patientmimicked serum has also been reported using amine-modified AuNPs [110]. LSPR immunoassays for activated leukocyte cell adhesion molecule show 20,000-fold higher sensitivity [111] than DNA-based assays [112]. Aptasensors using LSPR such as the one developed for VEGF165 show high specificity against VEGF165, osteopontin, and platelet-derived growth factor [113]. The single-step detection results in undiluted serum were consistent with ELISA measurements. Different AuNP morphologies have been explored for LSPR based cancer biomarker sensing along with promising neoteric material fabrication methods. In the example of IFN-γ, thermal lithography was used to synthesize LSPR chips, which were subsequently modified with gold nanorods [114]. LSPR chips with gold nanoislands have also been fabricated for the same biomarker with the help of electron beam evaporation [115]. Colorimetric sensors have become popular within the last two decades due to the rising demand for POCT platforms. Adapting colorimetry to these systems is facile since signal generation is quick, visible to the naked eye, and does not require additional steps for washing. Conventionally, colorimetric assays relied on enzymes such as HRP for inducing a color change in a substrate. The cost of enzymes in addition to their poor operational and thermal stability greatly hinders their widespread use in colorimetry. Modern colorimetric detectors instead use nanomaterials that undergo color change via several different mechanisms such as LSPR, aggregation, growth, and etching, or enzyme mimicry. For instance, a recent study targeted the abundance of

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Aptamer

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Uptake Removed pellet

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FIGURE 9.7 Colorimetric discrimination between (A) cancer cells and (B) normal cells [116].

nucleolin receptors on tumor cell membranes using DNA-conjugated AuNPs and nucleolin aptamers [116]. In the presence of normal cells, the nanoconjugates hybridized with the aptamers to form purplecolored aggregates. However, on the introduction of as few as 10 cancer cells per milliliter, the aptamers were captured by the nucleolin receptors and the individual nanoparticles retained their red color (Fig. 9.7). In such assays, attention must be paid to the salt content, pH and buffer composition since minor changes in these parameters may contribute to false negatives or positives. In nonaggregation assays, a color change is associated with a simultaneous shift in the LSPR. This can be achieved in two ways. First, a biomarker such as alkaline phosphatase (ALP), which is expressed abnormally during carcinogenesis, can be targeted to reduce a noble metal cation to “grow” on a plasmonic nanoparticle such as gold nanostars. Such sensors show a hypsochromic shift in the LSPR spectrum resulting in a great sensing performance with a fivefold higher sensitivity than ELISA [117]. Second, the biomarker can be used to reduce an ion such as iodate to iodine, an oxidant that etched nanomaterials such as gold nanorods into nanospheres [118]. This approach has reported one of the lowest LOD (0.01 U/mL) of all colorimetric assays used for cancer biomarker sensing. Nanomaterials are often auspicious alternatives to peroxidase and oxidase enzymes in catalytic sensors. These nanosensors can be further improved by incorporating different types of nanoparticles that show synergy. For instance, adding platinum and magnetic nanoparticles to a

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graphene oxide platform for breast cancer cells improved its peroxidasemimicking capabilities thereby accelerating the assay reaction by 30 times and generating a response within 5 mins [119]. Due to the oxidative nature of H2O2, alternative strategies are required for detecting sensitive analytes. Therefore oxidase-mimicking nanoparticles are common in H2O2-free colorimetric assays. Manganese dioxide nanosheets, one such material, have been reported in the detection of acetylcholinesterase [120] and similar nanomaterials have also been harnessed for sensing ALP (CuOOH nanoflakes) [121] and MCF-7 cells (Pt-Co bimetallic nanoparticles) [122]. Other pathways to perform colorimetric sensing of cancer biomarkers have been reported using dual enzyme-mimicry [123,124], nano photocatalysts [125], conformational changes in conjugated polymers [126 128], and chromogenic-reactions [129]. When monochromatic incident light interacts with the sensor surface, its photons are adsorbed, reflected, or scattered. The bulk of photons that undergo scattering do so elastically wherein the frequency and energy of the photons are conserved. However, 1 in 10 million scattered photons undergoes inelastic or Raman scattering [130]. The difference in energy of these photons from the incident photons is dependent on the scattering molecule and therefore unique in each case. This unique vibrational spectrum can be considered as the fingerprint of the analyte. Although Raman scattering is very sensitive, it produces weak signals. Therefore nanoroughness is introduced to the scattering surface, which promotes the inelastic scattering. This phenomenon is known as surface-enhanced Raman spectroscopy (SERS). SERS-based biosensors are usually operated in one of the two formats: direct/intrinsic and indirect/extrinsic. In indirect SERS sensors, the signal response is measured with the help of a SERS tag. Such indirect SERS immunoassays have been developed for VEGF with a substrate of gold triangle nanoarray and a label consisting of a gold nanostar-malachite green isothiocyanate nanoconjugate [131]. The 3D hierarchical sandwich structure led to the generation of dense hotspots as a result of photonic excitation contributing to substantial signal enhancements. SERS immunoassays can also be fabricated for the simultaneous detection of p53 and p21 proteins by conjugating silver nanorods to two different Raman tags attached to the respective antibodies [132]. Such nanosilver morphologies have been adapted for HER2 sensing as well [133 135]. Immobilization-free colloidal SERS assays for CEA comprising of hollow gold nanospheres show up to 1000 times the sensitivity of ELISA [136]. The same assay can also be adapted for multiplexed detection with AFP however a considerably reduced sensitivity was reported in this case [137]. Colloidal colorimetric assays using AuNPs are a popular choice in detecting circulating tumor cells [138 140]. In recent years,

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SERS-based sensors have been integrated into microfluidic systems to quantify CEA [141]. Another popular optical detection strategy for cancer biomarkers is fluorometry, which uses ligand-binding fluorophores to monitor changes in fluorescent intensity in the presence of analytes. Fluorometric sensor responses can be linked with enzymatic signal amplification for the femtomolar detection of interleukin-8 [142]. Fluorescent sensors are capable of detecting trace amounts of cancer biomarkers such as HER2 [143 145], VEGF165 [113], and HCG [146] in undiluted serum due to their extraordinary specificity. Fluorescence sensors with nanomaterial components allow cancer detection at the single-molecule scale [147,148]. Lectinmodified trifunctional nanosensors are also capable of mapping cancer cell glycoproteins [149].

9.5 Piezoelectric biosensors for cancer biomarker detection The change in the mass of a receptor once it binds with its target is monitored directly in piezoelectric biosensors. This mass change is monitored by detecting the changes in the resonance frequency of the sensing platform. Such acoustic waves propagate either through the bulk of the sensor or along its surface. Quartz crystal microbalance (QCM) is a popular example of a bulk acoustic wave piezoelectric sensor along with a film bulk acoustic resonator (FBAR). In these sensors, the wave propagation is not guided throughout the substrate volume. Most of the surface acoustic waves used in biosensing are also generated on the surface itself. Such systems can further be classified into shear-horizontal, love-wave, Leaky, lamb wave, and flexural plate wave biosensors. QCM sensors consist of an AT-cut quartz crystal with lithium niobate. QCM shows mass sensitivity at thin layer depositions (rigid) and viscoelastic sensitivity for thicker depositions (flexible). This behavior is essential for investigating the electrical features of ambient molecules with the help of asymmetric electrodes [150]. QCM sensors show long-term durability and are affordable as well as simple to manufacture. Their two major pitfalls are their big size and poor sensing resolution [151]. One such QCM sensor was developed by depositing a 31.1 nm thick layer of poly(2-hydroxyethyl methacrylate), which was subsequently activated with transferrin and carbodiimide for recognizing highly metastatic breast cancer cells [152]. The LOD improved more than 40-fold when the other face of the QCM chip was functionalized with notch-4 receptor antibodies (Fig. 9.8) [153]. Nanomaterials are often used for signal amplification in QCM-based cancer biosensors [154]. They allow biomarker recognition in a concentrated serum environment [155]. In a study on the sensing of different varieties of leukemia cells, it was reported that the

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FIGURE 9.8 The scheme followed for a nanoparticle-mediated piezoelectric sensor for breast cancer cells [153]. Ab, antibody; AFM, atomic force microscopy; NP, nanoparticles; PHEMA, poly(2-hydroxyethyl methacrylate); QCM, quartz crystal microbalance.

aptamer-mediated QCM provided 6.4 times higher signal response after the addition of aminophenyl boronic acid-modified AuNP [156]. Since these cell samples are obtained invasively, alternative cancer biomarkers have also been targeted for QCM-assisted detection. These biomarkers include CEA [157], poly (adenosine diphosphate-ribose) polymerase-1 [158] as well as small lung cancer biomarkers like hexanal, propanol, decane, and ethyl benzene [159]. QCM modules with dissipation features are useful in identifying and monitoring the status of cancer cells. Such platforms can be used to differentiate healthy cells from anaplastic carcinoma [160] or melanoma cells [161]. In recent work, CD63 exosomes were also detected in 75% serum with QCM-based dissipation sensors within 30 mins [162]. In FBAR-type sensors, the piezoelectric thin film is acoustically insulated from the atmosphere with two electrodes. Their resonance frequency ranges between 0.1 20 GHz. FBAR has been exploited for biosensing mainly within the last two decades. The thin film can be synthesized with Si3N4, ZnO, or AlN, and their sensitivity averages between 740 1.425 105 cm2/g [150]. FBAR sensors are small, light in weight, consume less power, and are compatible with complementary

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metal-oxide semiconductors. Although these properties make FBAR an interesting detection mechanism for POCT, they show considerable interference due to noise. Furthermore, the piezoelectric membrane is extremely fragile making signal measurement and control difficult. As the literature on FABR-based detection is not as abundant as QCM, they have been harnessed for cancer biomarker sensing only since 2010 [163]. FABR platforms readily bind with proteins as well as nucleotides. Although aptamers have also been immobilized on FABR substrates a poor mass sensitivity was observed toward CEA since only resonance frequencies below 2 GHz could be achieved [164,165]. In a recent study, PSA detection from whole blood was performed without any pretreatments within 5 mins [166]. The FABR assay quantified the real component of electrical impedance and exhibited a sensitivity of 101 kHz mL/ng. A similar platform for heart-type fatty acid-binding protein, a possible biomarker in breast cancer, presented a high scope for clinical use due to its rapid response time (1 min) and high sensitivity (14 pg/mL) in serum [167]. Analogous FABR platforms have been reported for few other cancer biomarkers such as AFP [168] and MUC1 [169]. The concept of sheer horizontal surface acoustic wave (SHSAW) sensors was employed for biosensing when it was discovered that phase shifts for the same immunochemical equation were significantly more pronounced at higher harmonic frequencies [170]. Different piezoelectric substrates can be employed for lithium niobate, lithium tantalate, AT-cut quartz, potassium niobate, aluminum nitride, and zinc oxide. SHSAW sensors consume less power, cost less, and allow wireless control. Therefore such biosensors have been integrated into semiconductor chips to detect interleukin-6 in undiluted serum in real-time [171]. Another POCT sensor was developed for AFP detection in whole blood with the help of monoclonal antibodies for SHSAW detection [172]. The energy loss originating in the impurity of the acoustic wave in SHSAW sensors is the main reason for limited systems developed for cancer biomarker detection on this principle. Horizontally polarized surface acoustic waves, so-called “love waves” have garnered a lot of attention for biosensing. Substrate materials for these sensors can be chosen from ZnO/SiO2/Si, ST-cut quartz crystal, or any of those used for constructing SHSAW sensors. The resonant frequency ranges between 80 1.586 3 103 MHz. In these sensors, the acoustic wave gets trapped inside a guiding layer, which minimizes signal dissipation to the environment. This wave guiding layer further imparts high corrosion resistance and ultrasensitivity to the love wave-type surface acoustic wave (LWSAW) sensor. This property makes them suitable for CEA detection in exhaled breath condensate [173,174]. The LOD reduces by an order of magnitude when a SAM of thioglycolic acid is formed on the surface of a gold substrate [175]. Nanomaterial amplification

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has been reported to enhance CEA sensitivity by 2 3 orders of magnitude [176]. Furthermore, AuNP nanocomposite with reduced graphene oxide and molybdenum sulfide nanosheets exhibited exceptional stability toward CEA detection with only 10% signal loss in 80 days [177]. Hydrophilic MIPs, as receptors for prostate-specific membrane antigen, can be facilely synthesized on a microfluidic LWSAW platform and such a system exhibits a high potential for use in lab-on-a-chip ensembles [178]. The two major challenges in LWSAW-based sensing include the guiding layer effect and the direct proportionality of its thickness to the insertion loss.

9.6 Other biosensors for cancer biomarker detection The vast majority of biosensors employed for cancer biomarker detection fall in one of the three categories discussed in Sections 9.3 through 9.5. Nevertheless, several other sensor types have been explored for cancer diagnosis as well as prediction. One of the most promising of these techniques is mass spectrometry. In a recent study, a mass spectroscopy pen was invented to enable ex vivo as well as in vivo cancer biomarker detection [179]. This device also allowed the distinction between healthy and tumor tissue by analyzing several biomarkers during surgery within 3 s of contact. In another study on thyroid carcinoma patients, matrix-assisted laser desorption ionization mass spectrometry imaging was performed on fine needle aspirations to identify tumor markers [180]. In a similar work, paper spray ionization mass spectrometry was integrated with machine learning to predict breast cancer with 87.5% accuracy [181]. A relatively higher accuracy has been observed when machine learning is complemented with gas chromatography-mass spectrometry (GC-Ms) targeting volatile organic compounds (VOCs) as biomarkers for predicting bladder cancer [182]. The GC-Ms approach has also been extended toward VOC sensing in the breast [183], lung [184], and urological malignancies [185]. Another chromatographybased approach for detecting cancer employed a polymers-mediated anion exchange column for quantifying the extent of DNA hypermethylation in the CpG island methylator prototype gene, an epigenetic aberration commonly observed in renal carcinoma cells with the help of high-performance liquid chromatography [186]. Although the detection of cancer biomarkers using mass spectroscopy shows high accuracy, it demands operation by experienced lab technicians. This complexity has prevented such approaches from gaining the same popularity as those mentioned in the previous sections. Thermistors or calorimetric sensors evaluate the heat generated in the exothermic reaction involved in the capture of a target by the receptor.

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Voltage

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Cell culture medium

Voltage

Streptavidin

Thermopile

Time

Biotin conjugated capture antibody Glucose oxidase conjugated detection antibody Glucose TNF-α

Voltage

Glass coverslip

Time

Time

FIGURE 9.9 Principle of a calorimetric immunosensor [188].

The change in enthalpy is a function of the analyte concentration. For cancer biomarkers, antibodies are the most popular type of receptors harnessed for calorimetric assays. For instance, HERCEPTINs is an engineered monoclonal antibody that can be adopted into a microelectromechanical system platform for the calorimetric detection of HER2 [187]. A calorimetric sandwich assay for TNF-α instead utilized enzyme-labeled antibodies (Fig. 9.9) [188]. The microfluidic assay displayed a LOD of 14 pg/mL with high reproducibility (R2: 0.9942). Nuclear magnetic resonance-assisted biosensors for cancerous cells have been developed by adopting cutting-edge techniques such as nanotechnology [189,190] and phage display [191]. Other seldomly used strategies that have also been applied for cancer biomarker detection include bioluminescence [192,193], resonant mirrors [194], and ultrasonic assays [195].

9.7 Conclusion and remarks In this chapter, we briefly discussed the occurrence and progression of cancer as well as the role of biomarkers in its diagnosis with a special focus on the various sensing techniques useful for the detection of these tumor markers. Delayed diagnosis of the disease impedes timely treatment and is largely responsible for the high number of cancer fatalities. On one hand, regular cancer screening tests almost exclusively aim at detecting one particular biomarker, which may not always be abnormally

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expressed during carcinogenesis whereas, on the other hand, the most accurate confirmatory tests rely on invasive and painful biopsies, which are themselves addled with complications of false positives and negatives. Additionally, conventional clinical assays are time-consuming, expensive, and require experienced lab technicians all of which make an early-stage diagnosis and screening inaccessible to a majority of the population in developing nations. All of these factors highlight the requirement of alternative strategies for detecting cancer biomarkers to predict and diagnose cancer as well as to monitor the effectiveness and suitability of any type of therapy. Biosensors are lucrative and facile options for such applications since they are not only facile to operate but also portable. They provide a rapid response with a capacity to detect multiple cancer biomarkers if needed. These features make them an auspicious strategy to decentralize cancer diagnosis and monitoring from professional labs to the local physician, doctor, or even the end consumer. Cancer is a group of diseases characterized by the unlimited growth and differentiation of cells, which successively invade and metastasize several organs. It is a genomic disease resulting from damage to the DNA in the form of mutations caused by various carcinogens. These carcinogens disrupt the regulatory cycle of several biomolecules involved in key cellular functions such as cell death, senescence, cell cycle, growth, and differentiation. Most carcinogens activate or “upregulate” cancercausing oncogenes and underexpress or “downregulate” tumor suppressor genes. Both these types of genes as well as the proteins they encode are useful biomarkers that enable the distinction between healthy and tumorigenic conditions. Electrochemical biosensors are some of the oldest and most sensitive biosensors available for determining the concentration of these tumor markers. There are several types of electrochemical biosensors depending on the mechanism of detection involved such as voltammetry, amperometry, potentiometry, conductometry, coulometry, and impedimetry. Most of these mechanisms function via the mediation of a redox marker, which indicates the binding of a biomarker to the receptors located at the sensor’s surface. Electrochemical measurements are performed within a few seconds to a couple of minutes, and they are capable of detecting as low as attomolar concentrations of cancer biomarkers. The use of a redox mediator often contributes to fallacies in computing the binding affinity. Therefore direct measurement techniques, commonly involved in optical sensing, are the most popular biosensors used in determining the levels as well as mutations in cancer biomarkers. Nowadays, almost all optical biosensors harness nanomaterials for their exquisite properties as labels, signal amplification tags, synthetic receptors, or plasmonic particles [196]. Optical detection strategies such as SPR, LSPR, SERS, fluorescence, and colorimetry facilitate accurate

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sensing by computing the change in the optical properties of incident light as a function of cancer biomarker concentration. Mass-sensitive or piezoelectric sensors note the change in mass resulting from the receptor analyte-binding. This change causes fluctuations in the sensor’s resonant frequency and manifests in the form of acoustic waves. Piezoelectric sensors for cancer biomarkers have been fabricated to sense bulk as well as surface acoustic waves. They are highly efficient and suitable for futuristic applications such as earlystage cancer detection from human breath. Piezoelectric biosensors exhibit long-term durability and affordability and recent developments have integrated them in lab-on-a-chip and acoustofluidics platforms, which are critical for developing POCT strategies. While the bulk of cancer biomarker detection is performed with the help of electrochemical, optical, or piezoelectric methods, other strategies such as mass spectrometry, calorimetry, nuclear magnetic resonance, bioluminescence, and a few others have also been probed for these purposes. The key reasons for the relatively lesser interest in these strategies for cancer diagnosis include the lack of sufficient literature, the requirement of expensive equipment and skilled professionals. Nevertheless, integrated approaches using these sensors help overcome these obstacles, and recent inventions such as the MasSpec Pen show promise for commercialization.

Acknowledgments Z.A. thanks the German Research Foundation (DFG, Grant number: 428780268) for the financial support as the principle investigator.

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C H A P T E R

10 Classical and new candidate biomarkers for developing biosensors in diagnosing diabetes and prediabetes; past, present and future Navvabeh Salarizadeh1,2, Sajjad Shojai3, Azam Bagheri Pebdeni4, Fahimeh Nojoki4, Seyed Jalal Zargar1 and Mehran Habibi Rezaei1 1

Department of Cell & Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran, 2Department of Biochemistry, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran, 3Department of Animal Science, School of Biology, College of Science, University of Tehran, Tehran, Iran, 4Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran, Iran

10.1 Introduction to diabetes mellitus In the introduction and next section, we will provide a picture of diabetes mellitus (DM) without using much biological jargon. DM is a disease caused by either when the pancreas cannot make insulin (type 1 diabetes mellitus; T1DM) or the body does not respond to the insulin (type 2 diabetes mellitus; T2DM). Gestational diabetes mellitus (GDM) is another type of the disease that occurs in pregnant women [1]. Western style diet

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and lifestyle, including elements such as high calorie foodstuffs and low physical activity, genetic predisposition, life history events, air pollution, and all sorts of stress are blamed for development of DM [2].

10.1.1 Prevalence Fig. 10.1 provides useful information about the worldwide distribution of DM. The prevalence of diabetes is about 8.8% of the adult population. Of all people with diabetes only 10%
15% have T1DM, yet T1DM is more prevalent among children less than 15 years old [3]. The highest prevalence of GDM is seen in Middle East and North Africa (median 15.2%; interquartile range 8.8%
20%) while the lowest prevalence and also the highest variation is observed in Europe (median 6.1%; range 1.8%
31%) [4].

10.1.2 Health issues related to diabetes Uncontrolled blood glucose is known as a silent killer [1]. It affects virtually every cell in the body. It damages heart and blood vessels, kidneys, eyes, nervous system, etc., and increases the risk of cancers [5].

10.1.3 Economic burden Diabetes prevalence is different according to income, age-group (Fig. 10.2), and sex. According to the International Diabetes Federation

FIGURE 10.1 Prevalence of Diabetes by Percentage of Country Population (2014). Middle eastern countries are among the countries with the highest prevalence of diabetes. By Walter Scott Wilkens, CC BY-SA 4.0.

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FIGURE 10.2

Prevalence (%) of diabetes by age and World Bank income group, 2017 [6]. HIC, High income; LIC, low income; MIC, medium income.

(IDF) in 2017 there were 451 million people (18
99 years old) with diabetes around the globe, which is estimated to reach 693 million in 2045. Furthermore about half of diabetics stay undiagnosed and are unaware of their condition. Meanwhile 374 million people live with an impaired state of blood glucose, which may further proceed to overt diabetes. Regarding gestational diabetes it is estimated that 21.3 million live births were affected by maternal hyperglycemia during pregnancy. In 2017 about 5 million deaths were related to diabetes (18
99 years old) and it was estimated that the disease imposed USD 850 billion on global healthcare expenditures in people 18
99 years old. This figure is expected to reach USD 958 billion in 2045 [6].

10.2 Pathophysiology of diabetes 10.2.1 Type 2 diabetes mellitus Insulin resistance is a state of decreased body response to insulin. Hence the usual amount of secreted insulin will not be able to lower the blood glucose. Therefore the β-cells of pancreas have to work harder to produce more insulin to account for the increased blood levels of glucose. Consequently β-cells are overloaded and start to deteriorate. Finally β-cells fail to supply enough insulin to control blood glucose levels and therefore overt T2DM appears [7].

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10.2.1.1 The role of insulin in energy metabolism Insulin is a polypeptide secreted by β-cells of the pancreas. It manages a host of metabolic functions related to carbohydrate, lipid, and protein metabolism [8]. Insulin secretion into the blood signals the abundance of energy sources, which are either consumed or are about to be consumed by the organism. That is, insulin secretion starts even before consuming the food [9]. When energy sources are abundant the energy provided to the body must be processed and stored for later use. The synthesis of new molecules from building blocks is known as anabolism and insulin in a major anabolic hormone. For example, the surplus of absorbed carbohydrates are turned into glucose, which is then stored as glycogen in liver and muscle. The same is true for lipids and proteins (i.e., insulin enhances both protein and lipid biosynthesis). On the other hand, when insulin levels are low the organism has to consume the stored energy. This process is managed by other hormones such as glucagon and epinephrine. The process of breaking larger molecules to their building blocks is known as catabolism and is important in providing body with energy and building blocks during no-food consumption period [10]. Insulin promotes glucose absorption from blood by insulin-sensitive cells. The ability of cells to absorb glucose under the effect of insulin depends on the glucose transporters1 found on the cell surface. GLUT4 is the main insulin-sensitive glucose transporter found on the surface of adipocytes (fat cells), muscle, and cardiac myofibers. Adipocytes and skeletal muscles are two main sources of insulin-mediated glucose transport into the cells, which reduce the blood concentration of glucose and therefore play essential roles in insulin action. Other cells have other glucose transporters that are not responsive to insulin [11]. 10.2.1.2 The ominous octet There are eight factors that play a major role in the pathogenesis and treatment of T2DM, which will be summarized below. These eight factors, also known as the ominous octet, were first proposed by R. A. DeFronzo in 2008 in a lecture of the American Diabetes Association [12] and are still valid (Table 10.1). 1. 2. 3. 4. 5. 6.

Decreased insulin secretion Increased hepatic glucose production Decreased glucose uptake Decreased incretin effect Increased glucagon secretion Increased lipolysis

1

GLUTs.

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TABLE 10.1 treatment.a

341

Eight important pathologies in T2DM with the drugs used in

Pathologic event

Drug therapy

Decreased insulin secretion

Sulfonylureas, TZDs, GLP-1 agonists, insulin

Increased hepatic glucose production

Metformin, TZDsb

Decreased glucose uptake

Insulin, metformin, TZDs, . . .

Decreased incretin effect

GLP-1 agonists, DPP-4 inhibitors

Increased glucagon secretion

GLP-1 agonists, DPP-4 inhibitors

Increased lipolysis

TZDs

Increased glucose reabsorption

SGLT2 inhibitors

Neurotransmitter dysfunction

GLP-1 agonists, metformin

a

Most drugs have multiple effects. For example, metformin decreases glucose synthesis in liver and is also known to decrease appetite. Materials provided in the table are collected from Refs. [1,12]. b Thiazolidinediones.

7. Increased glucose reabsorption 8. Neurotransmitter dysfunction Decreased insulin secretion marks a critical point in development of T2DM from prediabetes. This is the point where β-cells cannot continue to produce insulin at the elevated rate imposed by insulin resistance. Therefore insulin levels fall and consequently fasting plasma glucose starts to rise. The vulnerability of β-cells to hyperglycemia therefore determines when prediabetes turns into diabetes. Increased hepatic glucose production is a result of hepatic insulin resistance. During periods of hunger or low blood glucose, liver is responsible for synthesizing the glucose (gluconeogenesis) needed to keep the blood glucose levels within normal range. However, in the postprandial state or otherwise high-blood glucose levels, insulin suppresses glucose production in the liver. Decreased glucose uptake to adipocytes and muscle fibers is another consequence of insulin resistance (i.e., a state of decreased response to insulin). During insulin resistance, usually the insulin receptors on cell surface exist but the intracellular mechanisms responsible for accomplishing the effects of insulin do not seem to work normally. Therefore in spite of high levels of insulin cells fail to start the pathways necessary for recruiting GLUT4 transporters to the cell membrane, which leads to decreased absorption of glucose in adipocytes and skeletal muscle fibers. Decreased incretin effect. If glucose is consumed via oral route rather than being directly injected into blood vessels, insulin secretion is

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greatly enhanced. This effect is supposed to be due to hormones that are secreted by the digestive tract and mediate the secretion of insulin after having a meal. These hormones are called incretins and have found their place in diabetes treatment. Two main incretins in T2DM pathogenesis are glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP); GLP-1 secretion is decreased during T2DM while GIP secretion is increased, but in T2DM there is resistance toward GIP effects. Both GLP-1 and GIP increase insulin secretion while GLP-1 is also a potent inhibitor of glucagon and mediates about half of hepatic glucose production suppression after a meal, the other half being suppressed by insulin. Increased glucagon secretion by α-cells of the pancreas is an important aspect of T2DM. The effects of glucagon are antagonistic to insulin effects; therefore increased glucagon production is a main driver of hepatic glucose production, which is an important source of blood glucose elevation in T2DM patients. In addition, the sensitivity of liver to the effects of glucagon in T2DM is increased, which adds to the detrimental effects of increased glucagon secretion. Increased lipolysis is a direct result of insulin resistance in adipose tissue. In normal state, insulin is needed to inhibit the breakdown of lipids in adipose tissue and further promote lipogenesis. Normally lipolysis occurs during periods of fasting or low-energy consumption to provide the body with energy and carbon groups; during insulin resistance blood is flooded with glucose and energy sources, but due to lack of proper insulin action insulin-responsive cells cannot absorb and utilize nutrients efficiently leading to a false signal of metabolic hunger. This signal in adipose tissue is expressed as increased lipolysis. Increased lipolysis and faulty fatty acid oxidation pathway may lead to diabetic ketoacidosis when the levels of molecular species known as ketone bodies are greatly elevated [10]. Increased glucose reabsorption in kidneys is another important feature of T2DM. Normally when blood is filtered through kidneys 99% of the filtered glucose is reabsorbed. In kidneys about 90% of glucose is reabsorbed by SGLT2 and the remaining 10% is reabsorbed through SGLT1. However the kidneys’ ability to reabsorb glucose is satiable at around 180 mg of glucose per mL of blood, which is called the threshold. Therefore from that point on, glucose begins to appear in urine, which is a common observation in people with T2DM. However, kidney function is compromised during T2DM, and we know that in T2DM, kidneys are thriftier toward glucose. Therefore we see that in T2DM the threshold for blood glucose concentration to cause glucose to appear in urine is increased to about 220 mg/mL and the total reabsorbed glucose is about 32% higher compared to nondiabetic subjects [13]. This further complicates blood glucose control in patients with T2DM.

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Neurotransmitter dysfunction is at the heart of T2DM. The current epidemic of T2DM is supposed to derive from obesity, which is a state of unhealthy weight gain due to overnutrition. Appetite is mainly controlled by neurochemical mechanisms in the brain. Therefore we see that insulin is an antiappetite hormone at the brain level; nevertheless this function of insulin seems to be faulty in obese individuals, because despite high levels of insulin, these people maintain a high appetite for eating. Hence one may suspect that the state of being nonresponsive to insulin also exists in the brain, causing insulin not be able to play its appetitesuppressing role in obese insulin-resistant individuals. In the brain two hypothalamic regions known as ventromedial nuclei and paraventricular nuclei are responsible for appetite control. Using magnetic resonance imaging (MRI) it was demonstrated that in obese individuals the magnitude of response of these nuclei to glucose ingestion was lower and the response was delayed compared to normal nondiabetic group.

10.2.2 Type 1 diabetes mellitus T1DM is an autoimmune disease in which the body attacks β-cells in pancreas resulting in insulin deficiency and hyperglycemia. This disease is more common in children younger than 15 years old and therefore is also called juvenile diabetes, but it may occur in older ages as well. The autoantibodies against insulin, GAD65,2 IA-2,3 and ZNT-8,4 in blood could be used as biomarkers to diagnose T1DM and differentiate it from T2DM. However, 70%
90% of cases in T1DM are diagnosed as an autoimmune disease; in a minority of cases no antibody is found and therefore these cases are known as idiopathic T1DM and have a significant genetic component. The development of T1DM could be divided into presymptomatic T1DM and symptomatic T1DM. Alternatively development of the disease could be explained in three stages as shown in Table 10.2 [3].

10.2.3 Differential diagnosis of T1DM versus T2DM T1DM usually occurs in children, which facilitates differential diagnosis, but in adults the symptoms are less severe and may be easily mistaken for T2DM. In children patients usually show classical signs of diabetes including polydipsia (drinking too much), polyphagia (eating too much), polyuria (urinating too much), weight loss, abdominal symptoms, headaches, and ketoacidosis; in affected children the symptoms 2

65 kDa glutamic acid decarboxylase.

3

Insulinoma-associated protein 2.

4

Zinc transporter 8.

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Development of type 1 diabetes mellitus. Presymptomatic Stage 1

Stage 2

Symptomatic Stage 3

Autoantibodies

Yesa

Yes

Yes

β-cell loss

Yes

Yes

Yes

Hyperglycemia

No

Yes

Yes

Symptoms

No

No

Yes

The appearance of autoantibodies may precede β-cell destruction by months to years.

a

develop rapidly within days to weeks. Diabetic symptoms, insulinopenia (i.e., low levels of blood insulin), and autoantibodies are three diagnosis criteria for T1DM. Family history may not be helpful for differential diagnosis, because T2DM is three times more frequent in families of people with T1DM. Ketoacidosis happens more frequently in people with T1DM and is due to low insulin levels, but in Africa about 30% of people with T2DM may show ketoacidosis at the time of diagnosis, which is due to low levels of insulin caused by hyperglycemia-induced β-cell toxicity. Therefore low insulin or C-peptide (a marker of insulin release) are more prevalent in T1DM, but care must be taken about special cases of insulinopenia in T2DM [3].

10.2.4 Gestational diabetes mellitus The pathophysiology of GDM like T2DM is related to insulin resistance and pancreatic β-cell dysfunction. It seems women that develop GDM during pregnancy are already susceptible to developing insulin resistance due to obesity and other factors. In addition, during pregnancy due to higher metabolic demand for insulin actions, β-cells are under additional stress. Women affected with GDM are also more vulnerable to develop T2DM after pregnancy. Antibodies against β-cells are found in 2%
13% of women with GDM while genetic variants of single genes involved in DM exist in 5% of GDM cases [4]. Risk factors before pregnancy make the individual susceptible to insulin resistance and β-cell dysfunction. Furthermore placental hormones exacerbate insulin resistance and enhance endogenous glucose production. These factors together lead to elevated glucose levels in the mother’s blood. Insulin resistance, hyperglycemia, and β-cell dysfunction create a vicious cycle that continues to deteriorate the mother’s state of hyperglycemia. On the other hand, glucose and amino acids pass the fetus and enter the fetus circulation and cause hyperglycemia and hyperinsulinemia. This can lead to increased body weight

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(macrosomia) at birth and neonatal hyperglycemia. Babies that are born to mothers with GDM are more prone to develop obesity and T2DM later in life [4].

10.3 Glucose as a diabetes biomarker (history, accuracy, advantages, and disadvantages) A variety of glucose biosensors are available, they have changed little in principle over several years (Table 10.3). Blood glucose levels reflect the interaction between various regulatory pathways in the body and the balance between glucose uptake and production. D-glucose plays a fundamental role in metabolic homeostasis as a source of energy. When taken up by human cells, glucose enters glycolysis pathway to produce energy and turns into various intermediate metabolites [14
17]. Checking the glucose levels in the blood, as a disease marker, has been demonstrated to prolong life expectancy by enabling diabetics to monitor their hyper- or hypoglycemia and thus better control their lifestyle and decrease or prevent the debilitating side effects of diabetes. The concentrations of glucose in the blood plasma in normal, prediabetic, and diabetic conditions are described in Table 10.4. Glucose as a diabetic biomarker is unstable in the whole blood, and stabilizing the glucose by glycolysis inhibitors can meddle with the activity of some glucose meters. Glycolysis inhibitors such as iodoacetate or fluoride are charged molecules, and it takes 1
2 h to cross the cell membranes and become completely effective, and meanwhile the glucose metabolism continues. Thus to utilize the whole blood samples for accuracy comparisons, TABLE 10.3 A concise history of glucose biosensors, showing major events related to this field [18,19]. First description of a biosensor by Clark and Lyons at the children’s hospital in Cincinnati (1962) Practical enzyme electrode by Updike and Hicks (1967) Glucose enzyme electrode based on detection of hydrogen peroxide (1973) Relaunch of first commercial biosensor (i.e., YSI analyzer) (1975) First bedside artificial pancreas (Miles) (1976) First needle-type enzyme electrode for subcutaneous implantation by Shichiri (1982) First ferrocene mediated amperometric glucose biosensor by Cass (1984) Launch of the MediSense ExacTech blood glucose biosensor (1987) Launch of a commercial in vivo glucose sensor (MiniMed) (1999) Introduction of a wearable noninvasive glucose monitor (GlucoWatch) (2000)

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TABLE 10.4 The values of plasma glucose concentration in blood during different stages of diabetes [15
17]. Fasting

2
3 h after eating

Random

Normal

, 100 mg/dL , 5.5 mmol/L

, 140 mg/dL , 7.8 mmol/L

, 200 mg/dL , 11 mmol/L

Prediabetic

100
125 mg/dL 5.5
6.9 mmol/L

140
200 mg/dL 7.8
11 mmol/L

Not specified

Diabetic

. 125 mg/dL . 6.9 mmol/L

. 200 mg/dL . 11 mmol/L

. 200 mg/dL . 11 mmol/L

FIGURE 10.3 Basic principles of a biosensors. In enzymatic glucose biosensors analyte is glucose and enzymes as biorecognition elements are glucose oxidase (GOx) and peroxidase.

glycolysis effects must be taken into account and the separation of serum/ plasma from the cells in the lab must be accomplished at most 30 min after glucose test by glucometer [18]. A biosensor consists of three main components: (1) biological recognition elements that distinguish target molecules in the presence of various chemicals, (2) a transducer that converts the biorecognition event into a measurable signal, and (3) a signal processing system that converts the signal into readable form. Receptors, enzymes, antibodies, nucleic acids, and lectins are examples of molecular recognition elements. Electrochemical, optical, thermometric, piezoelectric, and magnetic transducers are the five main types of transducers (Fig. 10.3). Because of its improved sensitivity, repeatability, and ease of maintenance, as well as its low cost, electrochemical glucose biosensors account for the bulk of modern glucose biosensors. Potentiometric, amperometric, and conductometric types of electrochemical sensors exist [18]. For glucose sensing, several transduction principles can be used; however, amperometric glucose biosensors are the most common. Glucose levels are usually determined by interactions with one of three enzymes:

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hexokinase, glucose oxidase (GOx), or glucose dehydrogenase (GDH), glucose dehydrogenase nicotinamide adenine dinucleotide (GDH-NAD), glucose dehydrogenase flavin adenine dinucleotide (GDH-FAD), glucose dehydrogenase pyrroloquinoline quinone (GDH-PQQ) [20]. GOx was employed as a selective enzyme for the design of the glucose sensor. Other enzymes, for example, hexokinase and glucose-1-dehydrogenase, were rarely used for glucose meters. GOx compared to other enzymes is more stable during changes in pH, temperature, and ionic strength and hence more frequently used in glucose monitoring devices. Self-monitoring methods including “finger pricking approach” are enzyme-based and involve sampling blood from a finger using pricking, to be analyzed by test strips and glucometer [21]. There are two parts in a glucometer: a detector and an enzymatic part that is packaged in a dehydrated form as a disposable strip or response cuvette. Blood rehydrates and glucose reacts with the enzymes to provide a detectable product. Several glucometers use hydrogen peroxide or an intermediary, which then reacts with a dye to produce a colorimetric response, which depends on the concentration of glucose. The benefit of glucose self-monitoring devices include no need for pipettes, using from capillary blood, low price, easy to use, and no color blindness and illumination problems. These devices have some disadvantages such as having limit linear rang, inaccuracy of measurement, matrix of samples and temperature can make false results, and higher costs of consumables [22].

10.3.1 Current glucose sensors in clinical practice (accuracy, advantages, disadvantages) 10.3.1.1 Enzymatic and nonenzymatic sensors Enzymatic glucose sensors have very high sensitivity and selectivity, so most commercial sensors are enzymatic. In contrast, nonenzymatic glucose monitoring systems show better stability, but more research is needed to overcome other problems, such as interaction with other molecules, anomeric selectivity, and miniaturization of detection systems [23]. GOx, glucose dehydrogenase, and hexokinase are three types of GOxs. Each enzyme has benefits and disadvantages. All measuring devices are sensitive to heat and cold because they contain an enzyme that can be denatured at extreme temperatures. Although the enzyme is packaged dry, the moisture can cause premature rehydration of the protein and limit its reactivity. Therefore disposable measurement reagents must be protected from extreme temperatures and humidity. This can happen when reagents are transported outdoors in summer or winter. Many meters have internal temperature control so that if the ambient temperature and humidity exceed the manufacturer’s limits the meter will not work or will display an error code. The measuring instrument like any electronic equipment should be protected

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from moisture. The blood glucose meter tests the whole blood. Determining the accuracy of a blood glucose meter is difficult because the glucose in the whole blood is unstable and the sample may need to be transported to laboratories for comparison with laboratory methods and delays in the delivery can cause discrepancies between measurement equipment and laboratory methods. Red blood cells metabolize glucose through glycolysis pathway, which reduces the glucose concentration in the sample at a rate of 5%
7% per hour, while the serum/plasma remains in contact with the red blood cells. Therefore plasma/serum samples should be used to compare measurement results with clinical laboratory methods. Many factors affect the accuracy of the measurement results, including the way the operator works, environmental influences, physiological effects, and patient medications [18,24]. Glucose meters have some limitations. For example, regarding calibration, to accurately determine the glucose level in the same sample, ideally a blood glucose meter test and a reference method should be used for comparison. Unfortunately, this is technically difficult, because a small amount of capillary blood can be obtained by a finger puncture [24]. In the past, for calibrating purposes, glucose measurement in laboratories could only be performed nonenzymatically, using the condensation and reduction properties of glucose, but due to problems related to nonspecificity, toxicity, and cross-reactivity with other drugs were quickly eliminated from clinical practice. Both enzymatic and nonenzymatic methods show a high level of accuracy, specificity, and minimal cross-reactivity and could be used in laboratories at the same time. On the other hand, on-site and home monitoring benefits from the enzymatic method because it is simple and relatively user-friendly [25]. 10.3.1.2 Continuous glucose monitoring systems Continuous glucose monitoring systems (CGMS) can help with diabetes management by delivering real-time data from an internal insulin release mechanism. A continuous subcutaneous glucose monitor and a continuous blood glucose monitor are the two types of continuous glucose monitoring (CGM) devices now in use. However, most CGMSs do not monitor blood glucose directly due to surface contamination of the electrode by proteins and coagulation factors, as well as the danger of thrombosis. As a result, subcutaneously implanted needle-type electrodes that measure glucose concentrations in interstitial fluid (ISF) and reflect blood glucose levels have been created [26]. 10.3.1.3 Invasive continuous glucose sensors Regarding invasiveness, blood glucose monitoring has three main categories: invasive, minimally invasive, and noninvasive glucose measuring (Fig. 10.4). Invasive continuous glucose sensor research involves four technologies: subcutaneous amperometric electrodes, microdialysis,

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10.3 Glucose as a diabetes biomarker (history, accuracy, advantages, and disadvantages)

Point sample

Invasive

Glucose monitoring techniques

Home monitoring

Glucometer

Laboratory test

Enzymac

Non-opcal Electromagnec Connuous

Hexokinase

Bioimpedance

Non-Invasive

Opcal

Microwave/ Skin sucon bister

Florescence Raman

349

Near/mid/farInfrared

Millimeter

Opcal coherence tomography

FIGURE 10.4 Different classifications of glucose monitoring techniques in point sample and continuous measurement of various invasive and noninvasive methods.

implantable intravenous devices, and micropores/microneedles. To date, only subcutaneous and microdialysis studies have produced commercial products. Subcutaneous needle sensors adapt protected strategies, and immobilize enzyme and mediator onto a polymer membrane and electrode surface. The limitation of these strategies are the lack of accuracy mainly due to environmental factors such as humidity and temperature. Additionally, physiological factors such as sweat and nonglucose blood concentration can also interfere with accurate glucose readings [27]. Blood is obtained from individuals on an empty stomach in the morning in hospitals, and the blood glucose content is analyzed correctly by an automated biochemical analyzer. Despite the fact that the results of this approach are exact and may be used as a foundation for diabetes diagnosis, it is unsuitable for continuous diabetic monitoring due to its time-consuming process, extended detection time, and high volume of venous blood extraction [28]. 10.3.1.4 Noninvasive glucose monitoring system Another objective of glucose sensor technology is noninvasive glucose analysis, and major efforts have been made to attain this goal. The most prevalent noninvasive glucose measuring techniques are optical or transdermal modalities. The physical characteristics of light in the ISF of the anterior chamber of the eye are used by optical glucose sensors [18]. Wearable glucose meters, in addition to finger prick glucose monitoring using a portable glucose meter, provide a noninvasive instrument for CGM, and are now one of the most popular wearable biosensors on the market [26]. 10.3.1.5 Optical sensors In general, optical methods include all methods that have been developed for the infrared and optical regions of the spectrum because they take advantage of the reflection, absorption, and scattering of light as it passes through biological media [29
32]. Intensive research has also been

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conducted on fluorescence, especially in the development of completely noninvasive devices for subcutaneous glucose measurement using fluorescence, such as Eversense from Senseonics (Maryland, Germany), which are already on the market. They have also been studied using various techniques, including light polarization, electrochemistry, fluorescence, and other photon-based methods [33
36]. In this process, the development of sensors and analyses that correlate measurement values with the concentration of glucose in the blood will play an important role in fostering the progress and future of glucometers. The optical methods include kromoscopy [37], photoacoustic spectroscopy, optical coherence tomography, scattering/ occlusion spectroscopy, polarimetry, thermal infrared, fluorescence, Raman spectroscopy, MIR (mid-infrared) spectroscopy, and NIR (near-infrared) spectroscopy (Fig. 10.4) [36,38]. All biological fluids used for noninvasive glucose measurement, including tears, sweat, and saliva, have lower glucose concentrations than blood, so accuracy and precision are important issues. The surrounding nerve cells and subcutaneous fluids present certain issues that need to be addressed, such as fluid drainage and changes in pH that affect the ratio of abnormalities in glucose. This means that fluctuations in pH between cells must be considered. Tears and sweat have many advantages, but they are equally challenging. Future research must focus on fundamental and practical questions, such as electrode structure and geometry, glucose binding devices, miniaturization of surfaces and molecules, and (theoretical) knowledge of the glucose mutarotation mechanism, including the effect of pH and temperature. By overcoming these challenges, it is possible to develop smart wearable devices for noninvasive CGM [26]. The using of noble metals (Ag, Au, Pd, and Pt) in novel metal nanoparticles (NPs) are considered suitable choices for constructing sensors. Nanomaterials with porous structure, excellent electrical conductivity, and catalytic skills will be widely used in enzyme sensors and nonenzymatic sensors so as to significantly improve the sensor sensitivity. Nanomaterialsbased sensors are expected to have better mechanical properties in order to further improve reliability and flexibility during daily activities. The nanomaterials with specific recognition of glucose may attract more attention to enhance the selectivity of the wearable glucose sensors, as there are various interferences (such as lactate, Na1, K1) in body fluids. Thus the nanomaterials should exhibit excellent biocompatibility to ensure the long-term wearable application of glucose sensors for the human body. In addition, the manufacturing processes for sensing nanomaterials are simpler and cheaper, which will reduce the cost of the sensors [39]. 10.3.1.6 Electrochemical sensors Electrochemical glucose sensors have suitable properties such as portability, selectivity, and simplicity, superior stability, quick response time, low cost, and low limit of detection (LOD). The best alternatives for the

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electrooxidation of glucose are highly conductive carbon-based nanomaterials; nonetheless, their stability is a major concern. Metal oxides such as Co, Ni, Zn, Cu, Ti, Mn, Ti, Ir, Rh, and their bimetallic nanomaterials, as well as their bimetallic nanomaterials, have the potential to stimulate and enhance sensor application. However, at high voltages, a few metal-based glucose sensors have showed reduced selectivity. The nanoporous layer’s thickness and pore size allow it to work on plasma, human serum, and blood without being diluted. Disturbances caused by various electroactive and electroinactive chemical species also require modifications [40]. Carbon-based nanomaterials (carbon nanotube, graphene, etc.) have been widely used in the production of sensing electrodes for wearable glucose sensors due to their superior electrical conductivity, excellent biocompatibility, and low cost. However, because carbon-based nanomaterials cannot directly process GOx must be changed on the sensing electrodes. As a result, the inactivation of enzyme causes a decrease in the service life of the glucose sensor, which inhibits its widespread application and commercialization [39]. NPs are used because they have a large specific surface area, good electrical conductivity, and reactivity. They have outstanding electroanalytical and electrocatalytic properties due to the large number of surface active sites. They can act as excellent wires or electron channels when combined with enzymes to speed up electron exchange between the electrode and the redox protein/enzyme interface. It can also create a milieu comparable to that found in nature for the immobilization of biomolecules such as proteins and enzymes as a biocompatible material. This preserves their enzymatic and electrochemical activity while allowing protein and enzyme molecules to move about more freely. In direct electron transfer, this can lessen the insulating character of the protein shell or enzyme 3D structure. Immobilization of NPs on the surface of the substrate electrode is a very critical step in the creation of composite electrocatalytic systems based on NPs. Graphene can be used as a conductive carrier for the deposition of electrocatalytic. Because of their good electrical conductivity and electrochemical inertness, carbon materials such as fullerenes, diamond, carbon nanotubes, graphene, and carbon nanofibers have been frequently used as electrode materials for glucose sensors. Graphene is the most widely used among them [41]. While carbon-based nanomaterials have been widely used in wearable glucose sensors due to their advantages such as good electrical conductivity, simple manufacturing process, and low cost, there still exists some limitations. The sensing electrodes are often modified with GOx since carbon-based nanomaterials generally cannot detect glucose directly. Besides the high cost of GOx, the poor stability of sensing electrodes is an important problem due to the inactivation of enzymes, limiting the wide application of enzymatic glucose sensors. The metal-based nanomaterials, especially noble metal-based ones with large specific surface area and high electrocatalytic effects, can directly detect

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glucose without GOx, featuring better stability. But the cost is obviously high [39]. For glucose monitoring, a variety of enzyme-based electrochemical glucose sensors have been created. Direct detection of glucose in the blood (invasive glucose monitoring) is the most accurate method, and detection kits for blood glucose have been extensively developed, particularly in portable or implanted forms. Tears, saliva, ISF, and perspiration can all be retrieved noninvasively or with low invasiveness, and they contain glucose in quantities that are similar to those seen in blood. These alternative biofluids have become novel analytes of interest for the painless monitoring of glucose in the body as a result of this association [42]. 10.3.1.7 Wearable biosensing Optical and electrochemical technologies are the most common approaches utilized in wearable glucose sensors. Electrochemical sensors are more suitable for wearable applications than other techniques because of their advantages of fast response, high precision, and ease of use. The GlucoWatch, invented by the Cygnus firm in 2001, was the first electrochemical-typed wearable glucose monitor [39]. The recent development of wearable biosensing devices that monitor glucose in these fluids has shown promise, and these glucose meters are demonstrating their potential as noninvasive, pain-free alternatives to the outdated and unreliable finger-pricking method for monitoring chemical components in matrix samples such as intertidal fluids to improve the quality of life of millions of people with diabetes. Wearable glucose monitors that provide continuous blood glucose levels rely on the precision and accuracy of biosensors to accurately and effectively convey blood glucose concentrations in real-time, 24 h a day [26]. The use of conductive polymer NPs in wearable glucose sensors aims to improve the sensor’s flexibility in order to ensure that the glucose sensor performs well after being connected to the human body. The output properties of wearable glucose sensors will not change as a result of varied human body movements. As a result, the catalytic properties of the NPs are not as good as those of metal-based nanomaterials. Different nanomaterials can be used to make wearable glucose sensors, depending on the needs of practical applications [39]. For example, a wearable tattoo wireless-based biosensor platform that can monitor sweat and ISF glucose simultaneously (Fig. 10.5). Noninvasive chemical sensors and biosensors, which are based on the transmission of chemical information, are still in their infancy, unlike wearable physical sensors for monitoring vital signs. Further advancement has been hampered by the scarcity of wearable chemical sensors. The scarcity of wearable chemical sensors has stymied further advancement in the field of continuous personal health monitoring. This is due to a number of critical issues that have yet to be overcome, including getting sensor response

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FIGURE 10.5 Different fabricated wearable glucose sensors in various structures are shown such as tattoo-based wireless sensors, eyeglass noninvasive sensors, and a portable sensor for measuring the glucose concentration in saliva.

with low analyte concentrations and tiny biofluid sample volumes, as well as mechanical robustness, biofouling, and biocompatibility of the sensors. Noninvasive wearable electrochemical sensors, which were recently introduced, try to overcome these critical issues and cover important gaps in wearable sensor technology [43]. Furthermore, wearables with dermal, oral, ocular, and cochlear interfaces are also being developed to increase the means for collecting medical data. Although blood remains the most dependable medium for health monitoring, other bodily fluids such as saliva, tears, and perspiration have been the subject of research. These media eliminate the need for intrusive blood testing, which is typically disliked by patients, and open the door to novel wearables that improve the users’ quality of life [26]. Noninvasive eyeglass sensors for data transmission and portable glucose monitoring for measuring glucose concentration have also been developed with flexible integrated sensor array with glucose sensor electrodes, including substrate, working electrode, counter electrode, and insulating material. Other patient-focused enhancements are still in the works, such as reducing insertion pain, reducing the size of the wearable component, and providing connection with the patient/health through an easy-to-use app. Contact lenses can be worn throughout the day, but users would have to revert to other kinds of glucose monitoring at night or while sleeping, and hypoglycemia situations would not be notified.

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10.3.2 The role of nanomaterials in glucose biosensors The use of NPs to aid glucose detection is the most use of nanotechnology for diabetes sensors. Increased surface area, improved catalytic characteristics, ability to be employed in biomolecule labeling and adsorption, more efficient electron transfer from enzyme to electrode, and the option to insert extra catalytic stages are all advantages of incorporating nanomaterials into these sensors. Because nanomaterials are comparable in size and dimensions to redox enzymes, they might be employed as electrical connections to provide a bridge between the electrode and the redox center, allowing for enhanced electron transport [44]. Metallic NPs and quantum dots have been employed in a variety of glucose biosensors with NPs immobilized on electrode surfaces. Other electrochemical and optical approaches have also been employed to sense glucose using solution suspensions of NPs. When compared to standard macroelectrodes, nanoelectrode arrays have better signal-to-noise ratios, improved mass transmission, and improved detection limits. The significant development in nanotechnology that has occurred in the last decade or so has been translated to biosensors and bioelectronics in order to take use of some of nanomaterials’ extremely desired properties. For the synthesis of nanomaterials with various forms and dimensions, such as spherical particles, rods, cubes, and so on, several techniques have been devised. Nanostructures have become a crucial component of many detecting technologies due to their structural, electrical, chemical, and mechanical capabilities [45,46].

10.3.3 Glucose biosensors for point-of-care testing Despite the fact that laboratory analysis is the most reliable approach for determining glucose levels, point-of-care testing (POCT) is extensively utilized to measure glucose levels in inpatient such as emergency room (ER) or intense care unit (ICU) and outpatient (office/home) settings due to cost and time delays. The majority of point of care (POC) glucose biosensors rely on disposable enzyme electrode test strips with screenprinting. The electrochemical cells in these plastic or paper strips include GDH or GOx as well as a redox mediator. A test strip is introduced into the meters first, and then a little drop of capillary blood is extracted from the fingertip and transferred to the test strip using a lancing instrument. Finally, according to the international federation of clinical chemistry (IFCC) guideline, a conversion factor is used, and the measurement findings are commonly reported as plasma glucose equivalents. POCT testing is a good prospect for continuous, long-term monitoring of a variety of disorders, including diabetes, as well as food safety analyses and environmental monitoring. According to the sensing target, wearable glucose sensors in POCT are categorized into six categories: blood, sweat, saliva, tears, ISF, and urine. Wearable bioelectronics have gotten a lot of press across the world because of their huge potential for predictive medical modeling and POCT. 2. Biomedical applications

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10.3.4 Perspective and glucose sensor developments With increasing diabetes prevalence, new glucose biosensor technologies have emerged in the past few decades, including point-of-care devices, contentious glucose meter devices, and noninvasive glucose monitoring systems. Glucose biosensors have become more reliable, faster, more accurate, and more compact and user-friendly. Research on advanced technologies including electrodes, membranes, immobilization strategies, and nanomaterials continues. Sensor technology is not as precise as the central laboratory methods. Compared with clinical laboratory reference methods, the analysis should demonstrate good linearity, accuracy and correlation, and immunity to common interferences. The equipment should be calibrated and quality controlled regularly according to the manufacturer’s instructions. Userrelated factors can also affect the quality of the data, which in turn affects the outcome of the treatment. Techniques for measuring glucose can be divided into four subgroups: optical, thermal, electrical, and nanotechnology processes (Fig. 10.4). Sensor reading will feed data wirelessly to a monitor, allowing users to see how their glucose levels fluctuate over time. These current CGM sensors are all classified as minimally invasive sensors, as the term “minimally invasive” refers to the use of a needle to detect glucose content in the ISF without collecting blood (Fig. 10.6).

10.4 Glycated hemoglobin and glycated albumin as diabetes biomarkers Glycation process and advanced glycation end-products (AGEs) are the agents involved in diabetic complications due to hyperglycemia [47]. Glycation is a nonenzymatic process of carbohydrate addition to proteins, which has significant impact on their physicochemical and functional properties. Most of the studies on glycation contribution to diseases have been primarily focused on diabetes. The process provides the preliminary ground to assess the relationship between elevated levels of HbA1c and diabetes disease [48,49]. Among the proteins, glycated albumin (GA) and fructose amine as indicators in plasma and HbA1c as a protein in blood are interesting biomarkers for prediabetes and diabetes [50]. Albumin containing arginine, cysteine, and especially lysine residues bound to glucose is called GA. Despite the efficiency, accuracy and cost-effectiveness of glycation measurement, this assay is not routine in clinical laboratories [51]. In some cases, GA may be used as an alternative biomarker or complementary test for glycemic control [52]. Reaction of albumin with reducing sugars such as glucose is ten times higher than Hb [53]. In clinical laboratories, HbA1c and fating plasma glucose (FPG) are potential screening tools for the assessment of blood glucose [54]. 2. Biomedical applications

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FIGURE 10.6

Glucose sensor designs for (A) CGM, a general layout of currently available subcutaneously implanted, minimally invasive glucose sensors for continuous glucose monitoring, (B) wearable sensor that uses a sample patch to detect glucose levels in the perspiration, and (C) self-monitoring of blood glucose (SMBG), including sampling locations and system components.

10.4.1 Glycated hemoglobin as a diabetes biomarker (history, accuracy, advantages, and disadvantages) Currently, in clinical laboratories three methods are used for screening of T2D: Plasma glucose index: (1) FPG and (2) oral glucose tolerance test (OGTT): A patient consumes a glucose solution containing 75 g of glucose before determination of 2-h plasma glucose (PG); (3) HbA1c levels with a clinical screening test [55,56]. HbA1c is the stable glucose adduct to the N-terminal group of valine from β chain of HbA0 [57]. HbA1c is a potential indicator for diagnosis of T2D. Through this indicator, assay of the beta-N1-deoxy fructosyl component at N-terminal of β chain of HbA0 is performed [58]. Since the lifespan of Hb in blood is approximately 2
3 months, the monitoring of HbA1c level is important for long-term blood glucose control in clinical laboratories for control of per-diabetes and DM [54]. This biomarker, to this day, can be called the

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“gold-standard” assay that reflects average glycemia [59]. The HbA1c has several advantages to the FPG and OGTT, including greater convenience (fasting not required), greater preanalytical stability, and less day-to-day perturbations during stress and illness [60]. It is important to take age, race/ethnicity, and anemia/hemoglobinopathies into consideration when using the HbA1c to diagnose diabetes. In 1997 and 2003, the Expert Committee on Diagnosis and Classification of Diabetes Mellitus recognized a group of individuals whose glucose levels did not evaluate the criteria for diabetes but were too high to be considered normal [61]. An HbA1c range of 6.0%
6.5% had a 5-year risk of developing diabetes between 25%
50% and a relative risk 20 times higher compared with an HbA1c. It is reasonable to consider an HbA1c range of 5.7%
6.4% as identifying individuals with prediabetes [60,61]. 10.4.1.1 Current hemoglobin sensors in clinical practice (accuracy, advantages, disadvantages) An International Expert Committee, appointed by the American Diabetes Association (ADA), the International Diabetes Federation (IDF), and the European Association for the Study of Diabetes (EASD) published a report in 2009 recommending the use of HbA1c for the diagnosis of diabetes [62]. Some of the developed methods have been used in clinical laboratories for monitoring of HbA1c including boronated affinity high-performance liquid chromatography (HPLC), HPLC, ionexchange HPLC, fast protein liquid chromatography (FPLC), immunoassay [63
66], and enzymatic assay [67]. Most immunoassays measure HbA1c specifically; antibodies recognize the structure of the N-terminal glycated amino acids (usually the first 4
10 amino acids) of the Hb β chain. Ion-exchange HPLC separates Hb species based on charge differences between HbA1c and other hemoglobins in boronated affinity method, m-aminophenylboronic acid reacting specifically with the cisdiol groups of glucose on Hb, and then absorbance of each of the Hb fractions measured at 415 nm. This method measures total glycated Hb, including HbA1c and Hb glycated at other sites [68]. But ion-exchange HPLC is interfered with by different hemoglobin variants [69]. Capillary electrophoresis uses the principle of liquid-flow capillary electrophoresis in free solution. With this technique, charged molecules are separated by their electrophoretic mobility in an alkaline buffer with a specific pH. Current automated methods use a very small sample size and offer the potential for precise separation of different hemoglobin fractions [57]. But this method is not cost-effective. Ion-exchange chromatography allows for the separation of hemoglobin species based on the difference in isoelectric point between HbA1c and HbA0 [57,65,68]. In some clinical laboratories fluorescence immunoassay (FIA) kit (Ichroma HbA1c, BodiTec) was applied for accurate detection [58,70].

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10.4.2 Glycated albumin as a diabetes biomarker (history, accuracy, advantages, and disadvantages) Cohen and Clements first reported that the concentration of GA in plasma, with a lifetime of 12
19 days, may be a potential indicator of recent glycemic status [52,71]. Albumin is one of the most important and the largest plasma proteins. This biomolecule comprises more than 80% of the total molecules and 60% of the total plasma protein concentration [72]. In diabetic subjects, arginine, cysteine, and especially lysine residues from albumin bound to glucose that leads to GA [51]. GA localizes throughout the whole body, including the blood, ISF, lymph, and cerebrospinal fluid, and the amount of GA is proportional to the glucose concentration in the whole body and the half-life of albumin [73,74]. Since GA is not related to hemoglobin metabolism, it may be an important glycemic control marker in patients with diabetes and diverse comorbidities. GA also appears to be a useful glycemic control marker at the start of diabetes therapy [72,74]. Many publications have reported the assessment of plasma protein indicators, specifically FA and GA, for glycemic control over the short term to intermediate periods (2
4 weeks) for 15
20 years. According to previous studies [52], GA can be utilized as a short term to intermediate glycemic indicator [75]. 10.4.2.1 Current GA biosensors in clinical practice (accuracy, advantages, disadvantages) Since plasma albumin can react irreversibly and nonenzymatically with glucose in diabetic patients and may convert to fructose amine [71], an alternative method can be developed based on fructo-amino kinase [76] and fructo-oxidases [67,77] for detection of GA. Design of enzyme-based systems, which are deglycating AGEs in human cells, can be a potent defensive system against nonenzymatic glycation [58]. These enzymatic kits including chemical reagents and buffers are made to be used in autoanalyzer. Direct enzymatic systems have simplicity, specificity, and accuracy in comparison with other analytical techniques that are available in clinical laboratories [58,70]. Some kits have been designed by commercial companies (Tables 10.5 and 10.6), and more enzyme-based kits are expected. Several methods are presently employed in the isolation and quantification of GA. These include (1) enzymatic assay [79]; (2) HPLC and affinity chromatography [80]; (3) immunoassay, including quantification by radioimmunoassay; (4) enzyme-linked immunosorbent assay (ELISA); (5) enzymelinked boronated immunoassay (ELBIA); and (6) electrochemical [52]. But at the moment, monitoring of GA is limited to an enzymatic assay kit (GlycoGap, Diazyme Company) [79]. GlycoGap is not a replacement for HbA1c assay, but rather is a complementary assay to HbA1c for improved quality in diagnosing diabetes and monitoring glycemic control, especially

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10.4 Glycated hemoglobin and glycated albumin as diabetes biomarkers

TABLE 10.5 diabetes.

Commercially enzymatic kits for HbA1c detection in prediabetes/

Type of kit

Brand

Utilized keto-oxidase

Target molecule

Direct Enzymatic HbA1c Assay

Crystal Chem, USA

FVO

HbA1c

Glycohemoglobin A1c kit

NorudiaN HbA1c, Sekisui Medical Co., Ltd. Tokyo, Japan

FPOX

HbA1c

CinQ HbA1c [78]

Arkray, Inc. (Japan)

FPOX

HbA1c

TABLE 10.6

Commercially enzymatic kits for GA detection in prediabetes/diabetes. Utilized ketooxidase

Target molecule

Lucica, Asahi Kasei Pharma Corporation, Japan

Not available

GA

Diazyme GSP assay (GlycoGap)

Diazyme, Poway, USA

Fructosaminase

GA

Glycated Serum Protein (GSP) Kit

Crystal Chem, USA

Fructosaminase

GA

Type of kit

Brand

Lucica GA-L kit

for those patients whose HbA1c levels do not truly reflect the mean blood glucose levels or those with glycation gap [81]. Utility of GA kit is not routinely in diagnostic laboratories. In the United States five laboratories apply affinity chromatography and one laboratory determine GA with an enzymatic assay with a reference range of 11%
16% [52].

10.4.3 Perspective and GA sensors (designed biosensors for GA and HbA1c monitoring) in development Recent advances using optical and electrochemical techniques in biosensing designs vary considerably by biomarker/bioanalyte and recognition element type used. The recognition elements involved in biosensing applications are enzymes, aptamers, antibodies, and other molecules [82,83]. It is the advantage of electrochemical biosensors that they can sense bioanalyte without damaging the system [84]. Different biosensors based on BA derivatives, aptamers, enzymes, and antibodies have been reported for monitoring of GA and HbA1c, as noted in Table 10.7 [66]. Several groups have studied the electrochemical determination of HbA1c levels based on FcBA and its derivatives. BA binds to HbA1c and thus discriminates it from nonglycated hemoglobin [85,86]. A further improvement in HbA1c sensors was reported

2. Biomedical applications

TABLE 10.7

Designed sensors based on HbA1c and GA biomarkers.

Recognition element (nanozyme/enzyme/ aptamer)

Method

Biomarker

Dynamic range

LOD

Response time

Year

References

50
170.5 ng/ mol

50 ng/mol

ND

2007

[85,86]

FcBA lab ‘anti-HbA1c antibody

E-chem/immunoassay

Au/4,4-dithiodibutyric acid (DTBA)-APBA monolayers

SPR

HbA1c

0.43
3.49 μg/mL

0.01 μg/mL

ND

2008

[87]

FcBA

Disposable biochip

Wide range of glycoproteins, for example HbA1c

6.8%
14%

ND

ND

2006

[88]

Zinc oxide NPs
polypyrrole film/ FAO

E-chem

Fructosyl valine (FV)/ endproduct from decomposition of glycated hexapeptides

0.1
3.0 mM

50 μM

2s

2012

[89]

Alizarin reds (ARS) /phenylboronic acid

E-chem (potentiometric)

HbA1c

0.6
1.8 g/dL

ND

2012

[90]

Self-assembled monolayer (SAM) thiophene-3-boronicacid

Affinity biosensors with impedance measurement

Glycoproteins

10
100 ng/mL

1 ng/mL

ND

2013

[91]

Aptamer
antibody on magnetic beads (microfluidic system)

Immunoassay

HbA1c

0.6
1.8 g/dL

0.65 g/dL

ND

2015

[92]

Aptamer (single strand DNA)-based gold nanorods (Au NRs) and magnetic beads (MBs)

LSPR

Glycated proteins

ND

15 ng/mL

ND

2011

[93]

rGO/aptamer

Fluorescent quenching GO monolayer

GA

0
3 mg/mL

50 μg/mL

ND

2016

[94]

Novel three-dimensional paper-based electrochemical impedance device (3DPEID)

E-chem

HbA1c

0.5
20 g/dL

0.08 g/dL

ND

2016

[95]

Au/TiNTs nanocomposites/FAO

Electrochemiluminescent (ECL)

HbA1c

4.0 3 1029 M
7.2 3 1027 M

3.8 3 1029 M

ND

2016

[96]

AuNPs/aptamer

E-chem

HbA1c

100 pg/mL 10 μg/mL

0.2 ng/ml

ND

2017

[97]

PtNPs/rGO-MWCNT/ FAO

E-chem

HbA1c

0.05
1000 μM

0.1 μM

,3 s

2017

[98]

(AuNPs-PtNPs)/poly indole-5-carboxylic/

E-chem

HbA1c

0.1
1000 μM

0.2 μM

ND

2017

[99]

anti-HbA1c antibody

Potentiometric immunosensor

HbA1c

1
10,000 μM

20
500 μM

ND

2018

[64]

(GO) and Cy5-labeled G8 aptamer

E-chem

GA

0.05
0.3 mg/mL

50 μg/mL

ND

2016

[94]

Aptamer/magnetic beads

Microfluidic system

HbA1c

0.7
2.1 g/dL

30 min

2016

[100]

(rGO/AuNPs)/aptamer

E-chem

GA

2
10 μg/mL

ND

2020

[101]

0.07 μg/mL

(Continued)

TABLE 10.7 (Continued) Recognition element (nanozyme/enzyme/ aptamer)

Response time

Year

References

0.36 μg/mL 2 3.6 mg/mL

ND

2020

[102]

1.3 μM for FV and 2.0 μM for FVH

20
500 μM

ND

2021

[103]

HbA1c

31.2 2 500 nM

4.2 nM

ND

2021

[104]

E-chem

GA

0
500 μM

1.2 μM

ND

2021

[105]

LTQ-Orbitrap Discoverer

LC-MS

HSA(Met147O)

ND

ND

ND

2021

[106]

Field effect sensor/ aptamer

E-chem

GA

ND

ND

ND

2021

[107]

Aptamer

Colorimetric paper fluidic dipstick

GA

50 2 300 μM

6.5 μM

ND

2021

[108]

Method

Biomarker

Dynamic range

LOD

Paper-based sensor/ sandwich immonoassay

LFA

GA

8.36 μg/mL

PES-modified PnFPOx

Flow injection analysis (E-chem)

HbA1c

SPCE/AQBA electrode/ Boronic acid

E-chem

Interdigitated electrode/ FAOx

ND, No Data.

10.5 Novel biomarkers/metabolites in diabetes and associated complications

363

by the same group, in which the piezoelectric sensors combined with antiHbA1c antibody. The sensor is incubated in the HbA1c-sample solution, followed by measurement of the resonance frequency of the sensor to evaluate the total amounts of glycated and nonglycated hemoglobin adsorbed. HbA1c on the sensor surface is recognized by anti-HbA1c antibody [66,109]. Recently, the system has been designed for HbA1c detection with a fingerstick. The A1CNow1 system provides healthcare professionals with a fast and easy procedure for obtaining accurate A1C results [110].

10.5 Novel biomarkers/metabolites in diabetes and associated complications Despite the development of modern biomarkers, common biomarkers such as glycosylated hemoglobin (HbA1c) and glycosylated albumin have limitations, including moderate sensitivity and specificity, and are not accurate under certain conditions. Therefore the combination of several biomarkers can more accurately identify individuals at high risk of developing prediabetes and subsequent diabetes. Here we describe recently identified biomarkers and their potential utility for addressing the burgeoning epidemic of dysglycemic disorders [111]. Different types of biomarkers (i.e., miRNA, proteins, and metabolites) can be used together to identify different disease stages. In this section, we briefly explain the characteristics and applications of new biomarkers, as well as the different methods used to identify biomarkers: general clinical biomarkers, such as glycosylated hemoglobin and fasting blood glucose, are often used to treat diabetes. A new method for detecting biomarkers must be fast, simple, and inexpensive, it must be stable at all times of the day, and samples must be readily available using invasive methods (blood and urine). Recent work to understand how changes in miRNA expression or proteins cause certain disease states has shown promising results [112].

10.5.1 Micro RNA Small noncoding RNAs called miRNAs (miRNAs) are involved in many pathophysiological processes, especially posttranscriptional gene expression. In addition, the circulating levels of certain miRNAs seem to reflect different tissues and disease pathologies [113]. For example, many miRNAs have been found to be elevated in patients with prediabetes. Type 1 and 2 diabetes (T1DM and T2DM) are related to blood miRNA profiles, and these miRNA profiles will soon be used to identify people at risk of diabetes and its devastating complications [114]. There are still technical obstacles to overcome, but due to their stability and presence in various body fluids, miRNAs have become

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promising biomarkers of diabetes and related complications [5]. In addition, the different expression of miRNA in different tissues has been described in diabetes and its related complications [1]. The MiRNAs that have been correlated to T2DM include miR-9, miR29a, miR-30d, miR-34a, miR-124a, miR-103 and miR-143, miR-146a and miR-375, miR-661, miR-571, miR-77813b, miR-77813b, and miR-77813b. In patients with T2DM, miR-103 and miR-143 regulate the development of subcutaneous adipose tissue. MiR-103 may also be involved in the regulation of fat and glucose metabolism [115]. MiR-25 modulates the expression of apoptosis genes and important β cell regulatory networks [116]. MiR-103 levels are suppressed in individuals with prediabetes and have been found to inhibit the expression of secreted frizzled-related protein 4, which is a biomarker of potential risk of diabetes (prediabetes) [8]. Also, the levels of miR-27a, miR-150, miR-192, miR-320a, and miR-375 increase in proportion to fasting blood glucose. MiR-375 levels in pancreas are inversely related to the levels of pyruvate dehydrogenase kinase 1 (PDK1). Low levels of PDK1 decrease GSK3-signaling, glucose-stimulated insulin gene expression and glucose-stimulated insulin secretion [117]. It has been demonstrated that miRNAs regulate the translation, secretion, and exocytosis of insulin in pancreatic cells [118]. MiR-196a has been shown to regulate insulin biosynthesis. Recovery of specific miRNAs can reduce disease progression in animal models [119]. Restoration of reduced miR-181b levels in adipose endothelial cells in a mouse model of obesity can improve glucose homeostasis and insulin sensitivity [120]. miR-126 is involved in endothelial function and insulin sensitivity and suppresses the glucose-stimulated proliferation via IRS-2 in INS-1 β cells [121]. These studies show the potential of miRNAs as biomarkers for T2DM; however, the heterogeneity of the results obtained emphasizes the need for large prospective studies to determine reliable miRNA signatures for T2DM diagnosis. On the other hand, using miRNAs in diagnostic kits is an active field of research. Nevertheless the use of miRNAs as novel biomarkers in biosensors for diagnosis of prediabetes and diabetes requires extensive studies [114]. In numerous studies, an increasing number of potential miRNA biomarkers have been reported, but their practical application prospects are still unclear. For example, quantifying miRNAs in the circulation is challenging due to issues with sensitivity and specificity. Many common miRNA biosensors require fluorescent labeling, which is complicated, time-consuming, labor-intensive, expensive, and not very sensitive. Due to inherent characteristics of miRNAs, such as small size, low frequency, and high sequence similarity, the detection of miRNA remains a major challenge. In Table 10.8, some of the most important microRNAs as diagnostic molecular biomarkers are listed that are involved in T1DM and T2DM to pinpoint new areas for further experimental studies.

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10.5 Novel biomarkers/metabolites in diabetes and associated complications

TABLE 10.8 Proteomic biomarker

Novel microRNA biomarkers involved in T1DM and T2DM.

Mechanism of action

Association with diabetes

References

miR-192

Regulates the tumor protein p53

MiR-192 associated with subjects with iFG and iGT; associated with high triglyceride levels and fatty liver index. Level of miR193b were elevated in those with IFG and IGT

[122]

miR-15a

Directly inhibit endogenous uncoupling protein-2 gene expression

Leading to increased oxygen consumption and reduced ATP generation, thus promoting insulin synthesis and β-cell function.

[123]

miR-193b

Important for the differentiation of brown adipocytes and inflammation reduction

Associated with subjects with iFG and iGT; associated with high triglyceride levels and fatty liver index. Level of miR-193b were elevated in those with IFG and IGT

[124]

miR-126

Abundant in endothelial cells playing a role in endothelial homeostasis and vascular integrity

Decreased in IGT/IFG and T2DM

[125]

MiR-326

—

Expression levels were upregulated in T1DM

[126]

miR-146

—

Expression levels is reduced in the peripheral blood mononuclear cells (PBMC) of newly diagnosed T1DM individuals

[127]

miR-375

Essential for normal glucose homeostasis, β cell proliferation, and β and α cells turnover, pancreatic islet cell-specific miRNA that targets myotrophin mRNA. Iinhibit glucose-induced insulin secretion

Expression levels were increased in T2DM individuals. miR-375 expression level is suitable for predicting β cell death

[114]

miR-200

Crucial miRNAs in insulin signaling pathway that targets FOG2.a Prevents disturbances in insulin signaling pathway

Expression alteration be associated with T2DM. Plays a critical role in beta-cell apoptosis

[128]

(Continued)

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10. Biosensors in diabetes and prediabetes

TABLE 10.8 Proteomic biomarker

(Continued)

Mechanism of action

Association with diabetes

References

miR-126

In high glucose (HG) decreases microRNA-126 (miR-126) expression by macrophages.

MicroRNA-126 overexpression rescues diabetes-induced impairment in efferocytosis of apoptotic cardiomyocytes. Significantly downregulated in diabetic patients compared with prediabetes

[129]

miR-29

An important role in the insulin signaling pathway by targeting phosphoinositide 3-kinase (PI3K) regulatory subunit 1 (PIK3R1), insulin receptor substrate1 (IRS1), AKT2, and PI3K regulatory subunit 3 (PIK3R3) miR-29a and miR-29c regulate glucose uptake and insulinstimulated glucose metabolism

Increased in skeletal muscle from patients with T2DM Elevated expression levels of miR-29 were found in rodent models of diabetes or obesity. important regulator of insulin-stimulated glucose metabolism and lipid oxidation

[130]

a

Friend of Gata 2.

10.5.2 Peptides/proteins Proteomics is the study of all expressed proteins and provides information about protein concentration, variation, modification, and interaction through the signal pathways through network analysis. Various methods such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), mass spectrometry, and protein microarray have been used to identify many proteins. [131]. Biomarkers can be used for a variety of purposes, such as early detection of chronic clinical diseases and conditions, patient risk stratification to suspect or confirm the diagnosis, selection of appropriate treatments, and monitoring of the patient’s responsibility for treatment. Current protein biomarkers such as glycated hemoglobin (HbA1c), fructosamine, and GA have limitations, which undermine sensitivity and specificity under certain clinical conditions. Therefore the identification of other biomarkers is rapidly being explored, because every single biomarker may also have inherent limitations. The best solution to overcome these limitations is to combine several biomarkers that can more accurately identify people at high risk of prediabetes. In addition, compared with healthy people, patients with insulin resistance and T2DM have different levels of many proteins, including interleukin 6, resistin, leptin, adiponectin, and visfatin, which can be used to identify the condition [111] (Table 10.9).

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10.5 Novel biomarkers/metabolites in diabetes and associated complications

TABLE 10.9 Proteomic biomarker

Novel proteomic biomarkers involved in T1DM and T2DM. Source a

Association with diabetes

References

Essential factor for the regeneration of pancreatic islets in diabetic patients Plasma GDF-11 levels were decreased in diabetes and diabetes with cardiovascular complications

[132]

Growth differentiation factor-11 (GDF-11)

TGF-β

Growthdifferentiation factor-15 (GDF-15)

Expressed in low concentrations in most organs

Strong prognostic protein in patients with diabetes. Increases in insulin resistance and chronic kidney diseases cardioprotective effect through activation of ALK (Activin receptor-like kinase) type 1 receptor (ALK 1
7) and GDF-15 phosphorylates Smad2/3 Role in growth, differentiation, and inflammatory response highly expressed in macrophages, endothelial cells and adipocyte, endothelial cells

[111]

Lp(a)

Lp(a) is a lipoprotein subclass that contributes to atherogenesis lipoprotein variant containing a protein called apolipoprotein(B). A risk factor for atherosclerosis and related diseases, such as coronary heart disease.

Lp(a) has an inverse relationship with prevalence of prediabetes and T2DM

[133]

GPLD1

Phosphatidylinositolglycan-specific phospholipase D Encoded by the GPLD1 gene

Postulated role in the insulin-mimetic signaling pathway of glycosylphosphatidylinositol compounds

[134]

(Continued)

2. Biomedical applications

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10. Biosensors in diabetes and prediabetes

TABLE 10.9 (Continued) Proteomic biomarker

Source

Association with diabetes

Found on blood cells

GPI-anchor degrading enzyme Hydrolyzes the inositol phosphate linkage in proteins anchored by phosphatidylinositol glycans and release the attached protein from the plasma membrane. GPLD1 cleavage generates second messengers that can act as insulin
mimetic molecules. GPLD1 positively associated with: • T2DM and prediabetes (less strongly) • MASP1, another novel prediabetes biomarker • HDLs in serum Directs formation of multiprotein Complexes that modulate cellular phenotype (e.g., stimulates/inhibits migration of vascular smooth muscle cells or endothelial cells, respectively) Positively associated with: Higher prediabetes prevalence Increased iR Increased 2-h glucose Adipose inflammation and metabolic dysregulation in obesity and T2DM

THBS1

Thrombospondin 1, encoded by the THBS1 gene. Thrombospondin 1 is a subunit of a disulfidelinked homotrimeric protein. bind to fibrinogen, fibronectin, laminin, collagens types V and VII and integrins alpha-V/ beta-1 and play roles in platelet aggregation, angiogenesis, and tumorigenesis.

MBLassociated serine proteases

Mannan-binding lectin serine protease 1 Enzymes for innate immune responses and activation of the lectin pathway of the complement system

MASP1 has been shown to positively correlate with prediabetes and diabetes Plasma levels of MASP-1 is elevated in diabetes and this fact emphasizes the importance of the lectin pathway in diabetes

References

[134]

[135]

(Continued)

2. Biomedical applications

TABLE 10.9 Proteomic biomarker

(Continued) Source

Association with diabetes

References

Ferritin and transferrin

Ferritin is an intracellular protein that stores and releases iron Transferrin is a shuttle protein, mainly synthesized in the liver, and its principal role is to transport ionic iron to the liver, spleen, and bone marrow

Elevated serum ferritin and transferrin saturation have been strongly associated with increased risk of prediabetes and diabetes. Iron decreased insulin secretory capacity, and interference with glucose uptake in skeletal muscles and adipocytes

[136]

HDL

High-density lipoprotein (HDL) A major lipoprotein HDL aggregate fat molecules in the blood and transporting up to hundreds of fat molecules per particle

HDL-C promotes insulin secretion Low HDL-C concentration may lead to progression from prediabetes to diabetes Increased proportion of small HDL3 over HDL-C in subjects with prediabetes Decreased proportion of HDL-LpPLA2 in prediabetes

[137]

Galectin-3

Member of the lectin family Contains a carbohydraterecognition-binding domain (CRD) of about 130 amino acids that enable the specific binding of β-galactosides and plays an important role in cell-cell adhesion, cellmatrix interactions, macrophage activation, angiogenesis, metastasis, apoptosis

Regulates various T-cell functions and innate immune responses Removal of Gal-3 accelerates AGE-induced kidney injury in diabetes enhances atherogenesis Accelerates high-fat dietinduced obesity Increases inflammation in adipose tissue and pancreatic islets Protective effect in obesityinduced inflammation and diabetes Low levels of Gal-3 were associated with insulin resistance in T2DM patients

[138]

Irisin

Hormone that is secreted by the heart, skeletal muscle, liver, and kidneys

Irisin levels were decreased and urotensin II (UII) levels were increased in type 2 diabetic subjects Circulating urotensin II levels were increased in diabetes and could inhibit the glucose transport in skeletal muscle in diabetic mouse and aggravated the insulin

[139]

(Continued)

370 TABLE 10.9 Proteomic biomarker

10. Biosensors in diabetes and prediabetes

(Continued) Source

Association with diabetes

References

Apelin

36-amino acid peptide Endogenous ligand of Gprotein coupled receptors (GPCRs) of apelin receptor Secreted from white adipose tissue

Apelin synthesis in adipocytes is stimulated by insulin Apelin plasma levels are markedly increased in obesity associated with insulin resistance

[140]

Prolactin

Pituitary hormone, known for its role in producing milk in mammals

Prevalence of obesity was increased in hyperprolactinanemic patients Circulating levels of prolactin increase in diabetic patients Increased prolactin levels were associated with lower prevalence of diabetes and impaired glucose regulation Low circulatory prolactin concentration is associated with increased T2DM risk Stimulates integrin-mediated adhesion of circulating mononuclear cells to endothelium and induces vascular smooth muscle cell proliferation

[141]

CCL3

Chemokine (C-C motif) ligand 3 (CCL3) Cacrophage inflammatory protein 1-alpha (MIP-1alpha) Chemoattractant cytokines for leukocytes

Their receptors belong to a family of specific G-proteincoupled 7 transmembrane domain receptors. Macrophage inflammatory protein-1α (MIP-1α), is a CC chemokine characterized as inducers of inflammatory process in various inflammatory autoimmune diseases Anti-CCL3 Abs were positive in nearly 87% of T1DM individuals

[142]

DOC2B

Double C2-like domaincontaining protein beta

Calcium sensor Regulates SNARE-dependent fusion of insulin vesicles with membranes in pancreatic beta cells Human DOC2B levels were reduced over twofold in platelets from new-onset T1D in human

[143]

(Continued)

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371

10.5 Novel biomarkers/metabolites in diabetes and associated complications

TABLE 10.9 Proteomic biomarker

(Continued) Source

Association with diabetes

References

Retinolbinding protein 4 (RBP4)

Release from adipocytes in the liver (T2DM)

The onset of adiposity and insulin resistance Acts on muscle and liver via mechanisms that are either retinol-dependent or independent

[144]

Leptin

Release from adipocytes in the small intestine Key role in energy metabolism

Fat accumulation in human body before the onset of T2DM promotes In insulin resistance in T2DM and obesity, a decreased sensitivity to leptin occurs Leptin deficiency leads to obesity, excess levels of circulating insulin, and insulin resistance, all of which are hallmarks of T2DM

[145]

Adiponectin

Derived from adipose tissues Exhibits insulinsensitizing An independent predictor of diabetes

Lower levels of adiponectin are associated with increased obesity, higher levels have been related to lifestyle intervention groups in diabetes prevention trials Adiponectin levels are inversely related to the risk of incident prediabetes Independent of ethnic or sex differences Adiponectin levels were directly correlated with insulin sensitivity Indirectly correlated with insulin secretion

[146]

a

Transforming growth factor beta.

10.5.3 Other novel biomarkers in diabetes and associated complications Metabolomics and lipidomics are other systemic techniques besides proteomics to study large-scale modifications in body fluids, and may be beneficial in biomarker discovery. After identification of the set of metabolites, lipids, and carbohydrates with assistance of LC-MS/MS

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10. Biosensors in diabetes and prediabetes

and different metabolomics methods, they may be used to design diabetes biosensors [147]. Fluctuations in the levels of amino acids are immediately associated with insulin secretion or insulin resistance in diabetic patients. For example, serum levels of the glycine is reduced in insulinresistant people; however, phenylalanine is related to insulin secretion and may be involved in early compensatory insulin secretion [111]. There are measurable modifications in serum and urinary levels of carbohydrates in different stages of diabetes. Analysis of T2DM patients confirmed reduced oxidative capacity in more favorable levels of plasma lactate. Due to dysfunctional fructose metabolism during hyperglycemia, the levels of fructose in the serum and urine of diabetic patients are significantly elevated [148]. Among lipids, vitamin D and ceramides are well-known novel biomarkers. Ceramides are lipid molecules jointly related to prediabetes and T2DM [149]. In recent decades, researchers have additionally detected that decreased vitamin D levels are related to T1DM, insulin resistance, obesity, hypertension, cardiovascular diseases, and obesity. Vitamin D may be used as a biomarker for biosensor design and disease prediction. However, for a better understanding of the position of lipids and different metabolites in the pathophysiology of diabetes and cardiovascular diseases, extra research with long-term follow-up is required [150]. Before considering new diabetes and cardiovascular disease biomarkers, there are many questions that need to be answered: For example, can the new biomarkers be specifically used as biosensors for diabetesrelated cardiovascular diseases? Can they be used with any other established markers to predict diabetes-associated disease?

10.6 Conclusion In this chapter the sensors used for diabetes diagnosis and treatment were discussed. Diabetes is a serious health issue that must be diagnosed as soon as possible. Furthermore after diagnosis of prediabetes/ diabetes continual monitoring of the disease status is needed. This monitoring is achieved by measuring biomarkers that report the physiological status of the diabetic patient. The biosensors make use of such biomarkers to facilitate the diagnosis and monitoring of prediabetes/ diabetes. Glucose has been traditionally used as a convenient biomarker of insulin and various biosensors have been devised to measure this molecule in the blood, or other body fluids, either invasively or noninvasively. More novel nanomaterials are being created and used in optical or electrochemical wearable glucose biosensors. The future outlook of glucose biosensor seems to be sensors that measure glucose continually without the need to prick the body. Furthermore other diabetic

2. Biomedical applications

References

373

biomarkers add valuable and significant weight compared to the glucose being used alone, and it is predicted that in the near future other biomarkers such as HbA1c, GA, miRNAs, etc., will make diagnosis of DM more precise.

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[122] M. Pa´rrizas, L. Brugnara, Y. Esteban, A. Gonza´lez-Franquesa, S. Canivell, S. Murillo, et al., Circulating miR-192 and miR-193b are markers of prediabetes and are modulated by an exercise intervention, J. Clin. Endocrinol. Metab. (2015). Available from: https://doi.org/10.1210/jc.2014-2574. [123] L.L. Sun, B.G. Jiang, W.T. Li, J.J. Zou, Y.Q. Shi, Z.M. Liu, MicroRNA-15a positively regulates insulin synthesis by inhibiting uncoupling protein-2 expression, Diabetes Res. Clin. Pract. (2011). Available from: https://doi.org/10.1016/j.diabres.2010.11.006. [124] L. Sun, H. Xie, M.A. Mori, R. Alexander, B. Yuan, S.M. Hattangadi, et al., Mir193b365 is essential for brown fat differentiation, Nat. Cell Biol. (2011). Available from: https://doi.org/10.1038/ncb2286. [125] Y. Liu, G. Gao, C. Yang, K. Zhou, B. Shen, H. Liang, et al., The role of circulating microRNA-126 (miR-126): a novel biomarker for screening prediabetes and newly diagnosed type 2 diabetes mellitus, Int. J. Mol. Sci. (2014). Available from: https:// doi.org/10.3390/ijms150610567. [126] G. Sebastiani, F.A. Grieco, I. Spagnuolo, L. Galleri, D. Cataldo, F. Dotta, Increased expression of microRNA miR-326 in type 1 diabetic patients with ongoing islet autoimmunity, Diabetes. Metab. Res. Rev. (2011). Available from: https://doi.org/10.1002/dmrr.1262. [127] M. Yang, L. Ye, B. Wang, J. Gao, R. Liu, J. Hong, et al., Decreased miR-146 expression in peripheral blood mononuclear cells is correlated with ongoing islet autoimmunity in type 1 diabetes patients, J. Diabetes (2015). Available from: https://doi. org/10.1111/1753-0407.12163. [128] B.F. Belgardt, K. Ahmed, M. Spranger, M. Latreille, R. Denzler, N. Kondratiuk, et al., The microRNA-200 family regulates pancreatic beta cell survival in type 2 diabetes, Nat. Med. (2015). Available from: https://doi.org/10.1038/nm.3862. [129] S. Suresh Babu, R.A. Thandavarayan, D. Joladarashi, P. Jeyabal, S. Krishnamurthy, A. Bhimaraj, et al., MicroRNA-126 overexpression rescues diabetes-induced impairment in efferocytosis of apoptotic cardiomyocytes, Sci. Rep. (2016). Available from: https://doi.org/10.1038/srep36207. [130] J. Massart, R.J.O. Sjo¨gren, L.S. Lundell, J.M. Mudry, N. Franck, D.J. O’Gorman, et al., Altered miR-29 expression in type 2 diabetes influences glucose and lipid metabolism in skeletal muscle, Diabetes (2017). Available from: https://doi.org/ 10.2337/db17-0141. [131] P. Seeree, P. Pearngam, S. Kumkate, T. Janvilisri, An omics perspective on molecular biomarkers for diagnosis, prognosis, and therapeutics of cholangiocarcinoma, Int. J. Genomics (2015). Available from: https://doi.org/10.1155/2015/179528. [132] G.P. Fadini, L. Menegazzo, B.M. Bonora, M. Mazzucato, S. Persano, S.V. De Kreutzenberg, et al., Effects of age, diabetes, and vascular disease on growth differentiation factor 11: first-in-human study, Diabetes Care (2015). Available from: https://doi.org/10.2337/dc15-0868. [133] L. Ding, A. Song, M. Dai, M. Xu, W. Sun, B. Xu, et al., Serum lipoprotein (a) concentrations are inversely associated with T2D, prediabetes, and insulin resistance in a middle-aged and elderly Chinese population, J. Lipid Res. (2015). Available from: https://doi.org/10.1194/jlr.P049015. [134] C. von Toerne, C. Huth, T. de las Heras Gala, F. Kronenberg, C. Herder, W. Koenig, et al., MASP1, THBS1, GPLD1 and ApoA-IV are novel biomarkers associated with prediabetes: the KORA F4 study, Diabetologia (2016). Available from: https://doi. org/10.1007/s00125-016-4024-2. [135] J. Dobo´, V. Schroeder, L. Jenny, L. Cervenak, P. Za´vodszky, P. Ga´l, Multiple roles of complement MASP-1 at the interface of innate immune response and coagulation, Mol. Immunol. (2014). Available from: https://doi.org/10.1016/j.molimm.2014.05.013. [136] A. Aregbesola, J.K. Virtanen, S. Voutilainen, J. Mursu, A. Lagundoye, J. Kauhanen, et al., Serum ferritin and glucose homeostasis: change in the association by

2. Biomedical applications

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2. Biomedical applications

C H A P T E R

11 Biosensors for drug detection Zahra Goli-Malekabadi1,2, Navvabeh Salarizadeh3,4, Mehrnoush Dianatkhah5, Maryam Amoo6 and Javad Shabani Shayeh7 1

Bioengineering Center for Cancer, Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran, 2Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran, 3 Department of Cell & Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran, 4Department of Biochemistry, Faculty of Medicine, Baqiyatallah University of Medical Science, Tehran, Iran, 5Department of Clinical Pharmacy, Faculty of Pharmacy, Isfahan University of Medical Science, Isfahan, Iran, 6Nanotechnology Group, Department of Material Engineering, Isfahan University of Technology, Isfahan, Iran, 7Protein Research Center, Shahid Beheshti University, Tehran, Iran

11.1 Introduction Biopharmaceutical analysis is one of the most important aspects of medical research. Pharmaceutical products are complex compounds that are very diverse in terms of biological effects and chemical properties. The action of these compounds is highly dependent on adequate drug levels at the target site [1]. Each drug has a defined therapeutic window. This means that plasma levels lower than the minimum effective concentration may cause an inadequate therapeutic effect. And concentrations above the maximum tolerated limit can cause adverse effects. Qualitative identification and quantitative measurement of a specific drug or metabolites in the biological fluids are two important aspects of drug analysis. The human biological fluids include whole

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00016-9

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blood, blood plasma, blood serum, urine, cerebrospinal fluid, breast milk, semen, saliva, sweat, ascites fluid, amniotic fluid, etc. Depending on the purpose of the drug assay and the pharmacokinetic profile of the drug, each of these fluids can be used for drug analysis [2,3]. Therapeutic drug monitoring (TDM) is one of the important aspects of medical management and involves quantitative drug analysis. TDM aims at improving patient care by individualization of dosing in order to obtain the optimum response. The process of TDM is based on the assumption that a specific correlation exists between the administered dose and the plasma drug concentration, and also between the concentration and the target clinical effects. So quantitative measurement of drug concentration in body fluids is the main feature of TDM. The optimal dosage of the drugs can be obtained through measuring the drug concentration and adjusting the dose accordingly [2 4]. The main focus of TDM is on drugs with a narrow therapeutic window and interindividual variations, such as digoxin, anticoagulants, antiepileptics, which can easily be over- or underdosed, as well as drugs that need a defined concentration at their site of action for their clinical effect like antibiotics. Studies have shown that TDM is associated with improved outcomes and decreased toxicity [2 5]. Determination of the safety of drugs during pregnancy and lactation is another usage of quantitative drug analysis. Estimating the extent of the infant/fetus exposure to the mother’s medication via breast milk or the placenta can be feasible through assessing the concentration of the drug in the maternal milk or the amniotic fluid. For instance, in general, breastfeeding can be considered safe when the concentration of the drug in the milk is below a defined level [6]. Also, drugs that readily pass placenta barrier and can be detected in the fetus’s umbilical cord blood should be used with caution in pregnancy. Fig. 11.1 gives an overview of drug analysis scopes. Qualitative identification is another aspect of drug analysis, and is used for toxicological investigations by forensic medicine specialists. In the case of suicide, early detection of the causative agent or drug through body fluid analysis can accelerate the detoxification process and rescue attempt. Qualitative analysis is also used for detection of prohibited drugs (e.g., opiates, amphetamines) for employment. Both qualitative and quantitative analyses are applied to the measurement of blood alcohol concentration in patients involved in an accident. According to the National Institute on Alcohol Abuse and Alcoholism, the legal limit of blood alcohol concentration is equal to 0.08%. Antidoping tests for the presence of performance-enhancing steroids in athletes is another usage of the drug identification technique [7,8]. Consistent with large advances in medical science in recent decades, demand for reliable and accurate drug analysis methods has increased. Many methods have been proposed and most of them have been approved by the Food and Drug Administration of the United States [7,8].

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11.1 Introduction

FIGURE 11.1

385

Clinical application of qualitative and quantitative methods of drug

detection.

Historically, the chromatography technique such as gas chromatography (GC) and high-performance liquid chromatography (HPLC) has been the most popular method for drug analysis. Both methods are based on the distribution of solutes between solvents according to their partition coefficient [7,8]. In most GC techniques, the mobile phase is an inert gas. The major limitation of GC is the fact that only volatile drugs with relatively low molecular weight can be analyzed with this technique. Additionally, this method cannot be applied to analyze polar compounds. In contrast to GC, HPLC can be used for the separation of both polar and nonpolar molecules. HPLC is generally a liquid liquid chromatography technique where the mobile phase and stationary phase are both liquids [7,8]. The specificity and sensitivity of GC and HPLC analysis depend on the choice of the detector. One of the most popular detectors is mass spectrometry (Ms), which is widely used in clinical toxicology laboratories for identification and quantification of drugs in biological fluids. Ms is capable of producing a mass spectrum of any compounds eluting from a gas

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chromatograph or column of an HPLC [7,8]. Although GC and HPLC were among the most reliable and precise assays, these time-consuming methods require equipped laboratories and trained personnel. Furthermore, chromatography is one of the most expensive techniques of drug monitoring, so these methods were gradually replaced by immunoassays because of automation, ease of operation, simplicity, and speed [7,8]. Table 11.1 summarizes some benefits and disadvantages of commercial methods for drug sensing. Immunoassay is a bioanalytical method that measures the presence or concentration of an analyte based on the antigen-antibody reaction. This method is widely used in clinical laboratories for TDM and drugs of abuse testing. The immunoassay requires very small amounts of the sample, and usually no specimen pretreatment is needed. Additionally, this method is fully automatic. Despite all their benefits, immunoassays have some limitations, including being subject to interferences. Compounds with structural similarities in the sample may interact with the reagent antibody. The cross-reaction of the antibody with other molecules unrelated to the analyte can cause false-positive or false-negative results. Moreover, immunoassays are not commercially available for all drugs currently monitored in clinical laboratories, such as antiretroviral and new generations of anticonvulsants. Thus HPLC/Ms and GC/Ms are still the only available method for the qualitative and quantitative analysis of these drugs [7,8]. In addition to the abovementioned methods, several other analytical methods exist for the detection and assessment of drugs in different biological fluids. The choice between these methods depends on the TABLE 11.1

Some advantages and disadvantage of commercial methods.

Disadvantages

Advantages

Method

GC

• High sensitivity and specificity

• Cannot be applied to analyze polar and nonvolatile compounds • Time-consuming • Sample pretreatment is needed • Requires equipped laboratories and trained personnel

HPLC

• High sensitivity and specificity • Can be applied for a variety of compounds

• Time-consuming • Requires equipped laboratories and trained personnel • Sample pretreatment is costly

Immunoassay

• Quick • Requires very small amounts of the sample • Usually no specimen pretreatment is needed • Fully automatic

• Subject to interferences • Less sensitive than chromatography techniques • It is not commercially available for all drugs

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11.2 Criteria of an ideal method for drug analysis

387

Biosensors Applications

Medical and Nutrition

Environment Monitoring

Drug Discovery

Soil Quality

Prosthetic Device

Water Quality

Disease Detection

Air Quality

Food Quality

FIGURE 11.2 Various applications of biosensors. This chapter will focus on the application of biosensors in drug monitoring.

physicochemical properties of the drug, accuracy requirements, and quantification vs. qualification analysis [7,8]. As has been mentioned before, there is not any ideal method for drug analysis, and each technique has its own benefits and drawbacks. Thus research is still continuing to develop new and improved techniques to detect or quantify different drugs in a rapid, practical, and inexpensive manner in bodily fluids. However, new technologies such as biosensors are quickly developed helping fast and accurate detection. Biosensors have different health applications (Fig. 11.2). They can monitor the environment, determine food safety, help medical diagnosis, and provide homeland security. This chapter focuses on the use of biosensors for drug detection (Fig. 11.2).

11.2 Criteria of an ideal method for drug analysis Although there is no exact definition for an ideal method of drug analysis, there are some criteria that have been recognized for selecting the best technique for drug analysis. These criteria include factors such as reproducibility, reliability, accuracy, ease of the operation, test speed, being able to analyze a small amount of biological sample, compatibility with different kinds of biologic fluids, and cost [7,8].

11.2.1 Reproducibility, reliability, and accuracy of the method Reproducibility and reliability are among the most important criteria for selecting a drug assay technique. An ideal method should not have false-negative or false-positive results, since it can damage the accuracy of the test. As an example, although the immunoassay technique is one

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of the most popular methods of drug analysis, cross-reactivity and being subject to the interface has limited its use [7,8].

11.2.2 Ease of operation An ideal technique for the drug assay is one that is not operator dependent. The provided instruction should be easy to learn and should not be dependent on specially trained personnel, and should produce the same result with different operators. For instance, the most important limitation of the GC and the HPLC is the need for an equipped laboratory and trained personnel, which is part of the reason these methods have been replaced by simple ones [7,8].

11.2.3 Using the minimum amount of biological sample The analytical method for drug analysis that needs the minimum amount of the biological sample is always preferred over others because it is sometimes hard to obtain large amounts of the specimen such as blood or cerebrospinal fluid. For example, if as low as 50 microliters of the sample would be enough for the drug analysis, sampling can be done as a point-of-care test similar to a finger-stick blood sugar test [7,8].

11.2.4 The speed of analytical process The test speed is another important aspect of drug analysis. In the case of TDM and medical toxicology, a minimum detection time can be lifesaving. Additionally, methods that do not need any pretreatment or preparation process on the specimen are obviously preferred over others, since pretreatment can reduce the speed of analysis [6 8].

11.2.5 Compatibility with different kinds of biologic fluids An ideal method for drug assay should be operable on all kinds of biological fluids such as whole blood, blood plasma, urine, breast milk, etc. The importance of this matter is emphasized in TDM because depending on the site of action, we have to use different body fluids for drug concentration evaluation [4,6 8].

11.2.6 The cost Finally, the matter of price is always important and is considered a priority when selecting the appropriate technique. The test cost should be affordable for the medical center or the relevant organization.

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11.3 Biosensor design

Obviously, applying a costly technique is not practical for routine practice, and in order to become extensively used, it is necessary for the analytical method to have economic justification [7,8].

11.3 Biosensor design A biosensor is a diagnostic analytical device that can detect the amount of specific substances such as glucose and transform it to a quantifiable and processable signal [9,10]. This signal is proportional to the concentration of the substance, called analyte. Biosensors can be applied to a large variety of samples including body fluids (saliva, urine, blood, sweat), food samples, cell cultures [11], and pharmaceutical formulations [12]. Furthermore, biosensors can detect drugs that are made from minerals, plants, and animals (natural products) with healing properties [13]. Samples are placed on a biosensor. Then, the biosensor can analyze various substances using considered parts. Fig. 11.3 schematically shows different parts of a typical biosensor including bioreceptors, transducer element, signal processor (e.g., computer software), and an interface.

Biosensor Samples

Bioreceptor

Signal Processor/

Interface/

Electronics

Display

Transducer

(Including Analyte)

DNA Conversion

Cell Culture

Light

Photodiode

Heat

Thermistor

to Physical Parameter

Cell

Transducer

Processed

Signal

Signal

or

10 µg

Human Samples Enzyme

pH

pH

change

Electrode Conversion

Food Samples

Aptamer Mass Nanoparticle

Quartz

from

Electrode

Analogue

or

15mV

to

change

Digital Environment Samples

Process 1:

process 2:

process 3:

Bio-recognition

Signalization

Quantification

FIGURE 11.3 Elements of a typical biosensor including bioreceptor, transducer, signal processor, and interface. A biosensor follows three steps; biorecognition, signalization, and quantification.

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• Analyte is a substance that we need to detect in a sample. For example, glucose is considered as an analyte in a biosensor that has been designed for glucose detection [14]. • Bioreceptors are molecules with the ability of specific recognition of analytes. Cells, enzymes, and DNA are examples of bioreceptors. The interaction between bioreceptor and analyte generates a signal in different forms such as heat, light, pH change, or mass change. This process is the first process in a biosensor and is called biorecognition [14]. • Transducer elements. In a biosensor, the biorecognition event is picked up by the transducer element. In fact, the transducer element is supposed to measure the effect that has been produced due to the interaction of the analyte with the bioreceptor and convert it into a measurable signal such as electrical or optical signal. The signal intensity is proportional to the amount of analyte bioreceptor interactions. This signal can be amplified if needed. Energy conversion is the second process in a biosensor and is termed signalization [14,15]. • Signal processor or electronics converts this transduced signal to a meaningful physical parameter for display. In fact, this complex part of the biosensor is able to convert the analog form of the signal into a digital one. This process in which signals are quantified for the display is known as quantification [14,15]. • Display or interface shows the results quantitatively to the user in a user-friendly manner. It can be a number, an image, a graph, etc. The manner for results display depends on the end-user requirements [14,15].

11.3.1 Basic characteristics of a biosensor To achieve the desired goal and an appropriate measurement, an acceptable biosensor with unique properties and significant characteristics of the measuring system is necessary. The basic and unique properties are the level of the sensitivity, selectivity, stability, dynamic respond, wide measurement range, and limit of detection (LOD). In fact, a biosensor is a measuring system and should have all the characteristics for a measuring system to work well. • LOD for a biosensor is the minimum amount of the analyte that the biosensor can measure theoretically. For example, if a biosensor used for determination of glucose in blood has an LOD of 5 ng/mL, magnitudes lower than this concentration cannot be detected in the sample. • Sensitivity is the magnitude of analyte that can create a distinguishable signal in the biosensor. For example, when a biosensor has a sensitivity of 5 ng/mL, levels of analytes lower than this cannot change the signal of the biosensor device. The sensitivity is required in

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11.3 Biosensor design

•

•

•

•

391

many cases of medical and environmental applications, where we need to confirm the presence of a specific analyte in the sample [14]. There are many parameters determining the sensitivity of a biosensor. For example, physical size of the sensing region is important for the sensitivity of biosensors. In the best condition, the sensitivity of biosensors should be constant in their lifetime and various operations should not reduce the activity of biosensors [10 13,16]. Selectivity of a biosensor is defined by the ability of its bioreceptor to detect only the target analyte so that there is no reaction with other chemical species. Otherwise, some problems occur in determining the concentration of the target analyte. The selectivity has an important value in the development of analyzing methods for pharmaceuticals [10,14,16]. Stability is a performance factor for a biosensor’s lifetime. It determines the period of time and number of measurements of biosensors. Greater stability follows greater advantages and can decrease cost [10]. Dynamic range is the range of analyte concentration in which the biosensor interacts with the analyte. This time is related to the mechanism of the biosensor and usually depends on parameters such as transducer surface, concentration of receptors, and active surface area of materials. It should be noted that it is advantageous for a biosensor to have a wide linear range [16]. Reproducibility of a biosensor means it can display identical responses when we duplicate the experiment. The precision and accuracy of the transducer element and electronics part characterize the reproducibility of a biosensor [14]. Precision is the ability of a measuring system to report the same results for duplicate measurement of a sample. Accuracy is the proximity of results to a true value.

In addition to the above parameters, other characteristics of biosensors are linearity and wide measurement range, fast dynamic response, low and affordable cost, instrumental simplicity, miniaturization possibility, and integration in portable devices [17]. Reusability (or reversibility) and using minimal sample for processing prior to analysis are also two valuable properties in biosensors [18,19].

11.3.2 Nanobiosensors Evaluation of biosensors for drug detection increased after nanomaterials entered this field and a created a revolution in biosensor technologies. Nanomaterials have unique physical and chemical properties and can used in the structure of biosensor transducers. High surface-to-mass ratio and suitable electrical, optical activity, and biocompatibility make

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nanomaterials capable of connecting the receptor to transducer and increasing the sensitivity of biosensors [20,21]. Carbon-based and metalbased materials are two major categories of nanomaterials. Carbon nanotube (CNT) [22], multiwall carbon nanotube [23], activated carbon [24], and graphene [25] are some famous carbon-based nanomaterials. CNT due to its size can connect to the active center of the bioreceptor on the surface of transducer and has many benefits in electrochemical biosensors. This and the high surface area of graphene and other nanomaterials have upgraded performance of biosensors [26]. Metal-based nanomaterials such as gold [27], silver [28], copper [29], and many other nanoparticles are common nanomaterials used in the structure of biosensors. Some nanoparticles have suitable optical and/or electrical activity. Optical nanoparticles can attach to the bioreceptors. Then, the interaction of receptor-target causes the change of the optical activity of nanoparticles. Nanobiosensors have excellent accuracy and sensitivity, rapid detection and portability as well as excellent interactions between the analyte and the bioreceptor molecule compared to conventional biosensors [30 33]. On the other hand, nanoscale biosensors can detect markers much better than common biosensors. Indeed, nanostructures offer special opportunities for detection and quantification of biological and chemical species [34]. Nanostructures are excellent tools to achieve great sensitivity in biosensors. Studies have confirmed that nanoparticles improve the sensitivity of biosensors [35]. Many nanostructures have shown binding of biomolecules in desired orientation with improved conformation and high biological activity, which improve the sensing characteristic of biosensors [36].

11.4 Biosensors for drug detection An efficient biosensor is utilized for analyzing pharmaceutical compounds in two areas. In the stage of drug production in pharmaceutical companies and in clinical applications and diagnostic steps [26]. Traditional methods for determination of drug compositions in biological samples and drug formulations are HPLC, fluorimetry, spectrophotometry, solid-phase microextraction, ultraviolet-visible spectroscopy, spectroscopy chemiluminescence (CL), flow-injection and nuclear magnetic resonance spectroscopy, application of immunoassays, and enzymatic systems. But these methods are very costly and time-consuming and require complex sample preparation. Also, these methods have to be implemented by skilled staff. Therefore researchers sought to develop a cost-effective, fast, simple, reproducible, and user-friendly method for analyzing body biological samples and drugs [17,37 39]. Fig. 11.4 demonstrates the percentage of recent publications on the use of biosensors for drug detection.

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393

11.4 Biosensors for drug detection

12 32

5

16 11

12

10 18

7

12

2010 2016

2011 2017

2012 2018

2013 2019

2014 2020

2015 2021

FIGURE 11.4

The percentage of publications on the use of biosensors for drug detection in recent years.

11.4.1 Electrochemical biosensors Electrochemical activity is one of the most helpful parameters when using as transducer. An electrochemical signal produced in the presence of analyte can be attributed to its concentration. There are three major techniques for electrochemical detection of drugs including amprometery, voltammetry, and electrochemical impedance spectroscopy (EIS). Many other techniques can be derived from them. In amprometery technique, the electrochemical signal is electrical current produced by biocatalytic performance of receptor/analyte or the change in electrochemical activity of the media. The first one occurs once the receptor/analyte reaction creates some electrons. The presence of salicylate prohibits this biocatalytic reaction and the magnitude of the created current will decrease. Another bioreceptor for drug detection is aptamer chains. The affinity behavior of aptamer chains to drug molecules can be monitored by the current that is created by media. Aptamer chains decorated on the surface of electrode and hexacyanoferrate molecules have a redox reaction under applied potential on electrode surface. After interaction of drug/ aptamer chains, the 3D shape of aptamer chains changes and some active surface areas of electrode are blocked, which results in a decrease of the number of active sites and therefore the current decreases. Another method used in this technique is the labeling of the aptamer chains by a redox material such as methylene blue (MB). MB is a redox molecule with high tendency to attach to the aptamer chains. Under applied potential, MB molecules are oxidized and create the current. The interaction of drug molecules with aptamer chains and their deformation cause detaching of the MB molecule and releasing of a solution causing the current to decrease.

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11.4.1.1 Impedometric biosensors EIS is a useful technique for results about the mechanism of charge transfer reaction and depends on electrode surface resistance. Bioreceptors such as aptamer and antibodies that have affinity behavior to drug molecules are decorated on the surface of electrode. Affinity receptors need an electroactive redox media for monitoring the analyte presence and by changing the electrode surface resistance the magnitude of charge transfer resistance will change. One of the benefits of EIS for amperometric techniques is its sensitivity at very low change in analyte concentration. The current signal change is negligible but change in EIS magnitude is remarkable. 11.4.1.2 Potentiometric technique Although the two mentioned techniques are commonly used in drug detection, the potentiometric technique can also be used for this purpose. In this technique, two electrodes are used as reference and working electrode. The reference electrode is not modified with receptor. In the presence of drug molecules, the potential of the working electrode will change compared to the reference one. Then, drug molecules are monitored. Performance of electrochemical biosensors are illustrated in Fig. 11.5 and Table 11.2 summarizes some electrochemical biosensors used in drug monitoring.

FIGURE 11.5 The performance of an electrochemical biosensor for determination of drug macromolecules. The electrochemical media at the surface of the electrode creates an electrochemical signal. The interaction of analyte blocks the active sites in the surface of electrode. Therefore the magnitude of the electrochemical signal decreases as a function of analyte concentration [26].

2. Biomedical applications

TABLE 11.2

Some electrochemical biosensors for drug detection.

Technique

Bio receptor

Drug

LOD

Dynamic range

Selectivity

Response time

Ref

Amperometric

Enzyme

Verapamil

1 pM

0.5 3 nM

0.5 3 mM

2h

[40]

Impedometric

DNA

Daunorubicin

1.0 nM

4 nM 250 μM

Glycine, Valine, and Methionine

15 min

[41]

Amperometric

Amoxicillin

AX specific IgEs

0.6 ng/mL

0 45 ng/mL

BSA, Nonfat Dry Milk, Fish Gelatin or Whole Serum

30 min

[42]

Amperometric

Acetylcholine esterase

CPT-11

1.6 ng/mL

10 2 10 000 ng/mL

Acetylcholine, Whole Serum

15 min

[43]

Impedance sensing

MCF-7

Cisplatin

10 μM to 50 μM

4h

[44]

Differential pulse voltammetry

DNA

Cytarabine

2.8 nM

0.01 and 90 μM

Uric acid, Sucrose, Citrate, Ascorbic Acid, Epinephrine, Dopamine, l-tyrosine

200 s

[45]

Differential pulse voltammetry

DNA

Epirubicin

0.01 μM

0.04 to 20.0 μM

Docetaxel, Tamoxifen, Paclitaxel

40 min

[46]

Square wave voltammetry

DNA

Zidovudine

0.003 pM

0.01 pM to 10.0 nM

Efavirenz, Sofosbuvir, Lamivudine, Acyclovir

200 s

[47]

Cyclic voltammetry

Enzyme

Ibuprofen

0.42 μM

1.56 μM25μM

Ascorbic Acid, Urea, Glucose, KCl, and NaCl

15 min

[48]

Amperometric and impedometric

Aptamer

Procaine

0.5 μM

1 1000 μM

5 min

[49]

396

11. Biosensors for drug detection

11.4.2 Optical biosensors The monitoring of drugs with minute concentrations in pharmaceutical formulations, environment, and biological samples is very important for TDM and drug discovery [50]. The assay of therapeutic drugs, especially the control of anticancer drugs in human body fluids, is important in the management of different adverse drug reactions [51]. Optical biosensors have been used by many researchers for drug monitoring in biological and chemical samples, clinical diagnostics, and other various industries [52,53]. Over the past decades, these optical-based systems have been applied for centrifugal lab-on-disc devices in CD/DVD/Bluray discs [54]. In optical biosensors, the recognition from a biomolecule/molecule is converted to a distinguishable optical signal by means of a transducing element. Optical biosensors are commonly used with enzymes, antibodies, aptamers, and biomimetic catalysts as the transducing units [53,55,56]. These biosensors have advantages such as high sensitivity, robustness, reliability, and potential to be applied in lab-on-a-chip and lab-on-a-disc devices. Optical sensors are classified according to signal production including fluorescence, colorimetric, CL, surface plasmon resonance (SPR), and surface-enhanced Raman scattering (SERS) [51,53]. SPR sensors are more sensitive and selective compared to localized surface plasmon resonance (LSPR) [51]. Optical biosensors are potential techniques for investigation of pharmaceutical compounds [57]. Biosensing of different drugs using optical biosensor systems has been reported by many researchers (Table 11.3). Fig. 11.6 shows how an optical biosensor works. Different types of optical biosensors in terms of their processing principles, features, and prospects have been used for drug monitoring in biological samples and medicine formulations discussed as follows. 11.4.2.1 Surface enhanced Raman scattering spectroscopy SERS is a surface-sensitive label-free system in which Raman signal is increased by electromagnetic fields of metal nanostructures (Fig. 11.7). This system identifies significantly small molecules and is able to get the single-molecule detection limit under environmental conditions. Several factors significantly influence SERS, such as the type and binding energy between the molecule and the nanoparticle, the morphology of nanoparticles, light excitation wavelength, and environmental physicochemical parameters. SERs is classified based on which recognition element (e.g., molecular imprinting polymers, molecular traps, aptamers and enzymes) is embedded in the sensor structure [53]. A wearable surface-enhanced Raman scattering was used for drug detection by Hye Koh and coworkers [70]. The SERS technique has been

2. Biomedical applications

TABLE 11.3 Different types of optical biosensors. LOD

Selectivity

Response time

Dynamic range

References

Fluorescent molecularly imprinted polymer nanoparticles

1 μM

Paracetamol

2 min

4 1000 μM

[58]

SPR

Anticortisol antibody

1 pg/mL

Cortisol

0.005 10 ng/mL

[59]

Cortisol and cortison

SPR

Antibody

, 10 microg l (-1)

Cortisol cortisone

9 132 μg/L for cortisol (30 143 μ/L for cortisone)

[60]

Ibuprofen, Ketoprofen, Flurbiprofen, Iodipamide, Magnesium salicylate

FIA-QCM

HSA/bilirubin

Sulfamethazine, sulfadiazine

SPR-Chip

Polyclonal antibodies

0.015 μg/mL 0.028 μg/ mL

Sulfamethazine, sulfadiazine

250 S

0.8 1000 ng/mL

[62]

Promethazine

Colorimetric

Potassium persulfate reagent

16.5 mg/L

Promethazine

10 min

50 to 500 mg/L

[63]

Drugs

Technique

Paracetamol

Fluorescence

Cortisol

Bio receptor/ receptor

15 20 min

Drugs, identifying drug-binding sites without the need of label

[61]

(Continued)

TABLE 11.3 (Continued) Drugs

Technique

Bio receptor/ receptor

Enrofloxacin and Ciprofloxacin

Optical Immunobiosensor

Methamphetamine

LOD

Selectivity

Polyclonal antibodies

1.5 |xg/kg

Enrofloxacin, Ciprofloxacin

Optical Immunobiosensor

Monoclonal antibody

0.02 100 ppm

Methamphetamine in the urine

5-fluorouracil

SERS

Silver-doped sol geL

150 ng/mL

Doxorubicin

SPR

Polyelectrolyte complexes

7 3 10

Anthracyclines (doxorubicin)

SPR

DNA aptamers

Nitroimidazoles

SPR-Chip

Polyclonal antibody

13

M

Dynamic range

References [64]

0.02 100 ppm

[65]

Smaller molecules, such as 5-FU and thiocyanate

1 30 μg/mL

[66]

Doxorubicin

1 3 10 12 to 1 3 1027 M

[67]

1021 102 nM

[68]

,1 μg/L/ 0.01 100 ng/g

[69]

daunorubicin and doxorubicin 1 and 2 μg/L

Response time

Nitroimidazole compounds and their metabolites including ronidazole, ipronidazole, metronidazole, hydroxymetronidazole, hydroxydimetridazole, hydroxyipronidazole in various species and sample types including porcine, bovine and ovine kidney, avian liver, serum, and eggs and bovine milk

7 min

30 S

11.4 Biosensors for drug detection

399

FIGURE 11.6 A schematic diagram of optical biosensor systems. An optical biosensor is a collection analytical system containing a biorecognition element integrated with an optical transducer system. The basic objective of an optical biosensor is to produce a signal that is appropriated to the concentration of a measured analyte. The type of output signal depends on the transducer used. SPR, SERS, wave fluorescence, fluorescence, chemiluminescence, and optical waveguide interferometry utilize the evanescent field in close proximity to the biosensor surface to detect the interaction of the biorecognition element with the analyte [6].

FIGURE 11.7 (A) A wearable surface-enhanced Raman scattering (SERS) sensor for label-free molecular detection in sweat. (B) Schematic diagram of the SERS system for drug detection. The monitoring process occurs in several steps including sweat absorption in the SERS patch, detection of molecule, Raman spectral analysis in the database, and drug monitoring [70].

2. Biomedical applications

400

11. Biosensors for drug detection

used to detect and illustrate the metabolic mechanism of the 6-mercaptopurine drug and its metabolite 6-mercaptopurine-ribose in living cells [51]. Also, SERS systems have been used for monitoring of small molecules including drugs in urine, neurotransmitters, bilirubin, and salicylate in blood samples [71]. 11.4.2.2 Colorimetric assays Colorimetric-based optical biosensors use a sensor that comprises a specific reagent entrapped within a polymer structure that can absorb light at specific visible wavelengths. The morphology of surface and functional groups of the colorimetric optical sensor are characterized using scanning electron microscopy and Fourier transform infrared techniques. The reaction process and formation of product can be tracked and observed qualitatively using the naked eye, or digital camera assays of target molecules are created quantitatively by converting the intensity of reflected light (red, green, and blue) from digital images into absorbance that is estimated using an external calibration technique [63,72]. Colorimetric methods use the intrinsic advantages of colorful species upon the addition of a specific analyte. In this regard, the use of noble metal nanoparticles, especially gold and silver, for colorimetric purposes have been considered. This method is an excellent platform for on-site and real-time screening of clonazepam from different biological fluids. 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol fabricated AuNPs were used as an optical probe for detection of dopamine. 11.4.2.3 Chemiluminescence assays CL assays have drawn considerable attention in a variety of applications. Chemiluminescence resonance energy transfer (CRET) is based on nonradiative energy transfer from a chemiluminescent donor to a suitable acceptor molecule without the presence of an external light source. The CL energy transfer depends on the optical transducer. The CL process can usually occur in gas, solid, or liquid phases. Liquid phases are the most common application in analysis of molecules. Luminol, acridinium esters, acidic potassium permanganate, dioxetane, manganese (III/IV), hypohalites, tris(2,2’-bipyridine) ruthenium (III), Ce (IV), and their derivative compounds are the common liquid phase CL molecules. There are several ways to improve CRET efficiency including: 1. Optimization of distance between energy acceptor and chemiluminescent substrates 2. Developing potential nanomaterials-based CRET devices 3. Designing CRET system for simultaneous analysis of multiple molecules

2. Biomedical applications

11.4 Biosensors for drug detection

401

4. Improvement of sensitivity and selectivity of CRET system via combination with other systems/techniques [73]. 11.4.2.4 Fluorescence assays These optical biosensors use florescence as an output signal to monitor therapeutic drugs and biomarkers. There are two main important optical processes for drug screening including integrated systems that utilize fluorescence for signal production based on wave excitation and automated SPR techniques [50,51]. The first technique is based on the propagation of an excitation wave along an optical fiber and emission of fluorescence at a planar or microcapillary immobilized recognition element. This permits better discrimination of specific binding from nonspecific adsorption of sample components and from interfering components in the sample bulk. This assay system was utilized for the design of a flimmobilize biosensor for cocaine-based immunoaffinity recognition [50]. DNA and oligonucleotide microarrays immobilized onto the optical fiber tip can be indirectly sensed and are labeled with a fluorescent dye [57]. Molecular imprinted polymer nanoparticles with fluorescent features have also been reported for drug quantification in real samples. Tetracycline-imprinted fluorescent nanoparticles utilizing a brush polymer have been employed for the detection of tetracycline. Among the techniques used for amoxicillin monitoring, the most important one is fluorescence spectroscopy due to simplicity, costeffectiveness, and the fact that it is less time consuming [50]. 11.4.2.5 SPR assays SPR-based technology is a suitable system for drug sensing [51]. SPR systems measure changes in the refractive index occurring at the surface of a metal thin-film [74]. Binding of molecules to the surface as a result of biospecific interaction changes the solute concentration in the surface volume, which influences the refractive index. The changes of SPR signal is followed continuously in real time, resulting in direct monitoring of the molecular interaction process. The detection principle depends on changes in refractive index. The SPR signal can be affected by type, size, and morphology of nanoparticles, and geometry of the plasmonic structure, environmental conditions and biomolecule/surface binding, magnetic nanostructures, Ag nanoparticles, Au nanoparticles, and carbon-based nanostructures increase SPR signal [52]. SPR biosensors can be designed based on aptamers, antibodies, enzymes, and a variety of nanostructures. Immobilization of recognition element on the sensor chip is performed when binding partner is added to the buffer flowing over the chip. Recently, derived techniques from SPR such as LSPR, surface plasmon resonance imaging, and long range surface plasmon have been

2. Biomedical applications

402

11. Biosensors for drug detection

developed by researchers [52,53,75]. LSPR can be defined when light interacts with particles much smaller than the wavelength of the exciting light [57]. The detection of the heparin oligosaccharide was reported using monoclonal antibodies against this drug immobilized on the surface of the SPR chip. The affinity of drugs toward plasma proteins and interaction of various drugs with immobilized albumin were characterized using direct SPR.

11.4.3 Photoelectrochemical biosensors Photoelectrochemical sensors are ultrasensitive and fast detection methods for medical diagnostics. These potential sensors can screen multiple analytes and provide rapid toxicity testing. Semiconductor nanoparticles (quantum dots), which are frequently used in optical biosensors, find use in electrochemical biosensors due to their electrochemical and optical properties. Electrochemiluminescence (ECL) biosensors are based on the electrochemical signals and the photoluminance feature of nanostructures. Quantum dots have attracted notable attention due to their broad excitation and narrow emission spectrum, high emissivity and photostability, control of surface chemistry electronic, and optical features in development of ECL sensors in medicine and clinical diagnostics [50]. The photoelectrochemical biosensor is generated through quantum dots as photosensitive elements generate photocurrent due to their electronic properties and may be sensitive to the chemical environment of the solution [50]. Photoelectrochemical-based nanosensors are potent alternatives to other related sensors for drug analysis and pharmaceuticals, and they are quite popular in nanotechnology-related analytical methods [50]. The ECL biosensors, which can translate electrochemical reactions to an optical signal, offer the advantages of high sensitivity, wide linear range, and low detection limit. It has been reported that an ECL-MIP system that was applied for doxycycline was prepared by electropolymerization of pyrogallol doped with alizarin red as a functional monomer. The ECL-based MIP sensor was applied to detect doxycycline in fish muscle residuals with picomolar LOD [76]. The process of detection is illustrated in Fig. 11.8.

11.4.4 Mass biosensors Another family of biosensors are the mass-sensitive sensors and piezoelectric transducer systems shown in Fig. 11.9 [77,78]. The principle of transduction results from the monitoring of the mass changes via the changes in some oscillating behaviors. Mass biosensors can be used for a variety of chemical and biological compounds due to high sensitivity. Micro/nanoelectromechanical systems (MEMS/NEMS) have generated

2. Biomedical applications

11.4 Biosensors for drug detection

403

FIGURE 11.8 Schematic diagram of photo-electrochemial/photo-electrochemiluminescence (PEC/PECL) biosensors based on enzyme/TiO2 QDs. A PEC cell consists of an optical excitation source, an electrochemical cell, and an electrochemical reader. In PEC bioanalysis, biological interactions between the analyte and the biorecognition element result in a change in the generated PEC current or voltage. Sensitive and selective PEC/PECL biosensors can be applied for assay/estimation of various biomolecules. Biorecognition elements such as enzymes, ssDNA, antibodies, and aptamers can be incorporated in PEC biosensors.

FIGURE 11.9 A schematic of a QCM system with a thin slice of AT-cut quartz crystal. The analyte exposed to the sensor surface where conjugation with immobilized ligand such as antibody occurs. For a QCM biosensor one side is in contact with an “acoustic thin layer” contacting a semiinfinite fluid medium, and the contribution of the coating and the liquid properties can be considered additive, which combines both effects on the frequency shift— the mass effect of the coating and the mass effect of the liquid. Acoustic waves are excited by a voltage applied to an electrode structure where the quartz crystal is sandwiched.

considerable interest as inertial mass sensors. MEMS/NEMS devices have been applied to detection and analysis of biological and chemical compounds [78,79]. Acoustic transduction is mainly based on quartz crystal microbalance (QCM) as piezoelectric sensing. Piezoelectric structures between two

2. Biomedical applications

404

11. Biosensors for drug detection

electrodes causes vibration at its resonant frequency. Change in thickness or mass can result in a shift of resonant frequency. These properties exhibit the information about film deposition or analyte mass, especially for chemical and biological sensor application. Mass sensors is demanded to assemble in the integrated silicon chip with the development of semiconductor systems, so the typical mass sensor QCM should be replaced by other modern devices, such as film bulk acoustic resonator [80]. Analytical interest in this physical transduction was demonstrated initially for sensors acting in air and vacuum environment, then in liquid media. The binding sites of some drugs on human serum albumin (HSA) as a drug carrier and reservoir were determined using the QCM system. Two sulfadrugs, sulfamethazine and sulfamethoxazole, were immobilized onto the gold electrode of the piezoelectric crystal using dithiothreitol as linker. The binding interaction of the immobilized drug with various proteins in solution (IgG, HSA, hemoglobin, etc.) was followed by changes in the resonance frequency of the modified crystal. Results obtained from this study indicated that the two drug ligands behaved quite differently in the molecular recognition process despite similarity in their structures [57]. The most recent developments of conventional SPR are in the growing field of proteomics in combination with matrix-assisted laser desorption/ ionization time of flight Ms (MALDI-TOF). In the combination of these two techniques, SPR allows to study biomolecular interaction analysis. MALDI-TOF allows to identify the molecules from the mixture that interact with the sensor. The combination of techniques is very efficient because the metal nanolayers sort in the laser desorption/ionization process. However, Tthe direct application of MALDI-TOF on the sensor chip is destructive. Therefore, a developed MALDI-TOF procedure with new anchoring sample supports causes the elution and capture of the binding partner(s) from the SPR instrument for further identification. Recently, GC Ms and HPLC Ms are the most commonly used techniques for pharmaceutical monitoring. LC Ms has a drawback compared to LC for quantitative detection since it is sensitive to matrix effects resulting from the complex nature of ground or sewage water samples. HPLC/Ms and GC/Ms are still the only available method for the qualitative and quantitative analysis of some therapeutic drugs.

11.4.5 Microfluidic-based (microfluidic-integrated) biosensors Microfluidic platforms are being quickly developed as powerful tools to model natural tissues and organs for pharmaceutical studies. Fig. 11.10 shows a microfluidic device for drug studies. Diffusion is the cause of

2. Biomedical applications

11.4 Biosensors for drug detection

405

FIGURE 11.10 Microfluidic device for drug screening application. A scaffold. mimicking heart tissue will be placed within the chamber. Then, the effects of new drugs on this tissue can be monitored in real-time [82].

mass transportation of nutrients and gases inside the channels of these small dimensional devices. This phenomenon properly simulates the in vivo delivery of nutrients. This technology needs low volume of different reagents that can reduce the cost of the equipment and sample. Due to their small dimensions, it is possible to control the system precisely and have better replication of human tissues in vitro. Microfluidic devices are fabricated using microfabrication techniques such as soft lithography, 3D printing, and injection molding. The common material utilized in microfabrication is poly(dimethylsiloxane) with its unique properties including transparency and biocompatibility as well as high gas permeability and provides sufficient oxygen for cell culture systems. As appropriate flexibility to create different designs is provided using microfabrication techniques, it is possible to combine a chemical gradient generator with sensing technology and develop the predictive microfluidic platforms for drug screening and pharmaceutical studies [81]. Microfluidic devices provide a controlled environment for cell culture. Different cells such as hepatic and cancer can be cultured on microfluidic platforms. Then, the effects of new drugs on them can be monitored continuously (real time). Another advantage of microfluidic devices is their ability to isolate and study on single cell, which avoids a heterogenic response of all cells to a drug. The development of combination therapies is another application of microfluidic devices in drug screening. Empirically, some drugs do not show intended effects when they are isolated. However, their efficiency will increase in tandem with other drugs. Therefore microfluidic devices can help in the determination of proper amount and type of drugs in combination therapies. It should be noted that biocompatibility of materials and surface modifications are two essential factors for designing a microfluidic cell culture platform [83]. We have discussed the use of microfluidic devices for pharmaceutical studies, but now we will focus on the advantages of microfluidic-integrated biosensors.

2. Biomedical applications

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11. Biosensors for drug detection

Recently, researchers have been developing the use of microfluidics coupled with biosensing technology that provides great advantages including miniaturization, higher selectivity, multiplexing, small sample volumes, and faster screening time. This coupling aims to use the minimum sample and involve the user in the detection process [81]. In fact, biosensors embedded in the microfluidic platform can react to intended analyte existing in the biological sample. In addition, they can monitor and report changes of cell behaviors because of the influence of a drug. One of the common applications of the microfluidic-based biosensors is the development of an anticancer drug that utilizes different techniques. For example, high metabolic activity of the cancer cells leads to the alteration of oxygen consumption and pH in the tumor microenvironment. Microfluidic-based biosensors have the ability to confirm or monitor the effectiveness of anticancer drugs such as tirapazamine and bleomycin by measuring oxygen concentration and its chemical gradient. As anticancer drugs are so expensive, microfluidic technology is a helpful tool to minimize required sample of drugs [81].

11.5 Recent trends in biosensors for drug detection Today researchers are developing nanosensor technology because miniaturization has advantages such as better signal-to-noise ratio as well as the possibility of using smaller sample volumes that leads to cost reduction. In addition, the surface-to-volume ratio of the sensing area will increase in nanoscale dimensions resulting in comparable changes in the sizes of the detecting electrode. These alterations reduce nonspecific binding while improve binding efficiency toward the target molecule. Also, nanobiosensors technology gives us the opportunity to detect a single molecule. A novel material that can be used in nanosensing technology is graphene (and graphene oxide). Graphene is a promising source for electrode fabrication that improves the detection process in a biosensor [14]. However, there are still some challenges for using nanomaterials in biosensors. For example, they need to be functionalized for working at room or body temperature, physiological pH, and under ambient conditions as well as to keep their stability for a long time [84]. Despite these problems, the development of nanobiosensors can create a revolution in drug detection processes and the pharmaceutical industry. Another new trend in drug detection studies is organ-on-chip technology, or as a better term, multisensor-integrated organ-on-chip platform [85]. These viable platforms have recently emerged to help personalized medicine and drug screening [82]. In fact, an organ-onchip platform recapitulates the important biological and physiological

2. Biomedical applications

11.6 Conclusion

407

parameters of a special organ such as heart or kidney, or even several organs. Organoid or 3D human tissue (e.g., liver organoid or heart tissue) is placed on the chip and then its reaction to a new drug can be monitored continuously. Organ-on-chip technology can be combined with sensing technology that results in sensor-integrated organ-on-chip platforms. This platform allows researchers to simply detect the effects of drugs on body organs as well as determine the optimum dose of new drugs for the treatment of the intended tissue or organ. In addition to the above technologies, wearable biosensors have recently been used for drug detection. These biosensors can provide continuous and real-time monitoring to gather physiological information. Wearable biosensors are noninvasive and dynamically measure biochemical markers in biofluids such as sweat, interstitial fluid, saliva, and tears [86]. Microfluidics, microelectronics, and electrochemical sensing technologies are utilized in wearable biosensors. Sweat-based ones are the most well-known wearable biosensors that use sweat as the biofluid. Sweat includes ions, drugs, metabolites, and various biomolecules. For example, levodopa and caffeine were target analyte in sweatbased biosensors where Au and carbon were considered as recognition elements, respectively. Levodopa is prescribed to treat patients suffering from Parkinson’s disease. As an individual’s response to levodopa is affected by various factors, continuous monitoring of the concentration is important. Therefore sweat-based levodopa sensing was investigated where blood-based monitoring is usually invasive [87]. One of the most important applications of wearable biosensors is continuous drug monitoring that will improve the knowledge of pharmacokinetic variability and individualized therapy. Wearable microneedle sensors, in addition to sweat-based ones, have mechanisms for monitoring drugs continuously. Implantable biosensors are also another category of biosensors that help scientists for continuous drug monitoring. It should be mentioned that biocompatibility is an important property in both wearable and implantable biosensors [88]. However, we should enhance our knowledge level on the correlations between analyte concentrations in the blood and noninvasive biofluids to improve reliability [86].

11.6 Conclusion Determination and monitoring of drugs is an appropriate method for evaluating their efficacy, cytotoxicity, and potential side effects during the initial stages of drug discovery. Disadvantages of conventional methods are high cost, low throughput, and long analysis time. Therefore biosensor technology opens a promising window for drug detection strategies and pharmaceutical studies. Biosensor technology is a multidisciplinary

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11. Biosensors for drug detection

field that brings together many disciplines like bioengineering, medical and pharmaceutical science, biochemistry and biophysics. Biosensors have properties such as selectivity, sensitivity, reproducibility, and stability that distinguish them from convention methods of drug detection. One of the ultimate goals of developing this technology is the possibility of use by a nontrained person such as with pregnancy test biosensors. In addition, biosensors can be used in personalized medicine and the prescription of individual drugs with special dosage. Moreover, recently nanomaterials have provided many advantages in sensing technology that make biosensors more accurate and sensitive. However, the biosensor industry still has some challenges that prevent the widespread use of biosensors in medical applications of our society such as drug detection.

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C H A P T E R

12 Micro alcohol fuel cells towards autonomous electrochemical sensors Maria Helena de Sa´ CIQUP-Chemistry Research Centre of the University of Porto, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal

12.1 Introduction The incorporation of a wide range of wireless, often very small and portable (or even wearable), sensor devices on our current life is already a reality. This has enabled numerous applications, namely for the new Internet-of-Things (IoT) paradigm, such as environmental sensing or healthcare monitoring [1 4]. Such IoT devices, most often, are powered by miniaturized electrochemical energy storage devices, like batteries. These however have a limited lifetime and therefore need to be replaced or recharged periodically in order to allow an extended operational time. Thus this has originated intensive research toward alternative micropower generation devices, featuring smaller size, with increased capacity and reliability [5 7]. Among them, micro fuel cells (FCs) are recognized to play an important role, as a supplement or a replacement of batteries for off-grid or portable electronic devices. They are considered as a clean and efficient energy sources [8 14]. FCs like batteries are electrochemical devices (galvanic cells) consisting of two electrodes in contact with an electrolyte, where the energy-providing processes (redox reactions) take place. Namely, oxidation at the anode (the negative electrode—where the release of electrons into the external circuit

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takes place) and reduction at the cathode (the positive electrode—which gains electrons from the external circuit). Different types of FCs exist, each adapted for a specific type of fuel, such as hydrogen, the fuel of choice for early devices and safer liquid hydrocarbons, like methanol, now preferred for operating smaller devices under ambient conditions. Moreover, alcohols are considered more attractive alternative fuels for portable applications due to their high-mass energy density, further detailed in next sections. Nevertheless, FCs convert the chemical energy of a fuel into electricity by reaction with an oxidant, most often oxygen from air, which simplifies the design of the devices. These electrochemical processes promoted by suitable catalysts release mainly heat and water as by-products with no emission of noise, and therefore can be considered efficient, environmentally friendly, and sustainable. In Fig. 12.1 the main components and basic working principle of a alcohol fueled FC are depicted [15 23]. Thus the biggest difference between the FC and battery technologies is that a battery eventually will stop producing electrical energy once the reactants are exhausted (closed system), while a FC will continue working until reactants (fuel and oxidant) are sufficiently supplied and the products of reaction are properly removed (open system). In practice, FCs do not run down or are electrically recharged; they just need to be fed (via a replacement or a refilled fuel cartridge/reservoir) to provide electricity. Thus while in batteries power and capacity are typically intertwined, in FCs independent scaling between power (determined by

FIGURE 12.1

(A) Comparison of the energy storage capability of fuel cells and batteries. (B) In FC stack the small incremental fuel volume to continue operation of supplying energy makes them more efficient for longer operations. (C) Basic design of a single alcohol fueled FC showing its core components and working principle (input/output) [15]. (B) Reproduced with permission from M. Winter R. J. Brodd, What are batteries, fuel cells, and supercapacitors? Chem. Rev. 104 (10) (2004) 4245 4269, https://doi.org/10.1021/cr020730k. Copyright r 2004 American Chemical Society.

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the FC type and size) and capacity (determined by the fuel reservoir size) is possible. Moreover, during long-term operation FCs also benefit from greater energy density (i.e., the available energy per unit weight) than that of advanced rechargeable batteries, as shown in Fig. 12.1C. On the other hand, the FC power density (i.e., the rate capability, or the speed at which the energy can be drawn) depends strongly on the type of fuel and the limitations of the-state-of-the-art technology. Namely, poor electrode kinetics, fuel crossover, and ohmic losses originate from the internal resistance of the FC, which cause output voltage to be substantially lower than that of the ideal performance. For this reason, a single cell can generate only small amounts of power and this can be compensated for by stacking multiple cells in order to meet the power and energy requirements inherent to the power source specifications. Thus for micro FCs reaching values that can vary from a few tenths of μW/cm2 up to several hundreds of mW/cm2 depend on the type of feed fueling used as well as the stack design [24,25]. In addition to the peak/maximum power density (MPD), obtained from polarization (current-voltage) curves, the performance of these power generators devices (single or stack) can be characterized by their open-circuit voltage (OCV), or voltage at which no load is being applied, besides the operating temperature. Notwithstanding these devices are considered to belong to the “low-temperature” category (typically bellow 95 C), they manage an uncomplicated start-up, along with a quick and easy response to changes in load and/or operating conditions, even at room temperature [20 23]. A useful tool for comparing the different types of energy supply systems and this energy/power trade-off is visually represented by the Ragone plot in Fig. 12.2 [26]. Conceptually, the vertical axis describes how much energy is available, while the horizontal axis shows how quickly that energy can be delivered, and the sloped lines indicate the required time to get the charge in or out of the devices. Thus due to their higher energy density despite lower power density, it is easy to understand why FCs are attractive as alternative micropower sources for long running devices, like autonomous sensors or other portable electronics [27 33]. Thus various FCs with highly efficient catalysts have been developed. Whilst the traditional FC uses inorganic catalysts (usually noble metals), the biofuel cell (BFC) employs materials derived from biological systems, biocatalysts, like enzymes or microorganisms and can be further categorized into enzymatic FC and microbial FC, respectively [34]. One should highlight a common misunderstanding that BFCs are named as such because they use biological fuels, which is ambiguous since the same fuel (e.g., methanol) can be produced from both biological (biomass) and nonbiological sources (fossil fuels). For those interested in more details regarding the history and state-of-the-art on FCs and BFCs, the review paper by E. Katz et al. [35] is recommend.

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FIGURE 12.2 A Ragone plot for various electrochemical energy storage and conversion devices. Reproduced with permission from Ref. V. Aravindan, J. Gnanaraj, Y.-S. Lee, S. Madhavi, Insertion-type electrodes for nonaqueous li-ion capacitors, Chem. Rev., Rev.-Art. 114 (23) (2014) 11619 11635, https://doi.org/10.1021/cr5000915. Copyright r 2014 American Chemical Society.

In this chapter, the focus will be given for alcohol-fed FCs using traditional/abiotic catalysts and their potential application as electrochemical self-powered sensor for alcohols, independently of the fuel’s source. Therefore BFCs are out of the scope of this work, which is inspired by the need to find innovative alternatives for the commercial FC-based breathalcohol sensors. These electrochemical sensors still use a porous polyvinyl chloride (PVC) membrane saturated with aqueous sulfuric acid (H2SO4), which is neither safe nor compatible with electronic devices. Besides employing an extremely high loading of the expensive platinum (Pt) catalyst [36 38]. It is well known that the quantification of ethanol concentration in exhaled breath sample is directly proportional to the ethanol concentration in the blood, and this blood-breath factor is generally acceptable for legal purposes. In an alcohol fueled FC, being limited by the fuel oxidation process, there is a direct correlation between the fuel concentration and its output signal (current and power density). Therefore in the case of FC-based breath-alcohol sensors, the concentration/amount of ethanol introduced into anode compartment is directly proportional to the output signal. Which after proper calibration the sensor, can be used to determine ethanol concentrations in unknown samples (see Fig. 12.3).

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FIGURE 12.3 (A) Schematics of the laboratory testing set-up, (B) working principle and (C) typical response curve, for commercial FC-based breath-alcohol sensors. Along with research results regarding membrane modified FC-based breath-alcohol sensors: (D) response curves obtained with different membranes; (E) response curves obtained with different ethanol standard solutions; (F) calibration curves (fit to a linear equation) resulting from the peak areas obtained from different conditions in (D); (G) response curves obtained in the presence of acetone in the same concentration of ethanol. Reproduced with permission from Ref. M.G. Gaopeng Jiang, F.J.E. Comeau, H. Zarrin, G. Lui, J. Lenos, A. Veileux, et al., Free-standing functionalized graphene oxide solid electrolytes in electrochemical gas sensors, Adv. Funct. Mater. 26 (11) (2016) 1729 1736, https://doi.org/10.1002/adfm.201504604. Copyright r 2016 John Wiley and Sons.

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A first investigation on using sulfonated graphene oxide (GO)-based solid electrolyte, already showed an alternative for replacement the PVC in FCbased breath-alcohol sensors, with low detection limit and selectivity for ethanol over acetone [38]. Herein, special attention will be given to the new miniaturized microfluidic membrane-less FCs (MM-FCs) fueled with alcohol. Since these devices are recognized as promising micropower sources, with enormous technical and economic advantages compared to membranebased micro FCs (discussed further in the text). The key challenge is to achieve the desired energy and power performance, while simplifying their design, as pointed out in a recent review by Y. Zhou et al. [39]. These are crucial aspects necessary to be optimized for using MM-FCs with the double function of power conversion and electrochemical sensing. Accordingly, in this chapter an initial section intends to provide the fundamental concepts and challenges regarding MM-FCs. Further details regarding design and flow considerations, followed by fuel (alcohols) electro-oxidation are addressed in the next two sections. While presenting implemented strategies to improve their performance, toward boosting electrochemical conversion and technology advances, before concluding with an outlook on opportunities for future research directions and application prospects.

12.2 Fundamentals There have been extraordinary developments in the field of emerging analytical microsystems, prompted by the synergistic relationship between microfluidic and electrochemical technologies [40 43]. The rich interplay between each of these areas and their overlaps/convergence, with an emphasis on new application-areas, was extensive detailed in the review by D.G. Rackus et al. [44] and also more recently focused by L. Schmidt-Speicher et al. [45] and F. Sassa et al. [46]. All showing how the new advances in the field of micro- and nanotechnologies have boosted the development of novel electrochemical systems, with enhanced features, such as miniaturization, portability, high sensitivity and selectivity, increased automation, minimized consumption of reagents, low-power requirements, and reduced manufacturing cost. Nowadays, different cost-effective and well-recognized technologies are applied for microfluidic fabrication, such as photolithography, soft lithography, polymer molding, lamination, laser ablation and more recently, 3D printing besides nanofabrication [47 51]. All these technologies enable mass-production of the microsize structures in a specific substrate, with structural and economic advantages. Moreover, due to their compatibly with microfabrication technologies silicon and glass

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have been initially the most used materials, however owing to polymers’ wide range of properties, materials such as polydimethylsiloxane (PDMS), polymethylmethacrylate (PMMA) along with SU-8 a epoxybased UV-sensitive resist have gained increased use [52 54]. Most recently the use of paper has aroused progressive interest, highlighted here due to its recognized low cost, effectiveness and convenience for microfluidic devices [55 61]. Thus FCs are among many of the devices that have been scaled down with the help of microfabrication, achieving precisely controlled geometry with high surface area-to-volume ratio, ease of integration, cost effectiveness and portability. The inception of the advantages of microfluidic devices in the field of FCs design was very soon explored to overcome some of the challenges/limitations, aforementioned [62 64]. Most micro FCs are fabricated using microelectromechanical system (MEMS) technologies, taking advantage of the precise repetitive steps of silicon etching and deposition processes, which enable microchannel structures for fuel and oxidant delivery to the electrodes. As the electrochemical processes occur at the surface of the electrodes, the performance of the FCs tend to improve with miniaturization, enhancing the fuel utilization. In the conventional design of micro FCs, the electrodes are most often separated by a physical barrier/solid polymeric materials, like a proton exchange membrane (PEM) (typically DuPont’s Nafion, a perfluorinated polymer with sulfonic acid pendant groups), or more recently due to their potential use with nonprecious catalysts, a anion exchange membrane (AEM) (where the hydrocarbon polymer backbone covalently bound pendant cationic functional groups, such as ammonium) [64 66]. These membranes play three vital functions, namely: (1) separate the fuel from the oxidant in the anodic and cathodic compartments; (2) act as electrolyte, allowing the selective ionic conduction of protons (H1) or hydroxide ions (OH2), for PEM or AEM, respectively; (3) act as electrical insulators, thus preventing short-circuiting of the FCs (the electron conduction occurs through an external circuit to power a load). However, until now these materials intrinsically pose limitations that prevent extended working life of the FCs, such as membrane fouling and degradation, besides electrode dry-out (at the anode) and flooding (at the cathode) due to the electro-osmotic drag and water management issues. Adding to the process of fuel permeation/crossover through the membrane (from the anode to cathode), which has been acknowledged as the most serious problem in PEM methanol fed FCs. Since the methanol that reaches the cathode is prone to undergo the same reaction that normally takes place at the anode, causing mixed potentials at the cathode and, thus, reducing the voltage delivered to the load, besides the dramatic decrease in fuel utilization and intensity of current generated,

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overall decreasing the performance. Therefore efforts are being made to promote further developments in the field in order to overcame these drawbacks [67 69]. One of the routes explored was to use microfluidic to create MMFCs, sometimes also called laminar flow-based FCs, since they take advantage of multistream laminar flow to delay convective mixing of fuel and oxidant [63,70]. Thus this liquid-liquid interface functions as a “virtual/liquid membrane” by transporting ions while minimizing mixing of the two solutions, thereby eliminating the need of a PEM or AEM. During operation of MM-FCs convective transport dominates over diffusion, which occurs transverse to the direction of flow. Moreover, addition of an electrolyte (acidic, alkaline or mixed) can be provided for both streams (anolyte and catholyte) in order to assure reasonably high ionic conductivity, besides stabilizing the colaminar flow with respect to electromigration of fuel and oxidant species, enhancing the electrochemical performance of the devices even at room temperature. Even so, as the fuel and oxidant streams travel down the channel, a mixing region (i.e., an interdiffusion zone) is established and grows thicker near the liquid-liquid interface, due to the transportation of reactants across the channel by diffusion. This mixing region poses resistance to passage of ions and also limits the amount of the reactants that can be potentially available to the electrodes. A depletion zone (i.e., a concentration boundary layer) is formed on each electrode, as a result of consumption of reactants. Both of these regions grow downstream of the microchannel and affect performance of the MM-FCs, due to increasing the mass-transport limitations and ohmic losses. In turn, these limit the fuel utilization and volumetric power density (i.e., available energy output per system volume) achieved. However, these have the very attractive characteristic of compactness, since all functions and components related to fluid delivery and removal, reaction sites, and electrode structures are confined to a microfluidic channel. Besides the clear advantage of eliminating the polymeric membranes and inherent technical and economic issues, the integration of the electrodes with sufficient separation from the mixing region prevents the fuel and oxidant crossover effects (their position and orientation also condition the fuel utilization, and this will be detailed in the next section) [63,64,71 74]. In Fig. 12.4, one of the first MM-FCs based on colaminar flow developed by E. Choban et al. is schematic represented, putting in evidence the previous considerations. This system consisted of a Y-shaped microfluidic channel incorporating Pt-coated electrodes on the side walls, with side-by-side streaming flow-by regulated with the use of syringe pumps. The researchers used graphite plates as catalyst support, current collector, and edificial element to assemble the device. The graphite plates were aligned (side by side) using polyurethane modified epoxy

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FIGURE 12.4 Schematics of a MM-FC laminar flow-based developed by E. Choban et al. (not drawn to scale). Reproduced with permission from Ref. L.J.M. E.R. Choban, A. Wieckowski, P.J.A. Kenis, Microfluidic fuel cell based on laminar flow, J. Power Sources 128 (1) (2004) 54 60, doi: 10.1016/j.jpowsour.2003.11.052; J.S.S.E.R. Choban, L. Gancs, A. Wieckowski, P.J.A. Kenis, Membraneless laminar flow-based micro fuel cells operating in alkaline, acidic, and acidic/alkaline media, Electrochim. Acta 50 (27) (2005) 5390 5398, https://doi.org/10.1016/j.electacta.2005.03.019. Copyright r 2004 and 2005 Elsevier.

separators and further modified by microfabrication (including drilling of inlets/outlet and sputtering of the Pt-based catalysts) as well as standard photolithographic techniques to obtain (in PDMS) the top and bottom wall of the Y-shaped microfluidic channel (0.5 3 mm in thickness). These were capped between more rigid support structures, such as polycarbonate, to provide robustness to the layered system [63,64,74].

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Their early experiments were conducted at room temperature using methanol (with or without supporting electrolyte, 0.5 M H2SO4) as the fuel and oxygen (dissolved in the same supporting electrolyte) as the oxidant and provided confirmation of the performance-limiting factors for these devices [74]. Namely, the researchers established that these MM-FCs become limited at the cathode beyond certain current densities (shown to be by that time . 7 mA/cm2), caused by the low solubility of oxygen in aqueous media and the consequent mass transfer limitations in the oxygen-depleted boundary layer. Additionally, they also confirmed the poor methanol oxidation reaction (MOR) kinetics at the anode, known to be due to mainly the Pt catalyst CO poisoning effect, which is considered a major limitation in low temperature methanol-based FCs [75]. Accordingly, the group showed that the use of (50:50) Pt and Ru mixture of nanoparticles (NPs) instead of Pt NPs may reduce the CO poisoning, leading to much higher OCV and current density. Furthermore, they also explored the media composition flexibility/pH, by working with acidic (H2SO4) and alkaline (KOH) electrolytes, as well under “mixed-media” conditions in which the anode and cathode used different types of electrolyte (acid/alkaline or vice-versa) [64]. Hence, they proved that without changing their system, only by simply operating under alkaline conditions had positive effects on the reaction kinetics at both electrodes, while the performance under “mixed-media” conditions offered an opportunity to increase the maximum achievable OCV. Thereby revealing the unique advantage of these devices, which enables individual tailoring of the composition of fuel and oxidant streams in order to optimize individual electrode kinetics as well as overall MM-FC potential. This media flexibility was pointed out as a means to lift the severe restraints on FC catalyst options aforementioned. Since these early studies, various research developments have been performed, as summarized in relatively recent books [11,14,18] as well as recent reviews. Most fundamental are the early studies and compilations provided by E. Kjeang et al. Namely, in Ref. [76] the researchers provided an experimental and theoretical framework, including a full electrochemical model as well as empirical mass transfer correlations and scaling laws for microfluidic devices operation in the high and low velocity regimes. They also explored the use of hydrogen peroxide (H2O2) as alternative oxidant for MM-FCs [77], since it is highly soluble in water (in opposition to oxygen, with a solubility of 2 4 mM) and exhibits a high standard reduction potential. In addition, H2O2 enables MM-FC operation in anaerobic conditions or where natural convection of air is limited, as in underwater and space. Moreover, in Ref. [78] the researchers presented for the first time MM-FCs based on graphite rod electrodes. Typically used as pencil refills, graphite rods have the advantages of being inexpensive, and as the researchers showed, serve effectively as both electrode and current collector in an unique three-dimensional (3D) MM-FC

2. Biomedical applications

12.3 Design and flow considerations

TABLE 12.1

423

Overview of the fundamental advantages and disadvantages of MM-FCs.

Advantages

Disadvantages

Media flexibility

Lower volumetric power density

Reduced cost

Higher ohmic losses

Size flexibility

Mass-transport limitations

Simple operation

Insufficient fuel utilization

architecture. The early studies by F. Brushett et al. [71] regarding evaluation MM-FC performance with five different fuels (formic acid, methanol, ethanol, hydrazine, and sodium borohydride) in either acidic or alkaline media are also mentioned to illustrate the diversity of common fuels that can be used. However, to keep it concise, the already mentioned reference [72] summarized the major developments in MM-FCs and briefly explained architectures, fabrication techniques, and types of fuels and oxidants. This knowledge was updated in Ref.[73], by M. Goulet et al., who also provided practical directions and recommendations for further research, toward achieving commercial applications. More recently, Y. Zhou et al. [39] and I. Hanapy et al. [10] also presented an overview of the major developments and issues in the field, for the last 20 years. Likewise, M. Tanveer et al. explored and discussed the matter in many different publications [79], [80 85]. Highlighted here is their extensive review [79] focused on how different flow configurations affect the diffusive mixing and depletion regions to enhance power density output, besides the research opportunities and challenges in the field for the near future. This was also addressed in the review [80], mostly focused on how the channel and electrodes design can enhance reactants mass-transport, by reducing mixing and depletion regions. Together with the discussion regarding the current status of theoretical and computational modeling for MM-FCs to improve their performance. To follow all this information, in a more detailed manner, is recommended for all with interest in the topic. A summary of the most prominent advantages and drawbacks for MM-FCs micropower generation devices found so far is presented in Table 12.1.

12.3 Design and flow considerations Although the pioneering studies for MM-FCs by R. Ferrigno et al. [62] and E. Choban et al. [63] used the basic Y-shaped microchannel architecture (shown before), this design was considered somewhat impractical, due to fabrication limitations, like the complicated techniques necessary

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors

as well as the insufficient power output for real applications [10,86,87]. One should keep in mind that typically the performance of these devices is limited by the electrochemical conversion process, which is conditioned by a combination of different physicochemical phenomena. These are well-known cross-related factors affecting the performance of FCs, which have been compiled before [86]. A concept map from that compilation is presented in Fig. 12.5 in order to provide a clear summary for understanding performance-related problems in MM-FCs. Hence, the MM-FC performance is fundamentally determined by the concentration of reactants, catalytic activity, and flow and mass transfer of reactants and products. In turn, these mass transfer losses can be effectively controlled by reactants’ flow rate and configuration, along with microchannel geometry and electrode architecture (as extensively covered in the recent reviews [79,80]). The goal has been to improve reactants utilization by reducing ohmic and mass transfer losses (in view of the coupled mass transfer to reactive sites at the electrodes surface), by reducing the mixing and depletion regions, so that higher performance MM-FCs can be achieved. Plus, in the case of substantially selective catalysts and stable reactants (that do not react with each other), a laminar flow regime is not required to prevent crossover effects and the fuel and oxidant species can be mixed in a single stream (I-shaped) flow, as shown in the studies reported by W. Sung et al. [88]. The researchers constructed a 100 μm-thick microfluidic channel on interdigitated microelectrodes array by means of photolithography, using nickel hydroxide and silver oxide, at the anode and cathode, respectively (see Fig. 12.6). These catalysts were able to minimize the effect of crossreactions within the MM-FC when using a single solution, containing the methanol (as the fuel) and H2O2 (as the oxidant), under the more favorable Performance of membranelessLFFC determined by

Acvaon loss Catalyst type and loading should be

highly active, resistant to poisoning, enough loading

Dispersion of catalyst particles should be

uniformly disturbed

Mass-tranfer loss

Open-circuit potenal

determined by

determined by

determined by

Ohmic loss determined by

Fuel & Electrodes Channel Electrolyte Interconnects Electrodes oxidant geometry spacing affects concentration conductivity concentration and should have electrode should be should have determine affects architecture least length affects reasonably pH of and Diffusion least affects determined by affects affects high enough affect fuel/oxidant resistance mixing resistance electrolyte (cross over) Nernst Equ. Replenishment rate of depletion Rate of removal of boundary layer

Fuel & oxidant type

Anodic and cathodic half-cell potentials

Flow and oxidant flow rate

product(s) and generated bubbles

affects

Fuel utilization

FIGURE 12.5 Concept map for the performance of membrane-less laminar flow-based FCs (LFFC) or MM-FCs (as herein being designated). Reproduced with permission from Ref. N.-T. N. Seyed Ali Mousavi Shaegh, S.H. Chan, A review on membraneless laminar flow-based fuel cells, Int. J. Hydrog. Energy 36 (9) (2011) 5675 5694, https://doi.org/10.1016/j.ijhydene.2011.01.063. Copyright r 2011 Elsevier.

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425

FIGURE 12.6 Pictures of a single-stream I-shape MM-FC: (A) microphotograph of the fabricated interdigitated microelectrodes used and (B) photograph of the packaged device for testing (a coin was included for scale comparison). Reproduced with permission from Ref. J.-W.C. Woosuk Sung, A membraneless microscale fuel cell using non-noble catalysts in alkaline solution, J. Power Sources 172 (1) (2007) 198 208, https://doi.org/10.1016/j.jpowsour.2007.07.012. Copyright r 2007 Elsevier.

alkaline conditions (KOH). In spite of the low power density achieved (28.73 μW/cm2), this planar and simple configuration with inexpensive catalysts, was considered feasible of high design flexibility and easy integration in a given volume or stacking, leading to higher volumetric power density output, capable of meeting the specific needs for the applications of microfluidic devices and portable power sources. As an alternative, for the laminar flow-based MM-FCs, one could also be tempted to consider that larger flow rates resulted in less mixing of the streams at the liquid-liquid interface and provided higher power output. However, in practice the fuel and oxidant may reach the end of the channel before they are consumed, with a consequent insufficient fuel utilization in the devices. Therefore in order to overcome the abovementioned challenges, the electrodes architecture for MM-FCs has evolved from the simple flat plate (considered 2D) flow-over or flow-by architecture used in the first Y-shaped devices previously discussed, where there is just a thin layer of catalysts, to porous (3D) flow-through electrodes configuration, providing substantially increased active area combined with enhanced transport characteristics. In the flow-through design the fuel and oxidant streams form parallel laminar flows in the central microchannel, only after they permeated through the porous anode and cathode, respectively. This new MM-FC configuration was first studied by E. Kjeang et al. [89], who demonstrated the importance of the hydrophilic porous electrode treatment on the effectiveness of the flow-through architecture, and the potential for significantly increased performance by reducing the ohmic resistance, as compared to previous flow-over MM-FCs. Moreover, because the fabrication procedure used was relatively quick and the MM-FC (excluding external wires and tubes) contained only PDMS and carbon paper (CP), this was considered a very economical approach.

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors

A similar strategy of fabricating the devices in all-polymer was proposed by A. Hollinger et al. [90]. The researchers used laser machined polyimide (brand as Kapton by DuPont) layers to facilitate rapid prototyping of channel and encapsulation of the electrodes, an alternative to heavy clamping designs to seal layers and metal plates as current collectors. So in their device, the electrodes were encapsulated between polymer layers and placed on the bottom of the central channel facing inward on either side of the channel, normal to the vertical colaminar flow of streams. This configuration improved utilization of the 3D active area inside the porous electrodes and provided enhanced rates of convective/diffusive transport to the active sites, corresponding to a flowthrough configuration. In addition they also tested different oxidant conditions, namely using a conventional air-breathing cathode and supplying O2 or air by pumping. The results gathered showed that exposing the cathode to an air stream enhanced performance by B30% over the air-breathing configuration, which increased by an additional B70% upon flowing oxygen over the cathode as opposed to air. Hence, with this approach the researchers were successful in their aim to fabricate a much thinner (mm-thick) polymer-based, lightweight methanol MM-FC and demonstrated its potential to be scaled and stacked, in order to achieve power levels sufficient for portable electronic applications. Later on, M. Goulet et al. [91] developed a novel method of in operando modification of flow-through electrodes, which allowed the record MPD of 2.01 W/cm2 (or 13.4 W/cm3) for a MM-FC at room temperature. This was achieved by dynamically deposition of carbon nanotubes (CNTs) on CP electrodes during the MM-FC operation. So that the electrochemical surface area and pore size distribution of the flow-through porous electrodes were greatly enhanced, which in turn improves both the reaction kinetics and mass transport. Similar findings had already been reported by W. Huo et al. [92], when studying the influence of catalyst layer materials and deposition methods on the MM-FC performance. They used CNTs to support Pt NPs (via electrophoretic deposition) to be employed as catalyst layer on both electrodes. This way the researchers showed that the peak power density achieved by the device could more than double, reaching 5.70 mW/cm2 (at 25 C), while plain Pt electrodes only gave 2.75 mW/cm2. Moreover, it is important to recall the triple-phase boundary (TPB) theory, which states that an electrochemical reaction can only occur at a TPB site, where the electrolyte, reactants, and an electrically connected catalyst are in contact [93]. Hence, in a porous (3D) flowthrough electrode the extension of these TPB sites are more favored than the planar (2D) flow-over, where they are more surface limited. In turn, this enhances the catalyst utilization and corresponding electrochemical processes. Thus it is expected that MM-FC performance can be greatly enhanced with a 3D porous electrode carrying catalyst inside the pores.

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427

This was demonstrated by Y. Kwok et al. [94], who proposed graphene aerogel (GA) with ultra-fine Pt NPs as a porous anode for MM-FCs running on methanol. The performance, between the flow-over type and the flowthrough type MM-FC and their corresponding anode catalytic activity, was evaluated by using the 3D Pt/GA in comparison with the commonly used Pt/C on CP (considered 2D for this application). This way, with the new 3D electrode the researchers observed much higher cell performance compared with the 2D electrode (using the same catalysts loading), which they attributed to the flow-through structure as well as the enhanced catalytic activity. This strategy was further pursued by the group, to demonstrate that the Rurich core coated with a Pt-rich shell NPs (Ru@Pt) decorated on a porous anode for flow-through MM-FC could achieve a much better cell performance. Also taking advantage of the fact that the combination of Ru with Pt has a synergistic effect, improving the MOR kinetics significantly [95]. A variation in this strategy, more frequently reported to improve the performance of MM-FCs when running on dissolved oxygen (as aforementioned, cathode-limited due to the poor solubility and slow transport of oxygen in aqueous media limited), has been the use of an air-breathing cathode, as noted. This was first introduced by R. Jayashree et al. [96], who replaced the graphite cathode by a gas diffusion electrode, since it enabled oxygen delivery directly from air, which significantly enhances the oxygen reduction reaction (ORR) rate. In addition, this type of electrode made over a gas diffusion layer (GDL) used as a transport medium for the reactants (most often CP) and hydrophobic by nature has the advantage of decreasing the thickness of the depletion boundary layer and generating a sharper concentration gradient, thus causing a higher flux of reactants through the depletion region near the cathode. Yet, airbreathing design requires a blank cathodic electrolyte stream with flow rate similar to the anodic stream in order to facilitate ionic transport between the electrodes and prevent fuel crossover. Another example of investigation using an air-breathing laminar flow MM-FC was reported by D. Whipple et al. [97] regarding the incorporation of ruthenium cluster-like chalcogenide (RuxSey) as a methanoltolerant cathode catalyst (compared to a conventional Pt catalyst). With this type of MM-FC, in their case F-shaped (both streams on the same side of the device), the researchers demonstrated that notwithstanding the absolute performance of the device with RuxSey cathode being B4 mW/cm2 (while Pt cathode was B16 mW/cm2), the methanol tolerant catalyst greatly simplified the overall system configuration, and opened up the way for a more compact and thus higher specific energy power source. Plus this configuration was considered to be much simpler to operate, as the crossover effects were eliminated. Further studies to explore these issues in a methanol fueled MM-FC, operated in alkaline and acidic media at room temperature, were also

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors

reported by R. Jayashree et al. [98]. The researchers, with this design change (see in Fig. 12.7A schematic for an air-breathing MM-FC), achieved significantly higher OCVs (than the membrane-based devices), namely 0.93 and 1.05 V, respectively, in acidic and alkaline media, attributed to minimization of methanol crossover in both media. Moreover, a MPD of 11.8 and 17.2 mW/cm2 were found for operation in acidic or alkaline media, respectively. The researchers considered several factors to explain the results achieved, namely: (1) improved reaction kinetics of MOR and ORR at high pH; (2) decreased ohmic losses due to electrolyte conductivity; and/or (3) better mass transfer as a consequence of the air-breathing cathode being exposed to a higher oxygen concentration, and more importantly, the rate of oxygen

FIGURE 12.7

Schematic for an air-breathing MM-FC (active/pumping) configuration. Reproduced with permission from Ref. H. P. Amit Kumar Rathoure, Electrooxidation study of methanol using H2O2 and air as mixed oxidant at cathode in air breathing microfluidic fuel cell, Int. J. Hydrog. Energy 41(34) (2016) 15287 15294, https://doi.org/10.1016/j.ijhydene.2016.07.058. Copyright r 2016 Elsevier.

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429

replenishment in the depletion boundary layer on the cathode being greatly enhanced, as a consequence of the higher diffusion coefficient of oxygen in air (opposed to that in aqueous media). By employing this type of device (air-breathing MM-FC, Fig. 12.7) more research exploring the electrooxidation of methanol (this time using H2O2 and air as mixed oxidant at cathode) was first reported by A. Rathoure et al. [99]. Moreover, the researchers produced in laboratory the GDLs (carbon cloth based) and electrodes used. Their goal was to optimize the TPB sites in both electrodes, so that they could keep the catalysts loading as low as possible while producing power at very low cost. This way, the researchers observed that MM-FC performance improved when H2O2 was used as mixed oxidant. They reported a maximum OCV of 0.68 V and current density of 32.4 mA/cm2, with a peak power density of 3.8 mW/cm2 when using the mixed oxidant, while only 0.97 mW/cm2 was obtained for air-breathing cathode condition. This enhanced performance when using the mixed oxidant at the cathode was attributed to a reduced activation and concentration polarization provided by the additional O2 on the surface of the cathode. Thus demonstrating that their strategy could be used to reduce production costs of the MM-FCs. A novel design for MM-FCs, named as on-chip FC (Fig. 12.8) proposed by S. Tominaka et al. [100], used also air-breathing cathode configuration. Hence, they used MEMS technology on Si substrate to develop a prototype that was both monolithic and air-breathing. It is considered a promising on-chip power source for miniature devices. In fact, the microfabrication techniques allowed a shallow trapezoidal channel (5 μm depth, 400 μm top width, 390 μm bottom width, 6 mm

FIGURE 12.8

Schematic for an air-breathing MM-FC (passive/no pumping) configuration, on-chip FC: (A) top-view of the whole cell and (B) cross-sectional view of the electrodes part: (i) an anode catalyst layer, (ii) cathode catalyst layers, and (iii) an anode channel containing fuel solution and electrolyte. Reproduced with permission from Ref. S. Tominaka, S. Ohta, H. Obata, T. Momma, T. Osaka, On-chip fuel cell: micro direct methanol fuel cell of an airbreathing, membraneless, and monolithic design,, J. Am. Chem. Soc. Rapid-Commun. 130 (2008) 10456 10457, https://doi.org/10.1021/ja8024214. Copyright r 2008 American Chemical Society.

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors

length) for the fuel supply, and easy integration of two cathodes at the top of the channel and an anode on the bottom. Their choice of catalysts was the conventional Pt 2 Ru alloy for the anode and Pd 2 Co alloy for the cathode, in view of their previous results showing this last to be a selective catalyst for ORR. They used H2SO4 as a supporting electrolyte but also tested Na2SO4, considering that operation of FCs under neutral conditions is attractive from the viewpoint of its high biocompatibility and safety. As so, the researchers demonstrated that a prototype device was able to generate electric power on methanol without pumps under both neutral and acidic conditions (with a maximum c. 1.4 μW in acidic media). In addition, further investigation with this type of device, regarding the feasibility of the use of alternative fuels, like ethanol and 2-propanol as well as the use of phosphate buffer to realize safe operation under neutral pH conditions, was also reported by the same group [101]. This way they proved that the on-chip FC was flexible to fuel solution and could be operated not only with methanol (MeOH), but also with ethanol (EtOH) and 2-propanol (2-PrOH), both under acidic and neutral conditions. Thus the researchers considered that the best solution should be chosen accordingly to the application requirements. More recently, a mathematical model presented by A. Rathoure et al. [102], for air-breathing MM-FC using methanol, ethanol, and sodium borohydride as fuels, was developed considering the various losses at the electrodes. This model was validated with experimental data from polarization curves for different fuel/electrolyte concentrations at different temperatures. The experimental setup used was similar to the one shown in Fig. 12.7. Finally, one should also mention the previous computational models of air-breathing MM-FCs, with flow-over and flow-through anodes, by B. Zhang et al. [103] and T. Ouyang et al. [104], developed by coupling multiphysics consisting of microfluidic hydrodynamics, electrochemical reaction kinetics, and species transport of fluid. The reliability of the models was demonstrated by the excellent consistency between simulation results and experimental data, therefore providing guidance for further design and operation optimization. Moreover, in Ref.[104] gravity, exergy, and parametric sensitivity were also studied, and showed tremendous impact on MM-FC performance, despite been frequently overlooked. Another way of classifying MM-FCs (and maybe more intuitive) is given by their microchannel geometry and electrodes position, which can be side-by-side streaming with a vertical fuel-oxidant interface, comprising the Y-shaped MM-FCs with flow-over or flow-by and flowthrough type electrodes and the I-shaped single-stream mentioned before. But other shapes can be considered, such as the H-shape crosssection design leading to a small passage between the anode and cathode channels, first proposed by H. Park et al. [105], where the small

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431

passage restricted the mixing of the anode and cathode fluids flowing side-by-side in the main channel. This design was also used by A. Hollinger et al. [106] to investigate further minimization of the fuel crossover in an colaminar MM-FC fueled with methanol and employing flow-over and flow-through type electrodes. Alternative configurations were established by considering vertically layered streaming with a horizontal fuel-oxidant interface, resulting in T- or F-shape when the inlets are on the same side. One of the first examples of the T-shape design using methanol were already mention in the several examples describing the air-breathing cathode configuration, as can be seen in Figs. 12.7 and 12.8. Examples of using the F-shaped MM-FCs can be found in the research work by F. Brushett et al. [71] and D. Whipple et al. [97]. A schematic representation of all these different types of MM-FCs shapes is given in Fig. 12.9. A last important way of classifying MM-FCs design has been based on variations of their flow(s) configuration, as shown in the compilation presented in Fig. 12.10. The first examples (Fig. 12.10A and B) correspond to the aforementioned initial innovative solutions of vertical colaminar flowover and side-by-side colaminar flow-by design, respectively. In Fig. 12.10C) the alternative configuration of single stream that takes advantage of the flow-through electrodes, as discussed before, is shown. Fig. 12.10D) is another different approach also supported on flow-through electrodes, proposed by K. Salloum et al. [107], using counter-flow design. They introduced a nonreacting electrolyte separating the reacting streams and led them to opposite and independent outlets. Hence, the separation electrolyte provided an electrochemical bridge from the anode to the cathode, while keeping the reactants remain separated throughout their residence time in the MM-FC. This alternative counter-flow design was further explored in a numerical and experimental comparative study with a colaminar MM-FC by Y. Wang et al. [108]. The researchers used H2SO4 as electrolyte and ethanol as fuel; no oxidant additive was needed since the air-breathing cathode

FIGURE 12.9

Schematic of MM-FCs with various microchannel geometries: (A) Yshaped, (B) T-shaped, (C) H-shaped, (D) F-shaped, and (E) I-shaped. Reproduced with permission from Ref. E.S.L. Muhammad Tanveer, Kwang-Yong Kim, Effects of channel geometry and electrode architecture on reactant transportation in membraneless microfluidic fuel cells: a review, Fuel 298 (2021) 120818, https://doi.org/10.1016/j.fuel.2021.120818. Copyright r 2021 Elsevier.

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors Fuel

Dual-Role Reactant

Fuel

oxidant

Fuel

oxidant Anode

Anode Electrolyte

Anode Cathode

Cathode Cathode

Cathode

Anode

Oxidant

(A)

(B) Fuel Entry

(C)

(D)

O-ring Grooves

Fuel Flow

Anode

Conjugate pad

Gap

Steel Plates

Cathode

Anode

Anode at boom Cathode at top

Steel Spacer

Test line

Cathode Reacon zone Oxidant (Paper strip)

Coetral line

Waste Collection

Oxidant Entry

Absorbent pad

Waste Outlets

(E)

Oxidant

(F)

(G)

FIGURE 12.10 Schematic of MM-FCs with different flow configurations: (A) vertically layered streaming colaminar flow-over, (B) side-by-side streaming colaminar flow-by, (C) single-stream flow-through, (D) counter-flow, (E) radial-flow, (F) lateral-flow, and (G) orthogonal-flow. Reproduced with permission from Ref. M. Tanveer, K.-Y. Kim, Flow configurations of membraneless microfluidic fuel cells: a review, Energies Rev. 14 (12) (2021) 3381, https:// doi.org/10.3390/en14123381. Copyright r 2021 MDPI.

was also employed. This way the researchers concluded that after structural optimization (by employing narrower electrode distance, smaller electrode area close to the flow interface, and double outlets) the counterflow MM-FC achieved the same level power output than its coflow counterpart. With the important advantage of much higher tolerance to extremely low flow rates than the coflow MM-FC, confirming superiority in fuel utilization and energy density. This high performance was attributed to the diffusive fuel crossover being impeded by the convective electrolyte flow in this new flow configuration, which makes counter-flow MM-FC more competitive for practical applications, such as powering portable electronic devices. The studies mentioned before by Y. Kwok et al. [94] also used this type of flow configuration. Another variation to the colaminar flow MM-FC design, also developed by K. Salloum et al. [109] is the radial-flow MM-FC (Fig. 12.10E). In this configuration the fuel and oxidant flow sequentially in series rather than parallel, through the porous electrodes (thin disk-shaped anode and ring-shaped cathode). Thus enabling independent control over the fuel and oxidant flow rate and the electrode areas, leading to distinct transport features. Due to the improved mass transport, the researchers showed that higher fuel utilization was achieved with this design. In Fig. 12.10F the paper made lateral-flow MM-FC self-pumping device is shown. It is considered another category, because reactants

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12.3 Design and flow considerations

flow laterally without external assistance (pumping). This is explained by the intrinsic porosity and spontaneous capillary flows within the paper channels, making this material an ideal platform for microfluidic devices, as mentioned before. This concept was first presented by J. Esquivel et al. [110], who inspired by the manufacturing process of the lateral flow test strips, placed the electrodes at the bottom and top of the reaction zone in the main paper strip (this will be further detailed later). Chromatography paper is conventionally used for manufacturing these devices and many different fabrication methods can be employed depending of the goal [59,61]. Finally, a short comment regarding the last variation represented in Fig. 12.10G, known as the orthogonal-flow streaming MM-FC and proposed by J. Hayes et al. [111]. These devices utilize a convective electrolyte flow orthogonal to the plane of the diskshaped electrodes, preventing mixed potentials caused by back diffusion of oxidant onto the anode. The researchers tested the system with methanol (in alkaline media) and hydrogen (in acidic media) as the fuel, while oxygen was the oxidant, reaching interesting fuel utilization and power densities (for methanol, a MPD of 46 mW/cm2 and OCV of 600 mV). This type of MM-FC was also adopted in the studies mentioned before by Y. Kwok et al. [95]. In conclusion and focused on the single MM-FC devices, one realizes that there is an interplay of structural parameters, including electrode architecture, shape, and flow configuration that condition their performance. Besides the initial choice of concentrations and types of electrolyte and reactants, catalyst nature and loadings as well as operational conditions, like flow rate and temperature, are also considered. Therefore proper grouping of these structural parameters is a first step in understanding how they can be used to improve the performance of the devices. With this goal in mind, Table 12.2 provides an overview of examples regarding alcohol fueled MM-FCs types and features just discussed. TABLE 12.2 features.

Overview of the most common alcohol fueled MM-FCs types and

Category

Type

Feature

References

Channel configuration

Y-shaped

Colaminar streaming, supports flow-over and flow-through type electrodes

[63,64,74,92]

H-shaped

Colaminar streaming, supports flow-over and flow-through type electrodes

[106]

(Continued)

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TABLE 12.2

(Continued)

Category

Type

Feature

References

T-shaped

Colaminar streaming, supports flow-over and flow-through type electrodes

[94,98 100,102]

F-shaped

Colaminar streaming, supports flow-over and flow-through type electrodes

[71,97]

I-shaped

Single stream, supports flow-over and flow-through type electrodes

[88]

Flow-over

Lower fuel utilization and mass transfer

[63,64,71,74,94,97 101,106]

Flowthrough

Improve mass transfer and fuel utilization

[92,94,95]

Airbreathing cathode

The rate of replenishment of O2 from air is much faster than from solution

[71,90,94,95,97 102,108,110,112]

Colaminar flow

Higher mass transfer losses

[63,64,71,74,90,92,97 102,106,112]

Counter flow

Minimize diffusive mixing region, decreasing mass transfer losses

[94,108]

Lateral flow

Self-pumping by capillary action on paper (no external pumps are used)

[110]

Single flow

Mixed-reactant condition, no crossover issues

[88]

Radial flow

Improve mass transfer and fuel utilization

Orthogonal flow

No mass transfer limitation but complex assembly

Electrode architecture

Flow configuration

[95,111]

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12.4 Fuels electrooxidation and micropower generation

435

12.4 Fuels electrooxidation and micropower generation Besides novel designs and integrations, the successful performance of MM-FCs demands the use of specific materials, like structural components and supporting electrolyte in different media, conditioned by the paramount options regarding their fuels and oxidants. Since this chapter is focused on alcohol fueled MM-FCs, it is important to extend the knowledge regarding their attractiveness. First of all, one should realize that the type of fuel predominantly determines the MM-FC performance (voltage, current, and power density output), through its inherent thermodynamic and kinetic characteristics concerning the electrooxidation process. This is summarized in Table 12.3, by Y. Wang et al. [113] on a prospective review regarding the most common types of fuel used for microfluidic FCs and associated features, including reversible (thermodynamic) cell voltage (ΔE0, corresponding to the maximum potential that an ideal galvanic device can attain), reaction kinetics and typical catalyst used, besides energy density and fuel cost. Remember however that in real electrochemical devices, the cell voltage is always lower than the thermodynamic value due to different losses mentioned, such as activation overpotential, crossover effects, ohmic losses, and mass transport limitations [114,115]. From Table 12.3 we can observe the advantages and disadvantages of each type of fuel. Again, focused on the alcohol fuels considering their low cost, besides easy handling and storage, combined with their higher energy density and theoretical (thermodynamic) energy conversion efficiency for FC devices [22] (see Table 12.4 for comparison with hydrogen in acidic media) it is understandable why these type of devices are considered the best candidates to replace batteries in small-scale portable applications. Even though their sluggish oxidation reaction and ORR require typically expensive precious metal catalysts, like Pt, which is much vulnerable to CO poisoning, intermediate formed during the alcohol’s oxidation reaction (AOR). As pointed out before, the alkaline media can be a way to circumvent this problem, since it enhances the kinetics of both reactions (AOR and ORR), thus allowing the use of less expensive catalysts [116]. This has been translated into the flexibility of MM-FCs devices, namely in the fact that they can be can be operated in acidic, alkaline, or mixed-media conditions, without the constraints associated with the presence of membrane (PEM or AEM). This was discussed before in the studies by E. Choban et al. [64] for a Y-shaped MM-FC using methanol and dissolved O2 reactants, able to reach a OCV of 1.4 V for the alkaline anode/acidic cathode mixed-media configuration. And also, by F. Brushett et al. [71], running an air-breathing F-shaped device, with common fuels (including methanol and ethanol) in either acidic or alkaline media, being only limited by the actual anode catalyst used.

2. Biomedical applications

TABLE 12.3

Comparison of different fuels for microfluidic FCs operation.

Type of fuel

ΔE0a/(V)

Reaction kinetics

Vanadium

1.25

High

Hydrogen

1.23

High

Glycerol

1.21

Borohydride

Typical catalyst

Fuel costb/(USD/kg)

Shortcoming

Carbon

14.1

Toxic

39.4

Pt

14

Storage issue

Medium

5.0

Pt/Pd

1

High viscosity

1.64

Medium

9.3

Pt

10

Expensive

Formic acid

1.45

Medium

1.73

Pd

1

CO2 bubbling

Ethanol

1.14

Medium

8.0

Pd/Pt Ru

0.42

Catalyst poison

Hydrazine

1.56

Medium

5.47

Pt

1

Toxic

Ethylene glycol

1.21

Medium

5.3

Pd (alkaline)

1

Catalyst poison

Methanol

1.21

Medium

6.1

Pt Ru

0.4

Toxic

Hydrogen peroxide

1.08

Medium

0.75

Pt

0.8

O2 bubbling

Specific energy/(kWh/kg)

c

Ammonia

1.14

Low

6.25

Ni Cu

0.5

Glucose

1.09

Low

4.43

Au

0.5

Urea a

1.15

Low

3.56

Ni (alkaline)

0.22

N2 bubbling Poor efficiency c

N2 bubbling

Complete reaction at standard equilibrium state (298.15K, 1 atm, 1 mol/L for each aqueous species) with O2 as oxidant (except for vanadium and H2O2). Recent price of industrial-grade product from Internet. c Free of cost if wastewater is used. Source: Reproduced with permission from Ref. S.L. Yifei Wang, H.Y.H. Kwok, W. Pan, Y. Zhang, X. Zhao, D.Y.C. Leung, Microfluidic fuel cells with different types of fuels: A prospective review, Renew. Sustain. Energy Rev. 141 (2021) 110806, https://doi.org/10.1016/j.rser.2021.110806. Copyright r 2021 Elsevier. b

437

12.4 Fuels electrooxidation and micropower generation

TABLE 12.4 General chemical equations and other estimated FC parameters associated with the electrochemical oxidation of some alcohols and hydrogen (under standard conditions in acidic media). Electrochemical equation

Standard potential (E0)/V

Anode

H2 - 2 H1 1 2e2

0.000

Cathode

2 H1 1 2e2 1 1/2O2 - H2O

1.229

Overall

H2 1 1/2O2 - H2O

1.229

Anode

CH3OH 1 H2O CO2 1 6 H1 1 6e2

0.016

Cathode

6 H1 1 6e2 1 3/ 2O2 - 3H2O

1.229

Overall

CH3OH 1 3/2O2 - CO2 1 2H2O

1.213

Anode

C2H5OH 1 3H2O - 2CO2 1 12 H1 1 12e2

0.084

Cathode

12 H1 1 12e2 1 3O2 - 6H2O

1.229

Overall

C2H5OH 1 3O2 2CO2 1 3H2O

1.145

Anode

C3H7OH 1 5H2O - 3CO2 1 18 H1 1 18e2

0.107

Cathode

18 H1 1 18e2 1 9/ 2O2 - 9H2O

1.229

Overall

C3H7OH 1 9/2O2 - 3CO2 1 4H2O

1.122

Anode

C2H6O2 1 2H2O 2CO2 1 10 H1 1 10e2

0.009

Cathode

10 H1 1 10e2 1 5/ 2O2 - 5H2O

1.229

Overall

C2H6O2 1 5/2O2 - 2CO2 1 3H2O

1.220

Fuel Hydrogen

Methanol

Ethanol

2-Propanol

Ethylene glycol

Energy density/ (Wh/L)

Reversible energy efficiency

180 (@ 1000 psi, 25 C)

0.830

4820 (100 wt.%)

0.967

6280 (100 wt.%)

0.969

7080 (100 wt.%)

0.971

5800 (100 wt.%)

0.990 (Continued)

2. Biomedical applications

438 TABLE 12.4

12. Micro alcohol fuel cells towards autonomous electrochemical sensors

(Continued) Electrochemical equation

Fuel Glycerol

Standard potential (E0)/V

Anode

C3H8O3 1 3H2O 3CO2 1 14 H1 1 14e2

0.019

Cathode

14 H1 1 14e2 1 7/ 2O2 - 7H2O

1.229

Overall

C3H8O3 1 7/2O2 - 3CO2 1 7H2O

1.210

Energy density/ (Wh/L)

Reversible energy efficiency

6400 (100 wt.%)

0.951

Source: Reproduced with permission from Ref. S.K.K.D.M. Fadzillah, M.A. Zainoodin, M.S. Masdar, Critical challenges in the system development of direct alcohol fuel cells as portable power supplies: an overview, Int. J. Hydrog. Energy 44 (5) (2019) 3031 3054, https://doi.org/10.1016/j.ijhydene.2018.11.089. Copyright r 2019 Elsevier.

Hence, since the first appearance of alcohol fueled FCs (membranebased and membrane-less), methanol (CH3OH or MeOH), being the simplest alcohol with no C C bond, has been studied widely, presenting the higher reactivity toward Pt electrode in acidic media [22,117,118]. Notwithstanding MeOH abundance (easily produced from fossil fuels or biomass) and low cost, it may cause safety issues due to its toxicity, as can been seen in Table 12.3. In contrast, ethanol (C2H5OH or EtOH) is nontoxic and can be produced in large amounts by fermentation of agricultural renewable crops and waste, like corn and sugarcane. Thus EtOH has been regarded as a preferable choice [119 121]. However, despite the increasing energy density, higher alcohols that contain the C C bonds (like ethanol, ethylene glycol, propanol, etc.) are increasingly more difficult to oxidize, as their oxidation to CO2 involves more intermediates and pathways than that of methanol. Thus reducing the efficiency of the FCs and requiring more active catalysts in particular at lower temperatures. To that end, Pt-based bimetallic catalysts are generally found to be more active toward the AOR than the monometallic counterpart, which has been generally attributed to the bifunctional mechanism and/or ligand effect [122 124]. For these reasons alcohol-fueled FCs in acidic media commonly use Pt Ru instead of Pt at the anode to promote AOR, while on the cathode side, Pt/C is generally adopted to enhance the ORR kinetics. However, in alkaline media Pt is frequently replaced by Pd-based electrocatalysts, which are effective for AOR, less expensive, and more robust [125,126]. These characteristics were further explored in the research performed on MM-FCs. Namely, for improving the ethanol-fueled devices operating at room temperature, several new anode Pd-based electrocatalysts

2. Biomedical applications

12.4 Fuels electrooxidation and micropower generation

439

were developed. Supported in green synthesis methods C. Lo´pez-Rico et al. [127] compared the performance of an air-breathing MM-FC made of PMMA using as anodes the commercial Pd/C catalyst and the one they developed via ionic liquids (IL), Pd/C (IL), while Pt/C was used as cathode. They found that this was enhanced by the Pd/C (IL), particularly when changing the cathode pH from an alkaline to an acidic (mixed media condition) with a MPD near 100 mW/cm2. Following their simple and environmentally friendly method, the researchers also synthesized a Pd-NiO anodic nanocatalyst, which they showed to give even better performance for both acrylic (PMMA) and paper-based devices, in mixed media conditions and using ethanol as fuel (see values on Table 12.5) [128]. Therefore proving by employing Pd-NiO/C as anode catalyst, MM-FCs on PMMA or paper can be operated in a simple manner and using low-cost materials. A. Armenta-Gonza´lez et al. [129] also presented alternative anode catalysts based on a PdAg/ MWCNT to improve ethanol MM-FCs. These were synthesized using a different procedure (microemulsion method) and tested in alkaline media under air-breathing and closed (no air contact, just pumping saturated O2 solution) conditions. This way the researchers showed not only that the new catalyst improved the kinetics of the AOR, but also that the air-breathing condition was more favorable, improving the performance of the ORR, resulting in a higher power density output and OCV in comparison with the closed configuration (see Table 12.5). Another major drawback regarding the use of Pt-based catalysts that has been observed is the mixed potential at the cathode reducing the overall FC voltage, due to the alcohol crossover effect. Albeit crossover effect is expected to decrease with the alcohol chain length [150] and much more limited if not eliminated when using MM-FCs, as mentioned before. One way to overcome this problem has been the development of nonprecious metal catalysts toward ORR, with Earth-abundant materials being more cost-effective. Their major characteristic/challenge should be inactive toward the AOR, but still able to promote efficiently and selectively the ORR, in order to prevent the potential drop from fuel crossover. Lately, much attention has been given to Fe-N-C catalysts, which have shown remarkable tolerance to the presence of alcohols, when employed at the cathode of conventional (membrane-based) alcohol-fueled FCs, both in acidic and alkaline media [151 153]. For the case of MM-FCs, this strategy of selective catalysts was already supporting the studies reported by D. Whipple et al. [97], regarding the use of RuxSey as alternative methanol tolerant cathode catalyst in an airbreathing MM-FC. As well as the studies reported by A. Gago et al. [130], who developed two other chalcogenide cathodes (based on PtxSy and CoSe2) for MM-FCs and compared their performance with a commercial Pt cathode catalyst. Thus showing that the performance of the

2. Biomedical applications

TABLE 12.5 Year

Chronological overview of publications on alcohol-fueled MM-FCs design (feature) and their performance.

Design

Anode-cathode catalysts

Fuel-oxidant

Electrolyte

OCV/V

MPD/(mW/ cm2)

MCD/(mA/ cm2) a

Reference

2005

Coflow (mixed media)

PtRu-Pt

MeOH-dissolved O2

H2SO4 and KOH

1.4

12

50

[64]

2005

Coflow

PtRu-Pt

MeOH-dissolved O2

H2SO4

0.7

2.8

10

[74]

2006

Coflow

PtRu-Pt

MeOH-airbreathing

H2SO4 and KOH

0.93 and 1.05

11.8 & 17.2

80a and 120

[98]

2007

Single-flow (mixedreactant)

Ni(OH)2-AgO

MeOH-H2O2

KOH

0.11a

0.029

1.2a

[88]

2008

Coflow (on-chip)

PtRu-PdCo

MeOH-airbreathing

H2SO4 and Na2SO4

c. 0.50 and 0.48

(1.4 and 0.2 μW)

2008

Orthogonal-flow

Pt-Au

MeOH-O2

KOH

0.600 (90 C)

46 (90 C)

2009

Coflow (on-chip)

PtRu-PdCo

MeOH-airbreathing

H2SO4 and (Na2HPO4 1 KH2PO4)

(0.45 and 0.50)a

(c. 2.0 and 0.7 μW)

[101]

2009

Coflow (on-chip)

PtRu-PdCo

EtOH-airbreathing

H2SO4 and (Na2HPO4 1 KH2PO4)

(0.45 and 0.62)a

(c. 1.2 and 0.6 μW)

[101]

2009

Coflow (on-chip)

PtRu-PdCo

2-PrOH-airbreathing

H2SO4 and (Na2HPO4 1 KH2PO4)

(0.80 and 0.78)a

(c. 6.3 and 1.3 μW)

[101]

[100] 280a (90 C)

[111]

2009

Coflow

PtRu-Pt

MeOH-airbreathing

H2SO4 and KOH

0.93 and 1.05a

11.8 and 17.2

(80 and 118)a

[71]

2009

Coflow

PtRu-Pt

EtOH-airbreathing

H2SO4 and KOH

0.41 and 0.70a

2.5a and 12.1

(35 and 85)a

[71]

2009

Coflow

PtRu-RuxSey

MeOH-airbreathing

H2SO4

0.65a

4.00

65a

[97]

2010

Coflow (with separator)

PtRu-Pt/C

MeOH-O2

H2SO4

0.65a (80 C)

70 (80 C)

650a (80 C)

[106]

2012

Coflow (with separator)

PtRu/C-Pt/C

MeOH-airbreathing

H2SO4

0.59

6.5

62.7

[130]

2012

Coflow (with separator)

PtRu/C-PtxSy/C

MeOH-airbreathing

H2SO4

0.48

4

54.7

[130]

2012

Coflow (with separator)

PtRu/C-CoSe2/C

MeOH-airbreathing

H2SO4

0.27

0.23

3.4

[130]

2012

Single-flow (mixedreactant)

PtRu/C-Pt/C

MeOH-airbreathing

H2SO4

0.23

1.3

26.7

[130]

2012

Single-flow (mixedreactant)

PtRu/C-PtxSy/C

MeOH-airbreathing

H2SO4

0.33

2.7

40

[130]

2012

Single-flow (mixedreactant)

PtRu/C-CoSe2/C

MeOH-airbreathing

H2SO4

0.32

0.23

3.2

[130]

2013

Coflow (all-polymer)

PtRu/C-Pt/C

MeOH-O2

H2SO4

0.65a

9.7

65a

[90]

2013

Coflow

Pd/MWCNTPt/C

GlyOH-dissolved O2

KOH

0.55

0.7

5

[131] (Continued)

TABLE 12.5

(Continued)

Year

Design

Anode-cathode catalysts

Fuel-oxidant

Electrolyte

OCV/V

MPD/(mW/ cm2)

MCD/(mA/ cm2)

Reference

2013

Coflow

Pd/C-Pt/C

GlyOH-dissolved O2

KOH

0.6

0.51

3.5

[131]

2014

Lateral-flow (paper)

PtRu/C-Pt/C

MeOH-airbreathing

KOH

0.52

4.4

22.55

[110]

2014

Coflow

AuPd/PANI-CP

EG-dissolved O2

KOH

0.53

1.6a

14.5a

[132]

a

2015

Coflow

Pt/CNT-Pt/ CNT

MeOH-H2O2

H2SO4

0.79

5.70

25

[92]

2015

Counter-flow

Cu@Pd/CNFPt/C

MeOH-airbreathing

KOH

0.621

17.1

100

[133]

2015

Counter-flow

Cu@Pd/CNFPt/C

EtOH -airbreathing

KOH

0.670

25.75

153.70

[133]

2015

Counter-flow

Cu@Pd/CNFPt/C

EG-air-breathing

KOH

0.652

19.95

142.55

[133]

2015

Counter-flow

Cu@Pd/CNFPt/C

GlyOH-airbreathing

KOH

0.622

20.43

111.95

[133]

2015

Counter-flow

Cu@Pd/CNFPt/C

Multifuel-airbreathing

KOH

0.612

18

108.67

[133]

2016

Coflow

PtRu/C-Pt/C

MeOH-H2O2&air

KOH

0.68 (34 C)

3.8 (34 C)

32.4 (34 C)

[99]

2016

Counter-flow

Pd/C-Pt/C

EtOH-airbreathing

H2SO4 and KOH

1.03

100

125

[127]

2016

Counter-flow (PMMA)

Pd-NiO/C-Pt/C

EtOH-H2O2&air

H2SO4 and KOH

1.11

108

242

[128]

2016

Lateral flow (paper)

Pd-NiO/C-Pt/C

EtOH-H2O2&air

H2SO4 and KOH

1.05

85.5

365

[128]

2016

Coflow

Pd-Ag/ MWCNT-Pt/C

EtOH-airbreathing

KOH

0.95a

14.5

34

[129]

2016

Coflow

Pd-Ag/ MWCNT-Pt/C

EtOH-dissolved O2

KOH

0.65a

4.4

26

[129]

2016

Coflow

PtRu-Pt/C

MeOH-airbreathing

KOH

5.5

67.7

63.1

[134]

2016

Counter-flow

Cu@Pd/CNFPt/C

GlyOH-airbreathing

KOH

0.724

17.4

97.62

[135]

2016

Counter-flow

Cu@Pd/CNFPt/C

GlyOH(crude)air-breathing

KOH

0.695

17.56

96.54

[135]

2016

Counter-flow

Cu@Pt/CNFPt/C

GlyOH-airbreathing

KOH

0.791

23.16

104.10

[135]

2016

Counter-flow

Cu@Pt/CNFPt/C

GlyOH(crude)air-breathing

KOH

0.749

21.77

108.03

[135]

2017

Counter-flow

Pt/GO-Pt/C

MeOH-airbreathing

KOH

0.65a

(9.15 mW/ mgPt)

(100a mA/ mgPt)

[94]

2017

Orthogonal-flow

Pt Ru/(CNTGO)-Pt/C

MeOH-airbreathing

KOH

0.85a

16.35 (10.15 mW/ mgPt)

(90a mA/ mgPt)

[95]

2017

Coflow

PtRu-Pt/C

EtOH-airbreathing

H2SO4

0.8a

2.9

29a

[108]

6 cell stack

(Continued)

TABLE 12.5 Year

(Continued)

Design

Anode-cathode catalysts

Fuel-oxidant

Electrolyte

OCV/V a

MPD/(mW/ cm2)

MCD/(mA/ cm2) a

Reference

2017

Counter-flow

PtRu-Pt/C

EtOH-airbreathing

H2SO4

0.8

2.9

27

[108]

2017

Counter-flow

Pt-Ag/Pt

MeOH-airbreathing

KOH

0.55

2.4

34

[136]

2017

Single-flow (mixedreactant)—2 cell stack

Pt-Ag/Pt

MeOH-airbreathing

KOH

0.89

1.4a (3.33 mW/ mgPt)

20a

[137]

2018

Coflow

Pt/C-Pt/C

MeOH-dissolved O2

H2SO4 and KOH

1.24

30.0

73.4

[138]

2018

Coflow

Pt/C-Pt/C

EG-dissolved O2

H2SO4 and KOH

1.30

30.3

75.0

[138]

2018

Coflow

Pt/C-Pt/C

GlyOH-dissolved O2

H2SO4 and KOH

1.23

39.5

77.1

[138]

2018

Counter-flow

Pt/C-Pt/C

GlyOH-dissolved O2

KOH

1.000

71.2

337.3

[139]

2018

Counter-flow

Pt/C-Pt/C

GlyOH-bleach

H2SO4 and KOH

1.970

315.3

637.8

[139]

2019

Single-flow (mixedreactant)

PtRu-Ag/Pt/G

EtOH-airbreathing

KOH

0.75

10

54

[140]

2019

Counter-flow

PtRu-Ag/Pt/G

EtOH-airbreathing

KOH

0.8

9.8

58

[140]

2019

Single-flow (mixedreactant)

Pt/CNTMn2O3/Pt/CNT

MeOH-airbreathing

KOH

0.54

2.16

24a

[141]

2019 2019

Counter-flow Counter-flow

PdAg/C-Pt/C Pd NC/C-Pt/C

EG-air-breathing EG-air-breathing

H2SO4 and KOH H2SO4 and KOH

1.02 1.2 a

60a

[142]

14.44

a

95

[142]

9.66

2019

Coflow

Pd52-Ni48/ NSCNT/CA -Pt/C

EG-dissolved O2

KOH

0.9

62.8

312.49

[143]

2019

Counter-flow

Pt/C-Pt/C

GlyOH-dissolved O2

H2SO4 and KOH

1.3a

38

80a

[144]

2019

Counter-flow

FePt/C-Pt/C

GlyOH-dissolved O2

H2SO4 and KOH

1.3a

54

100a

[144]

2020

Coflow

PdPt/C-Pt/C

GlyOH-airbreathing

KOH

0.88 (34 C)

1.6 (34 C)

7a (34 C)

[145]

2020

Coflow

Pd/C-Pt/C

GlyOH-airbreathing

KOH

0.8 (34 C)

1.3 (34 C)

6a (34 C)

[145]

2020

Coflow

Pd/C (com)-Pt/C

GlyOH-airbreathing

KOH

0.52 (34 C)

0.49 (34 C)

4a (34 C)

[145]

2020

Counter-flow

Pt/C-GO/C

GlyOH-bleach

H2SO4 and KOH

1.72

110

354.4

[146]

2020

Counter-flow

Pt/C-CP

GlyOH-bleach

H2SO4 and KOH

1.68

83

267.1

[146]

2020

Counter-flow

Pt/C-GO/C

GlyOH-dissolved O2

H2SO4 and KOH

1.00

0.38

3.7

[146]

2020

Counter-flow

Pt/C-CP

GlyOH-dissolved O2

H2SO4 and KOH

0.83

0.09

0.9

[146] (Continued)

TABLE 12.5

(Continued) Anode-cathode catalysts

Fuel-oxidant

Electrolyte

OCV/V

MPD/(mW/ cm2)

MCD/(mA/ cm2)

Reference

Counter-flow (3D printed)

Pt/C-Pt/C

GlyOH-bleach

H2SO4 and KOH

1.8

175.2

310.7

[147]

2021

Counter-flow

PtRu/C-Pt/C

MeOH-airbreathing

KOH

0.46a (33 C)

14a (33 C)

[102]

2021

Counter-flow

PtRu/C-Pt/C

EtOH-airbreathing

KOH

0.43a (33 C)

28a (33 C)

[102]

2021

Counter-flow

PdPt/C-Pt/C

GlyOH-airbreathing

KOH

0.70 (35 C)

2.77 (35 C)

13a (35 C)

[148]

2021

Counter-flow

PdPt/C-Pt/C

GlyOH-airbreathing

KOH

0.78 (75 C)

4.03 (75 C)

19a (75 C)

[148]

2021

Counter-flow

PdNi/C-Pt/C

GlyOH-airbreathing

KOH

0.42 (35 C)

1.27 (35 C)

10a (35 C)

[149]

2021

Counter-flow

PdNi/C-Pt/C

GlyOH-airbreathing&Ca (ClO)2

KOH

0.72 (35 C)

3.43 (35 C)

15a (35 C)

[149]

Year

Design

2020

a

Estimated from figure. MCD, Maximum current density; MPD, Maximum power density; OCV¸Open-circuit voltage.

12.4 Fuels electrooxidation and micropower generation

447

MM-FC with the PtxSy cathode was superior to its counterpart with commercial Pt when using a CH3OH of concentration 10 M or more when mixing the anode and cathode streams in a single stream (mixedreactant condition) (see results in Table 12.5). Hence, this strategy of selective electrodes can be used to simplify the fuel delivery and the pumping ancillary, by operating the device with solely one stream. Recall the early studies by W. Sung et al. [88], who employed nonnoble metals with relatively mild catalytic activity, like Ni(OH)2 and AgO, as anode and cathode catalysts to minimize the effect of crossreactions, in a ethanol-fueled MM-FC in alkaline media, discussed before. Or the more recent research reported by M. Estrada-Solı´s et al. [140] regarding the use of a bilayer Ag-Pt cathode to selectively perform the ORR (in alkaline media), even if a high concentration of ethanol was used. This new catalyst was synthesized (by pulsed laser deposition) on graphene-modified CP substrate, due to its excellent electrical conductivity properties. The researchers compared the performance of the mixed-reactant MM-FC with the more conventional two-stream flow configuration with this new electrode (see Table 12.5), and showed that it was possible to achieve similar performance with the simpler mixed-reactant configuration. This simpler configuration of mixed-reactant MM-FC was also used by the group to test another bimetallic catalyst (Mn2O3-Pt) synthesized by the same technique, but this time supported on CNTs [141]. The researchers explored this alternative material, since its relatively cheaper and presents higher corrosion resistance than Ag. The anode catalyst consisted of a Pt film on CNTs synthesized in similar conditions. In addition, this time the operation of the MM-FC was even simpler, since the single solution containing MeOH in 0.5 M KOH as electrolyte was supplied under passive (no pumping) conditions. The results achieved (see Fig. 12.11 and Table 12.5) demonstrated that the new material was able to perform selectively the ORR even at high concentration of MeOH as 5 M, allowing the device to achieve higher OCV and power density than when Ag-Pt was used in similar conditions. It is important to mention that the group started by testing the bilayer Ag-Pt catalyst, using methanol (in alkaline media) as fuel, known to be more prone to crossover [136]. Because they supported the work in the previous evidence (from typical three-electrode or half-cell studies) the new catalyst exhibited ORR activity similar to Pt and no activity toward MOR, with the Ag layer acting as a protective coating for the Pt layer [154]. Thus they were able to demonstrate the effectiveness of the new Ag-Pt cathode to reduce the methanol crossover effect by using a conventional MM-FC with two-stream flow configuration operated with high concentration of fuel under passive conditions at room temperature. These advantages were even further explored by the group in the design, fabrication, and evaluation of a mixed-reactant and airbreathing MM-FC stack [137]. The stack consisted of two cells connect

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FIGURE 12.11 (A) Schematic of the air-breathing MM-FC tested under mixed-reactant configuration and (B) polarization curves (full symbols) and power density curves (open symbols) recorded during the passive operation with different concentrations of MeOH in 0.5 M KOH. Reproduced with permission from Ref. J. C. Abrego-Martı´nez et al., Nanostructured Mn2O3/Pt/CNTs selective electrode for oxygen reduction reaction and methanol tolerance in mixed-reactant membraneless micro-DMFC, Electrochim. Acta 297 (2019) 230 239, https://doi. org/10.1016/j.electacta.2018.11.199. Copyright r 2019 Elsevier.

in series that were operated in the mixed-reactant configuration with a mixture of (5 M MeOH 1 0.5 M KOH) also in passive mode, given the use of the highly methanol-tolerant Ag-Pt cathode. According to the researchers this configuration allowed to double the OCV with virtually no cost increase and showed good stability throughout a 10 h test at zero-flow under mixed-reactant conditions. Moreover, the performance achieved (see Table 12.5) was considered not far from that needed for low-power applications. One should mention that the pioneer study reported by Y. Wang et al. [134], stacking six air-breathing MM-FCs, despite very different conditions, when operated with methanol and 3 M KOH, showed that OCV of 5 V and a peak power density of 108.7 mW/cm2 were achievable. In the pursuit of sustainable energy production alternative alcohol fuels like ethylene glycol (C2H6O2 or EG) have also been considered most promising [155]. Compared with MeOH (see Table 12.4) EG oxidation reaction is theoretical more efficient and with higher volumetric energy density, being more favorable under alkaline media and promoted with Pd-based electrocatalysts. Hence, N. Arjona et al. [132] reported one of the first studies regarding a EG-based MM-FC operated at room temperature. They used Pd-Au/polyaniline as the anode for the first time (owing to its synergic effect toward the AOR) and showed that the device exhibited high electrocatalytic performance and stability for the conversion of cheap and fully available EG as fuel. Years later, the same group reported an improvement in the performance of the devices (compared to the conventional Pd/C catalyst) [142]. This time they prepared a novel Pd-Ag and Pd nanocube (NC) catalysts, containing abundant (100) facets, which

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they showed to enhance the EG oxidation. Therefore testing the anodes in a EG-fueled MM-FC (similar to the one shown in Fig. 12.11) revealed better performance for both anodes under mixed media (anodic/alkaline and cathodic/acidic electrolytes) condition. Despite some stability problems regarding the dissolution of Ag in acid media (since some protons may have reached the anode surface), they obtained similar performance with the Pd-Ag and Pd NCs (see Table 12.5), which can be considered an excellent achievement, taking into account that they used two- to sixfold lower Pd mass content on the Pd-Ag catalyst. Hence, showing the importance that bimetallic wellaordered materials can have for the further development of highly active cost-effective electrocatalysts. A different strategy however was followed by T. Raj kumar et al. [143], who reported a biomass-derived 3D carbon aerogel (CA) with carbon shell-confined Pd-Ni catalysts on CNTs (Pdx-Niy/NSCNT/CA) as efficient electrocatalysts for EG MM-FCs. They showed that the 3D porous carbon network and the synergistic effect of carbon shell-confined bimetal NPs enhanced electrocatalytic activity toward EG oxidation reaction compared to the commercial Pt/C catalyst. Namely, Pd52-Ni48/NSCNT/CA when used as flow-through anode in a Y-shaped MM-FC run with EG (alkaline media) was considered to demonstrate superior performance (increased power density and durability) compared to other examples the researchers found in the literature. However, one should also consider that this strategy had already been successfully applied by J. Maya-Cornejo et al. [133] to present a MM-FC that run with several fuels (individually or mixed): MeOH, EtOH, glycerol (C3H8O3 or GlyOH), and EG, in alkaline media, at room temperature. This outstanding achievement was possible due to the synthesis and integration of a highly active Cu@Pd core shell electrocatalyst, as an flowthrough anode using nanoporous carbon nanofoam (CNF), together with the use of an air-breathing MM-FC, by comparison with the performance achieved when using commercial Pd/C anode. All of the individual fuels and the fuel mixture were tested at a concentration of 0.1 M in order to study the effect of the fuel nature while avoiding possible CO poisoning effects. The polarization curves (shown in Fig. 12.12) exhibited an OCV between 0.620.7 V for all fuels tested. The highest value of 0.670 V was achieved with the use of EtOH, which also gave higher power density, while the lowest was observed using MeOH. Moreover, the stability tests performed showed that the current density remained almost constant for every fuel over duration of 150 mins. Best results again were obtain with EtOH, but the multifuel system showed higher current density than individual MeOH or GlyOH. These results were attributed to the excellent theoretical energy density of EtOH and synergetic effect of fuels mixed together, optimized by the use of the new efficient flow-through anode; in addition to the

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12. Micro alcohol fuel cells towards autonomous electrochemical sensors

FIGURE 12.12 (A) Polarization (open symbols) and power density curves (full symbols) for several individual alcohol fuels at 0.1 M in 0.3 M KOH and the multifuel mixture used in an air-breathing MM-FC cell, with Cu@Pd/C as the anode; (B) Response curves from stability tests of the device. Reproduced with permission from Ref. E. O.-O. J. MayaCornejo, L. A´lvarez-Contreras, N. Arjona, M. Guerra-Balca´zar, J. Ledesma-Garcı´a and L. G. Arriaga, Copper palladium core shell as an anode in a multi-fuel membraneless nanofluidic fuel cell: toward a new era of small energy conversion devices, Chem. Commun 51 (2015) 2536 2539, https://doi.org/10.1039/C4CC08529A. Copyright r 2015 Royal Society of Chemistry.

improved cathode performance, by using a combination of dissolved O2 and air as the O2 source. This study was considered to open the possibility of power nanodevices for multiuses purposes, regardless of fuel re-charge employed, setting the begin of a new era of small energy conversion devices. The fuel-flexible characteristic of alcohol MM-FCs was further explored, this time in mixed-media conditions, by C. Martins et al. [138]. Who focused their attention on the biomass-derived fuels (methanol, ethylene glycol, and glycerol) to provide energy for lowpower devices. Thus by employing flow-through porous electrodes based on Pt/C-modified CP, with the anode interchangeably using MeOH, EG, or GlyOH in alkaline media, while saturated O2 in acidic media was fed to the cathode, a MPD of B30 40 mW/cm2 was generated (see Table 12.5). This was achieved by proper balance of the fuel concentrations tested, since they observed that the power output was proportional to the fuel concentration for EG and MeOH, contrarily to what happened with GlyOH, which showed higher value at lower concentration (see plot in Fig. 12.13). This was rationalized as a consequence of the poisoning effect due to the limited glycerol catalysis on the Pt-based electrodes surface at higher GlyOH concentration. Finally, some additional considerations regarding the use of glycerol as fuel in MM-FCs. This has been considered a very promissory fuel, since besides its high volumetric energy density and environmentally friendly properties, it can be easily obtained from biosustainable sources (being a waste byproduct from biofuels generation) and its electrooxidation products

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12.4 Fuels electrooxidation and micropower generation

0.15 M MeOH, 0.075 M EgOH, 0.05 M GIOH 3.0 M MeOH, 1.5 M EgOH, 1.0 M GIOH

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FIGURE 12.13 Plot of maximum power densities achieved with a fuel-flexible solution for biomass-derived alcohol MM-FCs. Operated on mixed-media conditions with flowthrough porous electrodes fed by low concentration (open dots) and high concentration (solid dots). The concentration values are given on top of the plot, being methanol (MeOH), ethylene glycol (EgOH), and glycerol (GlOH). Reproduced with permission from Ref. C. A. Martins, O. A. Ibrahim, P. Pei, E. Kjeang, Toward a fuel-flexible direct alcohol microfluidic fuel cell with flow-through porous electrodes: Assessment of methanol, ethylene glycol and glycerol fuels, Electrochim. Acta 271 (2018) 537 543, https://doi.org/10.1016/j.electacta.2018.03.197. Copyright r 2018 Elsevier.

are considered high value chemicals [156 158]. Therefore from a limited number of studies using GlyOH as a fuel in MM-FCs, one reported by A. Dector et al. [131], using Pd/C and Pd/MWCNT-based anodes, and others by D. Panjiara et al. [145,148,149,159] using laboratory-synthesized electrocatalyst Pd/C, Pd-Pt/C, and Pd-Ni/C-based anodes. All using Pt/C cathodes and alkaline media, with not so impressive performances (see Table 12.5), but demonstrating the relevance of laboratory-synthesized electrocatalysts and operation conditions, in the route to such achievement. A major breakthrough was provided by the study of J. Maya-Cornejo et al. [135], who used for the first time crude glycerol (waste from biodiesel) in a MM-FC as a fuel. They diluted the fuel with KOH (supporting electrolyte) and used Cu@Pd/C and Cu@Pt/C supported on CNF (flow-through electrodes), so that more efficient electrooxidation of the fuel was possible to be reached, improving the performance of the device (see Table 12.5). This way the researchers demonstrated the potential of a novel and environmentally friendly application for the crude glycerol in small energy-supplier devices, like MM-FCs.

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In addition to the flow-through electrodes, C. Martins et al. [139] besides following the aforementioned strategy of mixed-media conditions, also explored a commercial bleach oxidant (sodium hypochlorite, containing 3% 8% hypochlorite ions (ClO2)). Their goal was to obtain high power density with negligible added cost, which they proved to be possible by reporting a record power density for an alcohol-fed MM-FC of 315 mW/cm2 with an OCV of B2 V. However, the researchers also realized that for the all-alkaline configuration, there were some instabilities and thus there was the need of developing new catalysts or electrode configurations. Hence, their following studies were focused on new multimetallic materials to be used in flow-through porous electrode configuration [144]. Namely, by in situ decoration of Pt/C-modified CP with Fe, which was subsequently used as an anode during operating the MM-FC fed with glycerol under mixed media conditions, and showed improved performance compared to a similar cell with nondecorated Pt/C anode (see Table 12.5). Moreover, they proposed the use of hypochlorous acid (HClO from bleach) as a potential liquid oxidant, together with GO-modified CP as a metal-free cathode for these devices [146]. With the GO-modification made and used for in situ flowing deposition on a CP, this time as the cathode. Hence, the prototype MM-FC, under mixed-media condition and with electrodes in a flow-through configuration, featuring glycerol electrooxidation coupled to HClO reduction on GO-modified CP showed unprecedented performance (see Table 12.5). Thus proving that a well-designed in situ electrode modification can be applied to reach high power density without the use of noble metals. Finally, a reference to the new 3D-printed MM-FC with a microchannel designed for flow-through porous electrodes also developed by the group [147]. The device was successfully tested to generate power out of glycerol anodic reaction coupled to hypochlorite cathodic reaction in mixed-media configuration (see Table 12.5). Therefore demonstrating the feasibility of the 3D-printed technology to fabricate also these type of electrochemical devices, in the pursuit of better performance with minor costs and easy manufacturing processes. This was proven by this prototype, with remarkable performance achieved using glycerol, but it is expected it can be extended to other fuels.

12.5 Examples toward sensing applications The need for powering small electronic devices and the quest for environmentally friendly energy harvesting systems, fulfilling the global energy demands, has provided motivation for the use of alternative micropower generators, like the alcohol-based MM-FCs. These devices provide many advantages, like intrinsically high surface-area-to-volume ratio, flow control/design variability, use of selective materials and

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conditions, along with the obvious absence of a membrane and its inherent drawbacks. Even so, this type of FC is hampered by several issues, including the electrochemical processes sluggish kinetics and several losses (activation, ohmic, mass), as well as the fuel crossover effect, leading to small-power generation. As discussed, one important strategy used to scale-up MM-FCs and extend their application is by stacking. However, any stacking implies a higher degree of complexity and proper structure optimization must be implemented. Therefore the goal must be to achieve a balanced and uniform flow distribution, assuring high fuel utilization and minimizing the cross-cell ionic connection (parasitic ionic currents) that can lead to self-discharging, limiting the practical operation time of the device [113]. This is an aspect that for MM-FCs is still at a very early stage and requires continuous efforts in improving, as seen in the investigation carried out by A. Zuria et al. [160] regarding air-breathing MM-FCs under mixed-reactant condition. Their work, focused on developing a passive mixed-reactant FC stack through numerical simulation studies, allowed better understanding of the phenomena occurring in the device and optimizing cell parameters so that better performing was achieved. Ultimately, these devices were tested in a new stack (four cells in series connected) in passive conditions with 4 M MeOH in 0.5 M KOH, which exhibited an OCV of 2.4 V and maximum current output of 2.2 mA, while a peak power of 1.0 mW was obtained with stable operation. Moreover, the proof of concept was demonstrated by using the stack for powering a green light-emitting diode (LED) during 4 h in conditions close to real application (i.e., room temperature and passive mode in a compact packaging; see Fig. 12.14). Thus demonstrating the good performance and capabilities of mixed-reactant MM-FCs for potential practical applications, as cost-effective candidates for the next generation of portable devices. Another major drawback one must note regarding the alcohol-fed MM-FCs is that due to the liquid nature of the reactants, the active configuration is most often used. This means that auxiliary pumps are required to drive the fuel and oxidant flow, which not only greatly increases the system complexity and cost, but also reduces the net power output further limiting miniaturization and portability [161,162]. One way to circumvent this problem has been to take advantage of some natural phenomena to create external power-free (passive) pumps. One example is evaporation, as demonstrated by Q. Zhang et al. [163]. These researchers developed an Y-shaped monolithic MM-FC, driven with stability by an evaporation pump, on a typical flow-by configuration. As a proof of concept, they showed that two LEDs were lighted by the stack of three cells in series. However, because they used vanadium redox species as the fuel and oxidant, this will not be further detailed.

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FIGURE 12.14 Results of testing a passive air-breathing MM-FC 4-cell stack, under mixed-reactant condition, with 4 M MeOH in 0.5 M KOH: (A) polarization and power curves of stack in parallel connection and comparison with single cell; (B) polarization and power curves stack in series connection and comparison single cell; (C) chronoamperometric curve response of the stack in series connection; (D) photographs of a LED off/on, connected to the stack and (E) voltage output of the stack during operation of the LED. Reproduced with permission from Ref. A.M. Zuria, J.C. Abrego-Martinez, S. Sun, M. Mohamedi, Prospects of membraneless mixed-reactant microfluidic fuel cells: Evolution through numerical simulation, Renew. Sustain. Energy Rev. 134 (2020) 110045, https://doi.org/10.1016/j.rser.2020.110045. Copyright r 2020 Elsevier.

Another example/phenomenon is osmosis. A novel approach was proposed by S. Kim et al. [164] using a very small osmotic pump embedded within an MF-FC device and capable of steady-flow generation for long-term operation. The researchers used two different types of fuel, vanadium and methanol, with Pt/C-based CP electrodes in all experiments. However, their results (see Fig. 12.15) showed very low power densities were achieved (0.33 vs 3.25 3 1023 mW/cm2, with an OCV of 1.12 vs 0.21 V, for vanadium vs methanol, respectively), even if vanadium fuel surpassed methanol performance. The researchers justified their findings with the necessity of further optimization and minimization of the channel cross-sectional geometry to improve overall efficiency of the device. Nevertheless, their research presented an innovative method toward the development of standalone MF-FCs, since according to them the osmotic pump can drive the system for a prolonged period (more than 24 h with a single pumping unit, easily replaced for continued use for days) with minimal dependency on the external environment. In addition to low-fuel consumption and minimization of crossover issues, this strategy presented a very simple and low-cost manufacturing process. Thus it is expected that with further

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FIGURE 12.15 Response curves for a passive pump (osmosis-driven) MM-FC, fed with different concentrations of methanol (A) and (C) and vanadium (B) and (D): polarization curves (on top) and power density curves (on bottom). Reproduced with permission from Ref. S.-H. Kim, K. Kim, M. Go, J.Y. Park, Stand-alone external power-free microfluidic fuel cell system harnessing osmotic pump for long-term operation, J. Micromech. Microeng. Text 28 (12) (2018) 125005, https://doi.org/10.1088/1361-6439/aae773. Copyright r 2018 IOP Publishing.

developments it will increase the attractiveness of MM-FCs for external power-free autonomous applications. The last natural phenomenon to be discussed, within the need to reduce peripheral equipment such as pumps, thus simplifying the system and reducing costs of MM-FCs, is the capillary effect. This was mentioned before regarding paper-based MM-FCs [110], which benefit from the laminar flow occurring naturally by capillarity in the porous paper, separating anolyte from catholyte. In order to combine the advantages of MM-FCs and the convenience and simplicity of lateral flow test strip format, the researchers used similar construction principles, as depicted in Fig. 12.16, to develop the first paper-based MM-FCs tested with methanol in KOH. In an incremental approach, they first demonstrated the concept with two differentiated but confluent sample

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Anode Au/COP Pt-Ru

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(C) Absorbent pad Cathode Carbon paper Pt

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FIGURE 12.16 (A) Schematic representation of a Y-shaped paper-based MM-FC; (B) concept of a lateral flow test strip MM-FC activated with water; (C) comparison of a paper-based MM-FC and commercial lateral flow test strip for dengue diagnosis. Reproduced with permission from Ref. J.P. Esquivel, F.J.D. Campo, J.L.G.D.L. Fuente, S. Rojas, N. Sabate´, Microfluidic fuel cells on paper: meeting the power needs of next generation lateral flow devices, Energy Environ. Sci. 7, (2014) 1744 1749, 2014/04/16, https://doi.org/10.1039/ C3EE44044C. Copyright r 2014 Royal Society of Chemistry.

pads for anolyte and catholyte (Fig. 12.16A), a Y-shaped device. Secondly, they showed how the incorporation of a conjugate pad to store the KOH electrolyte in a solid form and a piece of a methanol-rich agar gel on top of the paper strip allowed the device to function similar to a conventional lateral flow test strip (Fig. 12.16C). It worked just by soaking a single sample pad with water (Fig. 12.16B). Hence, the researchers showed that the two independent electrochemical reactions required in the MM-FC (AOR and ORR) can be performed in a single paper strip. The anode was placed under the paper strip, while the cathode was attached on top to facilitate O2 access from the atmosphere. Initial tests with the Y-shaped device run with different concentrations of the fuel and electrolyte (Fig. 12.17A and B) showed that when using 4.0 M MeOH and 2.0 M KOH, the MPD reached the value of 4.4 mW/cm2 with an OCV of 0.55 V and maximum current density of 47 mA/cm2. After that, more tests were performed under current load of 1 mA, first placing an absorbent pad on the Y-shaped configuration (Fig. 12.17C) and finally the lateral-flow strip configuration with different positions of the pad

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FIGURE 12.17 Paper-based MM-FC performance tests: (A) polarization curves at different methanol concentrations in 1.0 M KOH (Y-shaped configuration); (B) polarization curves at different KOH concentrations using 4.0 M methanol (Y-shaped configuration); (C) power output at a current load of 1 mA with different methanol concentrations (Yshaped modified with a pad configuration); and (D) power output at a current load of 1 mA (lateral-flow strip configuration with different positions of the pad). Reproduced with permission from Ref. J.P. Esquivel, F.J.D. Campo, J.L.G.D.L. Fuente, S. Rojas, N. Sabate´, Microfluidic fuel cells on paper: meeting the power needs of next generation lateral flow devices, Energy Environ. Sci. 7 (2014) 1744 1749, https://doi.org/10.1039/C3EE44044C. Copyright r 2014 Royal Society of Chemistry.

(Fig. 12.17D). In both cases, the researchers considered that the power density obtained exceeded that of printed batteries, even though there was still plenty of room for optimization. All together these results clearly showed the potential of implementing a MM-FC in a paper platform. Moreover, the simple and light structure associated with their low cost and easy manufacture makes this device emerge as promising clean energy power source for small-scale electronic applications. This was considered the starting point in the development of a power source for capillary-based autonomous sensing systems capable of harvesting the energy needed for the measurement from the sample to be analyzed. Excellent review papers specific to paper-based MM-FCs best practices [59] and electrochemical applications [61] are recommended for further reading.

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12.6 Conclusion and future outlook Significant advances have been made in MM-FC technology since its inception in 2002, and it is considered a promising power source to replace batteries in small electronic devices, including sensors for the new IoT paradigm. Empowered by a synergistic relationship between microfluidic and electrochemistry, these devices not only can outperform traditional membrane-based FCs as small-power generators, but also pave the way as new analytical platforms capable of extracting sufficient energy from the sample/fuel and simultaneously perform the measurement, thus acting as self-powered electrochemical sensors. Herein an updated summary on the evolution and state-of-art regarding alcohol-fueled MM-FCs was provided. More importantly the intrinsic problems raised by the use of this type of fuel were also covered, including crossover effect and sluggish kinetics, besides the charge and mass limitations on operating the devices. These problems can be minimized by using specific catalysts in improved electrodes architecture, combined with structural optimization of the devices and operational conditions, as extensively discussed. However, significant progress is still required for further development of the technology in terms of energy density and fuel utilization, targeting sustainable energy production, with minimal fabrication and operational costs besides other applications. Thus research efforts should also address the development of specific but low-cost catalysts, as well as suitable fabrication technologies for large-scale production together with niche applications. For now, the air-breathing mixed-reactant and the paper-based MM-FCs seem to be much closer to the market, for sensor applications like in point-of-care testing, which is mainly attributed to the successful minimization and simplification of the devices. Nevertheless, the adoption of alcohol-fueled MM-FCs devices toward sensing applications is still in a very early stage. An integrated research effort from chemists, chemical engineers, materials scientists, renewable energy scholars, physicists, and scholars from related backgrounds are needed for further development of the technology, which will ensure a future avenue to reach self-powered devices with minimal sizes and significantly reduced costs. This work intends to provide not only an updated reference to researchers on alcohol-based devices, but also inspiration for the future development of all types of MM-FCs.

References [1] A. Raj, D. Steingart, Review—power sources for the internet of things, J. Electrochem. Soc. 165 (8) (2018) B3130 B3136. Available from: https://doi.org/10.1149/2.0181808jes. [2] A.S. Dahiya et al. Review—energy autonomous wearable sensors for smart healthcare: a review, J. Electrochem. Soc. Text 167 (3) 037516 2020. Available from: https://doi. org/10.1149/2.0162003JES.

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2. Biomedical applications

C H A P T E R

13 Biosensors for organs-on-a-chip and organoids ¨ ztatlı1, Zeynep Altintas2,3 and Hayriye O Bora Garipcan1 1

˙ Institute of Biomedical Engineering, Bog˘ azic¸i University, Istanbul, Turkey, 2 Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 3Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

13.1 Introduction The inherent difficulties to study experimentally human biological processes render it challenging to investigate and understand human development and disease mechanisms [1]. Hence, model systems from single cells to animal models, which vary in complexity and size, have been used in biomedical research to identify molecular and cellular signaling pathways underlying disease mechanisms and develop treatments [2]. Research into the progression of growth and development as well as disease processes in animal models contributes to a thorough mechanistic explanation of organ physiology and disease pathology through the development similitude that all species share to a certain degree [3]. However, low concordance of the findings from animal models and humans evokes significant questions about the reliability of results from organism models to predict risks to humans and tackle problems that need the comprehension of human biology [4]. Advancements in developmental biology, stem cell technology, and tissue engineering have fostered the emergence of 3D in vitro models that recapitulate fundamental aspects of a multicellular organization, physiology, and function of in vivo counterpart for applications of basic research in human development, disease modeling, and drug discovery [5]. In particular, organoids and organ-on-a-chip (OoC) have arisen as landmark breakthroughs to generate in vitro 3D organotypic models.

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00007-8

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Organoids are 3D multicellular structures generated by the differentiation of pluripotent or adult stem cells into spatially organized organotypic cell lines to recapitulate the structure and physiological functions of the corresponding organ subunits [6]. On the other hand, OoC, also known as a microfluidic chamber, is in vitro cell culture platforms that are lined by the cells to emulate the microphysiological environment of in vivo tissue in terms of fluid flows, mechanical stimulations, and biochemical cues [7]. These platforms have shown an unprecedented aptitude for the regulation of related parameters precisely and mechanistic investigation of the role of those parameters on morphological, biochemical, and metabolic activities of the cells into biomimetic organ models [8,9]. Advances in these technologies have contributed to studies explicating the biological pathways responsible for the development and disease pathogenesis as well as discovering novel personalized therapies, pharmaceutical compounds, and screening their toxicity [10]. The development of reproducible 3D in vitro models is crucial to obtaining reliable information on morphogenesis and organogenesis of human development as well as accurate responses to stimuli as in native tissue [11]. In this regard, characterization of cellular physiology and microenvironment provides control on regulation of cellular behaviors to build complex model organisms with high fidelity to in vivo counterparts [12,13]. Conventional characterization methods depend on off-line analysis such as ELISA, cell-culture stains, and imaging [14]. However, the need for sampling from the system, large operating volume, and repeated system perturbation compromise reliable assessment of the functionality of models and dynamic cellular responses to the stimuli of interest evaluated [15]. Recently, integration of automated in situ continual monitoring platforms into 3D in vitro model system is preferred for readily detection of a change in physical, chemical, and biological markers with higher sensitivity and spatiotemporal resolution [16]. In particular, biosensors for real-time tracking cellular behavior [17], physical (electrical and mechanical) [18], and chemical [19] properties as well as the change in microenvironmental parameters [20] have been studied in recent years. The focus of this book chapter is to shed a light on the recent advancement of biosensor technology in the rapidly evolving organoids and OoC fields. We first give a glimpse of types of biosensors, based on bioreceptors, physiological responses, and transduction mechanisms. Subsequently, the emerging biosensors technologies used in various applications of organotypic models, in particular on human organ development and disease models along with drug discovery and screening applications, are summarized. Finally, the problems imposed by emerging sensing technologies and the potential possibilities of biosensors for applications in organotypic models are highlighted (Fig. 13.1).

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FIGURE 13.1 Schematic illustration of biosensors integrated 3D in vitro cell culture platforms.

13.2 The use of biosensors in organotypic models The key determinant of the development of organotypic models is the microenvironment of cell culture in which cells are subjected to the gradient of nutrients and secreted metabolites [21]. The incorporation of sensing platforms into in vitro 3D cell culture tools ensures a traceable and reproducible cellular microenvironment [22]. Biosensors exploit the special ability of highly specific and sensitive bioreceptors to perceive target analytes selectively and translate signals detected into measurable quantitates by transducers [23]. Hence, bioreceptors act as a bridge between the cellular microenvironment and the transducer for the detection of bioavailability of target analytes. A wide range of biological and chemical elements have been utilized as bioreceptors (e.g., antibodies, affibodies, nucleic acids, aptamers, peptides, imprinted polymers, cells) [24]. Depending on the type of bioreceptor the sensor platforms can be classified as molecular, cellular, and tissue biosensors [25].

13.2.1 Molecular biosensors Molecular biosensors rely on the delicate interaction of biomarkers (e.g., proteins, nucleic acids, metabolites, cytokines) with the detector [25]. The implementation of sensor modules in organotypic cell culture units has become possible with the recent advancements in 3D in vitro cell culture technologies [26]. In particular, continuous monitoring of expressed biomarkers in organotypic models can be achieved by the immobilization of bioreceptor molecules (e.g., antibody, peptide, enzyme, nucleic acids, and

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others) on the detector. Bioreceptor interacts selectively with a particular target biomarker of understudy for minimizing the risk of nonspecific binding of substances. The shift in physicochemical signals led by the interaction could be converted into measurable signals for in-line monitoring Aguilar [19,27]. The remarkable specificity, selectivity, and signal-tonoise ratio of molecule-based biosensors have contributed to a multitude of advances in in situ detection of biomarkers secreted from organotypic models [28]. By the integration of aptamer-based electrochemical sensors with microfluidics platform, Shin and coworkers evaluated the performance of the sensor by online continual monitoring of creatine kinase antigens secreted from cardiac organoids-on-a-chip upon exposure to the cardiotoxic drug. The researchers showed that aptamer immobilized receptors could detect the trace amounts of antigens with high sensitivity for several days [29]. Moreover, Zhu et al. used antibody conjugated biosensors to monitor concurrent secretion of proinflammatory and antiinflammatory cytokines from adipose tissue-on-a-chip in real-time. Using this platform, the authors were able to characterize phenotypic differentiation of macrophages in the inflamed adipose tissue and quantify the stagespecific gradient of multiplex cytokines secreted by adipocyte surrounded with macrophages in adipose microtissue as a physiological reaction to inflammatory stimulation [30].

13.2.2 Cell-based biosensors Living cells can sense physiological changes and biochemical stimulations in their microenvironment through a variety of naturally expressed receptors. They are also able to identify multiple analytes simultaneously and form a quantitative response to stimuli [31]. By incorporation of the natural sensing and identification ability of cells into a biosensor, noninvasive detection of a labile analyte of interest with high throughput and specificity is possible in a rapid, facile, and cost-effective way [32]. Hence, cell-based biosensors have emerged as a functional analysis platform, which can provide an understanding of the role of target analytes on cellular physiology, for medical diagnosis, disease progression, and drug discovery applications [33]. Specifically, cell-based biosensors have been implemented in diverse biological applications depending on the characteristic features of cells. For example, electrically excitable cells such as neurons, cardiomyocytes (CMs), and muscle cells have been pervasively employed in cell-based biosensors to study the molecular pathways modulating the activity of ion channels, receptors, and neurotransmitters as a reaction to pharmaceutical compounds, agonists, and antagonists. Furthermore, by using dielectric properties of the cells on the chips, cell behaviors and cell-matrix interactions can be evaluated with the impedance

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FIGURE 13.2 The use of biosensors for monitoring organotypic models. (A) 3D ECIS electrodes inserted vertically in 3D cell culture. (B) Adipose-tissue-on-chip with Local Surface Plasmon Resonance (LSPR) sensing platform for detection of cytokines. (C) (i) Muscle-on-achip system integrated with multiplexed electrochemical sensors for real-time monitoring IL6 and TNF-α cytokines and (ii) detection mechanism of cytokines [40,41]. Source: (A) Reproduced with permission from S.M. Lee, N. Han, R. Lee, I.H. Choi, Y.B. Park, J.S. Shin, et al., Real-time monitoring of 3D cell culture using a 3D capacitance biosensor, Biosens. Bioelectron. 77 (2016) 56 61. https://doi.org/10.1016/j.bios.2015.09.005. Copyright 2016. Elsevier Publications. (B) Reproduced with permission from J. Zhu, J. He, M. Verani, A.T. Brimmo, A. Glia, M.A. Qasaimesh, et al., An integrated adipose-tissue-on-chip nanoplasmonic biosensing platform for investigating obesity-associated inflammation, Lab. Chip. 18 (2018) 3550 3560. https://doi.org/ 10.1039/c8lc00605a.An. Copyright 2018. Royal Society of Chemistry Publications. (C) Reproduced with permission from M.A. Ortega, X. Fernandez-Garibay, A.G. Castano, F. De Chiara, A. Hernandez-Albors, J. Balaguer-Trias, et al., Muscle-on-a-chip with on-site multiplexed biosensing system for in situ-monitoring of secreted IL-6 and TNF-α, Lab. Chip. 19 (2019) 2568 2580. https://doi.org/10.1039/x0xx00000x. Copyright 2019. Royal Society of Chemistry Publications.

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measurements [34]. On the other hand, physiological changes in single-cell within cell population as a response to stimuli can also be detected by disregarding cell-cell interactions [35]. For example, single cells in the pancreatic islets generate short-lived high-frequency action potentials (AP) while the islet as a whole microtissue produces low-frequency slow potentials (SP). Since each electrical signal represents discrete levels of islet activation, both AP and SP should be measured and analyzed continuously to assess the diagnostic performance of pancreatic islets. Koutsouras et al. developed a novel pancreatic islet on-a-chip combined with an electrode covered by the conductive composite of poly (3,4-ethylene dioxythiophene) and polystyrene sulfate polymers. This system allows in situ detection of a singlecell and microorgan activity on-chip simultaneously [36].

13.2.3 Tissue-based biosensors In tissue-based biosensors, 3D multicellular structures are used as a bioreceptor to identify the effects of unprecedented stimuli in a physiologically realistic environment [37]. These multicellular structures recapitulate the structural and functional features of native tissue. Hence, organotypic models engaged with a range of sensory systems in a platform are built to monitor the integration of cells to form organotypic models and gain an insight into dynamic effects of biochemical agents by anticipating, tracking, and diagnosing the reaction of organ models [38]. Perrier et al. developed pancreatic islet-based bioelectronic sensors to monitor the activity of islets upon exposure to different concentrations of glucose by reading SP, which are directly related to glucose and hormone concentration. Results showed that biphasic electrical activity of islet cells could be detected with such a platform to profile islets’ response after glucose stimulation [39] (Fig. 13.2).

13.3 Biosensing technologies for monitoring organotypic models Organotypic models evolve from cell-cell and cell-matrix interactions mediated by intracellular and pericellular signals [42]. Accordingly, monitoring molecular, structural, and functional responses of a model organism is crucial to specify which physical and biochemical signals orchestrate these responses and divulge the underlying mechanism. In that regard, 3D in vitro platforms integrated with biosensors are used to manipulate and identify the response of organotypic models to physicochemical and biologic factors through the characterization of cellular behavior, metabolic, electrical, and mechanical activity [22].

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13.3.1 Biosensors for cell behavior The development of functional organotypic models is contingent on the precise organization of cells through the formation and perpetuation of adhesive cell interaction to one another and the matrix [43]. Cell adhesions also have influential functions in a multitude of physiological and pathological mechanisms such as migration, differentiation, immunologic reactions, and cancer metastasis [44]. Therefore noninvasive monitoring of cellular adhesion in organotypic models contributes to the lending comprehensive understanding of organogenesis and diseases [45,46]. Diverse sensing approaches can be conducted for the analysis of cell adhesion [47,48]. Mainly, electrical transduction methods provide exclusive information about cell-cell and cell-matrix contacts with high sensitivity and temporal resolution in 3D cell culture platforms [49]. Electrical cell-substrate impedance sensor (ECIS) is a convenient electrochemical method for probing the dynamic nature of cellular activities in real-time through in situ impedance measurements at a different frequency [50]. When cells come in contact with the electrodes, owing to their insulating bilipid membranes they engender impedance and elicit change in conduction path. The conduction of current through gap junctions, tight junctions, and free space at low frequency provides information on the density and strength of cell-cell and cell-matrix adhesion [51]. On the other side, the conduction of current through the membrane into cells at high frequency enables to control of the permittivity and integrity of the cell membrane [52]. Paracellular and transcellular propagation pathways of current give insight into the morphology of cells, as well. Hence, time course impedance measurement can be performed to detect morphological changes, which are induced by spreading migration, differentiation, apoptosis, or cellular degeneration. Following cell adhesion, consecutive stages of proliferation, cell differentiation, and apoptosis can also be analyzed via impedance measurements [51]. For example, by inserting a 3D capacitance sensor into 3D cell culture, Lee et al. monitored change in real-time capacitance of GFP-MCF-7 and human mesenchymal stem cells (hMSCs) encapsulated in alginate hydrogels at a different height to assess viability, apoptosis, and migration of cells. They found that the increase in cell numbers led to an increase in capacitance whereas apoptosis induced by anticancer drug exposure resulted in a decrease in capacitance. Also, they demonstrated that the migration of hMSCs could be followed in real-time by determining the corresponding shift in measured impedance [40]. Furthermore, multiple studies have shown that ECIS has enabled the distinction of different cell lines derived from stem cells by using distinguishing cell dielectric behavior dependent on the characteristic size and structure of each cell lineage [53]. Bagnaninchi et al. assessed variation in cell dielectric properties upon

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differentiation of adipose-derived stem cells into osteoblast and adipocytes via impedance sensing. They showed that as cell-cell connections became more tightly packed, measured impedance increased throughout osteogenic differentiation. However, during adipogenic differentiation, cell-cell junctions loosened and impedance decreased accordingly [54]. Low et al. reported comparable shift in dielectric values following the differentiation of induced pluripotent stem cells (IPSCs) into germ layers. They showed that upon ectodermal and mesodermal differentiation, the change in the size and morphology of cells affected the cell permittivity, facilitating quantitative discernment of cell lineages [55].

13.3.2 Metabolic activity Highly precise and selective analysis of cellular metabolism is of paramount significance for ascertaining the functionality of the 3D organotypic model, cytotoxicity, and action mechanism of therapeutic compounds and pathological progression of a disease [42]. In this context, exploitation of biochemical sensitive biosensors into 3D cell culture platforms allows in situ spatiotemporal detection of physicochemical changes in the microenvironment involving fluctuations in oxygen, pH, glucose, lactate, and cytokines level. Oxygen Oxygen is the main element in cellular metabolism for the generation of energy through mitochondrial respiration or glycolysis process as well as it modulates a vast range of cellular activities such as stem cell differentiation, structural organization, and organ development [56]. Normal level of oxygen in healthy tissue changes all over the human body from 4 μM in the large intestine lumen to 150 μM in lungs pulmonary vein depending on tissue demand. However, standard in vitro cell culture is mainly carried out under 182 μM oxygen tension. This increased exposure of cells to oxygen is called hyperoxia, which can influence cell activity [57]. Also, overlong exposure to low oxygen level conditions, referred to as hypoxia, can lead to unenviable stress in the cells, which may be followed by several pathologies like necrosis, cancer development, and stroke [58,59]. Hence, the ability to control oxygen level dissolved in cell culture media is important for regulation of cellular activity in the organotypic model and preclusion from mitochondrial dysfunction [60]. Optical and electrochemical oxygen sensors are considered as the two leading sensing systems for real-time and precise measurement of dissolved oxygen in 3D cell culture platforms [61]. Among these sensors, electrochemical sensors are the extensively employed first-generation

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biosensors, hinged on redox reaction of dissolved oxygen on a noble metal electrode [62]. Oxygen concentration is measured directly in cell culture medium via amperometric sensor [63] or indirectly through external electrolyte sensor isolated with an oxygen-permeable membrane, which is called Clark-type sensor [64]. Recently, Weltin et al. designed an amperometric microsensor system inserted into 96 well plates for rapid, sensitive, and continuous monitoring of lactate and oxygen levels of single hepatocyte spheroids cultured in each well. However, the consumption of oxygen by the sensor itself hinders accurate measurement in particular in the case of monitoring low oxygen levels [65]. In another study, Moya et al. presented built-in three Clark type microelectrode, printed in microporous polytetrafluoroethylene membranes, the inside liver-on-a-chip platform for in situ, continuous, and online readout of oxygen gradient of the hepatic culture platform mimicking liver sinusoid [66]. On the other hand, optical sensors rely on quenching fluorescent oxygen-sensitive dye upon interaction with molecular oxygen. To measure spatial variation in oxygen levels, the fluorescent dye can be infused into a polymer acting as an individual sensory point or dispersed over the entire surface of the cell culture platform [67,68]. The fluorescence intensity, decay kinetics, and phase modulation are principal measurement parameters to determine oxygen concentration [69]. Optical biosensors have provided the impetus for intracellular oxygen sensing with nanoparticles comprised of luminescent probes [70]. Nichols et al. elaborated on a new approach to devise a spheroid penetrable oxygen sensor by conjugation of poly(amidoamine) dendrimers on phosphorescent core composed of oxygen-sensitive dye porphyrin to measure intracellular oxygen tension vary along with depth without disturbing the spheroid. They demonstrated that oxygen-sensing nanoconjugates enable to measure oxygenation level continuously in 3D spheroid cancer models with high sensitivity [71]. Small molecules of energy metabolism Glucose, an essential constituent of the cellular energy production process, is initially processed into pyruvate, which is further metabolized to produce acetyl CoA and transmitted to the Kerbs cycle inside mitochondria [72]. However, in the oxygen deprivation case, pyruvate is fermented to lactate in the cytosol [73]. Accordingly, elevated lactate concentrations are regarded as the indication of perturbation in the Kerbs cycle led to cellular stress and impairment [74]. Given the fact that toxic metabolites, biological agents, and medications can harm enzyme reactions in the process of the Kerbs cycle, knowledge of glucose and lactate concentrations inside the 3D cell culture platforms is crucial to recon their effects on cellular dysfunctions and toxicity [75].

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Multiple types of glucose and lactate sensors, in particular electrochemical and optical sensors, are designed to decipher metabolic pathways [76] fostering cancer progression [77], a metabolic adaptation of CMs [78], molecular mechanisms of brain function [79] as well as obtaining information on cytotoxicity and cell metabolism [65,80]. Electrochemical biosensors based on enzyme electrodes enable in situ reading of the glucose and lactate concentration with high sensitivity. In these sensors, pertinent enzymes are conjugated on a redox polymer, which is directly deployed on a transducer. Glucose oxidase and lactate oxidase are usually used as enzymes for the detection of glucose and lactate. Once the enzyme interacts with a target analyte (e.g., glucose or lactate), electrons produced via redox reactions are transmitted directly to the transducer across polymer, generating electrical signal by voltametric or amperometric transduction [81]. The concentration of an analyte is directly related to electrical response [82]. In the oldest generation of enzyme electrochemical biosensors, instead of the concentration of the analyte (glucose and lactate), the concentration of hydrogen peroxide, a by-product of a redox reaction, is measured and correlated to the concentration of the analyte [83]. For example, Bavli et al. introduced a multisensor-integrated liver-on-a-chip system for monitoring mitochondrial function as a response to drug toxicity. In addition to screening concentrations of glucose and lactate with an amperometric electrochemical sensing unit, the system enables monitoring oxygen levels with phosphorescent microprobes for tracking the glycolysis process under mitochondrial stress [84]. Although electrochemical biosensors constitute a substantial advance in simultaneous monitoring glucose or lactate concentration, they still face some technological issues for long-term use. For example, in amperometric biosensors, the detection system relies on the availability of oxygen, which constrains their applications in the hypoxic microenvironment [85]. Optical glucose and lactate sensors depict many benefits over electrochemical sensors owing to their ability to label-free, precise and realtime readout concentration of target analyte [86]. In the optical sensory system, microcapsules comprising of enzymes and fluorescent indicator probes detect analyte upon cleavage of the fluorescent probe by hydrogen peroxide, which is a by-product of an enzymatic reaction. Accordingly, the fluorescent intensity indirectly reflects the concentration of glucose or lactate [87]. Optical glucose and lactate biosensors are widely used to monitor cancer progression, in particular for the identification of cancer malignancy [77]. Measurements of lactate secreted from cells and microtissue provide information on the metabolic activity of tumorigenic cells and their degree of malignancy [88]. For example, Mongersun et al. designed a droplet microfluidic system permitting rapid and sensitive quantification of the concentration of lactate released from K562 leukemia and U87 glioblastoma in extracellular

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microenvironment by measurement of time-resolved fluorescent intensity. They demonstrated that cellular characteristics could be discerned by determining lactate efflux rate [89]. Cytokines Cytokines are small glycoproteins synthesized and released by diverse cells, prevalently immune cells to regulate and disseminate immune responses. They essentially act as chemical messengers, controlling and organizing cellular activities including cell growth, differentiation, migration, and others. Other than that, they function in a myriad of reactions in the body such as cell-cell signaling, immunity, wound healing, and cancer development [90]. Comprehending protein secretion kinetics with high spatiotemporal resolution from a particular tissue in a 3D cell culture platform is crucial to the development of disease diagnosis and drug screening [91]. The integration of cytokine detection sensors is therefore an apparent requirement for studies on cell signaling, cancer progression, and drugs impacting the immune system. Electrochemical and surface plasmon resonance (SPR) sensors can be exploited to detect secreted cytokines instantaneously The instantaneous monitoring of secreted cytokines can be achieved by established techniques such as electrochemical and SPR sensors [92,93]. The most popular in situ detection approach for the identification of cytokines and small proteins secreted from the organotypic model is based on immunoassay immobilization on the electrode of electrochemical sensory systems [94,95]. Concerning cytokine detection, Ortega et al. demonstrated a sensitive (ng/mL) and high-throughput electrochemical sensing unit using antibody-based amperometric sensors built-in muscle-on-a-chip. The biosensor system enabled them to monitor concurrently the temporal profile of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) secreted by 3D microtissue of skeletal muscle cells during electrical and lipopolysaccharide stimulation [41]. Aptamers are synthetic nucleic acids or peptides that specifically capture target molecules with high sensitivity [96]. They compete with antibodies for highly precise and selective identification of cytokines owing to their high affinity to target analytes, amenability to detect multiple analytes, long-term bioactivity as well as chemical and thermal stability even in harsh conditions [97]. Zhou et al. developed a microsystem with chambers, enabling coculture of hepatocytes and stellate cells, for spatiotemporal monitoring cell-to-cell communications through released transforming growth factor-beta (TGF-β) molecules by both cell types upon liver injury. Once the door between chambers was opened, secreted TGF-β molecules propagated and coupled to aptamers adsorbed on the electrode connected with voltammetric sensors [98].

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FIGURE 13.3 Biosensors for monitoring cellular behavior and metabolic activity of organotypic models. (A) Diagrammatic representation of (i) ECIS system for monitoring cellular activities, (ii) intercellular and intracellular current flow model. (B) Schematic depiction of (i) gastrointestinal human microbe interface in vitro model integrated with oxygen and TEER sensors (ii) delineation of each layer individually and (iii) perfusion and sensing system incorporated into the platform. (C) Schematic of microphysiological system for identification of growth factors released by hepatocytes (i) top view, (ii) side view, and (iii) micrograph of the microfluidic device [102,103]. Source: (A) Reproduced with permission from A. Anchan, P. Kalogirou-Baldwin, R. Johnson, D.T. Kho, W. Joseph, J. Hucklesby, et al., Real-time measurement of melanoma cell-mediated human brain endothelial barrier disruption using electric cell-substrate impedance sensing technology, Biosensors. 9 (2019). https://doi.org/ 10.3390/bios9020056. Copyright 2019. MDPI Publications. (B) Reproduced with permission from P. Shah, J. V. Fritz, E. Glaab, M.S. Desai, K. Greenhalgh, A. Frachet, et al., A microfluidics-based in vitro model of the gastrointestinal human-microbe interface, Nat. Commun. 7 (2016). https://doi. org/10.1038/ncomms11535. Copyright 2016. Nature Publications. (C) Reproduced with permission from K.J. Son, P. Gheibi, G. Stybayeva, A. Rahimian, A. Revzin, Detecting cell-secreted growth factors in microfluidic devices using bead-based biosensors, Microsyst. Nanoeng. 3 (2017) 1 9. https://doi.org/10.1038/micronano.2017.25, Copyright 2019. Nature Publications.

Compared to stationary sensing modules, the implementation of movable bead-based sensors into a 3D cell culture platform is promising for online monitoring cytokine secretion [99]. Son et al. achieved continuous

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monitoring of the concentration of transferrin and albumin secreted from hepatocytes, cultured in a bioreactor, as a response to the drug (acetaminophen) during five days by using microbeads based electrochemical sensors [100]. Plasmonic biosensors can also be used for in situ measurements of cytokines. Lehman et al. developed plasmonic scaffolds comprised of gold nanoparticles (AuNP) embedded in poly-2-hydroxyethyl methacrylate (pHEMA) composite hydrogel. They showed that scaffolds served functionally as both supporting matrix for 3D microtissues and a biosensor for in situ screening cell metabolites with accompanied surface-enhanced Raman spectroscopy [101] (Fig. 13.3).

13.3.3 Mechanical activity The intrinsic mechanical forces of cells are essential in different cellular processes including motility, adhesion, contractility, and tissue morphogenesis. Also, multitudinous disease conditions, such as cardiomyopathy, are often distinguished by disrupted force excitation or alterations in ECM mechanical properties, which correspondingly influence the expression of mechanosensitive transcription factors like in fibrosis [104]. Hence, biomechanical sensors for tracking cell mechanical properties in 3D cellular structures are very precious for mapping forces generated by cells in healthy and pathological conditions [105]. Recently, several biosensing technologies have been built in 3D cell culture platforms to investigate various biomechanical functions of cells and microtissues such as contraction [106], motion [107], and mass changes [108]. Contraction is integral to striated muscles, namely skeletal and cardiac muscles during movement and blood circulation [109]. Comprehension and recapitulation of the biomechanical properties of tissue interest is crucial to develop a functional in vitro model for disease research and drug toxicity [110]. A number of advanced sensing technologies have been introduced for the detection of spatiotemporal change in mechanical properties and identification of its function in microtissue development [111]. Among these techniques, micropillar arrays (mPAs) [112], ECIS [113], and quartz crystal microbalance-dissipation monitoring (QCM-D) [108] have been extensively used in contractile motion studies. mPAs have been perceived as an appealing tool for the measurement of the force generated during contraction in CMs [114], skeletal muscle cells [115], endothelial cells (ECs) [116], and neural cells [117]. The technique is based on an assembly of micropillars with adjustable geometries, dimensions, and flexibility. The contractions of adhered cells deform micropillars, whose displacements are measured optically in real-time and quantified according to beam bending theory for functional screening of contractile force [118]. Agrawal et al. established a 3D muscle model on bioinert

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hydrogel pillar array, which is highly capable of gauge strain in real-time throughout tissue culture without destruction of in vitro muscle model. In particular, the deflection of fluorescent particles embedded in hydrogel pillars due to contraction of tissue was tracked by imaging with confocal microscopy. The displacement of the fluorescent particle was quantified with particle image velocimetry to determine passive tension related to the growth of muscle tissue. Muscle tissue injury induced by cardiotoxin was also detected by a decline in passive tension [112].

FIGURE 13.4 Biosensors for monitoring mechanical behavior of organotypic models. (A) Heart-on-a-chip platform coupled with mPA and piezoelectric sensing systems for recording contraction of microtissue. (B) Interdigitated electrodes integrated into the hearton-a-chip platform for monitoring electromechanical changes as a response to drug stimulation. (C) QCM-D-integrated cell culture platform for detection mechanical changes in endothelial cells as a response to biochemical stimulants [122]. Source: (A) Reproduced with permission from M. Sakamiya, Y. Fang, X. Mo, J. Shen, T. Zhang, A heart-on-a-chip platform for online monitoring of contractile behavior via digital image processing and piezoelectric sensing technique, Med. Eng. Phys. 75 (2020) 36 44. https://doi.org/10.1016/j.medengphy.2019.10.001. Copyright 2020. Elsevier Publications. (B) Reproduced with permission from X. Zhang, T. Wang, P. Wang, N. Hu, High-throughput assessment of drug cardiac safety using a high-speed impedance detection technology-based heart-on-a-chip, Micromachines. 7 (2016). https://doi.org/10.3390/ mi7070122. Copyright 2016. MDPI Publications. (C) Reproduced with permission from L. Tan, P. Lin, B. Pezeshkian, A. Rehman, G. Madlambayan, X. Zeng, Real-time monitoring of cell mechanical changes induced by endothelial cell activation and their subsequent binding with leukemic cell lines liang, Biosens. Bioelectron. 56 (2014) 151 158. https://doi.org/10.1016/j.bios.2014.01.004. Real-Time. Copyright 2014. Elsevier Publications.

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Cell-based impedance biosensor, relying on the concept of electrical impedance sensor (EIS), is one of the eminent biosensors as they render possible in situ, noninvasive, and simultaneous analysis of cell contractions [113]. Recently, researchers have shown that the ECIS system could discern the alterations in cellular activities, in particular the deformation and attachment propensity of cells triggered by the mechanical movement of CMs [119]. Zhang et al. introduced a high-speed impedance detection system assemble into a heart-on-a-chip platform providing simultaneous tracking contractile periodicity of CMs during beating through detection shifts in impedance. They demonstrated that contrary to conventional EIS systems, high-speed technology enables the measurement of fast impedance variation stimulated by contraction of CMs. This platform also enabled the evaluation of drug toxicity by analyzing contraction characteristics of heart microtissue including frequency and amplitude [120]. QCM-D is an acoustic-based sensor providing noninvasive and concurrent detection of interaction of cells with each other and matrix quantitatively through measurement of shift in resonant frequency and energy loss related to deformation in piezoelectric crystal [121]. Tan et al. studied mechanical changes induced by the interactions of ECs with substrate and leukemia cells (HL-60 and KG-1) on the QCM electrode. They demonstrated an increase in resonance frequency and decrease in motional resistance upon stimulation of ECs with TNF-α, which led to shrinkage in cells, reduction in viscosity of cell layer, and elevation in osmotic pressure. Also, they showed that exertion of mechanical force on activated ECs by leukemia cells in suspension gave rise to a reduction in frequency and increase in motional resistance while the effect of mass was negligible [108] (Fig. 13.4).

13.3.4 Electrical activity Cellular electrophysiology is a predominant approach for investigating cellular signaling in numerous cells range from electrogenic cells such as neurons, myocytes, or pancreatic islets alpha and beta cells to nonelectrogenic cells like immune cells or hepatocytes [123]. Electrical signals move across cell membranes, facilitating transmission of information between various areas of the cell, evoking the secretion of messenger molecules such as neurotransmitters, and inducing muscle cell contraction [124,125]. Hence, fluctuations in AP are indispensable physiological activity in particular for the proper function of electrogenic cells. In that regard, analysis of electrophysiological properties of engineered 3D microtissues is subsidiary to gain a deeper insight into cellular processes underlying communications of cells with each other [126].

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Recently, electrophysiological analysis of single cells and microtissues in 3D cell culture platforms were analyzed with a variety of biosensors including a multielectrode array (MEAs) [127], pillar arrays [128], and field-effect transistors (FETs) [129]. Breakthroughs in microfabrication technologies lay the foundation for the invention of MEAs enabling excitation and recording intracellular electrical potential of electrogenic cells and 3D microtissue [130]. MEAs detect the change in AP caused by ion movement across the cell membrane by multimetallic microelectrodes on which cells adhere [131]. Spatiotemporal recording of electrical signal characteristics (e.g., amplitude, frequency, and duration) render understanding physiological electrical activities of cells and cell-to-cell communication pathways [132]. Furthermore, recent studies have reported that embedding of electrodes inside engineered microtissues has revealed significantly enhanced reading out ability in 3D in vitro [133]. Li et al. introduced mesh nanoelectronics, which were coupled into cell monolayer in the initial phase of organoid growth and then expanded as the organoid evolved into 3D microtissue. This proposed method allowed the integration of equidistant electrodes around the whole microtissue, which enabled the measurement of the electrical signals throughout the growth and maturation of microtissues and to obtain an understanding of microtissue development [134]. Also, Feiner et al. developed flexible and electroactive scaffolds, which accommodated both cardiac cells and microelectrodes for studying the electrical function of cells during maturation via monitoring electrical signals in real-time and modulation of cell contraction by application of electrical stimulation. In addition, the change in the ion permeability of the membrane as a response to pathologic conditions or biochemical stimulants can be identified by analyzing the shifts in signal parameters [135]. Liu et al. contrived a heart-on-a-chip platform to explore the influence of oxygen deprivation on cardiac activity by reading out intracellular AP with Pt nanopillar arrays. They demonstrated that after the electroporation, provision penetration of Pt nanopillars into cytosol facilitated recording AP of cells. They discovered that AP exhibited a narrow width signal during hypoxia, which is triggered by inhibition of ion channels as a response to oxygen deprivation. They reported that these nanopillars offered precise measurement of the AP, which included information about the function of potassium channels that was critical to evaluate the adaptation of cells to hypoxia [136]. Field-effect transistors (FETs) have also been extensively studied for high throughput reading out the electrical activity of cells by using semiconductor nanomaterials as transducers [137]. Recent advances in FET have paved the way to the development of electronically active constructs which brought sensory and biological systems together for real-time screening electrical activity of organotypic models and their

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FIGURE 13.5 Biosensors for monitoring the electrical activity of organotypic models. (A) (i) Nanowire FET embedded vascular scaffolds (ii) image of rolled electronic scaffold consisting of hASMCs (iii) conductance measurement over time, (B) The electrospun cardiac patch consists of MEA for stimulation and recording electroactivity of engineered cardiac tissue. (C) Nanoelectronic mesh embedded 3D hIPSCs derived cardiac organoid for continuously monitoring electrophysiological behaviors. Source: (A) Reproduced with permission from B. Tian, J. Liu, T. Dvir, L. Jin, J.H. Tsui, Q. Qing, et al., Macroporous nanowire nanoelectronic scaffolds for synthetic tissues, Nat. Mater. 11 (2012) 872 876. https://doi.org/ 10.1038/nmat3403. Copyright 2012. Springer Nature publications. (B) Reproduced with permission from R. Feiner, L. Engel, S. Fleischer, M. Malki, I. Gal, A. Shapira, et al., Engineered hybrid cardiac patches with multifunctional electronics for online monitoring and regulation of tissue function, Nat. Mater. 15 (2016) 679 685. https://doi.org/10.1038/nmat4590. Copyright 2016. Springer Nature Publications. (C) Reproduced with permission from Q. Li, K. Nan, P. Le Floch, Z. Lin, H. Sheng, J. Liu, Cyborg organoids: implantation of nanoelectronics via organogenesis for tissue-wide electrophysiology, Nano Lett. 19 (2019) 5781 5789. https://doi.org/10.1101/697664. Copyright 2019. ACS Publications.

responses to biochemical compounds through monitoring fluctuations in the AP of electrogenic cells [138]. Tian et al. established a novel paradigm for the development of electrical sensors embedded scaffolds enabling to monitor simultaneously transmembrane potential of CMs, the response of cardiac and neural microtissues to pharmaceutical compounds, and detection of pH difference across engineered smooth

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muscle tissue [129]. In another study, Kalmykov et al. concentrated on the coupling 3D self-rolled biosensor array (3D-SR-BA) with cardiac spheroids for mapping electrical signal propagation between cells and studying the relationship between the electrical activity of engineered microtissue and arrhythmia caused by exposure to myosin inhibitor, blebbistatin. 3D-SR-BA involving both microelectrodes and graphene transistors enabled to record of electrical signals in 3D of the spheroid, which provided more precise information about the electric activity of cells and signaling between cells. The results of these studies established a basis for the new research area in which sensory system is integrated into 3D engineered tissues, providing a wide variety of possibilities ranging from generation of implantable electroactive tissues allowing real-time detection and stimulation of pathologic conditions to monitoring the influence of novel biochemical compounds on cellular electrical activity [123] (Fig. 13.5).

13.4 Applications of biosensors in in vitro culture platforms of organotypic models 13.4.1 Biosensors in barrier models In the human body, epithelial and endothelial barriers demarcate interconnected tissues and structures and modulate their environment by controlling the movement of ions and biomolecules. Among them, endothelium constitutes the interface between blood and adjacent tissue. On the other hand, epithelium delimits organs from the outer environment as wells as covers the whole surface of the organism. They have multifarious physiological functions depending on adjacent tissues and the state of organs, including protection, transportation, filtration, and sensation [139]. Endothelial and epithelial cell sheets are both retained by intercellular tight junction complexes that control stability, selective permeability, paracrine signaling between cells, and diapedesis of immune cells [140,141]. Therefore monitoring barrier permeability is important to determine the integrity of barriers and identify factors that led to barrier impairment in 3D cell barrier models [142]. The comprehension of functions of the placenta as a barrier in the protection of fetus and regulation transportation of nutrients, oxygen, and biomolecules between fetus and mother provide a basis for assessment of drug safety during pregnancy [143]. Kreuder et al. engineered the placental barrier model recapitulating microenvironment of the placenta. To achieve this, human trophoblast and human placental ECs were cocultured on both surfaces of a 3D bioprinted semipermeable membrane encapsulating human placental fibroblast cells. Transendothelial electrical resistance (TEER)

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measurements were performed for evaluation of the structural integrity and function of the barrier. TEER measurements consist in the determination of ion permeability across cell monolayer for the characterization of integrity and tightness of barriers besides understanding epithelial behavior under biochemical stimulation [144]. In another study, Peters et al. presented gastrointestinal organoid models to investigate barrier dysfunction as an adverse effect of diarrheagenic drugs. TEER measurements were carried out for quantification of the integrity of tight junction complexes in microtissues and detection of time course stability of barrier to identify inhibitory concentrations that

FIGURE 13.6 Biosensors integrated into 3D in vitro barrier models. (A) diagram of 3D in vitro intestinal barrier model (ii) epilntestinal microtissue stained with hematoxylin and eosin. (B) placenta-on-a-chip embedded with impedance electrode arrays for monitoring placenta barrier integrity and cytotoxicity of nanoparticle penetrated across the barrier. (C) Lung-on-a-chip consisting of MITO electrodes for monitoring electrochemical and mechanical changes across lung alveolar barrier. Source: (A) Reproduced with permission from M.F. Peters, T. Landry, C. Pin, K. Maratea, C. Dick, M.P. Wagoner, et al., Human 3D gastrointestinal microtissue barrier function as a predictor of drug-induced diarrhea, Toxicol. Sci. 168 (2019) 3 17. https://doi.org/10.1093/toxsci/kfy268. Copyright 2018. Oxford Academic Press. (B) Reproduced with permission from P. Schuller, M. Rothbauer, S.R.A. Kratz, G. Ho¨ll, P. Taus, M. Schinnerl, et al., A lab-on-a-chip system with an embedded porous membrane-based impedance biosensor array for nanoparticle risk assessment on placental Bewo trophoblast cells, Sens. Actuators, B Chem. 312 (2020) 127946. https://doi.org/10.1016/j.snb.2020.127946. Copyright 2020. Elsevier publications. (C) Reproduced with permission from Y. Mermoud, M. Felder, J.D. Stucki, A.O. Stucki, O.T. Guenat, Microimpedance tomography system to monitor cell activity and membrane movements in a breathing lung-on-chip, Sens. Actuators, B Chem. 255 (2018) 3647 3653. https:// doi.org/10.1016/j.snb.2017.09.192. Copyright 2017. Elsevier Publications.

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were classified as minimum drug concentration led to 15% (IC15) and 25% (IC25) recoverable damage and threshold concentration led to 50% (IC50) incorrigible damage, indicating barrier impairment [145]. However, commercial TEER analysis systems are not amenable for measurements in dynamic and small cellular microenvironment such as lung and kidney model systems while custom-designed TEER electrodes suffer from unevenly distributed current along the cell sheets led to errors in TEER values [146]. In lung-on-a-chip models, native microarchitecture, mechanical motion, airway pressure, and permeability of alveolar barrier are simulated to study physiological and pathological mechanisms [147]. A microimpedance tomography (MITO) system can be implemented on the chip models to monitor integrity, resistivity, and mechanical stretch of alveolar barrier in real-time during breathing [148,149]. Mermoud et al. constructed MITO built-in alveolar barrier-on-a-chip model mimicking alveolus and capillary interface on elastic and cyclically stretched poly(dimethylsiloxane) membrane to analyze permeability, resistivity, and deflection of the membrane during artificial breathing. They reported that in addition to tightness and resistivity of cell monolayers, small biomechanical changes in artificial membrane could be monitored in real-time with high sensitivity via electrodes placed 1 mm underneath membranes [107]. In addition to monitoring permeability, resistivity, and biomechanical properties, biophysical (e.g., migration [102], tumor extravasation [150], cell-cell [151] and cell-matrix [152] interactions), biochemical (e.g., cytokines [153], and metabolites [154]) and physicochemical signals (e.g., oxygen [103] and pH [155]) can be monitored in various 3D in vitro models of barriers (Fig. 13.6).

13.4.2 Biosensors in neural models The complexity of the nervous system and its intercellular connectivity has complicated the study of its functions and the structure of the neural network in vivo. Thereby, researchers attempted to recapitulate microphysiology of nervous systems into 3D in vitro models for investigating functions of neural cells, neurological diseases, and blood brain barriers (BBB) [156]. To accomplish this purpose, real-time monitoring systems must be implemented into 3D in vitro neural model [157]. Since the communication mechanism in the neuronal network is based on electrical signaling through the generation of AP and secretion of neurotransmitters, the functionality of neuronal models can be probed through monitoring electrophysiological behaviors and neurotransmitters releasing profile [158,159]. Neurotransmitters as the messenger molecules are key regulators of signal transmission from neurons to other neurons, gland cells, or muscle

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cells. Also, neurotransmitters play a decisive role in the modulation of neurogenic activities, psychosis, and neurodegenerative diseases including Alzheimer’s, Parkinson’s, and dementia [160]. Hence, monitoring neurotransmitter secretion is a useful tool for assessing the activity of neural cells and their interconnectivity [161]. Dopamine is one of the enticing neurotransmitters as a marker for investigating the differentiation potential of neural stem cells (NSC) and neuron maturation. Kim et al. developed nanocup electrodes grafted with reduced graphene oxide as a sensing platform for the electrochemical measurement of dopamine levels in real-time to evaluate the tendency of NSC to differentiate into dopaminergic neurons [162]. Moreover, the degeneration of dopaminergic cells has been linked to several neurodegenerative diseases such as Parkinson’s disease, schizophrenia, and depression [163]. Accordingly, the quantification of dopamine levels is a powerful method for the diagnosis of pathological conditions in the neural model. In another study, a novel cylindrical gold nanoelectrode array integrated with in vitro culture platforms was introduced for in situ label-free detections of dopamine secreted from neuroblastoma cells using electrochemical assays [164]. Electrophysiology is a distinctive feature of functionality characterization for nerve cells and microtissues. In that regard, it is crucial to record neuronal electrical behavior for a variety of neural model applications, particularly disease modeling and drug screening [165]. The multitude of electrophysiological technologies allows the investigation of diverse electrical properties of neurogenic tissue models ranging from extracellular potentials of neural cells to functions and interconnectivity of the cells in aggregate [166]. Among these technologies, MEAs are a prominent method to acquire insight into the dynamics of spatiotemporal signal propagation in the neuronal network of various layers within organotypic models with a high signal-to-noise ratio [167]. Three-dimensional MEAs have been designed and integrated into the 3D neural organoid model by Soscia and others. They demonstrated that neural AP and synchronized electrical activity of astrocytes and neurons in the 3D neuronal network could be recorded with a high spatiotemporal resolution by biocompatible flexible probes coupled with electrodes [168]. Also, meticulous extracellular potential and connectivity analysis in 3D neural disease models provide precious information about neurodegeneration or synaptic reconfiguration observed in diseases such as Amyotrophic Lateral Sclerosis, Alzheimer’s, and epilepsy diseases [169]. Pelkonen et al. developed a compartmentalized epilepsy disease model on a chip using human pluripotent stem cells-derived neuronal cells. They analyzed the development and maturation of neuronal networks through reading out synchronized electrical activity with integrated MEAs over 98 days. They reported that upon stimulation with kainic acid, the generation of localized seizure in the neural

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network, which was characterized with elevated burst activity could be monitored via MEAs recordings [170]. The BBB is a selectively permeable barrier that controls the movement of various compounds between blood flow and the central nervous system (CNS) to maintain stability in CNS and defend CNS against toxic molecules and infection [171]. The function of BBB heavily relies on the junctions formed between adjacent microvascular endothelial cells (BMECs). In particular, tight junctions are critical to maintaining the integrity of BBB and form TEER. However, tight junctions between BMECs impede the penetration of biochemical agents into CNS

FIGURE 13.7 Biosensors integrated into 3D in vitro neural models. (A) Human BBB model with the astrocytic network-on-chip integrated with impedance electrodes for online monitoring the integrity and permeability of endothelial barrier. (B) Epilepsy in vitro model coupled with MEAs platform for detection seizure-like electrophysiological activity. (C) Nanoelectrode array combined cell culture platform for monitoring dopaminergic differentiation of neural stem cells through measurement of dopamine. Source: (A) Reproduced with permission from S.I. Ahn, Y.J. Sei, H.J. Park, J. Kim, Y. Ryu, J.J. Choi, et al., Microengineered human blood brain barrier platform for understanding nanoparticle transport mechanisms, Nat. Commun. 11 (2020) 1 12. https://doi.org/10.1038/s41467-019-13896-7. Copyright Springer Nature Publications. (B) Reproduced with permission from A. Pelkonen, R. Mzezewa, L. Sukki, T. Ryyna¨nen, J. Kreutzer, T. Hyva¨rinen, et al., A modular brain-on-a-chip for modelling epileptic seizures with functionally connected human neuronal networks, Biosens. Bioelectron. 168 (2020). https://doi.org/10.1016/j.bios.2020.112553. Copyright Elsevier publications. (C) Reproduced with permission from T.H. Kim, C.H. Yea, S.T.D. Chueng, P.T.T. Yin, B. Conley, K. Dardir, et al., Large-scale nanoelectrode arrays to monitor the dopaminergic differentiation of human neural stem cells, Adv. Mater. 27 (2015) 6356 6362. https://doi.org/10.1002/ adma.201502489. Copyright John Wiley and Sons Publications.

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to treat neural disorders. In addition, a variety of diseases can arise from impairment or disfunction of BBB. Hence, a functional model of the BBB is necessary for the accurate evaluation of drug diffusion across the barrier and the effect of BBB disruption on the development of neurodegenerative diseases [172]. Ahn et al. built a human BBB-on-achip model mimicking the structure and function of healthy and inflamed BBB using BMECs, pericytes, and astrocytic networks for detection diffusion dynamics of nanoparticles in both BBB models. The TEER measurements revealed that BMECs founded intact monolayer structure with low permeability in 60 h whereas the permeability of endothelial monolayer increased upon exposure to shear stress. Dispersion analysis showed that tight junctions between ECs restricted the penetration of nanoparticles to perivascular space [173]. In another study, Buzhdygan et al. designed a 3D BBB-on-a-chip platform to simultaneously monitor the effect of spike 2 protein secreted by severe acute respiratory syndrome coronavirus-2 on the integrity of BBB. The platform enabled detection of the changes in the integrity and function of BBB with real-time electrical resistance and intercellular permeability measurements of ECs as well as proinflammatory responses of cells with flow-cytometry analysis. They demonstrated that spike protein activated the proinflammatory response of ECs, which significantly deteriorated the integrity and function of BBB [174] (Fig. 13.7).

13.4.3 Biosensors in cardiac models Heart is one of the complex organs in the human body and serves as a pump that maintains the movement of blood throughout the circulation system for delivering oxygen and nutrients to all the cells in the body while removing carbon dioxide and metabolic wastes [175,176]. The cardiac blood pumping cycle depends on three major cardiac functions involving excitation of cardiac conduction network via production of an electrical signal, coordinated contraction and relaxation of the myocardium, and electrochemical integration of excitation and contraction processes through intracellular calcium transients [177]. The intricate functions of the heart are mostly carried out by CMs, which are specialized based on requirements of the specific anatomical site to fulfill their distinct functionality. The atrial and ventricle CMs have highly structured cytoskeletal and contractile components that enable them to generate force and pressure required to pump blood while pacemaker cells produce and deliver electrical signals to regulate the heartbeat [178]. To garner broader insight on morphology, functions, and cardiotoxicity, researchers have studied the development of 3D in vitro cardiac models that reconstitute in vivo like myocardial blood perfusion,

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conduction and contraction activity, and cellular interactions. Notably, advances in biosensing technologies as well as the advent of 3D in vitro culture models have propelled the fields to integrate sensory systems with 3D in vitro cardiac models that mimic heart physiology and functions to monitor their functional activities in real-time. The electrical activity is paramount for a healthy myocardium, as it facilitates unidirectional pumping of blood by inducing consecutive and synchronized contractions and relaxations of cardiac muscle [179]. Comprehensive research on the electrophysiological characteristics of CMs in 3D cardiac models is crucial for an in-depth understanding of electrical signal propagation pathways, physiological development of myocardium, and pathological mechanisms of cardiovascular diseases (CVDs), which establish information on the diagnosis of CVDs and advancement of treatments [180]. MEAs continue to be a mainstream technology for recording electroactivity of 3D in vitro cardiac models in real-time [181]. Recent advances in 3D multimodal MEAs system coupled with 3D in vitro cardiac model have enabled online high throughput measurements and modulation of electrical activity within the cardiac model [182]. Daus et al. developed a biohybrid system that integrates 3D spheroids of CMs obtained from the chicken embryonic heart with MEAs to monitor electrophysiological behaviors of spheroids upon treatment with cardioactive drugs. They demonstrated that this biohybrid system conduces the analysis of the production and transmission of electrical signals throughout single spheroids, beat rates, and rhythmicity, as well as spatiotemporal fluctuations in the extracellular potential of beating spheroids as a response to the drugs [183]. Similarly, Wei et al. generated a porous scaffold coupled with MEAs as a novel bionic tissue-engineered cardiac patch for monitoring extracellular electrical signals of CMs cultured on the scaffold [184]. Additionally, Liu et al. introduced a heart-on-a-chip model combined with planar MEAs as an extracellular bioelectronic and Pt nanopillar arrays as an intracellular bioelectronic for the assessment of shifts in electrophysiological parameters under acute hypoxia conditions. Extracellular bioelectronic was reported to characterize the frequency and regularity of beats and the propagation rate of the electrical signal, which altered depending on oxygen dynamics of in vitro model, while intracellular bioelectronic exhibited the ability to enter electroporated cells’ cytosols and measure APs accurately in realtime. In particular, they highlighted that nanopillar arrays offered an opportunity to analyze precisely the intracellular electrical activities of both individual cells and the cardiomyocyte network, which allowed them to identify local changes in cellular activity [136]. Far beyond conventional electrophysiologic sensors, organic electrochemical transistor arrays (OECT) are an intriguing alternative for

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measurements of electrical signals with improved electrical interactions with cells and accordingly highly sensitive and precise readouts [185]. Also, they can be used to investigate chemical processes occurring on the surface of individual cells or 3D in vitro cardiac models [186]. Recently, OECT was used to monitor and identify the electrophysiological response of 3D cardiac microtissue to chronotropic drugs by analyzing beating frequency and change in direction and duration of AP propagation within cardiac microtissue. They showed that OECT can be applied to determine the effectiveness and adverse effects of chemical agents [187]. Functional CMs have distinct electromechanical connections based on the excitation-contraction coupling process through which an electrical impulse is translated into a mechanical response, cardiac contraction [188,189]. Synchronized contraction of CMs is regulated by the fibrous structure of the myocardium, whereas its mechanical and structural disruption can culminate in impairment of heart function. Hence, contractile characteristics of CMs have been studied thoroughly to delineate the etiology and pathogenesis of heart failure and to find innovative therapies [190]. However, primary cardiac cells are challenging to obtain and retain in cell culture for long-term experiments, so IPSCs-derived cardiac cells have emerged as an exceptional cell source for development of 3D cardiac models [191]. Recently, Hu et al. devised a sensor chip that brought microelectrodes and interdigitated electrodes (IDEs) detection technologies together for concomitantly tracking shifts in the electrophysiological and mechanical status of human-IPSCs-derived cardiomyocytes (hiPSC-CMs) beating after drug administration. This novel chip platform was revealed to be capable of continuous and consistent recording of the excitation and contraction behaviors with high accuracy and spatiotemporal resolution. This ability provides valuable information about malfunctions in the coupling that occur if functions of myofilaments and calcium channels are disrupted. Also, this system could measure both extracellular potential and mechanical beating concurrently, allowing the determination of time lag between both signals precisely. Moreover, it was indicated that with the use of this novel chip system, it was possible to analyze arrhythmia triggered by pharmaceutical agents by measuring alterations in the electrical activity of CMs [192]. Monitoring contraction of CMs helps to understand beating behavior under different conditions. However, it has been challenging to quantitatively map the contractile forces, which direct the dynamic interactions of cells with each other and ECM [193]. Hence, recent studies have attempted to design 3D in vitro models integrated with contractile force sensory technologies for functional cardiac contractile behavior analysis. Sakamiye et al. engineered a heart-on-a-chip platform that enables to

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simultaneously monitoring of the contractile activity of 3D cardiac microtissue with mPAs and polyvinylidene fluoride piezoelectric film through analysis of contraction force, frequency, and rhythm. mPAs surrounded by cardiac microtissue were utilized to acquire multisite force recording, twitch speed, and direction of contraction with high

FIGURE 13.8

Biosensors integrated into 3D in vitro cardiac models. (A) Cardiac spheroid cultured on MEAs. (B) Bionic engineered scaffold (i) schematic diagram of the production process of cardiac scaffold integrated with MEAs (ii) MEA chip (iii) underlying concept of extracellular potential measurement with the MEA sensor. (C) hIPSC-CMs cultured on ME-IDEs (i) schematic diagram of ME-IDE platform (ii) schematic representation of multimodal sensory platform (iii) recorded extracellular potential, mechanical beating, and electromechanical integration signal graphs. Source: (A) Reproduced with permission from A.W. Daus, P.G. Layer, C. Thielemann, A spheroid-based biosensor for the label-free detection of drug-induced field potential alterations, Sens. Actuators, B Chem. (2012). https://doi.org/10.1016/j.snb.2012.02.011. Copyright 2012. Elsevier publications. (B) Reproduced with permission from X. Wei, Q. Gao, C. Xie, C. Gu, T. Liang, H. Wan, et al., Extracellular recordings of bionic engineered cardiac tissue based on a porous scaffold and microelectrode arrays, Anal. Methods. (2019). https://doi.org/10.1039/ c9ay01888c. Copyright 2019. Royal Society of Chemistry publications. (C) Reproduced with permission from N. Hu, T. Wang, H. Wan, L. Zhuang, R. Kettenhofen, X. Zhang, et al., Synchronized electromechanical integration recording of cardiomyocytes, Biosens. Bioelectron. (2018). https://doi.org/10.1016/j.bios.2018.06.017. Copyright 2018. Elsevier Publications.

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efficiency by measuring the bending dislocation of pillars and mapping time interval between contraction of different pillars with an integrated digital microscope. Meanwhile, the piezoelectric film was exploited to measure the strain caused by the twitching motion of mPAs, which reflected the contraction and relaxation behavior of the cardiac microtissue. They showed that combining multimodal sensors with a heart-on-a-chip system allowed to obtain detailed and complementary information on the contractility function of cardiac microtissue and investigate the effects of drugs on cardiac contraction activity [122] (Fig. 13.8).

13.4.4 Biosensors in liver models The liver has intricate microarchitecture with a multitude of functions including regulation of blood sugar level, modulation immune response, excretion of toxins, metabolization of drugs, lipids, and amino acids, production of hormones, secretion of bile for digestion, and many other functions [194]. In vitro models that mimic human liver physiological and pathophysiologic functions have critical importance for studying the morphology and functions of the tissue [195]. Recently, in vitro liver models were combined with biosensor technologies for in situ monitoring of various cellular parameters and changes in the microenvironment to obtain realistic information on molecular and pathological responses of the tissue to xenobiotics [196]. Liver organotypic models have been constructed to investigate cellular behaviors in an effort to understand the reaction of the tissue to biochemical agents [197]. Recently, Vernetti et al. designed a liver in vitro model, which simulated the multicellular structure of the liver acinus module to investigate the interaction between the toxicity and liver injury induced by drugs. Sentinel cells transformed into fluorescent protein biosensors were also embedded into the liver model to monitor main liver functions related to hepatoxicity. They demonstrated that fluorescent biosensors allowed in situ multiplexed detection of cellular behaviors including migration, organization of cells in layered tissue, apoptosis, and production of reactive oxygen species (ROS) under drug stimulation [198]. Furthermore, ECIS- and TEER sensor-incorporated liver models provide unique opportunities to monitor real-time physiological and pathological responses of liver microtissues [199]. Recently, Farooqi et al. fabricated liver fibrosis-on-a-chip model embedded TEER and ECIS sensors to assess the influence of fibrotic stimulated TGF-β1 on the development of fibrotic tissue. Real-time electrical resistance measurements revealed that resistance was increased during the formation of intact hepatocytes monolayer, whereas exposure to fibrotic-stimulated TGF-β1

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led to a decline in resistance until the fourth day of culture, after which resistance values increased because of secretion and accumulation of fibrotic ECM by activated fibroblasts. Electrochemical detection of released ROS also confirmed stimulation of hepatocytes with TGF-β1, which induced stress in hepatocytes and the activation of fibroblast [200]. Additionally, electrochemical biosensor-integrated liver models have been extensively used for monitoring a numerous physiological parameter including glycolysis metabolites [84], secretion of cytokines [98], oxygen [64,66], and pH level [201] in the microenvironment to shed a light on the various function of the liver, drug toxicity, and disease models as explained in previous sections of the chapter (Fig. 13.9).

FIGURE 13.9 Biosensors integrated into 3D in vitro liver models. (A) Liver fibrosis-ona-chip combined with ROS and TEER electrode for monitoring the integrity of endothelial cells and secretion of reactive oxygen species upon TGF-β1 stimulation. (B) Oxygen sensor-integrated liver-on-a-chip platform. (C) Electrochemical aptasensor built-in liver injury-on-a-chip platform and TGF-β1 detection mechanisms elucidating signaling between hepatocytes and stellate cells. Source: (A) Reproduced with permission from H.M.U. Farooqi, B. Kang, M.A.U. Khalid, A.R.C. Salih, K. Hyun, S.H. Park, et al., Real-time monitoring of liver fibrosis through embedded sensors in a microphysiological system, Nano Converg.. 8 (2021). https://doi. org/10.1186/s40580-021-00253-y. Copyright 2021. Springer Nature publications. (B) Reproduced with permission from A. Moya, M. Ortega-Ribera, X. Guimera`, E. Sowade, M. Zea, X. Illa, et al., Online oxygen monitoring using integrated inkjet-printed sensors in a liver-on-a-chip system, Lab. Chip. 18 (2018) 2023 2035. https://doi.org/10.1039/c8lc00456k. Copyright 2018. Royal Society of Chemistry Publications. (C) Reproduced with permission from Q. Zhou, D. Patel, T. Kwa, A. Haque, Z. Matharu, G. Stybayeva, et al., Liver injury-on-a-chip: microfluidic co-cultures with integrated biosensors for monitoring liver cell signaling during injury, Lab. Chip. 15 (2015) 4467 4478. https://doi.org/10.1039/c5lc00874c. Copyright 2015. Royal Society of Chemistry Publications.

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13.4.5 Biosensors in kidney models Kidney is the main organs modulating the filtration of wastes from the blood flow for disposal by urination [202]. Also, it regulates fluid, salt, and pH stability through the filtration and reabsorption processes as well as they have critical functions in the production and secretion of several hormones involved in the production of red blood cells, resorption of calcium, and hypertension [203]. These renal functions are in control of the glomerulus and tubule structures of nephrons. The intricate 3D structure of a nephron has not been simulated in vitro yet. However, recently developed in vitro models of glomerulus and proximal tubules have allowed studying renal activities [204]. The interdigitation of epithelium monolayer of the proximal tubule and endothelial monolayer of glomerulus has been extensively researched for understanding their filtration function in health and disease conditions [205]. Asif et al. contrived a proximal tubule-on-a-chip that emulated diabetic nephropathy triggered by glucose stimulation. Changes in pH and tight junctions between epithelial cells were monitored in real-time with an optical pH sensor and TEER measurement. They demonstrated that nephrotoxicity associated with high glucose exposure led to the decrease in both resistance of epithelial monolayer and pH over 5 days, after which antiinflammatory drug-containing serum was used to lessen the cytotoxic effect. TEER measurements have revealed that drug administration supported rebuilding epithelial integrity and elevated pH to physiologically normal values [155]. Additionally, it is a tough challenge to investigate in vivo renal microenvironment, which drives a need to establish in vitro kidney models coupled with a sensory system to gain insight into metabolic mechanisms of various ions and molecules into the kidney [206]. To understand the response of proximal tubule-derived cells (PTCs) to drug-induced nephrotoxicity, Cho et al. built a kidney-on-a-chip model involving fluorescent nanoparticles to immunocapture γ-glutamyl transpeptidase (GGT) molecule, which was expressed on PTCs in normal conditions and released into the tubule upon exposure to toxins. They have concurrently monitored the availability of GGT molecules on PTCs or outflow solutions with a custom-made fluorescent microscope integrated with a smartphone [207]. In another study, Curto et al. fabricated OECT integrated into a microfluidic chamber lined with canine kidney cells (MDCK II). OECT functioned as a generator of a wound in a defined size, shape, and location, an electrical wound healing probe, and a multiparametric sensory system. They showed that OECT enabled to concurrently monitor the shift in capacitance and resistance of cell monolayer both in health and pathologic conditions as well as cell metabolism by measuring glucose uptake by cells [208] (Fig. 13.10).

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FIGURE 13.10 Biosensors integrated into 3D in vitro kidney models. (A) Proximal tubule-on-a-chip platforms with impedance electrodes and pH sensors for real-time detection nephrotoxicity of high glucose exposure. (B) OECT-integrated kidney microfluidic system. Source: (A) Reproduced with permission from A. Asif, K.H. Kim, F. Jabbar, S. Kim, K.H. Choi, Real-time sensors for live monitoring of disease and drug analysis in microfluidic model of proximal tubule, Microfluid. Nanofluidics. 24 (2020) 1 10. https://doi.org/10.1007/s10404-02002347-1. Copyright 2020. Springer Publications. (B) Reproduced with permission from V.F. Curto, B. Marchiori, A. Hama, A.M. Pappa, M.P. Ferro, M. Braendlein, et al., Organic transistor platform with integrated microfluidics for in-line multi-parametric in vitro cell monitoring, Microsyst. Nanoeng. 3 (2017) 1 12. https://doi.org/10.1038/micronano.2017.28. Copyright 2017. Springer Nature publications.

13.5 Conclusion and future perspectives 3D organotypic models, which are usually referred to as organoids and organs-on-a-chip models, are precious in vitro cellular platforms to recapitulate intricate human physiology, as a complement and alternative for animal models. Structural and functional analyses of organotypic models are crucial for the delineation of the cellular microenvironment to reproducibly modulate cellular behavior and direct cell fate. In this regard, the integration of biosensors in organotypic models is a factual approach that allows real-time and in situ recording signals from cells and their microenvironment for investigating the reorganization of cells to generate functional microtissues, evaluating the responses to therapeutic stimulants, and exploring the initiation and progression pathways of diseases. These platforms are captivating but are still confronted with a range of challenges. In particular, the focus on the production of 3D in vitro models relies on the simulated basic functional module of an organ instead of constructing its hierarchical complexity. However, with the emergence of new in vitro models, a combination of multiple functional parts and cell types of an organ in a platform is expected to be available in near future. Additionally,

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in situ phenotypic analysis of organotypic models is also a concern, which proves the need to develop advanced characterization technologies for 3D culture. Therefore advances in the fields of biosensors and 3D in vitro culture technologies may significantly tackle the challenges of developing high-fidelity organotypic models. We envision that 3D in vitro models integrated with biosensors will be extensively used in preclinical personalized therapy development and drug screening research.

Acknowledgments The self-made images (i.e. Fig. 13.1) were created with Biorender.com

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[199] H.M.U. Farooqi, M.A.U. Khalid, K.H. Kim, S.R. Lee, K.H. Choi, Real-time physiological sensor-based liver-on-chip device for monitoring drug toxicity, J. Micromech. Microeng. 30 (2020). Available from: https://doi.org/10.1088/1361-6439/ababf4. [200] H.M.U. Farooqi, B. Kang, M.A.U. Khalid, A.R.C. Salih, K. Hyun, S.H. Park, et al., Real-time monitoring of liver fibrosis through embedded sensors in a microphysiological system, Nano Converg. 8 (2021). Available from: https://doi.org/ 10.1186/s40580-021-00253-y. [201] F. Yu, R. Deng, W. Hao Tong, L. Huan, N. Chan Way, A. Islambadhan, et al., A perfusion incubator liver chip for 3D cell culture with application on chronic hepatotoxicity testing, Sci. Rep. 7 (2017) 1 16. Available from: https://doi.org/10.1038/ s41598-017-13848-5. [202] B. Du, M. Yu, J. Zheng, Transport and interactions of nanoparticles in the kidneys, Nat. Rev. Mater. 3 (2018) 358 374. Available from: https://doi.org/10.1038/s41578018-0038-3. [203] M. Epstein, Alcohol’s impact on kidney function, Alcohol. Heal. Res. 21 (2016) 93 96. [204] N. Ashammakhi, K. Wesseling-Perry, A. Hasan, E. Elkhammas, Y.S. Zhang, Kidneyon-a-chip: untapped opportunities nureddin, Physiol. Behav. 176 (2017) 139 148. Available from: https://doi.org/10.1016/j.kint.2018.06.034.Kidney-on-a-chip. [205] N.Y.C. Lin, K.A. Homan, S.S. Robinson, D.B. Kolesky, N. Duarte, A. Moisan, et al., Renal reabsorption in 3D vascularized proximal tubule models, Proc. Natl. Acad. Sci. U. S. A. 116 (2019) 5399 5404. Available from: https://doi.org/10.1073/ pnas.1815208116. [206] R. Paoli, J. Samitier, Mimicking the kidney: a key role in organ-on-chip development, Micromachines. 7 (2016). Available from: https://doi.org/10.3390/mi7070126. [207] S. Cho, A. Islas-Robles, A.M. Nicolini, T.J. Monks, J.Y. Yoon, In situ, dual-mode monitoring of organ-on-a-chip with smartphone-based fluorescence microscope, Biosens. Bioelectron. 86 (2016) 697 705. Available from: https://doi.org/10.1016/j. bios.2016.07.015. [208] V.F. Curto, B. Marchiori, A. Hama, A.M. Pappa, M.P. Ferro, M. Braendlein, et al., Organic transistor platform with integrated microfluidics for in-line multi-parametric in vitro cell monitoring, Microsyst. Nanoeng. 3 (2017) 1 12. Available from: https://doi.org/10.1038/micronano.2017.28.

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C H A P T E R

14 Sensors for water and wastewater monitoring Abdul Shaban1, Larbi Eddaif1,2 and Judit Telegdi1,2 1

Research Centre for Natural Sciences, Institute of Materials and ´ buda University, Faculty Environmental Chemistry, Budapest, Hungary, 2O of Light Industry and Environmental Engineering, Budapest, Hungary

14.1 Wastewater pollutants The term wastewater refers to any polluted water that does not pass the regulations for animal and human usage intake. Wastewater can originate from suburban sites or industrial output stream sources. The most common causes of wastewater are watery and drain contamination that come from industrialized or agrarian manufacturing activities, hospitals, rainwater drainage, and any other activities that necessitate water involvement in their operation. Unprocessed wastewater might comprise different types of pollutants: biological, chemical, and physical contamination [1]. Chemical or physical pollutants can contain heavy metals (HMs), organic materials, soluble organic material, macrosolids, emulsions, and toxins, and many more types. On the other hand, biological pollutants can be some forms of bacteria, viruses, helminths, fungi, etc. Besides all of that, wastewater can also contain nonpathogenic organisms such as fish and bugs [2].

14.2 Sources of water pollutants Water is a solvent that can dissolve more chemical materials than any other known liquid, which makes it very susceptible to pollutant contamination. Poisonous materials from agricultural farms, habitant municipalities, and industrial plants daily dissolve into and pollute nearby water sources stream endangering water quality. Wastewater contaminants might originate

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from point-sources, where water pollutant reaches the water stream from a pipeline such as sewage, or from dispersed sources, where contaminates can enter from unconfined areas, such as runoff from farms. Point-source contaminants are controllable whereas dispersed-source pollutants are not usually possible to govern [3]. The physical characteristics of wastewater can be gathered by the appearance, chemical composition parameters, and plenty of microorganisms [4]. The composition and concentration of polluting matters in wastewater fluctuate extensively and can be categorized as organic pollutants, inorganic substances, and microorganisms [5 7].

14.3 Types of water pollutants It is well known that living creatures cannot live without water. The quality of the water is very important. Water pollution can have disastrous consequences on human beings as well as on flora and fauna because the pollutants affect human health as well as the environment. Although natural waters (lakes, rivers) contain enormous volumes of water, they cannot absorb contaminants beyond measure. Very few parts of freshwater (.1%) are disposable for us now. It is necessary to save (and increase) the quantity of drinking water in the future. Water pollutants (chemicals: inorganic, organic; micro-, and macroorganisms, as well as their metabolites and degradation products) can contaminate water. But not only is surface water important but the groundwater (though it is generally cleared by the soil) flowing slowly through the soil layers in the subsurface is affected by the contamination. The groundwater pollution (pesticides, fertilizers, etc.) can contaminate the under water rivers and waterbeds, thus compromising the quality of wells and other places from which groundwater is extracted for human use. The salt content of the water in coastal areas can also be high. We have to keep in mind that in the future the necessity of saving our natural water is increasing as the polluted water not only disrupts the aquatic ecosystem but make the water unsafe for humans [8]. When citing water pollution one must distinguish between the pointsource (single source of contaminants like industrial effluents); nonpoint source (the pollution originates from different sources: oil industry, sewage, wastewater, agriculture, farm run-off, constructions); and transboundary contamination (the contamination happens inline) [9]. It is necessary to mention that not all water contaminants have the same origin. It is important to know that a polluted aqueous environment can cause dangerous alterations in the environment. Not only plants and animals are exposed to danger but after consuming the contaminated plants and/or animals the human body is exposed.

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There are four classes of water pollutants: organic and inorganic materials, microorganisms, and microscopic contaminants.

14.3.1 Organic pollutants Organic pollution is a type of chemical pollution caused by carbon pollutants such as organic matter. The source of organic pollutants is very diverse: oil industry (effluent oil); agriculture (herbicides and insecticides); livestock operation; laundries (detergents); and human activities that produce domestic sewage. They are mainly toxic and resistant to degradation. Their effects include convulsion, nausea, and other health disorders. Among the organic contaminants, one of the most dangerous is tert-butyl ether (though it is presently banned), which is an air-cleaning gas additive. The removal of this chemical from water takes a long time, and it can cause lymphoma, tumors, and leukemia. Contaminated water is toxic if it contains carcinogenic, mutagenic, teratogenic or radioactive, explosive, or so-called bioaccumulative (their concentration is increasing in the food chain) materials that can originate from different industries. As there are billions of people on the earth, an enormous amount of waste is produced. If this goes into water without any cleaning process it can cause dramatic water pollution (not to mention the diseases their microbial contamination can cause). The biodegradation of organic compounds in wastewater consumes a lot of oxygen and the lack of oxygen can negatively influence aquatic life.

14.3.2 Inorganic pollutants Inorganic pollutants can come from wastewater or runoff emitted by industries. The presence of ammonia and heavy metal ions (lead, mercury, cadmium, zinc—mainly from batteries—copper, barium, chromium, arsenic) at very low concentrations is not harmful, but at higher concentrations, they are dangerous to people, plants, and animals, and can not only cause health problem but can also result in death. Mercury can also derive from the paper industry. These contamination can be the consequence of the disposal of industrial waste and bioleaching processes. Another very dangerous waste is the radioactive discard from nuclear power plants.

14.3.3 Microbial pathogens Microorganisms are generally present in natural water as well as in domestic sewage. But when they start to grow above a limit they can

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seriously contaminate the aqueous environment with disease-causing microbes. Microorganisms (i.e., protozoa, bacteria, viruses) can contaminate water. Humans are very sensitive to this type of pollution. The microbial activity can be increased by the nutrients that originate from agricultural and industrial activity. Another important factor is the presence of oxygen. Oxygen depletion increases the activity of the so-called anaerobic microorganisms that produce chemicals that are harmful to people, plants, and animals (e.g., sulfide, ammonia, and other harmful toxins). Domestic sewage can be the source of pathogen microorganisms, of putrescent organic materials originating from feces; this is the reason they can threaten human health. The microbes destroy organics in the sewage. Not only the decomposed products can be dangerous for the health, but also the decreased level of oxygen caused by the microorganisms results in the lack or the decrease of oxygen, which is dangerous for the aquatic organisms. The level of the pathogens and the toxic organics are decreased in the sewage treatment. Pathogen bacteria that can cause diseases are Salmonella and Escherichia coli; the most often occurring protozoa are the gastrointestinal Gardia lamlia as well as the Cryptosporidium parvum (a parasite that infects the mammalian intestinal tract). The Norovirus causes gastroenteritis. Among the pathogens, E. coli bacteria are mainly responsible for the pollution and cause diseases, which can be the result of contamination by animal and human waste as well as by inadequate sewage and sanitation discharge. On the other hand, the sewage can increase the growth of algae causing significant eutrophication that leads to a decrease in the oxygen content and the final consequence is the death of several aquatic creatures. The main source of pathogens (and organics) is wastewater, and while sewage treatment reduces the level of the microbes it cannot eliminate it 100%.

14.3.4 Macroscopic pollutants Macroscopic pollutants are visible objects in water that are intentionally placed into the water or simply washed in from discarded objects. This is a real and serious problem especially when we remember that plastic bottles pollute the rivers and oceans by the tons. The problem is not only that they “invade” the water surface but, because of different effects (e.g., erosion, microbial, chemical degradation), they degrade into small plastic pellets. These microplastics are consumed by animals in water and sometimes can appear in the human body. It is important and urgent to remove macroscopic objects from the water to avoid their decomposition.

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14.3.5 Thermal pollution This type of pollution is mainly the consequence of human activity. One example is the effluent of cooling water from different industries (e.g., chemical manufactural, power plants) that can raise the water temperature as much as 15 C (at a higher temperature the solubility of the oxygen is lower). Elevated temperature can decrease the oxygen content, which is dangerous for gill-breathing species (e.g., fish). The decreasing number of different fish not only influences the food chain but reduces biodiversity. The change in the surface water influenced by urban wastewater has a significant impact on drinking water. Thus thermal (not only the contaminants) control of waste and drinking water is essential.

14.3.6 Emerging water pollution In this section some special sources and effects of contamination are discussed and clarified. So-called municipal wastewater collects industrial waste and can contain high amounts of pollutant. Its treatment is very important and started only a century ago. The population-equivalent municipal discharge shows the relation between the number of inhabitants in a given place and the untreated waste. When the population increased, it is necessary to handle separately the sanitary sewage and storm-water runoff. Agricultural wastes come not only from cities but also from the animals that are kept in relatively small pens (to produce more food in the same places, at the same time). Their effluence wastes heavily contaminate water. The sediments (which are considered to be a special type of pollutant) originate from mining, construction, and cultivation. These small particles muddle the water and, by diminishing the light intrusion, can interact with the gill organs of the fish and, additionally, the fish cannot find the food easily. The oil that can come from oil spills due to accidents of tankers on seas or when oil finds its way from drain-pipes into the soil and then to water can cause pollution that destroys ecosystems. Birds and aquatic life can be seriously affected. Other sources of pollution are the waste stockpiles, which are the side products of mining. For example, in the case of pyrite, which is always found together with other sulfide ores like sphalerite (ZnS) and galenite (PbS), the microorganisms in the presence of rainwater and heat dissolve the sulfide from the ores and convert it into sulfuric acid. This is washed into the groundwater, thus causing acid and toxic ions pollution. Additionally, abandoned mines can also increase aquatic ecosystem contamination.

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It is important to note that the removal of these pollutants requires special purification methods. It is not enough to use, for example, hot water or special natural materials to soak up the oil, but special detergents should be used in contamination cleanup. But before a cleaning method is applied it is important to consider its consequences (e.g., some detergents can damage coastal areas).

14.4 Indicators of water pollution The concentration of dissolved chemicals in water can qualify an aqueous system. The purpose of the water (e.g., industrial, domestic, drinking) will define the limits of dissolved materials (e.g., organic, inorganic, microbial contamination) to safeguard human health. Pure water contains dissolved materials. Water that is proper for swimming is not suitable for drinking. It is essential to follow standards that limit and regulate impurities and specify the quality of different types of water. Different standards are applied for rivers, lakes, and swimming pools. The most important characteristics are the dissolved chemicals and oxygen, pH, and turbidity, to mention only a few. For effluent water, special requirements are used to regulate not only the dissolved substances but the biological oxygen demand (BOD), the nitrogen, and the suspended solids as well. The regulations are stricter for drinking water that can contain only a few specified contaminants. In the case of industrial waste discharge, the concentrations of toxic chemicals are strictly regulated. Pollution of water is undesirable due to the effect on ecosystems. To control and manage pollution it is crucial to identify pollutants and chemical, physical, and biological indicators can play a big role in this process. Different indicators can characterize water quality and its pollutants such as physical, physicochemical, and biological contaminants [10,11]. Briefly, the most significant chemical/physical indicators are pH, reactive and total organic carbon (TOC), dissolved oxygen (DO) and chemical oxygen demand (COD), BOD, nitrogen, nitrate, anhydrous ammonia, and metal (especially heavy metal) ions.

14.4.1 Chemical indicators of water quality The chemical pollution that determines the quality of an ecosystem is in close connection with buffering capacity, an organic component that plays an important role in nutrient cycling and in the quality of water [12]. The quality criterion index can be used almost in all contaminated systems.

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Good chemical indicators promote biodiversity and can also regulate nutrient and carbon cycling [10,13]. Chemical parameters used to characterize water are as follows: 1. DO content: The DO value is a balance between the oxygen content of the aqueous system and the atmosphere. Generally, this value is close to saturation, which depends on the atmospheric pressure, the oxygen content of the air, temperature, and on reducing dissolved chemicals. The DO is a very good indicator of aqueous pollution [14], and the average level is 9 mg/L; when this value is less than 2 mg/L, fish will perish; for different groups of fish, this value should be at least 4 5. The DO content alters with the depth of the water, in the presence of plants, and can be disturbed by turbulence. At low DO values fish die. At a high DO value, the rate of corrosion increases. 2. Biochemical/BOD: This is the amount of oxygen microorganisms consume when they metabolize and decompose organic materials [15]. 3. COD: This is the measure of oxidizable organic materials in surface water, in sewage, and in industrial wastewater [16]. The decomposition of organic pollutant requires a lot of oxygen. This will change the ratio between the oxygen and the organic materials, and the water quality will be worse, which negatively influences aquatic life. A decrease in the oxygen content caused by organic pollutant can only indirectly characterize the organic material. On the other hand, the BOD can directly measure the chemical contaminant [15]. The COD shows the quantity of the oxidant under special conditions. It can be used as a chemical indicator for pollution in the presence of organic materials, iron salts, sulfides, and nitrates, and represents the oxidation of organics except for polycyclic aromatic compounds and dioxin-like chemicals. The COD is an indicator of system conditions. Potassium dichromate or permanganate can be used to determine the COD [16]. 4. TOC is an indicator of pollution that involves all organic compounds in the aqueous system [17]. Compared with the COD and the BOD the TOC directly estimates the organic pollutants in the ecosystem. 5. Alkalinity, acidity, and the pH-buffer effect: The alkalinity represents the quantitative capacity to neutralize the acid in water. The origin of the acid can be rainfall or wastewater. It is appropriate to measure and control the water treatment. The HCO32 reacts with hydrogen ions forming H2CO3; in the presence of OH- ions CO322 arises. Different microorganisms prefer different pH ranges. Bacteria like pH 5 9, blue-green bacteria: pH 6 9, fungi: 2 7, protozoa (single-cell eukaryotes) live between pH of 5 and 8, and actinomyces (special bacteria) like a pH between 6.5 and 9.5.

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6. Phosphorus content: Total phosphorus, soluble phosphorus, and phosphates (PO432, HPO422 (e.g., orthophosphates, metaphosphates, polyphosphates) are nutrients for algae [18]). The P content of the Earth’s crust is about 1000 ppm, mainly in the form of phosphate [8]. The phosphorus can cause acute poisoning to biota; 1 mg/kg is lethal for humans [19]. The concentration of P in synthetic industries in the domestic and industrial effluent can be high. When it is higher than 0.2 mg/L overreproduction of algae occurs that causes eutrophication [20]). This justifies its use as an indicator of P content. 7. Nitrogen content: Organic, total, in the form of ammonia (characteristic for wastewater, when the pH . 9.5, it is in the form of the very toxic NH3), nitrite, and nitrate (mainly from the agriculture; level of nitrate in drinking and natural water: max. 10 mg/L, 90 mg/L respectively; origin of nitrate: car exhaust, manure heaps, animal waste, runoff of fertilizers; the consequence of the enhanced nitrate: positive effect: increases in the plant production, and the fish population; negative effect: significantly increases algae population that leads to decreased oxygen level that is dangerous for fish), nitrite. The discharge of sewage (industrial and domestic) into natural water increases the organic and inorganic material content and consumes the DO content of water and at the same time reduces the water quality (increased phytoplankton reproduction and eutrophication). This is the reason the total nitrogen concentration can characterize water quality. In the nitrogen cycle, the nitrate is intermediate; it originates from organic materials. The microorganisms can immobilize the nitrogen and convert it into organic compounds. In ecosystems, the Nitrosomonas and Nitrobacter species convert the NH41 and the NH3 into NO32. In biological processes, the nitrate ions are converted into N2O, NO, and N2 (this is an anaerobic process). In clean water the nitrate concentration is low; in polluted water it is high. When it is tens of mg/L, it is already toxic for the biotas. This is the reason nitrate/nitrite is a proper indicator of the cleanliness of water [21]. The other form of nitrogen is ammonia, either as NH3 or NH431, depending on the pH and the temperature (at low pH the NH1 form, at high pH the NH41 content is dominant). The source of ammonia in water is organic molecules that are decomposed by microorganisms. But different microorganisms can convert it into NO2 and N2. Botas are very sensitive to the concentration of ammonia. 8. Major ions: Calcium, magnesium, sodium, copper, chloride, hydrocarbonate, sulfate, and sodium adsorption ratio, the ratio between the sodium ion content and the (calcium 1 magnesium) ions. 9. Sulfide: H2S is the metabolite of the anaerobic microorganism Desulfovibrios. 10. Fluoride: Mainly in groundwater. 11. Arsenic: Mainly in groundwater.

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12. Chlorophyll: The measure of eutrophication. 13. Organic micropollutants: There are more than 150 different organics on the official list. Dichlorodiphenyl-trichloro methane, printed circuit basics, anthracenes, benzene and its derivatives, chloroalkanes, naphthalene, and organic metals (with Cd, Hg, Ni content) to mention only some of them. 14. Radioactive pollutants: Radioactive waste materials emit radiation. They can originate from uranium mining, military weapons, nuclear plants as well as from the waste of hospitals and research laboratories. The main problem is that they are stable for a very long time (some of them for hundreds of years, others for thousands of years). 15. Heavy metal ions are known to cause toxicity by forming extremely stable complexes with amino acid residues on enzymes containing sulfur which deforms and renders the enzymes inactive [22,23]. Table 14.1 shows the influences and health effects of different HMs on human health. TABLE 14.1

Influences of different metal ions and their limit values [WHO (μM)].

Metal

WHO limit (µM)

Health effects

References

Pb

0.25

Penetrates through the blood-brain barrier: a risk for Alzheimer’s disease, and dementia, causes neurodegenerative diseases

[24,25]

Hg

0.005

Influences the digestive and immune system and lung, skin, hypotonia, hypertonia, kidney

[26]

Cd

0.045

Hypertension, fatigue, hypochromic anemia, lymphocytosis, pulmonary fibrosis, lung cancer, osteoporosis, hyperuricemia

[27]

Cr

0.97

Embryotoxicity teratogenicity, mutagenicity, carcinogenicity, lung cancer, dermatitis

[28 30]

Zn

76.5

Neural disorder, bronchiolar leukocytes, prostate cancer risk, macular degeneration, respiratory disorder

[31,32]

Cu

20.5

Allergies, alopecia, cystic fibrosis, hemorrhaging, kidney disorder, adrenocortical hyperactivity

[33]

Ag

0.93

Gastroenteritis, mental fatigue, cytopathological effect, keratinocytes, neuronal disorder

[34,35]

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14.4.2 Physical indicators of water pollution Physical indicators of water pollution take into account different factors such as water temperature, color, odor, turbidity, transparency, pH, salinity, conductivity, oxidation-reduction potential, and the level of CO2 among others. These parameters can be classified as follows: 1. Hydrological parameters: Flow, discharge in natural water. 2. Temperature: The change of the water temperature is influenced by the source of the water, by the season, the turbidity (described earlier), and the vegetation at the shore. It is the measure of kinetic energy. The lowest temperature in natural water is 0 C and the highest is 36 C that can be tolerated by animals. When the temperature is out of this range aquatic animals are more sensitive to diseases and, in extreme conditions, can perish. 3. Odor: Rotten organic materials, metabolic products of anaerobic microorganisms (Desulfovibrios, which produce H2S, as well as ammonia/ammonium, are mainly responsible for the unpleasant odor). 4. Turbidity: The measure of when the water loses transparency because of suspended small particles. It can be characterized by the total suspended solids. There is a scale that characterizes the measure of turbidity: 1 NTU is ideal, but if it is more than 5, the water is very turbid. The turbidity is measured by different techniques (Secchi disk, light scattering). There are different sources of turbidity: sediments (from erosion and stirred-up bottom), phytoplankton, algae, waste discharge, and urban runoff. High turbidity causes higher sunlight adsorption that increases the temperature of the water and decreases the amount of DO, which has a dramatic consequence on the living organisms in the water. 5. Light: The change between day and night will increase the eutrophication. 6. pH value: Low value shows the presence of hydrogen ions. When the pH is between 0 and 7, the aquatic environment is acidic. At pH 7 the media is neutral and above 7 it is basic. Characteristic pH values are as follows: the pH in the surface freshwater is between 6.0 and above; salty water has 7.7 and 8.1 pH values; and water in swamps is around 4.3. Changes in natural water can be caused by waste dumps, industrial or agricultural runoff, or simply the natural environment. When the pH changes, even only by two units, it results in the death of the animals living in the water. In eutrophic natural water (mainly in lakes and reservoirs) the in situ pH is . 9 10. It is important to mention that the pH values show the alkalinity/ acidity in logarithmic units. An infinitesimal change in the pH value can lead to a significant change in the chemical composition as well as in biological processes that result in a change in the ecosystem.

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The hydrogen ions originate from the decomposition of organics, the root respiration, and from the atmosphere; they can come from the nitrification process and sulfur reactions. The extent of resistance to pH change is called the buffer capacity of a solution. The rise or drop of the pH values is determined by the buffer capacity. The presence of CaCO3 in an acidic environment can increase the pH value. The other reason for the pH value increase is the application of fertilizers (e.g., (NH4)2SO4, NH4NO3). This justifies why the pH value is a very good pollution indicator. It can control the toxic materials and the nutrient indicates the buffer-cation exchange capacity. It is understandable that in an acidic environment there are fewer calcium, magnesium, boron, phosphorus, and nitrate ions. On the contrary, in the alkaline system, the quantity of copper, zinc, iron, phosphorus, and boron decreases. The pH value in a system also influences microbial life. Some bacteria prefer acidic conditions, but most grow under near-neutral pH conditions. Most fungi prefer acidic conditions [36]. 7. Electrical conductivity (EC): This informs about the presence of cations and anions (it should be in the range of 100 and 200 μS/cm). Salinity is the concentration of dissolved salts found in water. It also is an important indicator that characterizes the dissolved chemical content in a natural water system. It influences the EC, which is determined not only by inorganic but also by organic components. A high EC value shows that the system is highly polluted. It is important to mention that some microorganisms like high salt concentration. According to the EC values (d/Sm) when this value is less than 2, the water is considered not saline; between 2 and 4 the water is very slightly saline; 4 8: slightly saline; 8 16: moderately saline, and when this value is higher than 16 the water is strongly saline [37]. The conductivity is influenced not only by the presence of dissolved chemicals but also by the temperature as well as by the viscosity of the aqueous system.

14.4.3 Biological indicators of water pollution The biological content of wastewater is evaluated to observe and control the presence of pathogens and microbiological organisms in wastewater sources. Dangerous pathogens can come into the wastewater stream via untreated sewage or naturally from rivers and lakes. Not only the abiotic factors (e.g., pH, temperature, DOC, nitrates, turbidity, etc.) but the biological indicators of water are very important. The number and diversity of the macroinvertebrates (that can be seen without magnification) depend mainly on the pollution of the water. Worms, crustaceans, mollusks, and insects (e.g., Dipteran, Isopoda, Amphipods, and Ephemeroptera) belong to this group, to mention only

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some of them. When their number and variety are high, it shows that the level of pollution is high. On the contrary, a low level of variety and number indicates low water pollution. Some of the pathogen microorganisms are as follows: Bacteria: Campylobacter jejuni and Anteropathogenic E. coli—causes gastroenteritis; Legionella pnemunophila-acute respiratory; Salmonella—Typhoid, paratyphoid, salmonellosis; Shigella bacillary dysentery; vibrio cholera—cholera; Protozoa: Christosporidium—diarrhea; Entamoeba histolytica—amobic dysentery; Enteroviruses: adenoviruses—respiratory illness, eye infection, gastroenteritis; Hepatitis-a viruses infectious hepatitis, to mention only some of them. Important indicators in drinking water are: E. coli/Streptococci/ Clostridia/protozoa/parasite must be 0 most probable number/100 mL. The biological monitoring index for freshwater is generally the measurement and monitoring of the presence and abundance of aquatic insect species (e.g., mayfly, caddisfly, and stonefly). In fresh and marine water, the presence and number of bivalve mollusks is a proper indicator to show the cleanness of the water. The number of bioindicators will be changed by fluctuation in the pH/temperature/nitrate level/ and DO values. Some of the mentioned macroinvertebrate are very sensitive to the oxygen level; their number can indicate the cleanness of the water. But some outer impacts (e.g., the substrate and nutrients) can disturb the evaluation. Calibration must be extensive and all-pervasive as only in this case can it be suitable for at least qualitative characterization of pollution level. To monitor the aquatic (fresh and marine) environment bivalve mollusks are used as bioindicators as their population, behavior, etc., indicate the level of contamination. They are useful as they are sessile and represent the place from where the water sample originates. When the biological indicators are the actinomycetes and fungi, we should keep in mind that these microorganisms are less sensitive to the dissolved salt content than bacteria. The microbial respiration and nitrification are influenced by the salt content (that determines the conductivity). That’s why the electric conductivity can be an indicator for an aqueous ecosystem polluted by macro- and microorganisms.

14.5 Analytical methods for the detection of wastewater pollutants 14.5.1 Introduction The danger of organic, inorganic, and macro/micropollutants has been demonstrated. Regular water quality monitoring helps to monitor undesired contaminants. This requires methods and instrumentation to

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monitor these chemicals/organisms with high selectivity and sensitivity. Today there are sensors and remote monitoring systems for measuring water characteristics (turbidity, DO, pH, etc.). The crucial challenge is getting the correct water sampling. The composition of water changes with time, location, season, temperature, and atmosphere. There are different pollutants monitoring methods such as electrochemical sensors, chemosensors, biosensors, and other techniques. Some of those methods will be summarized in the next section. Since water can dissolve a lot of chemicals, there is no pure water in nature. It is necessary to characterize aquatic systems with the best analytical methods to be sure the water is safe for its purpose (e.g., swimming, drinking, fishing, etc.) by measuring different characteristics (DO, BOD toxic ions, pH, turbidity, etc.). Generally, the devices applied for the detection of organic compounds are for heavy metal pollutants [38]. As an example, chromatographic methods are usually applied for analysis of organic matter while atomic spectroscopic approaches are employed for heavy metal detection. It is worth noting that electrochemical devices can be used for both organic and HMs contaminants.

14.5.2 Electrochemical methods During the first decade of 2000, new European directives were evolved that strictly regulated the emission of four toxic metal ions (e.g., Hg, Pb, Cd, Ni). Thus new monitoring systems were required. In this field, electrochemical techniques proved to be very advantageous (Table 14.2). A great challenge in the area of heavy metal trace detection is the development of electrochemical techniques and devices that are user-friendly, robust, and selective, with low detection limits and fast evaluation. They are generally simple, and do not need special reagents, allowing in situ and online measurements and high selectivity in a complex solution. The detection and monitoring of heavy metal ions are important from a human health point of view. It was necessary to find real-time, sensitive sensors to detect in the early period the presence of toxic ions [39]. Intensive development of electrochemical methods began more than 60 years ago. The first method to measure heavy metal ions with polarography was developed by Heyrovsky [49]. Later remote electrochemical sensors and different voltammetric methods were developed [44 46]. The electrochemical methods for metal pollutant detections are diversified. We mention only some of the important methods in Table 14.2 used in the pollution detection of metal cations.

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TABLE 14.2

Different electrochemical techniques applied in the pollution detection of metal cations.

Technique

Mechanism of measurement

Measuring limits

Advantage/disadvantage

References

Amprometry

The current at fix potential.

The analyte on the electrode surface as a function of the current is very low; 0.05 15 ppm

• Rapid; simple apparatus; • Inaccuracy due to coprecipitation

[40]

Conductometry

The change in the resistance.

The total ion concentration of toxic organic compounds and metal ions are measured via ionophores-based polymeric membrane; molecular imprinted polymers.

• It does not make a distinction between special species.

[41]

Potentiometry

Measures the change or the generation of potential.

The ion recognition is converted into the potential value via ionselective electrodes; very low detection limit, up to pico molar.

• Small size, portability, low energy consumption, low cost.

[42,43]

Voltammetry

Current is measured as a function of potential.

Measuring limit: pico molar concentration.

Cyclic voltammetry

Measures the current as a function of applied potential; scans the surface at different rates for several cycles with a triangular waveform; follows the dynamics and records all the corresponding changes in the electrical signals.

Reduction and oxidation reactions alternately occurring on the electrode, a current potential curve is recorded.

[44 46] • Speedy analysis with high selectivity and sensitivity; automated and miniaturized, low energy needs; provides more data about the dynamics and mechanisms of reactions taking place on the electrode surface. • Low sensitivity and selectivity; undesired background noise.

[47,48]

Pulse voltammetry

Measures the pollutants at very low concentration, with high sensitivity.

• High selectivity and sensitivity; monitoring of trace metals in water bodies; • Pollutants need preconcentration and stripping of ion of the interest.

[49,50]

Anodic stripping voltammetry

Hanging mercury drop electrode (HMDE: the creation of new electrode surface: increases the reliability of the results) Mercury film electrodes (MFE: more robust, more selective than HMDE due to their high surface to volume ratio).

For the quantitative determination of specific ionic species.

• Detect trace metals with ultrahigh sensitivity without preceding separation; more than 30 elements of the periodic table are detectable. • Intermetallic compound in an amalgam; low cathodic potential limit; undesired large background in the measurement.

[51]

Adsorptive cathodic stripping voltammetry

Electrochemically pretreated glassy carbon electrode.

About 20 elements can be detected by cathodic stripping voltammetry.

• Simple and high selectivity.

[46,52]

Square-wave anodic stripping voltammetry

AC differential signal induced by square wave signal diagram of the electrode scanning potential.

After applying a preconcentration voltage for enriching the metal ions in solution around the working electrode before applying a scan signal; 4 150 μg

• Speedy; time-saving measurement; low limit of the detection (up to 1 10 ppb); • in situ preparation of the sensing electrode, complex matrices at the electrode surface; interference by other ions with a negative impact on the stripping process.

[52]

(Continued)

TABLE 14.2

(Continued)

Technique

Mechanism of measurement

Measuring limits

Advantage/disadvantage

References

Chronocoulometry, chronopotentiometry

Chronocoulometry is similar to the EIS; it measures the interfacial properties of the sensing surface where the analyte adsorbs to and desorbs from; the cumulative charge is monitored vs. time. chronopotentiometry: the step potential is recorded vs. time; the rate of charge at the electrode is measured at constant current.

Very low detection limit (0.03 μM) and high sensitivity (1.03 3 103 μA/mM).

Polarography

The analyzed solution is electrolyzed under diffusion control at DC, linear, sweep, pulse, cyclic, hydrodynamic, stripping conditions.

Inorganic, organic, and biological samples are analyzed qualitatively and quantitatively.

• Prior separation is not needed; the capillary can be plugged; changing of mercury drop size the area of the microelectrode is changing as well as the mercury can easily oxidized.

[45,49]

Electrochemiluminescence microscopy techniques

Based on fluorescence detection by electrochemiluminescent reactions.

Detects at the electrode surface or its vicinity specific metal ions in solution with high sensitivity at ppb-ppt concentration.

Simple and inexpensive

[37]

AC electrochemical impedance spectroscopy (EIS)

Measures variations of electrical impedance at the sensor-analyte interface; software determines the real impedance of the sample.

Its detection limit is in the pico molar range.

• Powerful, versatile, quick, simple, and inexpensive technique; it cannot distinguish different ions.

[54]

[53]

Piezoelectric sensors. quartz crystal microbalance

Determines the mass change per unit area by measuring the change in frequency of a quartz crystal resonator; the mass is transmuted into a force.

Influencing variables are constant as mass is deposited on the surface, with increasing thickness the oscillation frequency decreases.

• A piezoelectric transducer in any desired shape; small; high sensitivity. • High-temperature sensitivity and water solubility.

[55]

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14. Sensors for water and wastewater monitoring

Potentiostatic tests: amperometry (fixed potential method); chronocoulometry, voltammetry (differential pulse), polarography (DC, linear, sweep, pulse, cyclic, hydrodynamic, stripping). Galvanostatic tests: controlled current coulometry, chronopotentiometry; and electrochemluminescence (quantum dots (QDs)). Among the electrochemical sensors, the potentiometric and voltammetric methods seem to be very important. In the case of potentiometric measurements at zero current, the change in the potential corresponds to the concentration of the dissolved polluting material. In electrochemical sensors, the electrodes are the transducers that allow the on-site detection of contaminants at high selectivity and sensitivity. Generally, an electrochemical signal (change in current or in voltage) means pollutants are present. These cells can work with two or three electrodes. 14.5.2.1 Amperometric techniques In the case of amperometric measurement in an electrolyte of high concentration, the presence of a pollutant will be determined by measuring the potential between the working and a reference electrode. These cells can consist of two or three electrodes. The analyte is measured on the electrode surface as a function of the current. In a conductometric electrochemical sensor, the correlation between the solution resistance and the concentration of pollutants of charge is registered. These sensors measure the total ion concentration, but do not make a distinction between species. This technique is very useful after separation of the different pollutant ions by ion-exchange chromatography. In the case of potentiometric sensors, the potential is measured through an ion-exchange membrane; it depends on the concentration of the analyte. The ion recognition is converted into the potential value. When a solid-state membrane consists of LaF3 doped with Eu/Nd/Sm (which enhances the ionic activity) it can measure the copper and lead ion concentration [56]. 14.5.2.2 Voltammetric techniques In this technique, the change in current is constantly measured as a function of potential, which is due to the pollutant ions in a cell where an electrolyte of high concentration allows the current flow. Electroactive species are also present. In the electrochemical cell, there is a reference electrode (electrode of steady potential), a working electrode (here goes the electrochemical change), and a counter electrode (measures the current at the working electrode). The current consists of two parts: faradaic (where the oxidation of the analyte happens) and nonfaradaic current.

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The current in the cell is continuously monitored. This method allows measuring the charge exchange at the anode surface with very high sensitivity. This is a very efficient method to measure the contaminant concentration in the nano/pico-mole range. This method provides the ability that different pollutants can be measured in situ simultaneously. There are two basic types of this technique: anodic stripping voltammetry and adsorptive cathodic stripping voltammetry. Other branches of this method is the pulse voltammetry which utilizes a regularly increasing pulse height that is applied at periodic intervals. It is a very useful technique [50], that allows measuring the pollutants at very low concentrations, with high sensitivity. Electrochemical sensors can be used in parallel. The advantages of electrochemical sensors are: 1. very selective (especially the voltammetric sensors): atoms, ions, and molecules have special reduction/oxidation potential; 2. selectivity can be improved by special electrodes; 3. very sensitive and selective at low pollutant concentration; 4. possibility to monitor the concentration of the pollutants on time, in situ; and 5. portable and small in size (battery-operated instruments).

14.5.3 Chromatography This technique that separates the components in the aquatic mixture of pollutants gives qualitative and quantitative information about the dissolved chemicals. It works with a fixed (stationary) and a mobile phase. The sample components move with the mobile phase, and their rate depends on the interaction of the chemicals with the mobile and the stable phase. There are different chromatographic techniques (depending on the mobile phase) like gas chromatography and high-performance liquid chromatography. The advantage of chromatography is that it separates the components, but the disadvantage is that it needs special pretreatment. This technique can be combined with other sensors (e.g., electrochemical sensors). 14.5.3.1 Gas chromatography In this case, the gas phase (e.g., nitrogen, helium, and argon) moves the sample given in vapor form to the system and is pressed through a capillary column. The separation is due to the distribution of the components between the stationary and mobile phases. The partition is influenced not only by the material quality of the capillary and the gas phase but also by the temperature. Different (e.g., flame ionization, electron capture, mass spectrometer, thermal conductive) detectors can help in the identification of pollutants.

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14.5.3.2 High-performance liquid chromatography The sample with pollutants is dissolved in a solvent or solvent mixture and goes through the column with the mobile fluid phase. The separation of the components depends on the distribution of the dissolved chemicals between the stationary phase and the fluid. With the proper selection of the fluid phase, polarity can be improved in the selection of the components. Similar to gas chromatography special detectors help in the identification of the separated materials. In chromatographic techniques, the separation of pollutants is very sensitive, and it can be improved by combining the chromatography with mass spectrometry

14.5.4 Atomic spectroscopy This method monitors the change of electromagnetic radiation in the form of absorption and emission. This is a very sensitive technique and can characterize a lot of elements by the wavelength of the absorbed/ emitted radiation and its intensity, which is proportional to the pollutant. Two techniques belong to this group of methods: atomic absorption spectroscopy (AAS) and inductively coupled plasma spectroscopy (ICP). 14.5.4.1 Atomic absorption spectroscopy This electroanalytical technique gives quantitative information about the elements via the absorption of light by metals. Important advancements in this technique began in the 1950s [57]. The atoms are excited by the energy of photons from a lower energy state to a higher one. The Beer-Lambert equation helps in the identification of atoms/molecules. Metals in particular are identified by atomization via flame of electrothermal impact. Their concentration influences atomization. The toxic metal ions are identified and quantified by this technique. The electrothermal atomization results in higher sensitivity. The method is quick and accurate for the investigation of about 70 elements. The only disadvantage of this method is the remarkable sample preparation time [43]. 14.5.4.2 Inductively coupled plasma spectroscopy This optical emission spectrometry technique is one of the most multipurposed methods that are appropriate for the analysis of metals and inorganic materials. While the excitation in AAS goes by flame (air-acetylene) at 2000K, in the case of the ICP (which is an optical emission spectrometry), the temperature of argon plasma is 5000K 7000K. This higher energy can excite most of the elements. The return of the excited

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atoms to the lower energy level results in an emission beam with a given wavelength that characterizes atoms under investigation. In this technique plasma (which consists of electrons and positive ions) is applied for the atomization of elements. Generally, argon is used for plasma production. The plasma can excite atoms ionizing and dissociating them and then the emission will characterize the particles. It has a lower detection value. The disadvantage of the atom absorption technique is that the oxidation states are not monitored [58,59].

14.6 Chemical sensors in water pollutant detection 14.6.1 Introduction The demand for designing and fabricating detection methods for potential employment in sensing toxicants (e.g., water pollutants) and quantifying their concentrations in several matrices has increased in recent times [60,61]. In the field of water pollutant detection, a distinction should be made between chemical sensors and traditional analytical devices, such as analyzers. Traditional analytical techniques are common detection devices combining various electrical, chemical, and mechanical elements. Their assembly is energy-intensive, costly, and bulky, making them poorly suitable for on-site measurements. Another disadvantage is the long analysis time due to the detection technique itself or the need for sample preparation, calibration, and handling [22]. Included in this category of instruments are spectroscopic (e.g., ICP-Ms, ICP-OES, AAS, etc.) and chromatographic [e.g., High-performance liquid chromatography, GCMs, etc.] techniques. Unlike analyzers, chemical sensors are miniaturized devices made up of two closely linked components, which are a detection platform allowing the identification of target analytes with which it reacts, and a transduction system transforming the chemical reaction into an exploitable output signal. Chemosensors are inexpensive small-sized devices with low energy consumption, short response time (RT), and are ideal for on-site measurements and in-line process controls [62]. It is worth mentioning that for studying complex matrices (e.g., blood, seawater, river water, etc.), an array of chemosensors for detecting the targets to be analyzed is needed [63]. Several subtypes of chemosensors are available and are usually distinguished by the transduction mechanism (e.g., electrochemical, piezogravimetric, optical, etc.), or the employed detection platform (e.g., polymers, macrocycles, nanostructures, etc.).

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14. Sensors for water and wastewater monitoring

14.6.2 Sensors and transducers Sensors are minidevices that implement an “input” function and “sense” a physical alteration in some characteristic that varies in response to some excitation. Devices that perform an “output” function are generally called actuators and are applied to regulate external devices. The sensor output is usually an “electrical quantity” and the measurand is a “physical quantity, property, or condition which is to be measured.” A transducer is an element that transforms a specified measurand into a serviceable output by using a transduction principle [64]. Measurement systems involve sensors, transducers, actuators, and signal processing units. Principally, a transducer involves two main mechanisms (Fig. 14.1): 1. Sensing element: the physical quantity or its rate of alteration is detected and responded to by this part of the transistor. 2. Transduction element: the output of the sensing element is passed on to the transduction element, which converts the nonelectrical signal into its proportional electrical signal. Sensor specifications are several parameters related to performance that notify the end-user about alterations from the ideal performance of the sensors. There are numerous specifications of a sensor/transducer structure to be considered [65]: 1. Sensitivity: The ratio of change in output value per unit change in the input value. 2. Hysteresis: The maximum difference in output at any measurement value within the sensor’s specified range when approaching the point with increasing and then decreasing the input value. 3. Resolution: The lowest detectable incremental change of input that can be detected in the output signal, and is normally expressed either as a proportion of the full-scale reading. 4. Range: Indicates the boundaries for the input deviation. 5. Span: The value difference between the max-min of the input bounds.

FIGURE 14.1 Schematic diagram showing transducer components.

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6. Accuracy: Describes how close the measured outcome to the actual measurand value are (expressed in %). 7. RT: Labels the speediness of change in the output on a step-wise variation of the measurand. 8. Nonlinearity: Labels the maximum deviation of the actual measured curve from the ideal one. Linearity is often specified in terms of % of nonlinearity. 9. Stability: Capability of a sensor device to provide the same output when used to measure a constant input over a time period. Generally, the deviation of the measurement is called “drift.” 10. Dead band/time: The range of input values for which there is no output (i.e., the time duration from applying an input until getting the response). 11. Repeatability: Specifies the ability of a sensor to give matching output for recurring measurements of the same input value (%).

14.6.3 Chemical sensors Applications of chemical sensors have attracted the consideration of researchers groups to further develop sensors with enhanced analytical performance. Fig. 14.2 shows the progression of the number of

FIGURE 14.2 Progression of the number of publications associated with various chemical sensors in water pollutant detection (Scopus database) for the period between 2000 and 2021.

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publications related to applications of chemical sensors in water pollutant detection (2000 2021) (Scopus database). Chemosensors are extensively employed in the quantification of groundwater and marine water pollutants (e.g., HMs, pesticides, nitrates, or else hydrocarbons (Table 14.3): 1. HMs: Their release in water bodies is due to industrial activities. Because of their high toxicity and nonbiodegradability, HM contamination is a huge concern, and thus the requirement for their early detection and monitoring. With this aim, Wang et al. prepared a Cd21 fluorescent sensor based on dual emission QDs. The fabricated chemosensor exhibited a lower detection limit up to 25 nM under optimal conditions [66]. Likewise, a chemoselective dye for fast quantification of Pb21 and Hg21 was designed, and the sensor showed decent LODs of 2.9 and 4.9 nM for Pb21 and Hg21, respectively [67]. 2. Pesticides or phytosanitary compounds: Mainly employed for agricultural purposes, and are used for test diverse molecules that have slow degradation, high persistence, and major health risks [e.g., methyl parathion (MP)]. MP is a molecule of interest in electroanalytical studies. For example, Lin et al. modified a gold quartz resonator employing a Molecularly imprinted polymers (MIP)-PVDF. The piezogravimetric TABLE 14.3

Chemosensors for water pollutant detection.

Sensing platform

Target element

LOD (µM)

Reference

21

25 3 10

21

Pb Hg21

2.9 3 10 4.9 3 10

Molecularly imprinted poly (vinylidene fluoride): MIPPVDF

Methyl parathion

68 3 10

Phthalocyanine conjugate

Methyl parathion

1.78 3 10

Silver nanospheres and zinc oxide (ZnO)

Nitrate Nitrite

3 3 10 3 3 10

Gold nanoparticles (AuNPs) and multiwalled carbon nanotubes

Nitrite

10 3 10

Quinoxaline cavitand

1,2,4-trichlorobenzene

0.94

Dual-emission quantum dots Chemoselective dye

AuNPs chemiresistor

Cd

Toluene, benzene, ethylbenzene, naphthalene, and p-xylene

a

ND, Nondetermined

3. Environmental applications

a

ND

3

[66]

3

[67]

3 3

3

[68]

3

[69] [70]

3 3

[48]

[71] [72]

14.7 Electrochemical sensors in water pollutant detection

541

chemosensing platform showed a detection limit of 68 nM for MP [68]. Similarly, a phthalocyanine-based sensor for MP recognition reached a LOD of 1.78 nM [69]. 3. Nitrates and nitrogen derivatives: Mainly released from agricultural activities, they are considered the main source of groundwater pollution today. For the detection of nitrate and nitrite, the synergistic effect of zinc oxide (ZnO) and Silver nanospheres was studied to prepare a chemosensor platform. The latter reached detection limits of 3 nM for nitrate and nitrite [70]. Afkhami and coworkers modified a carbon paste electrode by the combination of AuNPs and multiwalled carbon nanotubes (MWCNTs), and the nitrite chemosensor achieved a 10 nM detection limit [48]. 4. Hydrocarbons: This type of pollution is due to industrial (petroleum) and domestic activities. Because of their poor biodegradability, hydrocarbons can persist for many years. The most soluble ones in water are labeled as BTEX, standing for Benzene, Toluene, Ethylbenzene, and Xylene. Multiple chemosensors were employed for hydrocarbons detection (i.e., a quinoxaline cavitand was synthesized and employed for the detection of 1,2,4-trichlorobenzene in water). This piezoelectric chemosensor exhibited a LOD of 0.94 μM [71]. Correspondingly, an AuNP chemiresistor was employed for the recognition of naphthalene and BTEX molecules in water, and showed partial selectivity [72].

14.7 Electrochemical sensors in water pollutant detection 14.7.1 Introduction Electrochemical sensors are based on chemically modified electrodes (CMEs), which appeared in the 1970s. The term CME has gained world recognition with the development of various electrode types as transducers and sensing materials as detection platforms [73,74]. However, today CMEs are known as electrodes possessing chemical reactivity along with the ability to transfer electrons to/from the analyte, which is due to the existence of chemically reactive substances on the electrode surface [75]. Consequently, besides ordinary electrochemical reactions, target analytes can undergo some chemical reactions with which the applicability of CMEs can be of broad aspect in electroanalysis [76,77], biosensing applications [62], and electrocatalysis [78].

14.7.2 Electrochemical transducers Recent advances in chemical synthesis, polymer development, nanotechnologies, and macrocyclic nanoscience have opened up new

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perspectives, particularly in the development of new highly sensitive electrochemical sensors [61], along with different advantages including the fast time of analysis, reasonable cost, decent sensing characteristics, and portability. Consequently, electrochemical sensors are employed in many sectors, namely, the automotive industry, defense and security, healthcare and medical services including the fabrication of point-ofcare devices and the early diagnosis of diseases [79], in addition to environmental monitoring comprising the detection of water pollutants [80]. Table 14.4 highlights examples of electrochemical sensors employed for the quantification of major hazardous water pollutants [HMs and Bisphenol A (BPA)]. Fig. 14.3 shows examples of heavy metal ion detection via screenprinted electrode (SPE) covered by an ionophore (C-dec-9-enylcalix[4] resorcinarene-O-(R1)-α-methylbenzylamine), where the measuring techniques are cyclic voltammetry (CV) as well as square-wave voltammetry (SWV). Both techniques can sensitively quantify the heavy metal ions when the sensor surface is properly pretreated with analytes capable of complexing with these ions. The CV plots (Fig. 14.3) clearly show that the presence of HMs alone does not produce analytical signals on the bare electrode. But when the SPE is coated by ionophore that can complex with the HMs, peaks immediately appear representing the presence of metal ions. In this example, the electrolyte was 0.2 M HCl with or without HMs. The influence of the character of the ionophores is also well demonstrated by TABLE 14.4

Electrochemical sensors for water pollutant detection. Target element

Sensing platform

LOD (µM)

Reference

Cu Pb21 Cd21

0.8 3 10 0.6 3 10 8.5 3 10

Zeolitic imidazolate framework/βcyclodextrin/ reduced graphene oxide (ZIF-βCD-RGO)

Ni21

5 3 10

Porous graphene/nafion/calcium lignosulphonate (PG-Naf-CaL)

Pb21 Cd21

10 3 10 3 3 3 10 3

Multiwalled carbon nanotubes and gold nanoparticles (AuNPs)

Bisphenol A

4.3 3 10

3

[84]

Magnetic MIP/carbon black nanoparticles/gold nanoparticles (MagMIP-CBNPs-AuNPs)

Bisphenol A

8.8 3 10

3

[85]

Electrosynthesized MIP

Bisphenol A

8 3 10

Ferrocene functionalized metallic organic framework

21

3. Environmental applications

3

3

3

3

[81]

3

[82] [83]

[86]

543

14.7 Electrochemical sensors in water pollutant detection

a b c d

0.10

Pb2+

Cu2+

0.05

I (mA)

Hg2+ Cd2+ 0.00

-0.05

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

E (V) vs Ag/AgCl FIGURE 14.3 Voltammograms of (A) bare SPEs in 0.2 M HCl; (B) bare SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl; (C) modified SPEs in 0.2 M HCl; and (D) modified SPEs in the presence of 1 ppm each of HMs in 0.2 M HCl, for ionophore (C-dec9-enylcalix[4]resorcinarene-O-(R 1 )-α-methylbenzylamine).

the well-defined oxidation current peaks (that correspond to the fast electron-transfer rate at the measuring platform) measured on the potential characteristics for the metal ions. As can be seen in Fig. 14.4, the SWV signatures are similar to the CV results. The electrodes without and with the coating of ionophore did not show anodic signals, but the presence of HMs on ionophore-coated surfaces resulted in well-separated peaks that represent the high accumulation/adsorption capacity on the modified surface. The differences in the peak intensities of different coatings show the importance of the ionophore characteristics. Wang et al. prepared a multiple HM electrochemical sensor, where the detection platform was based on the functionalization of a MOF employing ferrocene, and the LODs were found to be 0.8 nM (Cu21), 0.6 nM (Pb21), and 8.5 nM (Cd21). Further application of the sensor in tap water was carried out successfully [81]. Likewise, Cui et al. fabricated a Ni21-sensitive electrochemical sensor combining RGO, β-CD, and ZIF. The real application of the constructed sensor was performed in water samples and a detection limit of 5 nM was acquired [82]. The synergistic effect of porous graphene, nafion, and calcium lignosulfonate was studied leading to the development of an electrochemical sensor targeting HMs ions. The sensor exhibited detection limits of 10 nM

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1.2

Idiſ (mA)

1.0

a b c d

Pb2+

0.8

Cu2+

0.6 0.4

Hg2+

Cd2+ 0.2 0.0 -1.0

-0.5

0.0

0.5

E (V) vs Ag/AgCl FIGURE 14.4

SWV spectra measured in 0.2 M HCl: (A) bare SPE; (B) bare SPE in the presence of 1 ppm HMs from all metals; (C) SPEs coated by an ionophore; and (D) SPEs coated ionophore (C-dec-9-enylcalix[4]resorcinarene-O-(R1)-α-methylbenzylamine) in the presence of 1 ppm of all HMs.

(Pb21) and 3 nM (Cd21), and the method was validated and applied for the simultaneous detection of these ions in tap and lake water [83]. BPA is a synthetic organic compound, mainly employed in the fabrication of plastic water bottles and storage containers for food beverages. BPA is considered a toxic pollutant that causes potential health risks, thus in recent years the development and design of BPA-based electrochemical sensors has increased tremendously. For example, Ben Messaoud et al. fabricated a BPA electrochemical sensing platform based on the combination of MWCNTs and AuNPs, and the sensor reached a 4.3 nM detection limit [84]; a year later they mixed CBNPs, AuNPs, and a magnetic MIP for constructing a BPA sensor, and the latter exhibited an 8.8 nM LOD [85]. Both sensors were applied in real water samples comprising mineral and tap water. In another effort, Beduk’s group electrosynthesized a MIP sensor for BPA detection that reached a lower detection limit of 8 nM and was applied in mineral and tap water, in addition to plastic samples [86].

14.7.3 Piezoelectric transducers Improvements in electroanalytical and biosensing applications have been made considering the design and fabrication of sensors based on

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14.7 Electrochemical sensors in water pollutant detection

piezoelectric phenomena [87]. Mostly employing the quartz crystal microbalance with dissipation monitoring (QCM-D) or comprising impedance variations (QCM-I), the piezogravimetric chemosensors were utilized as detection platforms to quantify water pollutants such as HMs and pesticides as shown in Table 14.4. Decent sensing characteristics were achieved based either on frequency shifts or full width at half maximum variations, proving that these types of sensors can be potential alternatives to traditional detection techniques. Sun et al. prepared a piezogravimetric chemosensor for the detection of Cr31 ions. The sensing platform was based on polystyrene and polyethyleneimine, and exhibited a detection limit of 0.1 μM [88]. Likewise, metal-ophthalocyanines were employed for the construction of metal ions based-QCM sensor. The latter seemed to be more sensitive toward copper ions and could detect a concentration up to 6.3 μM [89]. Our group synthesized and fully characterized two novel enantiomeric resorcinarene derivatives. The macrocycles were functionalized on quartz crystal resonators and applied for the sensitive detection of Pb21 ions [90]. The ability of these sensors was tested for detecting other heavy metal ions, such as Cd21, Hg21, and Cu21. The two piezogravimetric sensors reached detection limits in the micromolecular range (Table 14.5) [55]. In a further study, we employed two other resorcinarene tetramers as HM-based piezogravimetric chemosensors; the sensing characteristics of the detection platform are given in Table 14.5 [91]. TABLE 14.5

Piezogravimetric sensors for water pollutant detection. Target element

Sensing platform Polystyrene and polyethleneimine Metalophthalocyanines

Cr

31

0.1

[88]

6.3

[89]

21

2.2

[90]

Pb

C-dec-9-enyl calix[4]resorcinarene-O-(R 1 )α-methylbenzylamine C-dec-9-enyl calix[4]resorcinarene-O(S-)-α-methylbenzylamine

1.4 3 10 Cd21

9.8

21

3.2

21

2.5

21

7.9

21

0.99

21

1.7

Hg Cu

C-dec-9-enyl calix[4]resorcinarene-O-(R1)α-methylbenzylamine

Reference

21

Cu

C-dec-9-enyl calix[4]resorcinarene-O(S-)-α-methylbenzylamine

LOD (µM)

Cd

Hg Cu

3

[55]

(Continued)

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TABLE 14.5

(Continued) Target element

Sensing platform C-dec-9-en-1-yl calix[4]resorcinarene

LOD (µM)

Reference

21

2.8

[91]

21

1.8

21

9

21

4.3

21

15

21

3.8

21

2.8

21

Pb

0.97

Deltamethrin

0.06

Fenthion

0.11

Methiocarb

0.17

Triadimenol

0.10

Cd

Hg Cu Pb C-undecyl calix[4]resorcinarene

Cd

Hg Cu

Phthalocyanines

[92]

For the quantification of common pesticides in water, namely methiocarb, deltamethrin, fenthion, and triadimenol, Erbahar et al. employed a group of phthalocyanines to construct pesticide-based QCM sensors. The detection limits are shown in Table 14.5 [92].

14.8 Optical biosensors for water pollution detection 14.8.1 Introduction One of the ways of classifying sensors is by output signal. Most sensor types are categorized by: 1. Magnetic parameters: Magnetic moment, magnetic flux density, etc. 2. Mechanical properties: Strain, stress, torque, flow, length, force, pressure, acceleration, etc. 3. Thermal characters: Flux, thermal conductivity, heat flow, specific heat, temperature, etc. 4. Electrical characteristics: Conductivity, voltage, resistance, charge, current, inductance, etc. 5. Acoustic properties: Wave characteristics (amplitude, phase polarization), spectrum, wave velocity, etc.

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547

6. Biological/chemical properties: pH, the concentration of gaseous species in the environment, additives or constituents in liquid, etc. 7. Optical properties: Refractive index, reflectivity, polarization, light intensity, wavelength, etc. Among the above-listed classes, optical sensors will be discussed at great length. Optical sensors are devices that transmute light waves into electronic signals. Optical sensors are commonly based either on measuring the magnitude of an intensity deviation in one or more light beams or by analyzing the phase changes in the light beams that interfere with one another. Therefore this type of sensor has been named as either intensity or interferometric sensors. There are various applications of biosensors in different capacities besides the regulation of industrial wastewater, such as diagnostics, medical applications, etc. On the other hand, a biosensor is an analytical instrument, which transforms a biological alteration into a quantifiable and assessable signal. As seen in Fig. 14.5, biosensors contain a biological element (biochemical receptor) attached to a transducer that transforms the biological signal into an electrical output. A biological molecule, like an enzyme or cells, tissue slice, organelle, peptide, antibody, nucleic acid, etc., provides molecular recognition and possibly will transform the analyte in some mode. Biosensors have the following advantages over other analytical methods: 1. Speedy, suitable and continuous detection 2. Direct measurement of real sample

FIGURE 14.5

An illustration of the general working principle of optical biosensors.

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High detection specificity fewer reagents necessary for calibration tests Very fast RT Capability to sense nonpolar molecules (conventional devices are not able to identify them)

Biosensors, being highly sensitive and specific, have a major disadvantage in that minor alteration and experimental artifacts may affect the affinity of the interactions with the sensing element. The stability of biological material (e.g., enzyme, cell, antibody, tissue, etc.) depends on the natural properties of the molecule that can be denaturalized under environmental conditions (e.g., pH, temperature, or ions). As a result, biosensors tend to be unstable and possess a deficiency in reproducibility, which is an important concern. The choice of the transducer depends on the biological reaction. Molecular recognition can be accompanied by the chemical conversion of the analyte to its products, which are determined by the biocatalytic sensor. In other cases, when an antibody is used, the biospecific recognition systems and interactions take place without analyte conversion, resulting in an affinity sensor. Considering the extensive variety of biorecognition approaches the different categories of biosensors are classified as (1) electrochemical, (2) optical, (3) piezoelectric, and (4) thermal biosensors, based on their transducing mechanisms [93]. This section offers a summary of the newest technological developments in the optical-biosensor-based detection of three focal types of contaminants in wastewater, namely organic materials, HMs, and microorganisms [62].

14.8.2 Recognition elements for chemical sensors and biosensors Recognition elements can be classified as standard and new elements as shown in Fig. 14.6. The standard or classical elements consist mainly of the following types [94]: 1. Enzymatic: Reacts selectively with the target analyte, by quantifying the catalysis or the inhibition of enzymes by the measurand. 2. Antibodies: Extensively used affinity-recognition elements. 3. Nucleic acids (DNA and RNA): DNA or RNA target is detected through the hybridization of DNA or RNA probe, owing to their high natural affinity. 4. Whole cells: The analytical signal is sensed by measuring the general metabolic status of such organisms (e.g., bacteria). This type has some benefits over enzymes, such as high measurement stability,

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FIGURE 14.6

549

Types of recognition elements used for chemical sensors and biosensors.

operational cost-effectiveness, lower purification necessities, and efficient cofactor regeneration. On the other hand, the other group of recognition elements is the newly developed (up-to-date) element types [94], such as: 1. Bacteriophages are viruses that are genetically engineered to bind with specific bacteria and display peptides or proteins on their surface. This type of recognition elements-biosensors are environmentally robust, inexpensive, simple to produce, and can have extended storage time without any loss of their binding affinity effectiveness. 2. Aptamers are short oligonucleotides [RNA or single-stranded DNA (ssDNA)] that have a high affinity (nanomolar to the picomolar range), and usually interact with their targets through complementary form and not their sequence. Aptamers are advantageous due to their thermal versatility, stability, reusability, and the ease of modification for their immobilization by integrating reporter molecules and functional groups. 3. MIP, also known as artificial antibodies, have a good possibility for substituting biological antibodies, for their use in nanosensors [94]. Their applications are on the rise due to their excellent selectivity and stability, short time to synthesize, high thermo stability, and economic efficiency.

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4. Affibodies are engineered affinity proteins that can reach considerable affinity and specificity to any target protein or peptide after their isolation. The recognition mechanism is critical in chemical sensing and biosensing and enhancements in the fabrication of recognition elements, such as employing a combination of nanomaterials, can significantly improve the analytical selectivity of these sensors.

14.8.3 Optical biosensors Numerous whole-microorganism-based biosensors have been developed for the detection of organic materials in water samples. Genetically engineered cell-based systems, coupled with luminescent as a reporter gene, have been widely promoted [95]. Whole-cell biosensors applied for herbicide detection employ nonengineered algae (such as Chlorella) to assess any variation in chlorophyll fluorescence [96]. Table 14.5 lists some studies on optical biosensors-based technologies in different fields.

14.8.4 Advantages and disadvantages of optical biosensors Some of the advantages of optical sensors include: Excellent sensitivity Chemically passive Mini size and lightweight Appropriate for remote sensing Insusceptibility to electromagnetic interference Wide-ranging dynamic range Ability to monitor an extensive series of physical and chemical parameters 8. Dependable application 1. 2. 3. 4. 5. 6. 7.

On the other hand, the disadvantages are: 1. Vulnerable to interference from environmental effect 2. Expensive 3. Prone to damage

14.8.5 Applications of optical biosensors 14.8.5.1 Detection of organic materials Novel optical-based technologies for biosensing of organic materials are listed in Table 14.6. Genetically modified cell-based systems as

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TABLE 14.6

Applications of optical biosensor-based technologies in different fields. LOD (µM)

Reference

Light emission/ bioluminescence

3

[97]

Whole-cell: E. coli

Light emission/ bioluminescence

400

[98]

Whole-cell: Acinetobacter sp. DF4

Light emission/ bioluminescence

26.6

[99]

Analyte

Bioreceptor

Transducer

Octane

Whole-cell: Escherichia coli DH5

4-chlorobenzoic acid Phenol

whole-microorganisms, along with luminescent reporter genes, are commonly employed as biosensors for the detection of organic pollutants in aqueous media [95]. For examining potential variations in chlorophyll fluorescence, the whole-cell biosensors for herbicide detection utilize nonengineered algae (e.g., Chlorella) [96]. Combined MIPs and surface plasmon resonance (SPR) are employed as consistent sensing techniques, aiming at developing MIP-SPR nanosensors for the precise recognition of several organic pollutants in wastewater (e.g., triclosan) through simulation of the bioreceptor function [100]. Based on immune-analytical methods such as fluorimetry, which is employed for enhancing the signal transduction in optical biosensing applications for the quantification of organic pollutants, the fluorimetric mechanism generally necessitates fluorescent antibodies having high affinities toward the target pollutants to be detected. Formerly, fluorimetry was employed for quantifying organic and environmental species, such as hormones, antibiotics, endocrine-disrupting chemicals, and pesticides in groundwater samples. By assessing bioluminescence variations, COD levels in wastewater can be quantified. Olaniran et al. reported that their system employed two biosensors based on whole-cell bacteria modified with pLUX plasmids (LuxCDABE) [101]. 14.8.5.2 Detection of heavy metals Bioluminescent microbial sensors are often applied for trace-level HM detection [102,103]. Based on the specific expression of fluorescent protein, bacterial sensors employ recombinant E. coli strains. When the latter is in contact with HMs, its specific fluorescence augments according to the amount of HMs in wastewater [104]. The inhibitory effects of HMs are used to power enzymatic biosensors. To improve the HM detection efficiency, and owing to the decent optical properties of porous silicon (PS) structures, PS was employed as the enzyme

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immobilization substrate to monitor chromium ions in water and wastewater [105]. Urease and acid phosphatase-modified optical fiber sensors were also employed to detect trace levels of HMs [106]. A fast and effective β-galactosidase colorimetric bioactive paper sensor capable of quantifying Ag1, Ni21, and Cr41 in ppm was constructed. Given the rapid advancements in DNA-based devices and nanotechnology, the construction of fluorescence resonance energy transfer (FRET) high-precision nanobiosensors represents an effective technique for multiplexed HM recognition. The selectivity and specificity of an innovative FRET system are improved due to the high photostability of the system’s receptor, which consists of QDs conjugated with a thin silica layer that was covalently coupled to DNAzymes and two quenchers. The QDs reflected different fluorescence signals against a series of metal ions [107]. Metallic nanoparticles (AuNPs or AgNPs)-based multidimensional aptasensors are offering promising systems for the simultaneous detection of HMs in several water samples [108]. 14.8.5.3 Detection of microorganisms The development of innovative optical biosensors has led to the construction of sensitive, selective, easy-to-use, and real-time systems to monitor various pollutants, drugs, and pathogen bacteria in wastewater [109]. For the detection of pathogens, optical techniques such as SPR [110,111] and resonant mirror [112] are widely employed. For example, specific antibody-modified gold SPR surface was employed to sense Salmonella enteritidis and Listeria monocytogenes. This biosensor can achieve a detection limit of 106 cell/mL. Another biosensor based on SERS molecular probes was employed for detecting E. coli (gram-negative bacteria) and Listeria (gram-positive bacteria). The detection mechanism corresponded to specific binding between the bacteria and the SERS probes, and the detection time seemed to be shortened. In Refs. [113,114] some capturing antibodies were immobilized on magnetic bead surfaces and biotinylated detection antibodies, aiming at the fabrication of an ecofriendly immune-sensor for the sensitive detection of E. coli O157: H7, through utilizing the chemiluminescence (CL) technique. The sensor’s mechanism was explained by the fact that the conversion of glucose into gluconic acid and H2O2 was catalyzed by avidin conjugated glucose oxidase, and the intensity of CL signals reflected the level of produced H2O2 and thus the concentration of E. coli O157: H7 via the catalytic activity of laccase on luminol. Yildirim et al. developed a portable biosensor employing fluorescently labeled specific aptamer to detect E. coli O157: H7 strain in wastewater. The free aptamers number could be determined by hybridizing them with complementary DNA probes immobilized on the surface of an optical electrode fiber. The higher fluorescent signal corresponded to a lower level of E-coli in the

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tested samples. For onsite wastewater testing, the developed system is low-cost, fast, highly sensitive, and portable [115]. Metallic nanoparticles (MNPs: AgNPs or AuNPs) have been identified as excellent substrates for pathogen biosensors employing the SERS technique. Microbial DNA detection via MNPs can also be done. Following hybridization with their complementary DNA, the colorimetric changes of AuNPs combined with single-stranded DNA samples can alleviate costly and complex fluorescence labeling needs. In particular, AuNPs are widely used for monitoring the trace amounts of organisms in dark water samples. Wu and coworkers [107] described a sensitive and simple colorimetric aptasensor for sensing E. coli O157: H7 and Salmonella typhimurium. Aptamers with a high affinity for target bacteria are adsorbed on the surface of AuNPs in this process. The existence of targeted bacteria causes aptamers to alter conformation, leading to the AuNPs aggregation and, as a result, a naked-eye red to pinkish/purple color change. The described method in this work showed its simplicity, specificity, and quickness, besides direct measurement of the whole bacteria with no need for specialized devices or pretreatment protocols. Another noteworthy method employs carboxyl functionalized graphene QDs to label a specific antibody for detecting E. coli O157: H7 via the antibody’s primary amines. The sensor could detect E. coli O157: H7 in a variety of environments, including water and food, and generated an enhanced fluorescent signal with a detection limit of 100 CFU/mL.

14.8.6 New trends in optical biosensors sensing and monitoring Due to stricter regulations and widespread human and environmental safety issues, extensive effort has been made to develop an effective approach for wastewater monitoring. The distinctive role of biosensors, optical biosensors in particular, in helping to monitor and manage wastewater sources can’t be overlooked and is widely supported. Even with many advantages achieved, there still exist some practical restrictions and difficulties to overcome to construct efficient and capable optical biosensors. Innovative sensing structures with a bigger detection range and an acceptable selectivity and sensitivity of wastewater pollutants must be developed. Furthermore, the limit of detection is a very important aspect to be considered in any new developments of biosensors. The major obstacles mentioned previously for using biomolecules as the recognition components can be overcome in two ways: (1) manufacturing of target-specific fragments and immobilization of antibodies and enzymes to stimulate speedy, inexpensive, and in situ

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sensing of organic pollutants in wastewater, and (2) the blending of nanomaterials that will increase sensitivity and selectivity of biosensors. This approach will improve the sensor detection efficiency by device miniaturization and the application of sensor arrays to detect multipollutant components. Up-to-date developments in the field of nanotechnology illustrate the unlimited possibilities and potential in improving some important characteristics of optical biosensors. This new opportunity can help to accomplish improved detection efficiency.

14.8.7 Uses of nanomaterials for water quality monitoring Observing water quality is essential to regulationg pollution sources and ensuring the well-being of living things. The use of nanomaterials in water quality control has received extensive consideration due to their unique properties (e.g., electrical, mechanical, catalytic, optical, magnetic, photonic). Several studies have revealed the possibility to apply numerous types of nanomaterials, including CNT, Au and Ag, and QDs, in the detection and sensing of organic, inorganic, and pathogen pollutants [116], Detection and monitoring of pathogens in water is crucial to the protection and safety of human and animal health. The mechanism of nanomaterial-facilitated pathogen detectors consists of recognition elements, nanomaterials, and transducers. Among the main sensor mechanisms, nanomaterials are utilized to increase the detection sensitivity and response of pathogens owing to their exceptional optical, electrochemical, and magnetic properties. Functionalized QDs were utilized to sense cells of bacteria serotype, and the results demonstrated that QDs were superior to customary techniques in sensitivity and stability [116]. Another application of nanomaterials is the detection of organic and inorganic contaminants. For example, nano-Au can detect chlorpyrifos and malathion at ppb concentration range in surface water [116]. Additionally, magnetic nanomaterials and CNTs have been used for sample concentration and purification. Nanofiltration membranes can be used in the production of potable water and the removal of metals, disinfection by-products, pesticides, and emerging contaminants from contaminated water. CNTs, iron oxide nanomaterials, and nTiO2 have shown high adsorption capacities. nZVI can be used to remove HMs and organic pollutants by reduction or oxidation. However, there are still some disadvantages in the application of nanomaterials, including fouling of nanofiltration membranes, the low absorption efficiency of visible light of nTiO2, regeneration of carbon nanotubes, and the dispersity of nZVI.

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555

In addition, nanomaterials, such as CNTs, magnetic nanoparticles, noble metal nanomaterials, and QDs can be used for water quality monitoring, especially for the detection of extremely low concentrations of organic pollutants, inorganic pollutants, and pathogens. Briefly, nanomaterials have received extensive attention in water pollution remediation and monitoring. However, there are still some hurdles to consider and resolve, including cost-effectiveness, technical obstacles, and the potential safety risk of nanomaterials.

14.8.8 Wireless sensor networks Wireless sensor networks relate to networks of spatially distributed and dedicated sensors that observe and record the physical circumstances of the environment and transmit the specifically collected data to a central place. WSNs can measure environmental variables such as temperature, contamination levels, etc. Previously the sampling and the analysis were separated, which required well-trained specialists to perform water sampling appropriately and the supplementary analytical work was completed in a specialized laboratory. Before the analyses, the quality of water samples could be altered. To overcome this problem WSNs were developed. In this case, the sensor (miniature size) is used at the location, the water sample is analyzed immediately after sampling, data are collected in real-time spontaneously, and results are sent to the operators as frequently as is needed. This instrument is comprised of an isolated sensor node, and processing signals are collected by a personal computer; there is a wireless connection that enables the communication between the sampling place and the end-users. This water quality monitoring instrumentation, which works on a cellular network, can collect, store, and transmit analytical data [117 120].

14.9 Conclusion Water quality has an extreme impact, in numerous phases, on all forms of hominids, animals, and environmental lifecycles. The applications of conventional approaches for characterizing wastewater have many disadvantages, such as high cost, low efficiency, and the need for specialized staff. Thus solutions were developed to resolve the limitations of traditional detection methods and their economic value. The chapter introduced different sources and types of pollutants (e.g., organic, inorganic, microbiological, macroscopic, thermal) that appear in water of different origins (e.g., industrial, domestic, drinking, wastewater, sewage, etc.). Organic, inorganic, and biological indicators were

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introduced that characterize the water and help in the determination of pollutants. Important analytical (e.g., electrochemical, chromatographic, spectroscopic, as well as chemical and biological) methods were summarized that allow measuring the undesired toxic components in water. New trends in optical biosensing and monitoring as well as the importance of nanomaterials in quality control were also covered. A new field of detection approaches is the applications of biosensors, which are reliable replacements for detecting environmental pollutants through in situ monitoring and real-time methodologies. The most common form of biosensors with the ultimate capability for the recognition of hazardous contaminants in water is the optical biosensor. The popularity of biosensor-based systems is due to their great sensitivity and selectivity for recognition of various components of environmental pollutants, simplicity of use, and low cost. In the field of the sensing of hazardous HMs, organic materials, and microorganisms, optical biosensors have achieved huge attention in research and development.

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C H A P T E R

15 Chemical sensing of heavy metals in water Omer Sadak Department of Electrical and Electronics Engineering, Ardahan University, Ardahan, Turkey

15.1 Introduction Water is the most essential natural resource for not only human beings but all species including plants and animals. It is at the heart of global sustainability and is vital to socioeconomic development and safe and healthy environments for human life. Due to increases in global population, especially in metropolitan cities, there is a rising need for clean drinking water for the populated centers. According to a report issued in 2012 by the United Nations Educational, Scientific and Cultural Organization, accelerating urbanization worldwide causes increasing vulnerability to improperly built or operated water supplies and inadequate access to sanitation and hygiene services [1]. The most recent examples of poor water supply in populated areas that effect the human health occurred in June 2019 in an island, Askøy, located northwest of Bergen, Norway. It was reported that over 2000 people became ill, 76 were admitted to hospital, and 2 died due to Campylobacter infection, according to publicly available data [2]. The Flint lead crisis is another example of a poor water supply in recent years. In April 2014, the water supply for the city of Flint, Michigan with a population of 100,000 people was temporarily changed by state-appointed emergency management from Detroit-supplied Lake Huron water to the Flint River in order to save money [3]. Water from the Flint River possibly corroded lead pipes that caused the elevated lead level in the drinking water [4].

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00010-8

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© 2023 Elsevier Inc. All rights reserved.

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15. Chemical sensing of heavy metals in water

Monitoring of water quality is very essential to reduce the harmful effects on human health and avoid environmental catastrophe. The US Environmental Protection Agency (EPA) reported the recommended core and supplemental water quality indicators based on use to help set levels of protection in water quality standards [5]. Much research has focused on simple detection and monitoring of pollutants in water including heavy metals, dyes, volatile organic chemicals, microbial contaminants (bacteria, viruses, and parasites), hazardous chemical waste, etc. Identifying some of the contaminants could be easily achieved based on taste, color, odor, and turbidity of the water [6]. Yet, most pollutants cannot be readily detected by a simple assessment, so testing is needed to determine whether or not the water is polluted [7]. Among these pollutants mentioned above, contamination of water resources by heavy metals are the main concern because their accumulation over time causes adverse effects in plants, animals, and human beings [8]. Heavy metals are commonly classified as metals with high atomic weight (63.5 and 200.6 g/mol), high density (.5 g/mL), and toxicity behavior to humans and living species even at low concentration [9]. Heavy metals are nonbiodegradable, toxic, and persistent nature, and they tend to accumulate in living organisms such as fish and vegetables due to their high solubility in water [10,11]. Some heavy metals show not only chemical toxicity but also radiological toxicity and these radioactive metals from nuclear power plants or existing nearby radioactive materials cause water and environmental pollution [12]. Heavy metals can form chemical bonds with the thiol group of peptides or biological ligands to enter the cell. Heavy metals are toxic because they change the biochemical lifecycle by entering cell [13]. Although living organisms need various concentrations of heavy metals including iron, zinc, copper, manganese, cobalt, manganese, molybdenum, etc., at sufficiently high concentrations these heavy metals are toxic to livings. On the other hand, certain heavy metals like, arsenic, cadmium, chromium, lead, and mercury are considered to be harmful even at lower concentrations [14]. Measuring water contamination usually requires large, sophisticated, and expensive laboratory-based techniques such as absorption spectrometry (AAS), inductively coupled plasma mass spectroscopy (ICPMS), X-ray fluorescence (XRF), ion chromatography, ultraviolet visible spectroscopy (IC-UV-vis), atomic emission spectrometry (AES), etc., to reliably determine toxicity. Sensors, alternatively, offer an accurate, fast, and easy solution to monitor water contaminants. The compact designs of sensor instruments have also made it possible for in situ contaminants monitoring, while preventing long-term and often costly analysis in laboratories [15].

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15.2 Heavy metal toxicity ranges and mechanism in living cells

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In this section, first, general information about heavy metals and their impact on livings is given and then toxicity ranges of heavy metals and their cell toxicity function are explained. Various types of biosensors for the application in heavy metal monitoring and their working principles are discussed in detail. In the end, emerging developments and future states of heavy metal monitoring are described.

15.2 Heavy metal toxicity ranges and mechanism in living cells The main sources of heavy metals in aquatic environments result from anthropogenic activities and natural processes. The natural processes are mainly bedrock weathering, volcanism, and erosion. Rapid industrialization and urbanization increases anthropogenic activities, which include industrial production, agriculture fertilization, sewage discharge, and smelting and refining processes during metal mining [16]. Heavy metal accumulations over the years or even centuries in aquatic environments are the global public health concern. The toxicity of heavy metals depends on the absorbed metals or metalloid ions dose and this dose varies drastically. For example, cobalt, copper, iron, manganese, nickel, and zinc are essential nutrients required for plants and animals. A number of deficiency disorders or syndromes arise from insufficient supply of these micronutrients [17]. On the other hand, heavy metals like arsenic, cadmium, chromium, lead, and mercury cause severe health problems upon consumption even at a trace level [18]. Table 15.1 summarizes the maximum level of heavy metals permitted in drinkable water by the World Health Organization (WHO) and the EPA along with their possible contamination sources and their health effects above the limits [19,20]. When a biosensor is designed for specific heavy metals, this threshold must be taken into consideration for meaningful results. The prolonged exposure to heavy metals is hazardous and causes serious health problems. Although the mechanism of heavy metal toxicity in living cells varies depending upon heavy metal species, there are four well-known mechanisms that affect biochemical activities at the cellular level: inactivating enzymes, blocking metabolically active molecules, displacing or removing essential molecules and elements, and compromising the integrity of the membrane [21]. Heavy metals have high electrondonating affinities that can lead to covalent attachments being formed. Electron-accepting groups in mammalian tissues and plants such as thiol and selenic groups are the key factor in toxicity because of heavy metal’s binding affinity to these groups. The tripeptide, glutathione (GSH), exist at millimolar concentrations in mammalian tissues and hence contributes for

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15. Chemical sensing of heavy metals in water

TABLE 15.1 The maximum level of heavy metals permitted in ground and drink water [19,20].

Metals

EPA limits (µg/L)

WHO limits (µg/L)

As

10

Cd

Health effects above threshold

Heavy metal sources in drinking water

10

Cause skin lesions, developmental effects, diabetes, pulmonary, and cardiovascular diseases

Production of electronics wastes, natural deposits erosion

5

3

Major cause of kidney diseases

Corrosion in galvanized pipes; batteries and paints waste

Cr

100

50

Carcinogenic and allergic dermatitis

Discharge from steel plants, natural deposits erosion

Hg

2

6

Kidney damage

Discharge from refineries and natural deposits erosion

Pb

15

5

Kidney damage, high blood pressure, developmental effects

Corrosion in house plumbing systems and natural deposits erosion

Sb

6

20

Carcinogenic and lung damage

Discharge from petroleum refineries, production of electronics and ceramics wastes

Se

50

40

Gastrointestinal disturbances, skin discoloration, teeth decaying

Discharge from petroleum refineries and mines, natural deposits erosion

about 90% of the overall nonprotein sulfur. This reaction between heavy metals and GSH play a critical role in the response to prolonged exposure to heavy metals in a metabolism [22]. One of the consequences of heavy metal toxicity is the overproduction of reactive oxygen species (ROS). ROS play significant roles in controlling the expression of various genes under normal circumstances. Nonredoxactive heavy metals (e.g., Ni, Cd and Pb) do not produce ROS directly by engaging in oxidation/reduction reactions, unlike redox-active metals (e.g., Fe, Cu, and Cr) [23]. ROS consist of chemically reactive oxygencontaining molecules such as hydroxyl radical (HO
), singlet oxygen (O2), superoxide radical (O2 2), and hydrogen peroxide (H2O2). Serious consequences may result from carcinogenic metal-generated ROS including apoptosis, genetic instability, autophagy, angiogenesis, metabolic reprogramming, etc. [24].

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15.3 Heavy metal measurement methods in water and their performance

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15.3 Heavy metal measurement methods in water and their performance Heavy metal ions are associated with a variety of metal and metalloids species in the environment. In order to avoid severe health effects of heavy metals, the determination and monitoring of such metal speciation has a significant effect on preventing them causing severe health problems both in animals and plants [25]. Conventional approaches (ICP-MS, AAS, XRF, IC-UV-vis, AES) have been employed for heavy metal monitoring that require manual sampling followed by a laboratory analysis. In this chapter, methods with easy sampling and analysis are discussed and the principle of electrochemical, optical, and surfaceenhanced Raman spectroscopy (SERS) sensors are introduced. These sensors require low-volume samples and offer easy handling since these devices can be designed as portable. These features would help with on-site monitoring of heavy metals and other pollutants in water.

15.3.1 Electrochemical sensors Electrochemical sensors, an analytical tool for detecting chemical and biological analytes, have drawn significant interest due to their portability, low cost, rapid response, and high sensitivity [26,27]. In principle, electrochemical sensors use the interaction between receptors/bioreceptors and specific analyte of interest to generate signal. These interactions produce or use ions or electrons that cause a change in the electrical properties of electrolyte solution. The transducer measures the change in electrical current or potential of electrolyte solution and produces electrochemical signal, which is associated with the quantity of analyte of interest in the sample [8,15]. Electrochemical sensors are typically achieved with a three-electrode system, working (WE), reference (RE), and counter electrodes (CE), as shown in Fig. 15.1 [28]. The CE is used for completing the circuit and allows the charge to flow and the RE is employed to deliver a stable potential for regulating the WE’s potential. The WE can be modified for the specific detection of heavy metals using various materials including graphene, mxenes, 2D nanomaterials, metal oxides, polymers, small organic molecules, metal films, biomolecules or their composites [2932]. This modification of WE enhances the selectivity, sensitivity, and stability of the sensor in the detection of heavy metals. When heavy metal ions are present in an electrolyte solution, they interacts with the WE, which causes a change in potential, current, electrochemical impedance, or capacitance [28]. The signal change, detected by an electrochemical

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15. Chemical sensing of heavy metals in water

FIGURE 15.1 Schematic description of the principle of electrochemical detection for heavy metal ions [28]. Reprinted from L. Cui, J. Wu, H. Ju, Electrochemical sensing of heavy metal ions with inorganic, organic and bio-materials. Biosens. Bioelectron. 63 (2015) 276286. Copyright (2020), with permission from Elsevier 4977141014036.

workstation, can be used to monitor heavy metals. On the basis of these signals, electrochemical sensors may be classified as potentiometry, amperometric, electrochemical impedance, and capacitance. Moreover, an electrochemical workstation can be designed as a portable sensor device that is suitable for on-site monitoring. A computer or a mobile device installed with required software platforms can be utilized to collect and interpret the data gathered from the experiments [33]. Fig. 15.2 shows the detailed categorization of electrochemical-based heavy metal sensors [33]. Each category needs a specific design of an electrochemical cell. Recent advances in nanomaterials and biomaterials for electrode materials have attracted the attention of many researchers due to their unique chemical, mechanical, and electronic properties [3436]. These materials can easily incorporate into sensing electrodes for heavy metals and potentially improve their electrochemical performance. Table 15.2 summarizes the recent advances in electrochemical sensors for heavy metals in aqueous solutions. Static techniques: potentiometry does not require current to pass through the solution of the analyte. An electrochemical cell is utilized to measure the potential under static condition and the response from the cell depending on its composition, kinetic and thermodynamic properties. Ion selective electrode (ISE), field-effect transistor (FET), and carbon paste electrodes (CPE) are the most common electrode systems used to detect metal ions in water samples. Although this technique offers high selectivity, low-cost, long lifetime, and quick response toward metals

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15.3 Heavy metal measurement methods in water and their performance

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FIGURE 15.2 Classification tree for different electrochemical methods for identification of heavy metals [33]. Reprinted from B.K. Bansod, T. Kumar, R. Thakur, S. Rana, I. Singh, A review on various electrochemical techniques for heavy metal ions detection with different sensing platforms. Biosens. Bioelectron. 94 (2017) 443455. Copyright (2020), with permission from Elsevier 4977141323182.

ions, this type of sensor has some limitation such as high detection limits, low sensitivity, and electrode miniaturization difficulties [78]. However, modifying the interface material of WEs with nanomaterials is of great interest since they may enhance the sensitivity and lower the limit of detection [79]. Li et al. immobilized conductive copolyaniline nanoparticles in a vinyl resin matrix for the detection of Pb21 and the results showed that the sensor had a very low detection limit (2.2 3 10211) with a wide linear concentration range (1 3 102101 3 1023) (Fig. 15.3) [44]. The fabricated Pb21 sensor has a long lifetime (15 months), which is a desirable feature for sensors. Moreover, the real water samples from the Printing House of Fudan University, China were tested for Pb21 after filtering 34 times until obtaining clear water and atomic AAS and the fabricated sensor showed very close results. Potentiostatic techniques (also known as controlled potential techniques) require an electrochemical workstation (potentiostat) to regulate the potential between RE and CE and maintain a potential difference between RE and WE while measuring current. In contrast to static techniques, there is an applied current for a measurement. This method is further classified into three subgroups; amperometry, chronocoulometry, and voltammetry/ polarography. Due to their high sensitivity, selectivity, compact and lowcost instrumentation, and a broad linear concentration range, potentiostatic sensors are commonly used for heavy metal monitoring [80]. Zhang et al. fabricated gold nanoparticle-decorated carbon nanofiber (AuNPs/CNFs) via electrospinning followed by a thermal reduction (Fig. 15.4). The assynthesized nanocomposite had an affinity to sense Cd21, Pb21, and Cu21 ions simultaneously using square- wave anodic stripping voltammetry (SWASV) [49].

3. Environmental applications

TABLE 15.2

Summary of electrochemical methods for heavy metal ions monitoring in water samples. Analyte

Static techniques

31

As

21

Cd

21

Cd

ISE ISE CPE

Active electrode materials TMOPP-Co NPOE LT

LoD

Linear range (M)

Reference

5

7.9 3 10 1 3 10

N/R 9

1.4 3 10

8

8 3 10

6

1

8

1

8

1

6

1

[37]

1 3 10 1 3 10

[38]

8 3 10 1 3 10

[39]

Cr

ISE

Rhodamine-B

1 3 10

5 3 10 1 3 10

[40]

Hg21

ISE

tBCIU

2.2 3 105

5 3 1051 3 101

[41]

Hg21

FET

SWCNTs

1 3 108

1 3 1081 3 103

[42]

Pb21

ISE

NPOE, BBPA

4.3 3 109

1 3 1081 3 101

[38]

Pb21

FET

GSP-AuNPs

1 3 108

1 3 1081 3 105

[43]

Pb21

ISE

Poly(AN-co-HSA)

2.2 3 1011

1 3 10101 3 103

[44]

As31

SWASV

Au/GNE

1.3 3 109

1.3 3 1091.2 3 107

[45]

As31

DPASV

AuNPs/SPE

1.5 3 109

1 3 1096 3 107

[46]

Cd21

SWASV

BiSnFEs

3.6 3 109

7.3 3 1084.1 3 106

[47]

61

Potentiostatic techniques

Method

21

Cd

21

Cd

21

Cd

61

Cr

21

Hg

21

Hg

SWASV SWASV DPSV ASC DPSV CM

Bi-C AuNPs/CNFs NG Bismuth NG GO/Ag NC

9

7.2 3 10

7

1 3 10

8

5 3 10

10

3 3 10

8

5 3 10 N/R

9

7

8.9 3 10 8.9 3 10 7

6

8

3

9

8

7

6

[48]

1 3 10 1 3 10

[49]

5 3 10 1 3 10

[50]

5 3 10 5 3 10

[51]

2 3 10 9 3 10 10

2.5 3 10

[50] 7

4.2 3 10

[52]

Hg21 21

Pb

21

Pb

21

Pb

21

Pb

21

Pb Galvanostatic techniques

31

As

21

Cd

21

Cd

21

Hg

21

Pb

21

Pb Impedance measurements

31

As

21

Cd

61

Cr

21

Hg

21

Hg

21

Hg

21

Pb

DPV SWASV SWASV SWASV DPSV DPV FTSC CCSCP CCSCP GSCP CCSCP GSCP EIS EIS EIS EIS EIS EIS EIS

MSO BiSnFEs Bi-C AuNPs/CNFs NG Bismuth GPPE Cysteine SPBFE Gold SPBFE RVC Aptamer Calix [4] arene Azacrown Gold GO-PTO-GES GST-SmtA Protein GH-DNA

5 3 1010 1.9 3 10

9

3.1 3 10

9

7

1 3 10

9

5 3 10

1 3 1091 3 107 6

5.9 3 10 3.1 3 10 9

7

4.8 3 10 4.8 3 10 7

6

8

6

7

4

1 3 10 1 3 10 1 3 10 9 3 10 8 3 10 5 3 10

N/R 9

2 3 10

3.6 3 10

9

7.1 3 10

8

9

5 3 10

4.8 3 10

8

9

6 3 10

7.4 3 10

11

6

1 3 10

2.7 3 10

11

10

1 3 10

9

1 3 10

15

1 3 10

10

5 3 10

[53]

8

[50] [54] 6

9

6

7

6

6.7 3 10 1.3 3 10 3.6 3 10 7.1 3 10 1.8 3 10 2.7 3 10 6

5 3 10

2 5 3 10

6

9

6

9.6 3 10 1.4 3 10 2.7 3 10 4.7 3 10 1.5 3 10

8

1 3 10

6

1 3 10 1 3 10

3

8

1.9 3 10 1.9 3 10 3

9

7

1 3 10 1 3 10 1 3 10 3 3 10 15

1 3 10

10

5 3 10

10

1 3 10

7

5 3 10

[56] [57]

[57] [59] [60] [61]

6

9

[55]

[58]

8

10

[48] [49]

9

9

[47]

[62] [63] [64] [65] [66] (Continued)

TABLE 15.2 (Continued) Analyte Electrochemiluminescence technique

31

As

21

Cd

21

Cd

Method ECL ECL ECL

Active electrode materials hairpin DNA

LoD

Linear range (M) 11

1.6 3 10

9

2.1 3 10

CdTe QDs

9

1.1 3 10

CdTe QDs

8

Reference

11

2.7 3 10

25

2.7 3 10

9

26

6.2 3 10 3.4 3 10 9

25

2 3 10 5 3 10 8

25

[68] [69]

61

Cr

ECL

GQD/S2O8

2 3 10

5 3 10

Cr61

ECL

Au@Pb-β-CD

3.4 3 109

1 3 108 2 1 3 1024

[71]

Hg21

ECL

CNNSs

1 3 1011

5 3 1010 2 1 3 1028

[72]

Hg21

ECL

Au-Ag NCs

5 3 109

1 3 108 2 5 3 1026

[73]

Hg21

ECL

DNA-Ag NCs

5 3 1012

1 3 1011 2 6 3 1027

[74]

Pb21

ECL

DNAzyme

1.1 3 1011

2.5 3 1010 2 1 3 1029

[75]

Pb21

ECL

Ru117E0

1.4 3 1012

5 3 10128 3 1011

[76]

Pb21

ECL

GR-5 DNAzyme

9 3 1013

2 3 10121 3 109

[77]

22

2 6 3 10

[67]

[70]

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FIGURE 15.3 A Potentiometric Sensor for Pb21 ions based on copolyaniline nanoparticles in a vinyl resin matrix and its linear curve fit [44]. Reprinted with permission from X.G. Li, H. Feng, Mr. Huang, G.L. Gu, M.G. Moloney. Ultrasensitive Pb (II) potentiometric sensor based on copolyaniline nanoparticles in a plasticizer-free membrane with a long lifetime. Anal. Chem. 84 (2012) 134140. Copyright (2020) American Chemical Society.

FIGURE 15.4 SEM images of gold nanoparticle grown on carbon nanofibers (AuNPs/ CNFs) as electrode materials (on left) and its potentiostatic response toward Cd21, Pb21 and Cu21 using SWASV (on right) [49]. Reprinted with permission from B. Zhang, J. Chen, H. Zhu, T. Yang, M. Zou, M. Zhang et al. Facile and green fabrication of size-controlled AuNPs/ CNFs hybrids for the highly sensitive simultaneous detection of heavy metal ions. Electrochim. Acta 196 (2016) 422430. Copyright (2020), with permission from Elsevier 4977131475571.

Galvanostatic techniques (also known as controlled current techniques) are similar to potentiostatic techniques, but with the use of a constant current rather than a constant potential. The other difference over potentiostatic techniques is that a simple instrumentation system is used for galvanostatic techniques since no input from the RE is required, so it can also be easily tailored to online measurements. The disadvantage of these techniques is the large double-layer-charging effects that arise while monitoring heavy metal ion concentrations [56,81]. Impedance measurements are some of the most widely used techniques since it is a nondestructive approach to monitoring heavy metals in biological and environmental samples. The impedance measurements can be performed either in a two- or three-electrode configurations via

576

15. Chemical sensing of heavy metals in water

measuring fractional change in the charge density on electrode surface due to the presence of heavy metals [82,83]. Cao et al. reported a novel approach to detect selectively Hg21 ions via electrochemical impedance spectroscopy (EIS) using thiol functionalized poly-T oligonucleotides modified gold disk electrode [63], as shown in Fig. 15.5. The fabricated biosensor selectively detected Hg21 ions due to affinity of Hg21 toward two DNA thymine bases (T). Hg21 ions stabilize the DNA duplexes by forming Hg21-mediated base pair and thymineHg21thymine. The fabricated sensor had a wide linear concentration range of 1 3 10291 3 1023 M with a low limit of detection of 1 3 10210 M. Electrochemiluminescence technique (ECL) is a unique approach because it is a combination of chemiluminescence and electrochemical sensors. It is a chemiluminescent process, caused by the electrochemical reaction of electrochemiluminescence-active species on the electrode surface, and the measurements are triggered and regulated by a potential. In recent years, quantum dots (QDs) such as CdTe, CdS, and CdSe have appeared as promising fluorescent probes for various applications including heavy metal monitoring. QDs-based ECL sensors offer high sensitivity (parts per billion/ trillion), fast response, and low background signal [84,85]. As seen in Table 15.2, although the ECL-based

MCH

T-Hg-T

Hg2+

-6000

Zim (Ω)

-4000

Fe(CN)63-/4-2000

0 0

4000

8000

12000

16000

Zre (Ω)

FIGURE 15.5 Schematic illustration of detection of Hg21 using EIS via thiol functionalized poly-T oligonucleotides [63]. Reprinted with permission from R.G. Cao, B. Zhu, J. Li, D. Xu, Oligonucleotides-based biosensors with high sensitivity and selectivity for mercury using electrochemical impedance spectroscopy. Electrochem. Commun. 11 (2009) 18151818. Copyright (2020), with permission from Elsevier 4977281268202.

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sensors do not offer a wide linear concentration range of heavy metals, they have a lower low limit of detection (LOD) than other electrochemical methods. Moreover, this method has a complicated procedure and high cost of operation [86].

15.3.2 Optical sensors Optical sensors and biosensors (OSs) have great potential for on-site monitoring of heavy metal ions. OSs are dependent on variations in optical properties (e.g., absorption, emission, transmission, lifetime, etc.) triggered by interactions between the receptor and analytes of interest [87]. Changes are correlated with the concentration of the analyte in the sample analyzed. OSs offer several advantages compared to electrochemical methods: insensitivity to electromagnetic interference, great potential for in vivo use, and ease of use [8]. Colorimetric and fluorescence sensors are the most common approaches used for optical sensing of heavy metal ions. Wang et al. developed a sensitive and selective colorimetric Hg21 sensor based on unmodified silver nanoparticles (AgNPs) and mercury-specific oligonucleotides [88]. It has been reported that Hg21 can selectively bind to thymine bases on single-stranded DNA (ssDNA), which fold it into hairpin or duplexes, so the authors used this phenomenon to detect Hg21 with the help of AgNPs under high-salt conditions. Yellow-to-red color change was observed with the addition of Hg21 into the assay. The linear range of 2.5 3 10285 3 1027 M and a LOD of 1.7 3 1028 M were reported. In other work, fluorescent gold nanocluster (AuNC)-decorated polycaprolactone (PCL) nanofibers (AuNC*PCL-NF) were employed for Hg21 detection [89]. The fabricated sensor was stable for more than four months. The mechanism of this sensor is based on an enhanced interaction between gold and mercury. A rapid Au-Hg amalgam formation during adsorption process enables the sensor to detect Hg21 at ppt level with a 30 s response time. Liu et al. developed a uranium sensor in natural water systems with high sensitivity and selectivity using a luminescent mesoporous metalorganic framework (MOF) [90]. Using luminescence spectra, a rapid response to uranium in aqueous solutions was obtained. The sensor that was developed showed a wide concentration range from 0 to 3 mg/L with a LOD of 0.9 μg/L. The sensor successfully tested for uranyl detection in two natural water systems: fresh lake water from Dushu Lake in Jiangsu Province, China, simulated saltwater, and actual seawater from the Bohai Sea, China. A highly selective As31 colorimetric sensor was designed based on 2-amino-5,6,7-trimethyl-1,8-naphthyridine (ATMND)/A hairpin probe (HP1) matrix mediated triple-helix molecular switch (THMS) (ATMND/ HP1-mediated THMS) by Pan et al. [91]. The fabricated sensor showed a

3. Environmental applications

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15. Chemical sensing of heavy metals in water

wide linear range from 10 ng/L to 10 mg/L (1.3 3 102101.3 3 102 M) and a LOD of 5 ng/L (6.5 3 10211 M). Tap, lake, and pond water samples were tested for their As31 using as-synthesized sensor and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The authors reported that there were no significant differences between measured values. Xu et al. reported an integrated lab-on-paper device based on enzyme-coated reduced graphene oxide (rGO)—PdAu probe for the identification of Pb21 [92]. The mechanism of this sensor is illustrated in Fig. 15.6. It relies on specific biorecognition between the target and

FIGURE 15.6 Schematic illustrated of integrated lab-on-paper device for Pb21 sensing [92]. Reprinted with permission from J Xu, Y Zhang, L Li, Q Kong, L Zhang, S Ge, et al. Colorimetric and electrochemiluminescence dual-mode sensing of lead ion based on integrated labon-paper device. ACS Appl. Mater. Interfaces 10 (2018) 34313440. Copyright (2020) American Chemical Society.

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Pb21-dependent DNAzyme. After addition of Pb21 into the prepared lab-on-paper device, Pb21-dependent DNAzyme would be destroyed. The cleaved rGO-PdAu-GOx probes in the visualization bar generated color change, which could be detected by a naked eye with a linear range from 5 3 10292 3 1026 M. Moreover, the lab-on-paper device was capable of sensing Pb21 ions using ECL technique with a wider concentration range (5 3 102102 3 1026 M). The as-fabricated device was suitable for on-site monitoring of heavy metal with a low cost and easy operation. In another study, Park at al. designed a Pb21 detection platform based on DNA aptamer-linked photoluminescent graphene oxide quantum dot (GOQD) and a microfluidic sample pretreatment device was utilized in order to preconcentrate Pb21 ions and remove foreign anions [93]. The as-fabricated integrated sensor offered wide linear range of 4.8 3 102101.2 3 1026 M with a LOD of 6.4 3 10210.

15.3.3 SERS sensors SERS is a highly sensitive and selective technique that can identify analytes of interest at extremely low concentrations and offers real-time analysis by gathering information on the vibrational level of heavy metals in water samples. In terms of working principles, the advantages of using SERS over other techniques mentioned in this chapter are it can generate fingerprint spectra for the metal ions under analysis, and it can detect heavy metals of interest without any sample treatment [94]. As a result, heavy metals can be identified simultaneously with high precision. Besides heavy metal monitoring, SERS can be utilized in analytical testing, security, drug discovery, forensic and medical diagnostic devices, etc. [95,96]. Raman scattering is defined on the basis of an inelastic scattering of light resulting in a distinct fingerprint spectrum of the analytes. The weak Raman spectrum intensity is the drawback associated with Raman scattering, resulting in limited sensitivity toward analytes. To overcome this drawback, nanostructured metallic surface such as (Ag, Au, Cu, etc.) were employed as a substrate [97]. These metallic nanostructures have been extensively studied as substrates in SERS due to their sensitivity, selectivity, and low cost of fabrication in heavy metal monitoring. Song et al. reported a SERS platform based on 4-mercaptobenzoic acid (4-MBA) and As (III) aptamer functionalized Au@Ag shellcore nanoparticle (approximately 40 nm in size) system [98]. Au@Ag shellcore nanoparticle, (4-MBA), and As (III) aptamer serve as substrate, Raman signal reporter molecule, and SERS donor, respectively. The as-fabricated system was capable of detecting As31 using an exposures time of 5 s with a detection limit from 6.7 3 1029 to 1.3 3 1027 M

3. Environmental applications

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15. Chemical sensing of heavy metals in water

with a LOD of 1.3 3 1029 M. Wastewater samples from East Lake of Wuhan, Hubei Province, China were analyzed after pretreatment with a 0.22 μm microporous membrane. Atomic AAS confirmed the feasibility of SERS sensor for the detection of As31. In another study, monolayer 2,5-Dimercapto-1,3,4-thiadiazole (DMcT) functionalized Au@Ag NPs (Au@Ag-DMcT) were introduced as label-free SERS probe for Hg21 ions by Zeng et al. [99]. As illustrated in Fig. 15.7, Hg21 coordinates with donor nitrogen atoms in DMcT, resulting in aggregation of Au@Ag-DMcT, which enhances SERS signals. The sensor could detect Hg21 in the concentration range of 5 3 10211 to 1 3 1027 M with a LOD of 1 3 10211 M. The water samples from East Lake of Wuhan, Hubei Province, China were tested for Hg21 ion presence using the Au@AgDMcT-based SERS sensor. Ma et al. described ssDNA-functionalized gold nanostar (GNS) dimers for the detection of Hg21 ions at ppt level [100]. This method was very selective and sensitive because of stable THgT base pairs and better SERS activity of GNS over regular spherical AuNPs. This ultra-sensitive Hg21 sensor showed a linear concentration range of 1 3 10214 to 5 3 10212 M with a LOD of 4 3 10215 M. In another study, a prototype for a label-free SERS sensor for the detection of Hg21 made

FIGURE 15.7 Schematic illustration of (Au@Ag-DMcT) synthesis as a SERS platform and its use for Hg21 sensor [99]. W. Ma, M. Sun, L. Xu, L. Wang, H. Kuang, C Xu, A SERS active gold nanostar dimer for mercury ion detection. Chem. Commun. 49(2013) 498991. Copyright (2020), with permission from Elsevier 4978460130828.

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from DNA aptamer-modified SiO2@Au core/shell nanoparticles (120 nm in size) was designed by Lu et al. [101]. While Hg21 ions were recognized by T bases in the DNA aptamer, change in Raman signals of (G) and adenine (A) bases was used to probe Hg21 ions. The T-rich fragment was arranged near the thiol binding site for a specific binding of Hg21 ions. Once T-rich fragment interacted with Hg21 ions and formed stable THgT base pairs, the orientation of DNA molecule became vertical. Therefore the relative Raman signals of (G) and adenine (A) bases were shifted, which were used to quantify trace amounts of Hg21 with a wide range from 1 3 1028 to 1 3 1023 M with a LOD of 1 3 1028 M.

15.3.4 Other sensors As mentioned earlier in this chapter, sensors with easy sampling and analysis for heavy metals in water is the focus. Besides electrochemical, optical, and SERS sensors, surface plasmon resonance (SPR), Fo¨rster resonance energy transfer (FRET), evanescent wave (EW), and microcantilevers sensors are the other major sensors used to monitor heavy metal in water. SPR refractometric sensors are based on excitation of surface plasmons. In principle, plasmons are formed once light appears on the metal surface, the distribution of which is very sensitive to fluctuations in the material refractive index. Biomolecular reactions or structural changes of the molecules on the sensor surface could be the reasons for variations in the material refractive index [102]. For example, Forzani et al. designed a SPR-based As31 sensor based on thiol-containing organic compound (namely GSH, dithiothreitol (DTT), or N-(dithiocarboxy)-N-methyl-d-glucamine (dTGluc))-modified old film (B50 nm thick) as sensor probes for testing groundwater [103]. The GSH-, DTT-, and dTGluc-modified probes had a LOD of 1.3 3 1028, 4 3 10211, and 2 3 10211 M, respectively. This simple and cost-effective device was employed for on-site arsenic testing for surface and groundwater, and showed very promising results. FRET (also known as fluorescence resonance energy transfer) is another method that allows heavy metal monitoring with great sensitivity. FRET arises from dipole-dipole interactions between an excited donor and an acceptor, and donor-acceptor distance highly effects the interaction between the donor and acceptor [104]. Li et al. developed a FRET-based mercury sensor with high sensitivity [105]. In this study, thioglycolic acid-functionalized CdTe QDs and butyl-rhodamine B (BRB) were utilized as donor and acceptor, respectively, in the presence of cetyltrimethylammonium bromide (CTMAB). The CTMAB were

3. Environmental applications

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15. Chemical sensing of heavy metals in water

employed to reduce the distance between the donor and the acceptor. The FRET-based sensor was highly sensitive and selective toward Hg21 ions at pH of 6.6 with a LOD of 2 3 1028 M. EW sensors use the evanescent field of optical fiber to identify various trace elements or molecules and relies on the principle of total internal fluorescence reflection. It has several advantages including enhanced sensitivity compared to conventional bulk-optic methods, none or limited sensitivity to electromagnetic interference, small size, and multiplexing capability [106]. Long et al. combined the advantages of EW sensor and structure-switching DNA for rapid on-site/in situ Hg21 ion monitoring [107]. Fig. 15.8 summarizes the mechanism as well as the EW sensor platform for the on-site/in situ detection of Hg21 ions. The analysis time was less than 10 min and the linear concentration range was from 5 3 10291 3 1026 M with a LOD of 1.2 3 1029. Microcantilevers sensors are basically micromechanical devices that can measure the concentration of analytes of interest based on the resonance frequency of a microcantilever shifts [108]. Zhang et al. developed a hydrogel-coated microcantilever for the detection of Cr61 at concentrations as low as 10211 M. This approach showed better sensitivity compared to ion selective electrode sensors.

15.4 Current trends in heavy metal monitoring Conventional techniques have been applied for heavy metal monitoring such as ICP-MS, AAS, XRF, IC-UV-vis, AES, etc. However, due to cost, time, and performance concerns, many researchers are focusing on developing a sensor technology that can monitor heavy metal ions in water samples In this chapter, electrochemical, optical, SERS, SPR, FRET, EW, and microcantilever sensors were discussed for heavy metal monitoring because of their superiorities over conventional methods. The desirable characteristics of these sensors are high sensitivity, selectivity, stability, repeatability, low cost of operation, real-time analysis, low consumption of sample volume, and on-site detection option, allowing for sensing in complex natural samples. Also, the current trend toward miniaturization of sensing instruments is of great interest to researchers. In situ water quality testing using portable devices allows on-site and real-time heavy metal monitoring. Among the sensors discussed in this chapter, electrochemical sensors can be considered as one of the most appropriate strategies for heavy metal analysis, due to their low cost, easy installation, none or limited sample preparation, and ability to detect multiple metal ions [109]. Moreover, integration of microfluidics into biosensing technology is another field of interest. In microfluidics system, the test can be done at

3. Environmental applications

15.4 Current trends in heavy metal monitoring

583

(A) Schematic illustration of sensing mechanism of EWS sensor for Hg21 ions. (B) sensing EWS platform [107]. Reprinted from F Long, A Zhu, H Shi, H Wang, J Liu. Rapid on-site/in-situ detection of heavy metal ions in environmental water using a structureswitching DNA optical biosensor. Sci. Rep. (2013) 3.

FIGURE 15.8

3. Environmental applications

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15. Chemical sensing of heavy metals in water

FIGURE 15.9

Schematic illustration of synthesis graphene aerogel (GA) and metalorganic framework (MOF) composite and it is application for multi-metal ion detection (Cd21, Pb21, Cu21, and Hg21) [112]. Reprinted with permission from M. Lu, Y. Deng, Y. Luo, J. Lv, T. Li, J. Xu, et al. Graphene aerogel-metal-organic framework-based electrochemical method for simultaneous detection of multiple heavy-metal ions. Anal. Chem. 91 (2019) 88895. Copyright (2021) American Chemical Society.

a closed and stable environment with a low sample volume, which improves the sensitivity of sensors [110]. Currently a microfluidic paper-based colorimetric sensor is commercially available for the detection of heavy metal ions [111]. In recent years, sensitive, selective, and simultaneous multiheavy metal sensing has become of great interest. With the developments in nanomaterial technology, there are many reports on detecting multiple heavy metals of interest. For example, Lu et al. reported a graphene aerogel and MOF composite for the simultaneous detection of Cd21, Pb21, Cu21, and Hg21 [112] (Fig. 15.9).

15.5 Current limitations and future prospective Sensor technology has shown great potential as a water quality analysis platform. Commercialization of some of the sensors is either currently available or may be available in the near future. We believe the current limitations are complexity of the sensor’s material preparation, repeatability, reproducibility, stability, and sensitivity and future studies will go in these directions to minimize these limitations.

3. Environmental applications

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On the one hand, highly complicated synthesis procedures have been employed for sensor materials in many studies. Although these procedures show very sensitive and selective detection of heavy metals, they are far from scalable. These costly and time-consuming procedures could be replaced with simple and fast approaches with the developments in material synthesis technologies at the nanoscale. Moreover, sensor materials like mercury and QDs have potential negative impacts on the environment. The mass production and accumulation of these materials may fill landfills and ultimately contaminate water sources. Before mass production of sensors, health and environmental concerns need to be taken into consideration. On the other hand, simple instrumentation and ease of use are the current advantages to using these advanced sensors in everyday life. In situ operations and online monitoring are the areas of future studies for water quality analysis. The poor repeatability, reproducibility, stability, and sensitivity in heavy metal monitoring in complex and real aquatic samples are the drawback of many sensors. This is a big challenge in mass manufacturing and complex systems. Thus more effective methods for heavy metal detection need to be thoroughly studied in the future. Furthermore, with the help of future studies in this field, in addition to environmental use of sensors, health, safety, and security fields will be areas of interest.

15.6 Conclusion Heavy metals cause serious health and environmental problems when present in water sources. In order to provide safe and clean water sources to the public, monitoring water contaminants is a crucial task, and the development of fast and reliable sensors may be the key to the sustainable management of our ecosystem. This chapter introduced principles of sensors designed for heavy metal monitoring including electrochemical, optical, SERS, SPR, FRET, EW, and microcantilever sensors, and the recent advancements and performance of these sensors were evaluated. Recent sensory systems, including wireless, compact, and in situ, were presented to show their huge potential for future use.

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16 Chemical sensing of food phenolics and antioxidant capacity Aysu Tolun1 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany

16.1 Introduction The growing interest in the field of phenolics and their antioxidant properties has transfused several food and health disciplines, including biochemistry, pharmacology, physiology, nutrition, food processing, and analytical chemistry [1
3]. Polyphenols present a large group of structurally related chemical compounds widely distributed in the plant kingdom with more than 8000 molecular variants [4,5]. They are important food component mainly present in fruits and vegetables contributing to their flavor and color. These molecules are secondary metabolites of plants and characterized by at least two phenyl rings and one or more hydroxyl substituents. The main two groups of polyphenols are flavonoids and phenolic acids. They can act as metal ion chelators and free radical scavengers neutralizing dangerous reactive oxygen species (ROS) by donating electrons. Both these activities are responsible for antioxidant properties. These properties can be helpful in the evaluation of food quality and creation of beneficial features of the products [4,6
8]. Although ROS such as the superoxide anion radical (•O2 2 ), hydroxyl radical (•OH) and peroxyl radical (ROO ) are produced during normal cellular metabolism and they are important for cellular



Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00004-2

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neutralizing, their accumulation in the cells can cause direct damage of signaling pathways, which is responsible for DNA damage. They can also be produced against exogenous environmental condition such as UV radiation and pollution. It is reported that their total dietary intake could be as high as 1 g/d, which is much higher than all known classes of phytochemicals and dietary antioxidants. In addition to fruit and vegetables, cereals, chocolate, tea, coffee, red wine, and dry legumes also contribute to the total polyphenol (TP) intake [9,10]. Clinical and epidemiological studies have revealed that there is an inverse correlation between the intake of phenolics and the occurrence of diseases such as cardiovascular disease, cancer, chronic inflammation, diabetes mellitus, and neurodegenerative disorders [11
13]. ROS and reactive nitrogen species (RNS), which cause oxidation of lipids, nucleic acids, and proteins, are unstable and aggressive molecules. They donate their unpaired electrons to other cellular molecules or snatch electrons from other molecules to gain stability. Antioxidants inhibit or delay the oxidation process by blocking the oxidizing chain reactions. Therefore they are known as free radical scavengers and chemoprotective agents and help avoid various diseases [6,10,12]. Several conventional methods have been used for measuring the antioxidant properties. These are based on the two major chemical mechanisms involved: 1. Hydrogen atom transfer (HAT); such as the oxygen radical absorbance capacity (ORAC), total radical trapping antioxidant parameter (TRAP), total oxidant scavenging capacity (TOSC), β-carotene bleaching by ROO•, and low-density lipoprotein (LDL) oxidation; 2. Single electron transfer (SET); such as ferric reducing antioxidant power (FRAP), trolox equivalent antioxidant capacity assay (TEAC), DPPH ((2,2-diphenyl-1-picrylhydrazyl)-based assay, and total phenolic assay by Folin- Ciocalteu (FC). Despite being well-established methods they are not sufficient to estimate general antioxidant activity because antioxidants have extremely short lifetime and complex structure of the sample can cause interference. Since these methods require long sample preparation steps, more solvent consumption, and expensive equipment, researchers have been focusing on developing newer and more sensitive analysis techniques [14]. In addition, there is no unique or standard analytical approach that offers reliable measurement of free radicals due to the structural diversity of phenolic antioxidants arising from their molecular structure and polarity, as well as the challenges presented by radical formation and stability. Moreover, there is no validated procedure for the assessment of antioxidant capacity (AOC) in food. This makes the critical evaluation of

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antioxidant profiles and the validation of new procedures a daunting task. A remarkable effort has recently been made toward designing more accurate and reliable procedures such as novel TP and AOC assays. The development of these assays based on advanced nanomaterials is an emerging area to fill the aforementioned gaps in the field [15
18]. Nanomaterials possess tunable and unique features [19]. It is possible to combine different types of nanomaterials and easily functionalize them for the use in advanced methods that offer advantages over existing conventional approaches [20]. Nanomaterials or their functionalized forms are used as catalyzer or immobilization platforms in optical and electrochemical sensing tools to improve detection performance in terms of higher sensitivity, stability, and specificity. In addition, nanoparticles have been used as matrices for immobilizing enzymes and impart magnetic capabilities owing to the fact that they play a direct role in detection as chromogenic label and transducers [21]. They have been used to enhance electrochemical signals and generate ROS as well [13,22
24]. The determination of antioxidants with nanoparticles can be achieved using metal (e.g., gold, silver, ceria etc.) and metal oxide (e.g., cerium oxide, iron oxide, zinc oxide etc.) nanoparticles that can interact with antioxidants and induce a photochemical change in the nanoparticle system. These methods rely on the change in the nanoparticle color. Metal nanoparticles and quantum dots are two kinds of nanomaterials used in AOC assays [22]. In addition to such optical (spectroscopic) methods, a number of highly sensitive electrochemical assays have been developed. Electrochemical methods such as cyclic voltammetry (CV), differential pulse voltammetry (DPV), and square-wave voltammetry (SWV) are good alternatives to conventional methods. These electrochemical measurement techniques can also be combined with chromatographic and capillary electrophoresis methods and do not require sophisticated and expensive equipment for detection. A variety of nanostructures have been used in electrochemical sensor designs to provide enhanced detection, protection from passivation, and as immobilization matrices for biomolecules [21,22,25]. In addition to direct electrochemical oxidation, the determination of phenolic compounds can also be achieved enzymatically. Nanoparticles offer possibilities for immobilizing enzymes such as tyrosinase and laccase, and a variety of immobilization approaches to increase the selectivity and the molecular recognition. Not only enzymes but also nucleic acids can be immobilized on electrochemical transducers. DNA layers can act as biomolecular recognition elements for the detection of polyphenols in food samples taking advantage of the hybridization reactions [26
28]. Within the next sections of this chapter, we comprehensively review the current literature about the determination of phenolics and AOC using the recently developed chemical sensing approaches, including nanomaterial-based

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sensing and biosensing strategies. We also provide the advantages and disadvantages of these strategies in comparison to conventional methods.

16.2 Conventional methods for the determination of total phenolics and antioxidant capacity Until now, a great number of analytical techniques have been used for determination of total phenolic content and antioxidant activity of foodstuff, beverages, processed food as well as medical and environmental samples [29]. Phenolics are health-beneficial compounds that can reduce reactive species (such as ROS and RNS) and prevent oxidative stress, which leads to various human diseases [10,29]. The design of practical methods to measure antioxidant activity is an important challenge in analytical chemistry because of their complex chemical structure. Several conventional methods for measuring the antioxidant properties of samples use spectrophotometric methods, such as free radical scavenging activity, reduction of metal ions, and competitive methods [30]. On the basis of the major chemical mechanism, these conventional methods can be roughly divided into two categories: (1) HAT reaction-based assays and (2) SET reaction-based assays. The former measures the ability of an antioxidant to quench radicals by hydrogen donation whereas the latter measures the ability of an antioxidant to transfer one electron to reduce any compound including metals, carbonyls, and radicals [14,23,29,31]. HAT and SET mechanisms almost always take place together, with the balance depending on antioxidant structure and pH [32]. The assessment of AOC using the SET reaction mechanisms can be performed by several techniques such as total phenolic assay by FC, FRAP, TEAC, DPPH-based assay, and the total antioxidant potential assay using 2,20 -Azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS). The most commonly used HAT-based spectrophotometric assays to evaluate the AOC include ORAC, TRAP, TOSC, β-carotene bleaching by ROO•, and LDL oxidation [11,29]. Conventional methods have shown some disadvantages. Although these methods provide useful data, they are not sufficient to estimate the general antioxidant ability of a sample. Quantifying antioxidant activities against some radicals such as hydroxyl radical is not easy because of their extremely short lifetime. Additionally, the results can be affected by the presence of various interferents (particularly reducing sugars and proteins commonly found in food). Moreover, comparison of the performance of these methods, relying on totally different strategies, may lead to complications due to very different sample preparation. Requiring

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long analysis time, pretreatment steps of colored samples, and expensive equipment are other disadvantages of these methods [14]. Chromatographic separation techniques such as high-performance liquid chromatography (HPLC) and gas chromatography (GC) coupled with a wide range of detectors can also be used for the quantification of the individual antioxidants present in samples, but they are time consuming and require costly instruments [23]. They also demand several extraction techniques, like solid-phase microextraction, supercritical fluid extraction, and microwave-assisted extraction. In addition, considering the complexity of the composition of foods and biological samples, separating each antioxidant compound and studying it individually is time consuming and inefficient [33
35]. While different assays (e.g., ABTS, FRAP, etc.) are generally used for the assessment of AOC, the most commonly used assay to determine total phenolics is FC. However, the FC assay measures total reducing capacity of a sample, and does not completely reflect the total amount of phenols [24]. Furthermore, these conventional methods require an extraction such as solid phase extraction (SPE) and liquid 2 liquid extraction (LLE). Both methodologies consume large amounts of solvents and long times of analysis. The accuracy of the procedure can be affected by extraction steps due to uncomplete recoveries as well [32,36]. Although there is a great multiplicity of conventional methods, there is no a single approved and standardized method to provide an adequate measure of AOC due to the complexity of the oxidation processes in food [16,23]. Therefore there is still great interest in the development of novel methods to determine antioxidant activity. Several works over the years have aimed to provide complementary methods to existing ones to improve their specification [16]. For more accurate estimation of antioxidant activity, the use of multiple-method approaches and the development of new methods are necessary/advisable as an alternative or complementary to the most commonly used methods [37].

16.3 Novel sensing methods of total phenolics and antioxidant capacity A remarkable effort in recent research has been directed toward the development of new sensing strategies useful for the determination of total phenolics and antioxidant activity/capacity because of the disadvantages of conventional methods. In this endeavor, nanomaterials play a crucial role in implementing the new approaches into progressive applications [24]. Recent developments in nanotechnology that offer

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opportunities to develop new nanomaterials are extensively used in electronics, sensing, and catalysis [19,25,38,39]. In parallel with the fast progress of nanotechnology, nanomaterials, becoming new tools, have received great attention for the development of analytical methods to improve analytical performance and sustainability. Nanomaterials, such as carbon nanotubes (CNTs), graphene quantum dots (GQDs), nanodiamond, nanorods, and metallic nanoparticles (MNPs), have opened up new directions in analytical chemistry toward innovative applications in sample preparation, separation, and sensing [19,23]. In many fields, especially in environmental monitoring, food processing, biological applications, and criminological investigations, nanomaterial-based biosensors have been used. The innovative use of MNPs (mostly gold and silver) and QDs-based tools offer reliable assessment of polyphenol determination as well as antioxidant activity [40,41]. Fig. 16.1 shows the basic components and working principle of a biosensor as a new tool for the determination of polyphenols. Nanoparticles can be used as matrices for immobilizing enzymes in addition to playing a direct role in detection as chromogenic labels and transducers. They have been used to enhance electrochemical signals and generate ROS as well [22]. The use of nanoparticles as catalytic tools, immobilization platforms, or as optical or electroactive labels improves the biosensing performance for antioxidant measurement because metal and metal oxide nanoparticles enhance the catalytic and electrochemical processes. Compared to conventional analytical assays for the detection of antioxidants, nanomaterial-based methods have

FIGURE 16.1

Components and working principle of biosensors for the detection of

polyphenols.

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exhibited higher sensitivity, long-term stability, and selectivity [22,42]. These methods can be used for the measurement of a target antioxidant in biological samples and exhibit very good LODs due to their specific AOC in the media [23]. We have provided further details about nanomaterials-based optical and electrochemical sensing of polyphenols and antioxidant activity in the following subsections.

16.3.1 Optical sensing of polyphenols and antioxidant activity In recent years, nanoparticle (NP)-based spectrophotometric antioxidant assays have attracted researchers’ attention because of their simplicity, convenience, reproducibility, and being low-cost, energyefficient, and nontoxic [43]. Spectrophotometric assays measure the color change when the reduction of an oxidant occurs. The degree of the color change correlates with the concentration of antioxidants in a sample [37]. With advances in nanoscience, the production of a variety of nanomaterials in different shapes and morphologies has become possible. Nanomaterials, such as gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), QDs, and graphene, have distinct optical properties that can be used as optical-translating units for manufacturing optical sensors [29]. NP-based assays with optical detection principle rely on the ability of antioxidants to reduce noble metals toward the formation of nanoparticles (e.g., AuCl4 to AuNPs, Ag1 to AgNPs), which cause the changes in optical properties. In addition, antioxidants are used as reducing agents in the seed-mediated growth technique, where small NP seeds serve as nucleation centers to the growth and further enlargement of NPs [44]. The optical sensing mechanism is based on the formation of noble metal NPs. In such a way antioxidants chemically reduce the noble metal salts such as HAuCl4, AgNO3, H2PtCl6, RhCl3, PdCl2, etc., and produce the metal NPs. The concentration of metal NPs changes depending on the nascency of aggregation, disaggregation, growth, or formation of NPs in the media, and this causes a change in the spectral absorbance. The absorbance measured at certain wavelight has a correlation with the amount of the antioxidants [23,29]. Optical colorimetry is one of the oldest and simplest methods of analytical chemistry. Depending on the properties of the surface resonance plasmon bands, intense colorimetric and chromatic transitions are observed on the absorbance of the colloidal NP suspensions. Endogenous food polyphenols reduce the Au(III) to AuNPs(0) in aqueous solvents. The optical absorbance change of the metal NPs at a certain wavelength within the visible region of electromagnetic spectrum (i.e., 380
740 nm) are used for the measurement of antioxidant activity

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[23,41,42,45]. A significant number of colorimetric methods, based on metal and metal oxide NPs, have been devised especially in the last decade for use in the food and clinical research. One of the latest applications of nanomaterials in analytical chemistry is the use of metal NPs for the determination of the antioxidant activity. Noble metal NPs, including gold (Au), silver (Ag), copper (Cu), palladium (Pd) oxide, vanadium (V) oxide, and cerium (Ce) system, can be used for this purpose [23]. In particular, the antioxidant determination based on Au and Ag NP formation in complex samples has been reported, thanks to their superior properties compared to others. These properties include higher molar extinction coefficients in the visible region, ease of preparation via different physicochemical, and green approaches. Au and Ag NPs exhibit extraordinary and switchable localized surface plasmon resonance (LSPR) characteristics of which frequency is associated with composition, size, distance between particles, and dielectric properties of the environment. Ag has a higher plasmon quality than Au. However, Au is more often used than Ag in surface plasmon resonance (SPR) studies owing to its higher stability against oxidation [46]. For example, the solution of AuNPs with a diameter of 13 nm results in an absorption peak at 520 while the solution of AuNPs with a diameter of 40 nm generates absorption maxima at 528 nm. Noble metal NPs exhibit intense spectroscopic signals in the UV and visible range of the electromagnetic spectrum due to their high extinction coefficients [47]. On the other hand, nanoparticle-based optical sensors lack selectivity. Noble metal NPs may create aggregates originating from the reaction conditions such as salt, pH, and solvent effects. Therefore they often need be supported with more sophisticated LC/GC-MS/MS methods, which offer higher precision and accuracy [29]. 16.3.1.1 Gold nanoparticles According to the studies in which AuNPs are used for the evaluation of antioxidant activity, it has been reported that the polyphenols present in food samples have the ability to drive the synthesis of AuNPs, reducing gold(III) to gold(0) [41]. The general principle of using AuNPs is based on the color change occurring while the presence of antioxidants in a sample reduces their metal complex to generate NPs. The types of reduction reagents employed, pH, and the chemical stabilizer used for the reduction reaction significantly affect the size and distribution of AuNPs [23]. As a principle of the detection assay, it is possible to selectively and sensitively determine a wide variety of molecules such as DNA, proteins, organic molecules, and inorganic ions, which are significant for clinical, food, and environmental fields [48,49]. The AOC of target samples toward monitoring the AuNP formation is also an interesting application since the antioxidant activity provides

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information about the total antioxidant content in a sample. However, it is still required to compare the results of AOC analysis obtained from the analytical potential of the AuNP formation with other wellestablished AOC assays for further consolidation [23]. Della Pella et al. developed an AuNP-based colorimetric assay for the antioxidant activity determination of extra virgin olive oil (EVOO) and the optical absorption peak of AuNPs was detected via an LSPR [16]. This study demonstrated that the EVOO polyphenols can reduce Au(III) to Au(0) in an aqueous solvent. AuNPs, with a diameter smaller than 25 nm, have provided maximum absorbance at around 540 nm for all the extracts. The results were also compared with a classical method for the polyphenol quantification. ABTS and DPPH methods, related to the radical scavenging activity, and FC method, based on the reducing ability of polyphenols, were used to validate the new assay. All the analytical methods used for the comparison of the AOC showed appreciable repeatability (ABTS 6%
15%, DPPH 6%
14%, FC 6%
14%). In this alternative method, the data demonstrated that the compounds with orthodiphenols had the highest activity in reducing Au(III), followed by gallic acid, while tyrosol (monophenol) had a significantly lower activity. Thus molecules with o-dihydroxyl functionalities seem to be characterized by high antioxidant activity [16]. Vilela et al. demonstrated that there is good correlation between the production of AuNPs and the concentration of polyphenols extracted from Mentha piperita (Hierba-buena), Cymbopogon citratus (Limoncillo) tea, the aerial parts of Calendula officinalis (Marigold), Tagetes lucida (Perigon) plants, and the heads of Cynara scolymus (Artichoke). The formation of AuNPs could be achieved in a well-defined size (all ,20 nm) with a reliable electron-transfer assay for AOC [23]. In this work, the process of AuNP formation was studied using four representative polyphenolic classes: chlorogenic acid (hydroxycinnamic acid) and the flavonoids rutin (flavonol), apigenin (flavone), and hesperidin (flavanone). The presence of AuNP formation process of these selected polyphenols followed a sigmoidal curve (R2 $ 0.990) and the absorbance from SPR was measured at 540 nm. The results obtained from this newly developed assay showed good correlation with the established AOC assays (FRAP and FC; P , .05) [23]. In another study, Vilela et al. studied the biosynthesis of AuNPs from selected endogenous soy isofavone classes aglycones (genistein and daidzein) and their glycosides (genistin and daidzein). According to the results, a sigmoidal curve indicating the efficiency of AuNP production toward the assessment of the concentration value and AuNP production was observed. Moreover, a high correlation was obtained between biosynthesis of AuNPs and a well-established FC AOC assay (R 5 0.93, P ,.05). The results revealed that genistein and soy extracts

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with high aglycone content showed the highest antioxidant activity with the most intense red colloidal solutions (at 540 nm) and smallest AuNP sizes (i.e., diameter # 15 nm). According to the results, complex soy isoflavone extracts with high total isoflavone aglycone content provided higher biosynthesis efficiency than those found with high total glycoside content [50]. Liu et al. reported an AuNP-based assay for evaluating antioxidant activity of Chrysanthemum extracts and tea beverages in vitro. AuNPs were formed upon the reduction of metallic ions to zero valence gold in the media consisting of hydrogen tetrachloroaurate, cetyltrimethyl ammonium bromide, sodium citrate, and phosphate buffer. After adding 1 mL test sample in 10 min reaction at 45 C, AuNPs were formed with a characteristic wine-red color. The optical properties of AuNPs at 545 nm exhibited good correlation with antioxidant activity of test samples. As a result, the researchers concluded that the new protocol offers great promise for estimating the antioxidant activity of Chrysanthemum extracts, tea beverages, and other plant-related food [37]. Tułodziecka et al. assessed AOC of Brassica oilseeds (rapeseed), white flakes, and meal extracts, which are rich sources of natural antioxidants such as polyphenols (mainly sinapic acid, sinapine, canolol, and condensed tannins). Besides proposing a simple, quick, precise, and accurate spectrophotometric method (AuNP) based on the reduction of gold ions to NPs, the pH of the reaction mixture, incubation time and temperature, as well as the conditions of sample preparation (solvent polarity) were carefully optimized. The reactivity of sinapic acid, caffeic acid, gallic acid, ferulic acid, and quercetin present in the white flakes and meal extracts formed AuNPs in an acetic buffer medium (pH 5 4.6). After 60 min the absorbance of purple solutions was measured at 540 nm and was compared with FRAP, DPPH, and FC methods. According to the results, positive significant correlations (R2 5 0.840
0.970) were observed [51]. In all the aforementioned studies, the classical methods required the extraction step for removing the possible interfering compounds, separating the polyphenols from the lipids, and concentrating the polyphenols. The extraction can mainly be achieved by two methodologies including SPE and LLE. Both techniques require large amounts of solvents and long times to complete different steps. Furthermore, it is possible that the accuracy of the procedure can be affected by the extraction step because of the uncomplete recoveries. In this context, Della Pelle et al. studied the development of a AuNP-based colorimetric assay that involved a new type of AuNP synthesis in organic medium requiring no sample extraction. In this extraction-free assay for the determination of polyphenols in fatty matrices olive oils and chocolates were used as a sample. Virgin olive oil, a fundamental ingredient in the Mediterranean diet, is recognized as a valuable source of natural phenolic antioxidants.

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Dark chocolates and chocolate pastes are also particularly rich in polyphenols, especially in flavan-3-ol and the corresponding polymers. In this work, dimethyl sulfoxide (DMSO) was used as organic solvent to solubilize the sample and stabilize the AuNP suspension. The results obtained at 540 nm exhibited excellent correlation with the FC, which is the most commonly used method for the determination of total phenolic content [41]. For the determination of antioxidant activity in food samples there is a clear demand for easy-to-use, portable and cost-effective tests. In this context, paper-based analytical devices constitute a remarkable platform. The principle of detection of paper-based analysis relies on the analyte-driven reduction of Au ions, which are immobilized onto the surface of a small paper patch. AuNPs, having intense chromatic transition from white or pale yellow to red, are indicative of the AuNP surface plasmon absorption bands. Choleva et al. developed a new paper-based device in the form of a sensor patch that enables the determination of antioxidant activity. These analyte-driven on-paper formation AuNPs were tested for the assessment of antioxidant activity in real samples (i.e., teas and wines). It was also indicated that the sensor can be stored for long periods of time at moisture-free and low temperature conditions without losing its activity [45]. 16.3.1.2 Silver nanoparticles Due to their small size (1
100 nm) and high specific surface area, AgNPs can be used in diverse applications for electrical conductivity, chemical stability, catalytic and antibacterial activity, DNA sequencing, and surface-enhanced Raman scattering. Reducing agents such as sodium borohydride, sodium citrate, ascorbic acid, and elemental hydrogen are commonly used for the production of stable AgNPs. Initially, the reduction of various Ag 1 species causes the formation of silver atoms (Ag0), which is followed by agglomeration into oligomeric clusters. These clusters finally generate the formation of colloidal Ag particles [52]. Szydłowska-Czerniak et al. proposed a AgNP method based on the electron-transfer reaction between silver ions and antioxidants present in rapseed including phenolic acids (sinapic acid is the predominant one) and condensed tannins with high antioxidant activities. In this work, the optimization of different extraction solvents (i.e., ethanolic, methanolic, methanolic-aqueous) and their influence on the AOC of rapseed at different stages of processing (i.e., ground, flakes, press cake, meal sample) were studied for the first time. The generation of AgNP formation was elaborated in ammonium buffer medium (pH 5 8.4) at room temperature. The absorbance of the yellow-orange suspension was measured after 60 min at 405 nm. The results were compared with FRAP and DPPH as

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two well-known procedures. A positive correlation was found between the novel AgNP and the modified FRAP, DPPH, and FC methods for all extracts of the rapeseed samples (R2 5 0.7564a0.8516, P , .001). The novel AgNP-based spectrophotometric method demonstrated satisfactory values of precision (RSD 5 1.2a4.4%) and accuracy (recovery 5 95.6a104.6%) for analysis of the AOC of rapeseed samples. The findings also highlighted good correlation between the absorbance spectral changes and the redox characteristics of the phenolic acids (i.e., sinapic, caffeic, gallic acids ascorbic acid, quercetin) [53]. ¨ zyu¨rek and team evaluated a colorimetric method for the sensitive O detection of polyphenols (i.e., flavonoids, simple phenolic, and hydroxycinnamic acids). The sensing platform relied on the citrat-stabilized Ag seed growth mediated by polyphenols. In this study, Ag1 ion-reducing ability of polyphenols at 25 C for 30 min was measured after the AgNP seed generated in the presence of trisodium citrate. Ag seeds, where electron transfer takes place, generated a very intense surface plasmon resonance absorption band of AgNPs at 423 nm. The developed method was used to screen the total AOC of some commercial fruit juices and herbal teas, revealing that interference between the phenolics and the common food ingredients (i.e., oxalate, citrate, fruit acids, amino acids, and reducing sugars) does not effect the assay performance. The results showed that spectral changes and plasmon absorbance well correlate with the reducing abilities of antioxidant compounds and provided linearity with the concentration [52]. Della Pella and team reported another optical method for the determination of AOC based on the ability of natural polyphenols to reduce Ag(I) and produce AgNPs(0). The researchers studied the reactivity of stabilized AgNPs(0) with 15 polyphenols belonging to different chemical classes (i.e., caffeic acid, catechin, catecol, chlorogenic acid, cumaric acid, epicatechin, epigallocatechin, ferulic acid, gallic acid, kaempferol, myricetin, phlorizin, quercetin, rutin, and trolox) and nine different samples (i.e., digestive infusion, fennel infusion, lemon tea, pink forest infusion, relax infused, sogni d’oro camomile, classic tea, green tea, and vanilla tea). Various pH and temperature values were examined to realize the optimum conditions for the developed method, for which pH 8.4 and 45 C turned out to be ideal. The formation of AgNPs was followed by the absorption band of LSPR at 420
440 nm. The research outcomes of the newly developed method were compared with ABTS, FC, and AuNP-based methods. The results demonstrated that the AgNP-based assays in all cases showed the highest correlation with ABTS (R2 5 0.956, P 5 , .0001) [54]. 16.3.1.3 Other metallic nanoparticles In recent years, other metallic NPs have also been introduced for optical sensing of the phenolics in food. Ceria NPs have been used for

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the detection of antioxidants by immobilizing them in sensors. For example, Sharpe et al. attached ceria NPs onto a filter paper to fabricate an active ceria-based sensing platform that provides a colorimetric measurement. The principle relies on the ability of antioxidant compounds to reduce cerium on the NP surface from Ce(IV) to Ce(III). When antioxidants interact with the ceria sensors in a sample, color change is induced as a result of redox and surface chemistry reactions. The observations claimed that this color change is proportional to the antioxidant concentration of the sample. The ceria NP- attached sensor was employed for the detection of common antioxidant compounds, including ascorbic acid, gallic acid, vanillic acid, quercetin, caffeic acid, and epigallocatechin gallate (EGCG). The developed nanoceria functionalized paper as a portable sensing platform was successfully applied for the assessment of antioxidant activity in teas and medicinal mushrooms without requiring any reagents and specialized equipment [11]. There has been increasing interest in iron oxide nanoparticles (IONPs) because of their unique physicochemical properties such as chemical stability and nontoxicity. For this reason, many research groups have started to synthesize IONPs by green methodologies in order to reduce or eliminate hazardous substances [55]. IONPs have great potential in biomedical and technological applications as food and antimicrobial additives and drug carriers [56,57]. Using IONPs, Szydłowska-Czerniak et al. developed a method based on the reduction of ferric ions by oil antioxidants in acidic medium and the formation of yellow solutions. This IONP-based method was validated using sinapic acid, caffeic acid, gallic aci, ferulic acid, vanillic acid, and trolox as standard antioxidant solutions. Upon addition of the extract after 50 min, the absorbance of IONP yellow-orange solutions were measured at 396 nm. The results obtained by Fourier transform infrared spectroscopy confirmed the presence of sinapic acid adsorbed on the IONP surface. To determine the antioxidant capacities of the acetonic, ethanolic, and methanolic extracts of rapeseed oils, a novel IONP-based method and two conventional methods (FRAP and DPPH) were compared and a significant correlation was found between the different methods [10]. Rhodium nanoparticles (RhNPs) have also been used for the determination of antioxidant activity and phenolics. In this direction, a new approach was reported by Gatselou et al. based on their interaction of phenolics with citrate-capped RhNPs. Phenolic compounds (i.e., catechins, gallates, cinnamates, and dihydroxybenzoic acids) have changed the size of RhNPs and the analyte-specific spectral and color transitions in the NP suspensions have increased due to the localized surface plasmon resonance of RhNPs. The dark green-brown color of the citratRhNPs exhibited strong broad adsorption bands in the UV region near 200 nm. Moreover, the researchers observed new absorbance peaks at

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350 and 450 nm when RhNPs reacted with phenolic compounds such as dithydroxybenzoate and trihydroxybenzoate derivatives. On the basis of these findings, the developed RhNPs-based photometric methods enabled to obtain a fingerprint of the total phenolic content and total catechin content of tea samples. The RhNPs-based photometric assays showed significant correlation with the commonly accepted methods (i.e., FC and aluminum complexation assay). The results from RhNPsbased photometric methods exhibited significant linear correlation with the total phenolic content (R2 5 0.9273, P 5 .05) determined by the FC assay and the total catechin content determined by the aluminum complexation assay (R2 5 0.8326, P 5 .05) [47]. 16.3.1.4 Quantum dots QDs have recently been the focus of attention for the development of diagnostic tools in many research areas because of their highly desirable features, including broadband excitation, narrow bandwidth, and high intensity emission [19,20,25,38,58,59]. To make QDs applicable for studying biological systems, their surfaces are generally modified with a ligand such as mercaptopropionic acid or cysteine [1,6,60]. Graphene, a single layer of carbon atoms, tightly bound in a hexagonal honeycomb lattice is an allotrope of carbon in the form of a plane of sp2-bonded atoms with a molecular bond length of 0.142 nanometers. Layers of graphene stacked on top of each other form graphite, with an interplanar spacing of 0.335 nm. The separate layers of graphene in graphite are held together by van der Waals forces, which can be overcome during exfoliation of graphene from graphite. GQDs, which are emerging luminescent carbon-based nanomaterials, have lately attracted immense attention among the scientific community due to their optical and electronic properties. GQDs, having 3
20 nm range diameters, consist of graphene sheets with lateral size smaller than 100 nm in single, double, and multiple layers [25], and these materials exhibit special properties such as low toxicity, high biocompatibility, and high fluorescent activity. They can be used to fabricate photovoltaic devices, bioimaging instruments, and biosensors. GQD photoluminescence (PL) generally ranges from blue to green or, less commonly, yellow to red. While smaller GQDs have longer PL emission wavelengths, the larger ones have shorter PL emission wavelengths. Of note, pH affects the emission intensity: some GQDs prepared under alkaline conditions exhibit strong PL, whereas others obtained under acid or neutral conditions exhibit maximal PL emission [1,61]. Benı´tez-Martı´nez and Valca´rcel developed a method for the determination of the phenol fraction of olive oil using an optical nanosensor. A GQD-based sensor obtained by pyrolysis of citric acid was developed for this purpose, where the principle of the method relied on the formation of stacking and noncovalent interactions and measuring quench resultant right after the reaction between phenols and GQDs. This

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optical method provided the LODs lower than 0.12 mg/L for the analytes with a high precision (1.7% .RSD) [1]. Akshath et al. aimed to utilize CdTe QDs for the ultrasensitive detection of individual and total polyphenols by monitoring laccase activity based on charge transfer PL quenching using the QD-laccase enzyme system. They also studied the interference of metal ions (i.e., aluminum, copper, zinc, iron etc., which may be present in trace amounts in plant extracts) with QD fluorescence. This work was claimed to be the first report that utilized QDs-based optical sensing of individual and total polyphenols with a detection limit of 1 ng/mL. The developed method was more efficient than those of other methods reported in the literature in terms of sensitivity, specificity, and selectivity. Moreover, the method was able to offer simultaneous measurements of different macromolecular systems [9]. QDs can be used as optical labels to detect polyphenols. In recent years, there has been growing interest in the construction of Laccase-based biosensors using nanomaterials due to their unique optical properties. QDs, having colloidal nanocrystalline structure and semiconductor nature, are superior materials for establishing QD-based biosensors due to their unique spectral properties such as high emission quantum yield, sharp emission spectra, broad absorption spectra, photostability, and tunable emission frequencies [20,58,59]. When electron acceptors like quinones adsorb on the QD surface, acceptors can lead to an efficient electron transfer and thus the quenching of QD fluorescence [6]. Hemmateenejad et al. synthesized L-cysteine-capped CdTe QDs for use in the assessment of antioxidant activity of a series of polyphenolic compounds, including quercetin, tannic acid (TA), caffeic acid, gallic acid, naringin, trolox, and four different tea samples. They developed a detection assay based on measurement of the inhibitory effect of the antioxidant/polyphenolic compounds on the UV-induced bleaching of chiral CdTe QDs with L-cysteine capping to evaluate the antioxidant/polyphenolic activity. The production of ROS under UV irradiation may be the main cause of the photobleaching of QDs. In this study, QDs exhibited excellent photostability without any UV exposure. However, under the UV irradiation, the excited QDs, by absorbing photons, reacted with the surrounding oxygen molecules and generated ROS. Once ROS are produced, they can initiate the oxidation of QDs. The photobleaching effect induced by ROS could be reduced in the presence of antioxidant/polyphenolic compounds. According to the results obtained, the QD-based assay showed very good correlation with the data acquired by FC assay [6].

16.3.2 Electrochemical sensing of polyphenols and antioxidant activity In recent years, a great number of electrochemical analyses have been devised for the direct determination of antioxidants as polyphenols or

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16. Chemical sensing of food phenolics and antioxidant capacity

AOC in food and beverages [14]. When considering the complexity of food matrix and the possibility of synergistic interactions among the polyphenolic compounds present in a sample, the total AOC determination is preferred instead of measuring individual antioxidants. Dealing with this fact, electroanalytical methodologies have become one of the most favorable options for the analysis of phenolic compounds [8]. Electrochemical nanosensing approaches offer versatile tools for fast and low-cost analysis of antioxidants in real samples for the majority of cases. The use of electrochemical nanosensing devices for the determination of total AOC is a good alternative to the optical methods because they do not require expensive reagents and equipment, while they provide high precision and sensitivity [27]. Moreover, having high surfaceto-volume ratio and electrocatalytic properties, the use of nanomaterials allows significant improvement in these electrochemical systems in terms of sensitivity and selectivity [22]. Besides these advantages of the electrochemical techniques, they also allow working with colored samples, which is not possible by spectrophotometric techniques [62]. The use of electrochemical tools offers some advantages such as short detection time, small sample volume, high accuracy, simplicity, and lack of interference from colored samples that avoid time-consuming pretreatments [14,22]. Furthermore, these techniques allow obtaining a large number of experimental parameters that help to characterize the phenolic compounds and quantify them with very low detection limits [15,63]. Electrochemical methods, including controlled potential techniques, electrochemical sensing, and biosensing, are based on the measurement of OH groups of the phenolics and their electron transfer behavior during the redox reactions [6,14]. The total reducing power is defined H. atom or electron transfer process, in other words, the ability of certain molecules to act as electron donors or protons receptors in oxidation reduction reactions. This radical scavenging activity and the reduction power during redox reaction is directly related to electroactive groups [62,63]. For the determination of total antioxidant activity CV, SWV, and DPV are the most widely used electrochemical techniques, which we discuss in detail in the following subsections. The antioxidant activity depends on the electron donor ability and is feasibly described by peak potentials (Ep) and also peak currents (Ip) [62], which are directly correlated with antioxidant activity [15,64]. Peak potential and current intensity are the most important factors for the determination of AOC. The higher peak currents indicate greater rate and number of electron transfer, which means the presence of more electroactive polyphenol species in a sample [15,62,65
67]. 16.3.2.1 Cyclic voltammetry CV is a commonly used technique for the determination of total AOC of low-molecular-weight antioxidants present in various samples such as

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16.3 Novel sensing methods of total phenolics and antioxidant capacity

edible plants, fruits, plasma, and biological fluids [68]. This technique, acquiring qualitative information about the properties and characteristics of the electrochemical processes, provides considerable information about the thermodynamics of redox processes and kinetics of heterogeneous electron-transfer reactions [27,69,70]. Some works in the literature used CV for the determination of antioxidant activity with modified or unmodified working electrodes. Different types of inorganic and organic nanomaterials as well as biological composites have been used for the modification of working electrode surface, especially CNTs and glassy carbon due to their proven electrochemical properties. Improving the signal-to-noise ratio by minimizing the nanofaradiac component of the current provides higher sensitivity with lower LODs and faster electron transfer kinetics than those of unmodified electrodes [18,71]. While performing these studies, the most important parameters obtained from cyclic voltammograms are Ip and Ep, where Ep is associated with the type of reductant, and the low oxidation potentials correlates with greater facility or strength of electron donation of the molecule [72], thus acting as an antioxidant. Ip is directly proportional to the concentration of the antioxidant concentration. Ip value is affected by scanning speed, concentration of electroactive species, and diffusion properties of electrochemically active molecules at the electrode surface [15,17,27]. Epc is the potential where oxidation of the species in the electrolyte occurs and Epa is the potential where reduction occurs. Ipc and Ipa are the peak currents from the oxidation and reduction [73]. The redox potential of the redox couple is defined as the mean value of Epc and Epa. A typical example for a reversible redox reaction is shown in Fig. 16.2.

15 Epc Current (µA)

10 ipc

5 0 ipa

-5

Epa

-10 1.1

0.9

0.7

0.5

0.3

Potential (V vs Ag/AgCl)

FIGURE 16.2 Typical cyclic voltammogram where Ipc and Ipa show the peak cathodic and anodic current, respectively, for a reversible reaction.

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16. Chemical sensing of food phenolics and antioxidant capacity

Many electrochemical methods have been developed for the detection of polyphenols using carbon-based electrode coated by composite nanostructured materials including CNTs, graphene, and imprinted conductive polymers [8,27,74]. To enhance surface area and selectivity, nanomaterials can be produced at different size and shape. While single-walled carbon nanotubes are comprised of one-layer cylinder graphene, double-walled carbon nanotubes are a blend of two-layer singlewalled cylinders, and multiwalled carbon nanotubes (MWNTs) consist of multiple rolled layers of graphene (Fig. 16.3). Screen-printing is a well-established technology to produce disposable, inexpensive, simple, and portable biochemical, sensors some of which are commercially available in the market [75]. Screen-printed electrodes have been used to establish such sensors [25,76]. Carbonbased conductive inks are the most widely used materials to produce screen-printed electrodes. The sensitivity and selectivity of the electrodes mainly depend on the composition of the ink used to fabricate the screen-printed electrodes, while the substrate of the screen-printed electrodes is responsible for ensuring some properties like improved water resistancy, flexibility, and high mechanical strength [77]. Usually, ceramic or plastic substrates are used for the fabrication of SPEs by printing or spreading a conductive ink on a suitable substrate. Paper is an alternative substrate for the fabrication of SPEs as it has some characteristics advantageous such as low-cost, recyclability, and biodegradability. Since their discovery in 1991, carbon nanotubes have rapidly attracted considerable attention in pharmaceutical and food analyses [78]. In recent years, CNTs have also been used as electrode-modifier materials due to their unique properties, such as enhanced electronic features, a large edge plane/basal plane ratio, and capability of electron transfer reactions [79]. CNT-based sensors offer high sensitivity with

FIGURE 16.3 Carbon nanotubes (CNTs): Single-walled CNTs (left), double-walled CNTs (middle), and multiwalled CNTs (right).

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16.3 Novel sensing methods of total phenolics and antioxidant capacity

611

low LODs even in complex matrices as well as fast electron kinetics [17,63]. Several factors have an effect on the electrochemical performance of CNT-based sensors, including the pretreatment of nanotube, the surface modification of CNTs, the method of electrode attachment, and the addition of electron mediators that can influence electrode performance [80]. CNTs have high electrical conductivity, chemical stability, and mechanical strength. On the other hand, the low wettability of their surface can induce a weak surface bond. For this reason, the mechanical properties of CNTs need to be improved with other materials such as chitosan. Banica et al. worked with three glassy carbon electrodes (GCEs): one unmodified and the other two newly manufactured GCE modified with CNTs and chitosan in different concentrations (i.e., 1 mg/mL CNTs 1 5% chitosan and 20 mg/mL CNTs 1 0.5% GCE). The authors used these electrodes for the determination of total phenolic content and antioxidant activity of three different pharmaceutical forms (i.e., capsules, tablets and tincture), containing Echinacea purpurea extract from the root or aerial part of the plant. The GCEs modified with CNTs were characterized by CV to observe oxidation/reduction peaks. The CNTs/Chitosan/GCE electrodes showed good selectivity for the detection of caftaric and chicoric acid. Organic substances (i.e., resorcinol, glucose, uric acid, phenol and tartaric acid) and inorganic ions added in different concentrations into the solution of caftaric acid and chicoric acid did not significantly alter the signal of the oxidation peak. The research findings showed that the electrode modified with 1 mg/mL CNTs 1 5% chitosan provided far better results than those of the other two electrodes [17]. In 2020, Araujo et al. produced highly efficient carbon screen-printed electrodes by using colorless nail polish, graphite, and polyester overhead projector sheets to determine caffeic acid in white, matte, and fennel tea samples. To improve the analytical performance of the method, SPEs were chemically modified with multiwalled carbon nanotubes (MWCNTs) and functionalized with acidic pretreatment as a small diameter (D 3 L 6a9 nm 3 5 μm, . 95%) and larger diameter (D 3 L 100—170 nm 3 5
9 μm, . 95%), which will be referred to as SDMWCNTs and LD-MWCNTs, respectively. It was reported that modified and functionalized LD-MWCNTs electrode (ΔEp 5 163 mV and Ipa 5 83 μA) showed much better performance than SD-MWCNTs electrode (ΔEp 5 292 mV and Ipa 5 22 μA). Its linear range and LOD were reported to be 2.0a50 and 0.20 μmol/L, respectively [18]. In another recent study, Lima and coworkers used the modified GCEs on the redox processes of different antioxidants such as caffeic acid, gallic acid, chlorogenic acid, catechin, rutin, and quercetin. The researchers observed the redox processes of the antioxidants using unmodified and alumina-modified GCE. The results demonstrated that

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16. Chemical sensing of food phenolics and antioxidant capacity

residual alumina on GCE (not properly cleaned) negatively affected the electrochemical analysis of antioxidants in food samples (i.e., green tea, black tea, mint tea, hibiscus tea, rosemary tea, phytotherapic tablets, and wine) when compared to the unmodified GCE. On the other hand, the alumina, properly immobilized on GCE surface, enabled the improvement of sensitivity and detection of the AOC compared to unmodified GCE [15]. It may be the fact that the modification of the electrode with alumina immobilization is directly related to the adsorption of electrochemically active polyphenol species on the electrode surface. The higher adsorption surface provided the best interaction of molecules with the alumina particles. Consequently, increased analytical signals resulted in greater sensitivity and lower detection limits. The electrochemical oxidation of iodide in the presence of resveratrol was investigated using both platinum and glassy carbon as working electrodes, Ag/AgCl (saturated KCl) as a reference electrode, platinum (Pt) sheet as a counter electrode. It was reported that the electrochemical oxidation mechanism of iodide to iodine and the reaction of the electrogenerated iodine with trans-R at both platinum and GCEs have applicability for the quantitative analysis of resveratrol revealing sensitivities of 0.98 6 0.03 and 4.22 6 0.20 μA/μM/cm2, respectively. According to these findings, it was concluded that the sensitivity obtained with the GCE was fourfold higher than that of Pt electrode, and the LOD was nearly 2.4 times lower. GC electrode provided a linear detection range between 5 and 75 μM of resveratrol and LOD of 2.3 μM while these parameters for Pt electrode were recorded as 15a120 and 5.5 μM, respectively. The results also showed good correlation (96.8%) with the HPLC analysis of resveratrol. In addition, no interference effects were obtained when trans-R was mixed with L-ascorbic acid, p-coumaric acid, glycine, saccharose, tyrosine, and trans-cinnamic acid. In all these studies, better analytical performance was achieved in the case of using GCE rather than Pt electrode [81]. Several other research groups have also studied the electrochemical determination of the same antioxidant by CV, DPV, and SWV using GCE [17,82
85]. Furthermore, electrodes based on (1) TiO2 nanocomposites GCE [86], (2) MWCNT-modified glassy carbon [17], (3) NiO-embedded SWCNT nanocomposite carbon paste electrode [87] and (4) carboxyl hydrogel particle film GCE [75] have been reported for the detection of phenolics/antioxidants in recent years. We list further examples in Table 16.1 for the electrochemical determination of food phenolics using CV method coupled with various electrodes. 16.3.2.2 Differential pulse voltammetry CV provides a lower sensitivity compared to the pulse techniques, such as DPV and SWV. The most important advantage of these

3. Environmental applications

TABLE 16.1

Electrochemical sensors based on nanomaterials for the determination of food polyphenols by cyclic voltammetry. Reference method

Working electrode

Target analyte

Food matrix

Linear range

Limit of detection

References

GCE

Caffeic acid, gallic acid, chlorogenic acid, catechin, rutin, and quercetin

Grean tea, black tea, mint tea, phytotherapic tablets, and wine

0.1a89 μmol/L

0.005a0.01 μmol/L N/A

[15]

Pt/MnO2/f-MWCNT/GCE

Catechin

Red wine, black tea, and green tea

2a950 μm

0.02 μM

N/A

[88]

Nanocarbon-GCE, Nanodiamond-GCE, Graphene-GCE

Catechol, hydroquinone, cresol, and phenol

Tea River water

Up to 100 M hydroquinone (nanodiamond)

0.04a0.11 M (nanocarbon); 0.10
0.2 M (graphene), 0.12
0.43 M (nanodiamond)

N/A

[89]

MWCNTs/SPE

Chlorogenic acid

Coffee beans

0.48—45 M

0.34 μM

HPLC

[75]

PASA/GCE

Chlorogenic acid

Honeysuckle and pharmaceutical

0.4 M
12 μM

40 nM

HPLC

[90]

CSPE/Tyr/gallic acid

Catechin

Black and green teas

0.05a80 mM

0.03 μM

HPLC

[91]

Ethylenediamine-Co complex

Ellagic acid

Raspberry and strawberry juice

0.1a929 mM

35 mM

N/A

[92]

Protein-coated magnetic particles (MPs) (αVβ3 integrin)

Gingerol

Ginger extract

0.85a20 mM

260 nM

N/A

[93]

CPE modified with chitosan and CNTs covered with DNA

Rosmarinic acid

Rosemary extract

0.040a1.5 μM

0.014 μM

HPLC

[94] (Continued)

TABLE 16.1

(Continued)

Working electrode

Target analyte

Food matrix

Linear range 24

Limit of detection

Reference method

References

Platinium disk

Epigallocatechin gallate, rutin, catechin, caffeic acid, gallic acid, L-ascorbic acid, chlorogenic acid, theophylline, theobromine, and xanthohumol

Ten natural antioxidants

2 3 10 and 2 3 1023 M

n.d

ABTS, DPPH, FRAP

[95]

Glassy carbon disk

Tannins, flavonoids, and sterols/triterpe`nes

Thymus vulgaris

n.d

n.d

DPPH, ABTS, FolinCiocalteu

[96]

Glassy carbon disk electrode

Catechin, syringic acid, vanillic acid, gallic acid, coumaric acid, caffeic acid, flavan-3-ol ferulic acid, rutin, quercetin, and ltartaric acid

Red wines, white wines

n.d

0.1a0.5 mM

HPLC

[97]

MWCNT/SPEs FLDMWCNTs/SPEs.

Caffeic acid

White, mate, and fennel tea

2
50 μmol/L

0.20 and 0.66 μmol/L

FolinCiocalteu

[18]

CNTs/CS/GCE glassy carbon electrodes (GCE) glassy carbon electrodes modified with carbon nanotubes (CNTs) and chitosan (CS)

Caftaric acid, and chicoric acid

E. purpurea

Caftaric acid: 0.850
4.084 mM; Chicoric acid: 2.691
4.661 mM

Caftaric acid: Folin0.283 mM; Chicoric Ciocalteu acid: 0.897 mM

[17]

Glassy carbon electrode/poly (3,4-ethylenedioxythiophene)gold nanoparticles-sinusoidal voltage (GC/PEDOT-AuNPsSV)

Caffeic acid

Apple and peach juices

1 3 1025
1 3 1023

4.24 3 1026 M

SWV

[40]

Glassy carbon

Ascorbic acid

Fruit pulp, seeds, bark, leaves and roots of Bunchosia. glandulifera

Ascorbic acid 0.0a3.5 mg/mL

n.d

ABTS, FRAP, DPPH, Folin

[98]

Gold nanoparticle-modified carbon working electrode

Chlorogenic acid, gallic acid, umbelliferone, luteolin 7-O-glucoside, vitexin, and isoquercitroside

Sea buckthorn extracts, and lavender extracts

n.d.

n.d.

Total antioxidant capacity (TAC) assay based on H2O2 scavenging

[99]

Laser-scribed graphene electrodes (LSGEs)

PCM (paracetamol)

Pharmaceutical tablets vit C

0.1a10 μM

31 nM

Screenprinted carbon electrodes (SPCEs)

[100]

Carbon paste electrode with NiO-embedded single-wall carbon nanotube nanocomposite and n-methyl3-butylimidazolium bromide CPE/MBIBr/NiO-SWCNTs

Ferulic acid

Corn milk, wheat flour, and corn cider

0.06a900 μM

20 nM

HPLC

[101]

(Continued)

TABLE 16.1

(Continued)

Working electrode

Target analyte

Food matrix

Linear range 23

Limit of detection 24

μM

Reference method

References

N/A

[102]

Ce-TiO2/carbon nanotube composite Ce-TiO2/CNTs

Caffeic acid

n.d.

10
10.0 μM

3.3 3 10

Glassy carbon

Quercetin

Apple peel extract

Quercetin 0.3— 4.8 mg/L

n.d.

FolinCiocalteu

[87]

Glassy carbon

Ortho-dihydroxy-phenol and gallate groups

Fruit tea (Hibiscus flower, rose hip fruit, apple fruit, and other (mostly wild berries, blackberry leaf, citric acid, and cinnamon)

n.d.

n.d.

FRAP, ABTS and DPPH assays

[71]

CPE modified with chitosan and CNTs covered with DNA, Chitosan/carbon nanotube composite-modified carbon paste electrode covered with DNA; CSPE/Tyr/gallic acid electrodes, Carbon screen-printed electrode/Tyrosine/Gallic acid; Ethylenediamine-Co complex, Ethylenediamine ligand based cobalt (II) complex modified glassy carbon electrode; GCE, Glasy Carbon Electrode; MPs, Protein-coated magnetic particles (transmembrane protein αVβ3 integrin); MWCNTs/SPE, multi-walled carbon nanotubes modified screen-printed electrode; PASA/GCE, poly(aminosulfonic acid) modified glassy carbon electrode; Pt/MnO2/f-MWCNT/GCE, Facile synthesis strategy of MnO2/carbon nanotubes decorated with a nanocomposite of Pt nanoparticles.

16.3 Novel sensing methods of total phenolics and antioxidant capacity

617

techniques is the ability to detect the faradaic current in the absence or minimal presence of the capacitive current. The difference between cyclic and pulse technique is the virtual elimination of the capacitive current, and therefore the faradaic current is highlighted using DPV and SWV, because the more intense current increases by these technique [15]. DPV technique involves applying amplitude potential pulses on a linear ramp potential. In this technique, a base potential value is chosen at which there is no faradaic reaction and is applied to the electrode. The waveform, which is a series of pulses, increases along a linear baseline, meaning that the base potential is increased between pulses with equal increment. The current is immediately measured before and after the pulse application and the difference between them is recorded [103]. Measuring the current immediately before each potential pulse and plotting it as a function of potential aids in minimizing the measurement of background (charging) current. This fact increases the sensitivity and decreases the LODs of the analyses [104,105]. Glassy carbon, carbon paste, and modified glassy carbon are some of the most commonly used electrode types in DPV techniques. Glassy carbon and carbon paste electrodes were utilized in the study of wine samˇ ples for catechin quantification with a LOD of 1.77 mg/L (Seruga et al., 2011), as well as for the analysis of gallic acid in the same drink with a LOD of 0.148 3 1027 M [106]. Such electrodes are often modified with certain nanomaterials to improve the efficiency of measurements. The most commonly preferred electrode modifiers are carbon nanomaterials such as nanotubes, graphene, graphene oxide, and their combination with metal and metal oxide NPs, polymers including electropolymerized materials and molecularly imprinted polymers. The modification of GCEs can be achieved in different ways. For example, Ziyatdinova et al. developed a technique by applying CNTs to the electrode’s surface for the determination of gallic acid, catechin, and (EGCG) [107]. GCE modified with MWCNT and polyquercetin (polyquercetin/MWCNT/GCE) was successfully applied for the evaluation of AOC in tea. The linear detection ranges of 0.50a10 and 10a750 mM for gallic acid, 0.10a10 and 10a250 mM for catechin, and 0.050a10 and 10a100 mM for EGCG were obtained in differential pulse mode with the LODs of 0.10, 0.024, and 0.014 mM, respectively. There existed a positive correlation between antioxidant activity and total phenolic content using this highly sensitive voltammetric method. The analytical characteristics obtained by polyquercetin/MWNT/GCE were found to be much better than the bare GCE and other modified electrode (MWNT/GCE). Thus the highest Ip value was also obtained with polyquercetin/MWNT/GCE electrode (4.3 6 0.1 μA for gallic acid; 0.64 6 0.02 μA for catechin; and 3.15 6 0.08 μA for ECGC). The electrode modification with electropolymerized quercetin also provided

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16. Chemical sensing of food phenolics and antioxidant capacity

significant sensitivity with LODs of 0.10, 0.024, and 0.014 μM for gallic acid, catechin, and EGCG, respectively [107]. pH is an important parameter to obtain accurate readings in DPV. Badea et al. electrochemically detected gallic acid under the different analytical conditions at pH values of 5.8, 7, and 8. Lazar ORP-146C reduction-oxidation microsensors and screen-printed carbon sensors were used for qualitative and quantitative determination of gallic acid via DPV. The researchers validated their proposed methods by analyzing some real samples such as wine, green tea, apple juice, and serum fortified with gallic acid. The results obtained at pH 5.8 were found to be more stable and sensitive with lower LOD values. Highly statistic correlations were found between DPV and DPPH (R2 5 0.9977) and between redox microsensor and DPPH (R2 5 0.9998) methods. According to the overall results, the two sensors (i.e., Lazar ORP-146C reduction-oxidation microsensors and screen-printed carbon sensors) were found to be superior due to their advantages such as fast responses, low detection limits, wide dynamic ranges, and good selectivity [108]. Various types of electrodes modified with inorganic and organic materials have been coupled with the DPV methods for the detection of total polyphenolic content in a variety of samples [63]. Table 16.2 summarizes some of these works with their key parameters. 16.3.2.3 Square-wave voltammetry SWV is considered as a special type of DPV [104]. SWV is a largeamplitude differential technique that uses a combined square wave and staircase potential applied to a working electrode [27,132]. High sensitivity screening can be obtained by SWV due to the minimal contribution of nonfaradaic currents. The use of a differential current plot instead of reverse current plots and significant time evolution between potential reversal and current sampling make this technique more sensitive than other electroanalytical techniques. For this reason, SWV is utilized as an electrochemical measurement technique in various research domains, including food, medicine, and environmental [133]. SWV has been used to identify and quantify phenolic compounds within different food and beverages’ matrices, with very low detection limits. Piovesan et al. established electrochemical detection strategies for the determination of total phenolic compounds in vegetables (i.e., spinach, cabbage, broccoli, chicory) by using a carbon paste electrode modified with poly(vinylpyrrolidone). In this study, SWV method provided the highest sensitivity for kaempferol with an LOD of 40 nmol/L compared to linear scan voltammetry and DPV [134]. The modification of electrodes with ideal nanomaterials facilitates and accelerates the electrochemical determination of compounds using

3. Environmental applications

TABLE 16.2

Electrochemical sensors based on nanomaterial for the determination of food polyphenols by differential pulse voltammetry.

Working electrode

Target analyte

Food matrix

Linear range

Limit of detection 27

GCE-SWCNTs

Gallic acid

Red/White wine

Gallic acid 5.0 3 1027
1.5 3 1025 mol/L

3.0 3 10

SWCNT-SubPc

Catechin

Green, rosehip fruit, Turkish and Indian black tea

0.1a1.5 μM

Pd
Au/PEDOT/rGO/GCE

Caffeic acid

Red wine

GR/CuO@Cu-BTC/GCE

Caffeic acid

F-GO/GCE

mol/L

Reference method

Reference

N/A

[109]

13 nM

N/A

[110]

0.001
55 μM

7.0 nM

N/A

[111]

Red wine

0.020
10.0 μM

0.37 nM

HPLC

[84]

Caffeic acid

Wine

0.5a100.0 μM

0.018 μM

HPLC

[112]

SPE-CB/MoS2

Catechin

Cocoa

0.12a25 μM

0.17 μM

FC and ABTS

[113]

TAPB-DMTP-COFs/AuNPs/GCE

Chlorogenic acid

Coffee, apple, honeysuckle

10 nMa40 μM

9.5 nM

HPLC

[82]

Alumina microfiber-modified CPE

Chlorogenic acid

Honeysuckle and soft drinks

28 nMa5.6 μm

14 nM

HPLC

[114]

MOF/TiO2/GCE

Chlorogenic acid

Coffee and tea

0.01a15 μm

7 nM

N/A

[86]

MCFs/CPE

Capsaicin

Hot pepper powder

0.76a11.65 μM

0.08 μM

HPLC

[115]

MgO/SWCNTs-[Bmim] [Tf2N]-CPE

Ferulic acid

Red wine and white rice

0.009a450 μM

0.3 nM

HPLC

[116] (Continued)

TABLE 16.2

(Continued) Linear range

Limit of detection

Reference method

Working electrode

Target analyte

Food matrix

Reference

ZrO2/Co3O4/rGO nanocomposite/FTO

Gallic acid, caffeic acid, protocatechuic acid

Fruit juice, rice, 6.24
477.68 nM (gallic and tea samples acid); 2.48
524.90 nM (ca eic acid); 5.40
424.96 nM (protocatechuic acid)

1.56 nM (gallic acid), 0.62 nM (caffeic acid), 1.35 nM (protocatechuic acid)

N/A

[117]

PLM/MWCNT/GCE

Gallic acid

Black and green tea, red wine

4.0 nM
20.0 M

3.1 nM

LC-Ms/MS

[118]

Fe2 O3 NPs/MWCNTs/GCE

Kaempferol

Broccoli

1
300 μM

0.53 μM

HPLC

[119]

NIPA/AA-MWCNTs-GCE

Luteolin/ baicalein

Peanuts shell, tomato

0.0001
1.5 mM/ 0.005
35 mM

0.0145 nM/ 0.0444 nM

N/A

[43]

ZnO/CNS/MCPE

Quercetin

Onion, and honey buckwheat

0.166
3.63 μM

0.04 μM

N/A

[120]

PB-rGO/TCD/AuNPs

Quercetin

Apple juice, red wine, and honeysuckle

0.005
0.4 μM

1.83 nM

N/A

[121]

poly(gallic acid)/MWCNT/GCE

Quercetin

Medicinal herbs extract

0.075
100 μm

54 nM

UV

[122]

GCE/PoPD/Pt

Rosmarinic acid, Rosemary and protocatechuic melissa extracts acid

2
10 μ M rosmarinic acid) 1
35 μM (protocatechuic acid

0.7 μM (rosmarinic acid and protocatechuic acid)

HPLC

[123]

(Continued)

TABLE 16.2

(Continued)

Working electrode

Target analyte

Food matrix

Linear range

Limit of detection

Reference method

MIS (TEOS-PTEOS-3 APTMS) Au electrode

Caffeic acid

Red and white wines

0.500
60.0 μM

0.15 μ M

HPLC

[124]

MIS (TEOS, PTEOS, APTMS) Au electrode

Chlorogenic acid

Coffee, tea samples

0.5
14 μM

0.15 μM

N/A

[125]

MIS (TEOS, PTEOS, APTMS)/ MWCNTVTMS /GCE

Chlorgenic acid

Coffee, tomato, and apple

0.08
100 μM

0.032 μM

N/A

[126]

MIP (MAA, EGDMA) -MWCNT
CPE

Gallic acid

Four different commercial juices

0.12
380.0 μM

47.0 nM

N/A

[127]

MIP/Pd/pGN-CNTs/GCE

Quercetin

Pule’an tablets, honeysuckle juice, and red wine

0.01
0.50 μM

5.0 nM

N/A

[128]

Screen-printed carbon electrode

Gallic acid

Wine, green tea, 0.1
2.0 mM apple juice, and serum fortified with GA

0.032 mM (pH 7) 23
103 μM (depending on pH)

DPPH

[108]

Electrochemical sensor based on [Cu(2) tpmc](ClO4) (4) immobilized in PVC matrix and coated on graphite (CGE) or classy carbon rod (CGCE)

Gallic acid

White, rose, and red wine samples

0.25
100 μM

0.148 μM (CGE) 4.6 μM (CGCE)

N/A

[106]

Glassy carbon electrode modified with multiwalled carbon nanotubes and poly-quercetin (polyquercetin/ MWCNT/GCE)

Gallic acid, catechin, and epigallocatechin gallate (EGCG)

Tea

10
750 μM

0.10 μM;

DPPH

[107]

Reference

(Continued)

TABLE 16.2

(Continued) Limit of detection

Reference method

Black tea, cortex 0.05
8.0 μM mouton, and urine samples

10.7 nM

N/A

[129]

Plant sample

0.3
150 μM

0.11 μM

N/A

[130]

Dioscorea plants (Yam)

5.0a80.0 μg/L

0.39 μg/L

N/A

[131]

Working electrode

Target analyte

Food matrix

Gold microclusters (AuMCs) electrodeposited on sulfonate functionalized graphene (SF-GR) electrode

Gallic acid uric acid

A modified electrode prepared by modification of the CPE with graphene nanosheets

Gallic acid

Poly-glycine/glasses carbon electrode

Quercetin

Linear range

Reference

SWCNT-SubPc, SWCNT-Subphthalocyanine hybrid material; Pd
Au/PEDOT/rGO/GCE, Pd
Au/Poly(2,3-dihydrothieno-1,4-dioxin)/rGO/GCE; GR/CuO@Cu-BTC/GCE, flower-like hierarchical graphene/copper oxide@ copper (II) metal-organic framework; F-GO/GCE, Fluorine-doped graphene oxide/Glassy carbon electrode; SPE-CB/MoS2, Carbon black/ molybdenum disulfide nanohybrid sensor; TAPB-DMTP-COFs/AuNPs/GCE, Gold nanoparticles-doped TAPB-DMTP-COFs (TAPB, 1,3,5-tris(4-aminophenyl)benzene; DMTP, 2,5dimethoxyterephaldehyde; COFs, covalent organic frameworks) composite; Alumina microfiber-modified CPE, Surface enhancement of porous alumina microfibers toward electrochemical sensing; MOF/TiO2/GCE metal, Organic frameworks/titanium dioxide nanocomposites; MCFs/CPE, Sensor for capsaicin based on mesoporous cellular foams; MgO/SWCNTs-[Bmim] [Tf2N]-CPE, MgO/SWCNTs-1-Butyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide paste electrode; ZrO2/Co3O4/rGO nanocomposite/FTO, ZrO2/ Co3O4/reduced graphene oxide nanocomposite; PLM/MWCNT/GCE, Gallic acid on poly (l-Methionine)-carbon nanotube composite electrode; Fe2O3 NPs/MWCNTs/GCE, MWCNTs-Fe2O3 nanoparticle nanohybrid-based highly sensitive electrochemical sensor; NIPA/AA-MWCNTs-GCE, Carboxyl hydrogel particle film; ZnO/CNS/MCPE, Zinc oxide supported on carbon nanosheet (ZnO/CNS/MCPE)-modified carbon paste electrode; PB-rGO/TCD/AuNPs,1-pyrenebutyrate functionalized reduced oxide graphene/mercaptoβ-cyclodextrin/Au nanoparticles composite film; poly(gallic acid)/MWCNT/GCE, Poly(gallic acid)/MWNT-modified electrode; GCE/PoPD/Pt, Poly(o-Phenylenediamine)/Pt nanoparticles-modified glassy carbon electrode; MIS (TEOS-PTEOS-3 APTMS) Au electrode, Electrochemical sensor based on molecularly imprinted siloxanes; MIS (TEOS, PTEOS, APTMS) Au electrode, Molecularly imprinted siloxane (tetraethoxysilane (TEOS), phenyltriethoxysilane (PTEOS), 3-(aminopropyl)trimethoxysilane (APTMS); MIS (TEOS, PTEOS, APTMS)/MWCNTVTMS/GCE, Nanostructured platform and imprinted sol-gel film; MIP, Molecularly imprinted polymer; MAA, Methacrylic acid; EGDMA, Ethylene glycol dimethacrylate; MWCNT
CPE, In silico design of short peptides as sensing elements; MIP/Pd/pGN-CNTs/GCE, Molecularly imprinted poly(para-aminobenzoic acid) on 3D Pd nanoparticles-porous graphene-carbon nanotubes composite.

16.3 Novel sensing methods of total phenolics and antioxidant capacity

623

SWV [80]. In a recently reported study, the SW-CNT-modified sensor dramatically increased the detection signals for quercetin compared to the bare GCE [83]. The satisfactory electrochemical performance of the modified electrode can be explained by the high surface area and the conductive nature of SW-CNTs. The CNTs can form electrical transmission paths across all nanocomposites that are responsible for the electrical conductivity. The surface roughness of the modified electrode increases the electroactive surface areas, allowing quercetin to remain in porous structures and creating more binding sites compared to the unmodified electrode. These desirables changes on the electrode surface enhance the selectivity of the sensor. Such a sensor has demonstrated excellent performance for the ultrasensitive detection of quercetin (LOD: 7.7. nM) in tea [83]. Arman et al. introduced SWV-based detection of antioxidants in various plant tea samples using a GCE modified with AuNPs and the copolymer of o-phenylenediamine-aniline (o-PDA-ANI) to increase the surface area and the conductivity during voltammetric measurements. The measurement system was based on the chemical reduction of hexacyanoferrate(III) to hexacyanoferrate(II) by antioxidants that leads to the decrement of the cathodic current intensity of hexacyanoferrate(III) in proportion to the concentration of antioxidants (i.e., gallic acid, vanilic acid, and quercetin). The SWV method was validated by DPV-cupric reducing antioxidant capacity (CUPRAC) method and no significant difference was observed between the measurements in terms of accuracy and precision. Of note, the possible interference effect of sulfite (a common food preservative) was analyzed as part of the same study. The DPV-CUPRAC method was adversely affected by sulfite interferences while the newly developed SWV method showed no sulfite interferences [64]. Concerning voltammetric experiments, besides Ep and IA, the area under the voltammogram indicates the response of more reactive phenolic antioxidants, meaning that an AOC corresponds the total area under the SWV peak. Newair et al. carried out a study using GCE and a three-electrode configuration comprising a screen-printed carbon electrode (SPCE), single-walled carbon nanotubes (SWCNTs-SPCE), and multiwalled carbon nanotubes (MWCNTs-SPCE). Several polyphenolic compounds (i.e., caffeic acid, gallic acid, catechin, and malvidin-3-glucoside) present in red wines were characterized by SWV. The first peak area expected to have the most significant electrochemical response in red wine was found between 55% and 75% of the total area value for SWCNTs- SPCE electrodes while it was between 28% and 68% for GCE. According to these results SWCNTs as working electrode material provided the best output, allowing to promote electron transfer for electrochemical reactions [76].

3. Environmental applications

624

16. Chemical sensing of food phenolics and antioxidant capacity

An electrochemical sensor based on molecularly imprinted polymer (MIP) was developed for the detection of dodecyl gallate at the surface of a GCE [79]. In this study, MIPs were used as synthetic recognition elements toward dodecyl gallate as they were generated by molecular imprinting of the target analyte in the presence of a functional monomer (o-phenylene diamine) solution. The stepwise preparation of MIP as well as the control polymer (nonimprinted polymer, NIP) was investigated by SWV using a redox marker solution (i.e., the mixture of ferrocyanide and ferricyanide). The performances of bare GCE, MIP/f-MWCNT/GCE, and NIP/f-MWCNT/GCE were compared. It was shown that the current response remarkably increased after GCE electrode was coated with f-MWCNTs and the current intensity of the oxidation/reduction peaks increased 64% in comparison to the GCE electrode. According to the current intensity of isotherm curves, it was concluded that selective cavities for dodecyl gallate were formed successfully on the MIP sensor. The MIP/f-MWCNT/GCE sensor showed good selectivity toward the target analyte. Such a sensing system represents a promising alternative for the determination of antioxidants [79]. Table 16.3 lists further recent studies to determine the AOC of samples or standards using different electrodes materials in combination with SWV method.

16.3.3 Nanomaterial-based enzyme electrodes Besides direct electrochemical oxidation, the determination of phenolic compounds can be performed enzymatically. Enzyme electrodes hold great interest because of the analytical potential of coupling selectivity and catalytic action on the sensing area. The conjugation of enzyme to solid electrodes is commonly preferred while developing biosensing devices [150,151]. Biosensors may have unique and creative configurations depending on the enzyme structure, the immobilization method [26], and the electrode material (carbon materials, gold, platinum, ITO as bare or modified) acting as support for the biological material bonding to promote electrochemical reactions [63,152]. Polyphenol oxidase (PPO) is the most commonly used enzyme on electrodes for polyphenol detection. PPO has copper as prosthetic group and catalyzes two reactions: (1) a cresolase activity, which adds a hydroxyl group to a monophenol at the ortho position in order to convert it into an o-diphenolic compound and (2) a catecholase activity that converts the diphenolic compound into quinone [153]. Depending on the substrate specificity and mechanism, PPOs are classified as different types such as tyrosinase, catechol oxidase, and laccase. PPO selectively catalyzes the oxidation of monophenols with molecular oxygen in order to generate

3. Environmental applications

TABLE 16.3

Electrochemical sensors based on nanomaterials for the determination of food polyphenols by square wave voltammetry. Reference method

Working electrode

Target analyte

Food matrix

Linear range

Limit of detection

Reference

Reduced graphene oxide (rGO)modified electrode/A glassy carbon electrode (3 mm diameter) was employed as the conductive substrate before GO modification. GO and rGO were investigated (rGO/GCE)

Gallic acid

Water and tea samples

8—400 μM

0.42 μM

FT-IR spectroscopy, CV

[85]

CPE-NiO/CNTs and ionic liquid

Quercetin and morin

Grape wine

1.0 3 1028 M

3.3 3 1029 M

N/A

[135]

GCE AgNPs-2-aminoethanethiol functionalized graphene oxide

Quercetin

Onion, apple, and capsule

0.08
400 μM

0.03 μM

N/A

[136]

CPE/MBIBr/NiO-SWCNTs

Ferulic acid (and butylated hydroxytoluene)

Corn milk wheat flour, and corn cider

0.06
900.0 μM (ferulic acid)

20 nM (ferulic acid)

HPLC

[101]

PDDA-GR-Pt/GCE

Gallic acid

Jianmin Yanhou tablets, cortex moutan, and green tea beverage

0.03
1 μM

7 nM

HPLC

[137]

CoPC-modified SPCE

Genistein

Derris scandens extracts

2.5a150 μM

1.5 μM

N/A

[138]

SPE (coarsely stepped cyclic SWV)

Capsaicin

Chili-derived sauces

0a5000 μM

1.98 μM

N/A

[139]

SPE-CB

Gallic acid

n.d.

1.0 3 1025
1.0 3 1024 mol/L

1.0 3 1026 mol/L

N/A

[140] (Continued)

TABLE 16.3 (Continued) Working electrode

Target analyte

Food matrix

Linear range 25

Limit of detection 26

CPE-SAMN/TA

Maghemite NPstannic acid Hydroquinone

Blueberry

2.5 3 10
5.0 3 1024 mol/L

8.6 3 10

Screen-printed sensors with carbon working electrodes

Ascorbic acid

Blood serum

n.d

Nucleotide-based biosensors with glassy carbon transducer

Ascorbic acid

Flavored water

Biosensor with purine base immobilized on glassy carbon

Ascorbic acid

4-[(4-decyloxyphenyl)-ethynyl]1- methylpyridinium iodidemodified glassy carbon

mol/L

Reference method

Reference

FolinCiocalteu

[141]

0.09 mM

FRAP

[142]

0.15
25 mg/L

0.04a0.035 mg/L

FRAP

[143]

Flavor and flavored waters and beverages

0.50a2.50 mg/L

0.29 mg/L

DPPH

[144]

Caffeic acid

Total polyphenol content of mate herb extracts

9.9 3 1027
3.8 3 1025 M 4.7 3 1025
9.9 3 1025 M

9.0 3 1027 M 8.7 3 1026 M

FolinCiocalteu

[145]

A single-walled carbon nanotube (SWCNT)-modified glassy carbon electrode (GCE)

Quercetin

Tea sample/tea samples were black tea, green tea, and local purple basil tea

7.7a25.6 nM

0.01
100 μM

N/A

[83]

Pt-PDA@SiO2/GCE

Quercetin

n.d.

0.050
0.383 μM

0.016 μM

N/A

[146]

Poly(vinylpyrrolidone)/CPE

Quercetin

Pharmaceutical formulation

0.5
5.5 μM

0.17 μM

UV-Vis

[147]

AuNP-modified GCE coated with poly(ophenylenediamineaniline film) (GC/P(o-PDA-coANI)-Aunano electrode

Gallic acid, vanillic acid, and quercetin

plant tea samples/green tea, lime, and coral moss such as green tea, lime, and coral moss

5 3 102 5a5 3 1024 Gallic acid; 5 3 1025a5 3 1024 Vanilic acid; 2 3 1025a2 3 1024 mol/L Quercetin

1.45 3 1025 mol/L Gallic acid; 1.4 3 1025 mol/L Vanilic acid; 5.75 3 1026 mol/L Quercetin

DPVcupric reducing antioxidant capacity (CUPRAC) method, TEAC

[64]

GCE Poly(Alizarin Red S)

Caffeine (CAF) and vanillin (VAN)

Food and beverage samples

0.5
250 μM for CAF and 10
450 μM for VAN

0.06 1 0.8 μM

N/A

[148]

CPT-BDD (cathodically pretreated boron-doped diamond)

Chlorogenic acid and vanillin Caffeine

Food and beverage samples

2.8 3 1026
1.7 3 1024 Chlorogenic acid 3.3 3 1026
3.3 3 1024 Vanillin 5.2 3 1027
2.1 3 1024M Caffeine

4.0 3 1027; 3.8 3 1027, 1.5 3 1027 M

N/A

[149]

CPE/PVP

Kaempferol

Plant extract

0.50—6.0 μmol L21

160 nmol/L

FolinCiocalteau

[134]

The GCE was modified with fMWCNT and the MIP synthesis Molecularly imprinted polymer (MIP) film for dodecyl gallate detection at the surface of a glassy carbon electrode (GCE)

orthophenylenediamine/ dodecyl gallate

n.d.

0.50 to 8.0 3 1029 mol/L

Dodecyl gallate 0.22 3 1029 mol/L

N/A

[79]

Screen-printed electrodes glassy carbon electrode (GCE) of 3 mm diameter/Both GCE and screenprinted carbon electrodes (bare SPCE, SWCNTs-SPCE, and MWCNTs-SPCE)

Gallic acid, caffeic acid, catechin, and malvidin-3glucoside

Red wine

n.d.

0.1 mM

ABTS, FC, HPLC

[76]

CoPC modified SPCE, Cobalt(II) phthalocyanine-modified screen-printed electrochemical sensor; CPE-SAMN/TA, Electrocatalytic nanostructured ferric tannates; CPE/PVP, carbon paste electrode (CPE) modified with poly(vinylpyrrolidone) (PVP); GCE (NGR-NCNTs)/NGR-NCNTs, nitrogen-doped graphene/carbon nanotubes./GCE 5 glassy carbon, Electrodeposited nitrogen-dopedgraphene/carbon nanotubes nanocomposite; PDDA-GR-Pt/GCE, Pt nanoparticle decorated polyelectrolyte-functionalized graphene modified electrode; SPE-CB, Communications carbon black as successful screen-printed electrode modifier.

628

16. Chemical sensing of food phenolics and antioxidant capacity

o-diphenols, which are then oxidized to o-quinones. The resulting quinones are reduced at low potential on the transducer surface generating a current signal proportional to the phenolic compound [24]. The most extensively used enzymes for the development of biosensors are laccase, tyrosinase, and peroxidase. Laccase is coppercontaining enzyme found in plants and fungi. This oxidoreductase enzyme catalyzes the degradation of organic and inorganic substrates, mainly phenolic derivatives, polyphenols, aminophenols, benzenethiols, aromatic diamines, and polycyclic aromatic hydrocarbons (PAH), with the reduction of oxygen to water [9]. It is highly advantageous that laccase does not require H2O2 as a cosubstrate and any cofactors for its catalysis. Laccase provides a wide range of specificity for reducing substrates and acts as a good source for immobilization on different electrodes to design electrochemical biosensors for the detection of phenolic compounds [28,60,151,154,155]. Immobilization of enzymes on the electrode surface is a required critical step for successful biosensor design. In all these steps, loading the highest enzyme amount and minimizing its leaching from the electrode surface are the major goals. Enzyme stability and activity should also be maintained upon binding. A major problem encountered during enzymatic biosensor application is related to the surface passivation of transducer by the electropolymerization of the enzymatic products. The surface passivation of transducer affects the analytical performance of electrodes [63]. Therefore even if the substrate of the working electrode plays a key role in the detection sensitivity, appropriate immobilization methods are essential to overcome complexity of the signal and poor selectivity of electrode surface [28]. Several promising materials have been used to perform the chemical modification of the electrode such as carbon materials (SWCNTs, MWCNTs), graphene nanosheets, gold, platinium, acetylene black NPs, and indium tin oxide (ITO). Enzymes can be effectively immobilized on these materials by simple adsorption such as electrostatic interaction, entrapment, cross-linking, covalent bond, etc. [14]. Enzyme conjugation to the aforementioned nanomaterials is an important subject for successful determination of phenolics as well as antioxidants because the immobilization strategy strongly influences the final hybrid properties and the catalytic performance. Magro et al. developed a functional core-shell hybrid nanoparticle (SAMN@TA) that exhibited specific electrocatalytic and surface properties. A shell of nanostructured ferric tannates was spontaneously developed on the surface of naked maghemite nanoparticles (SAMNs, the core) by a simple wet reaction with TA. Laccase from Trametes versicolor (TvL) was successfully self-assembled on the ternary functional nanobioconjugate (SAMN@TA@TvL) for the detection of polyphenols in blueberry extracts. The final biosensor configuration was characterized by simply

3. Environmental applications

16.3 Novel sensing methods of total phenolics and antioxidant capacity

629

immobilizing SAMN@TA@TvL on a commercially available gold electrode via an external magnet (Au/SAMN@TA@TvL). It was reported that the SAMN@TA@TvL ternary hybrid immobilized on a gold electrode (gold/SAMN@TA@TvL) provided great sensitivity (868.9 6 1.9 nA/μM) with a LOD of 81 nM and linear range of 0.1
10 μM for hydroquinone. This sensor was also tested for 9 h per day by storing overnight at 4 C, and it was stable for 6 months [151]. A similar approach was followed by Ibarra-Escutia et al. using polyvinyl alcohol photopolymer (PVA-AWP; azide-unit pendant watersoluble photopolymer) on the disposable graphite screen-printed electrodes to immobilize laccase from Trametes versicolor (LTV). The optimized laccase biosensor (LTV-screen-printed electrode) showed good reproducibility and operational stability, and it was calibrated for o-, m-, and p-diphenol as well as caffeic acid. LTV-screen-printed electrode was used for the determination of the equivalent phenol content in tea infusions (i.e., arnica, basil, orange leaves, fennel, mint, hibiscus, and palo azul). The highest sensitivities were recorded as 9.44, 18.8, and 24.9 nA/μM for hydroquinone, catechol, and caffeic acid, respectively. Moreover, the developed sensor could maintain its relative signal up to 60% during one year storage under 14 C [156]. In a recent study, Zrinski and coworkers introduced a laccaseimmobilized gold nanoparticle/graphene nanoplatelet-modified SPCE for the determination of hydroquinone and other phenolic compounds. The optimum electrocatalytic activity of the biosensor was obtained toward oxidation of hydroquinone at a potential of 20.05 V (vs Ag/ AgCl, 3 M KCl) in phosphate buffer as supporting electrolyte (0.1 M, pH 7.0). The laccase immobilized onto a gold nanoparticle/graphene nanoplatelet-modified screen-printed carbon electrode (LACC/AuNP/GNPl/SPCE) remarkably enhanced electron transfer kinetics. This can be attributed to some properties of the nanomaterial, such as metallic conductivity in combination with large surface area of AuNP and GNPl. The sensor provided the best electrocatalytic activity in a phosphate buffer solution at pH 7.0 with a linear range of 4
130 μM and LOD of 1.5 μM. The results were found to be in good agreement with the output of the reference method (TEAC assay) with an R2 value of 0.9989. With its simplicity, rapidity, and cost-efficient properties, such a sensor is a promising alternative to standard analytical methods for the quantification of phenolic antioxidants in complex food matrices such as wine and sirup [28]. A great variety of enzyme-based approaches for the determination of phenolics have been reported in the literature. Table 16.4 summarizes some of these works with their important characteristics.

3. Environmental applications

TABLE 16.4 Enzyme-based electrochemical sensors for the determination of food polyphenols. Working electrode

Enzyme

Immobilization

Target analyte Food Matrix

Limit of Detection

Linear Range 28

28

2.6 3 10

Reference

Carbon black paste electrode/ SWV/CBPE/CMB-LCE/1carboxymethylated derivative of botryosphaeran (carboxymethylbotryosphaeran, CMB)/ carboxymethylbotryosphaeran (CMB)/CBPE carbon black paste electrode/laccase (LCE)

Laccase from Botryosphaeria rhodina

Laccase from Botryosphaeria rhodina MAMB-05 was covalently immobilized on carboxymethylbotryosphaeran by 1-ethyl-3-(3dimethylaminopropyl) carbodiimide and Nhydroxysuccinimide (EDC/ NHS) in aqueous solution.

Quercetine

Beverages, pharmaceuticals, and biological samples

4.98
50.0 3 10

Carbon screen-printed electrodes (CSPE)

Tyrosinase from mushroom

(CSPE/Tyr/GA), entrapment The enzyme was immobilized by coreticulation with glutaraldehyde on carbon screen-printed electrodes (CSPE).

Catechin

Black and green tea

0.05a23.0 μM

0.03 μM

[91]

GCE-GRO-MWCNTs

Laccase from: Trametes versicolor/ Tyrosinase from mushroom

BSA reticulated with GA/ chitosan entrapping

Catechol

Fruit juices

Up to 300 μM 1.0 3 1026
3.0 3 1024 mol/L

0.3 μM

[154]

GRQDs-MoS2/nanoflakes

Laccase from: Trametes

Electrostatic interaction laccase/GRQDs

Caffeic acid

Red wine

caffeic acid: 3.8 3 1027
1.0 3 1024 mol/L

3.2 3 1027 mol/L

[60]

mol/L

mol/L

[155]

Fullerene-AuNPs

Laccase from: Trametes versicolor

Covalent onto Au-SAM/ AuNPs-Linker/Fullerenols/ TvL

Gallic acid

Red and white wine

Gallic acid: 3.0 3 1025
3.0 3 1024 mol/L

6.0 3 1026 mol/L

[157]

Screen-printed electrodes (SPEs)/ferrocene-modified SPE immobilizing cross-linked tyrosinase with BSA
GA

Tyrosinase

Cross-linked /Cross-linking of tyrosinase by BSA
GA: (bovine serum albumin (BSA); glutaraldehyde (GA))

Catechol

White and red wines

1.0 3 1026
100.0 3 1026 M

4 μM

[158]

PhOSubPc-Tyr /Tyrosinase and Laccase biosensors (deposited on ITO glass)

Tyrosinase and laccase

π-π interactions between subphthalocyanine rings and the active sites of the enzymes

Catechol and hydroquinone

Red wine

2 3 1025 mol/L to 5 3 1027 mol/L

1 3 1027 mol/L

[159]

Gold/SAMN@TA@TvL

Laccase from Trametes versicolor

(SAMN@TA@TvL) was successfully self-assembled by incubating laccase

hydroquinone

Blueberry extracts

0.1
10 μM

81 nM

[151]

Polyvinyl alcohol photopolymer PVA-AWP (azide-unit pendant watersoluble photopolymer) onto disposable graphite screenprinted electrodes (SPE)

Laccase from Trametes versicolor (LTV)

Entrapment in polyvinyl alcohol film

Catechol, caffeic acid, and hydroquinone

Tea infusion

Catechol 0.5
175 μM Caffeic acid 0.5a130 μM Hydroquinone 1.1
30 μM

Catechol 0.5 μM Caffeic acid 0.5 μM Hydroquinone 1.1 μM

[156]

Laccase immobilized onto a gold nanoparticles/graphene nanoplatelets-modified screenprinted carbon electrode (LACC/AuNP/GNPl/SPCE)

Laccase

Laccasse immobilized onto AuNP/GNPl-modified screenprinted electrodes

Hydroquinone Wine and blueberry sirup

from 4 to 130 μM

1.5 μM

[28]

632

16. Chemical sensing of food phenolics and antioxidant capacity

16.3.4 Nanomaterial-based DNA electrodes Despite the fact that enzyme-based biosensors are promising tools for rapid screening, sometimes it is not easy to work with enzymes because they lose their activity during binding events and can not response to the external stimulus [14,160]. The immobilization process may lead to significant alterations in enzyme structure [161]. Even the binding to a solid surface can induce protein denaturation by affecting the catalytic properties, which consequently leads to the complete loss of biological activity [162]. Therefore preservation of catalytic activity of an enzyme is a crucial task for producing biologically active biosensors [151]. In this regard, DNA-based sensing systems provide alternative solutions by eliminating the aforementioned limitations of enzymatic systems. To establish an electrochemical DNA sensor, nucleic acid molecules are immobilized on the working electrode surface as a recognition layer. Interaction with free radicals causes damage in the structure of nucleic acids. The degree of these damages can be determined by voltammetric measurement on the basis of the changes in the DNA signals [160,163]. The guanine, adenine, and 8-oxoguanine (8-oxo-7,8-dihydro-20 -deoxyguanosine, 8-oxodG) signals are commonly considered as an indicator of DNA oxidative damage [164
166]. The hydroxyl radicals, one of the most reactive forms of ROS, cause oxidation of DNA and deoxyribose and hence alter its chemical structure, which leads to release of the bases and strand breaks. During biosensing process, •OH radicals can be generated from H2O2 by the Fenton-type reaction in the presence of transition metal cations such as Fe21. Natural or synthetic antioxidants present in the sample act as radical scavengers. They reduce the free radicals and thus protect DNA against oxidative damage. The surviving DNA is then measured by voltammetric methods [167,168]. As simple and widely applied techniques for the quantification of DNA on a surface, voltammetric sensing approaches require DNA probes, intercalators, and the [Fe(CN)6]3
/4
indicator present in solution. A combination of voltammetric and impedimetric methods has been used to obtain complex information on the type and degree of DNA damage [169]. The damage to DNA is estimated according to an initial increase in the DNA bases responses at the helix opening followed by a decrease in DNA bases and intercalator responses at the degradation of DNA by breaks of its strands. The damage of the negatively charged DNA layer on the electrode is also confirmed by the change of the shift of CV and electrochemical impedance spectroscopic (EIS) data for the DNA-covered electrode to those typical for the bare electrode without DNA [160]. New nanostructured materials have been used to increase the surface area of the electrode thanks to the development of nanotechnology and

3. Environmental applications

16.3 Novel sensing methods of total phenolics and antioxidant capacity

633

biotechnology. The increased surface area as well as the protected surface against fouling and interference increase the sensitivity and selectivity of measurements [14]. Polymers such as cellulose acetate, chitosan, nafion and phospholipids, which are generally biodegradable and nontoxic, prevent the unwanted fouling and act as a barriers for small and large molecules [168,170,171]. DNA biosensors with external protective membranes have been successfully developed in the last decade to evaluate the antioxidant properties of several beverages, such as fruit juices [172], coffee, tea, and beer [160]. Chitosan, the most abundant cationic polymer similar to cellulose, is widely used for biomedical and pharmaceutical applications due to its ability to immobilize the enzymes [173,174] and to entrap the DNA molecules [175]. The original biopolymer not soluble in water can become soluble after hydrolysis depending on the amount of free amino groups in the chain. The completely protonated amino groups make chitosan soluble in acids, such as acetic, nitric, hydrochloric, perchloric, and phosphoric acids [176,177]. Nafion, a polymer with cation exchange properties, is used to reduce the diffusion of small neutral and negatively charged interfering species. Nafion is extremely resistant to a chemical attack in case of the addition of sulfonic acid groups into the bulk polymer matrix. This also helps nafion to not release fragments or degradation products into the surrounding medium [178]. This negatively charged polymer diminishes the diffusion of small neutral or negatively charged interfering species such as ascorbic acid and uric acid. Being chemically inert nafion is biocompatible to enzymes since it has both hydrophilic and hydrophobic properties [173]. Antioxidant activities of various white wines (e.g., Mu¨ller Thurgau, Pinot Blanc, Traminer, Green Veltliner, Riesling, Chardonnay) have been studied by voltammetric DNA biosensors coupled with the external membranes of nafion (NAF), chitosan (CHIT), and polyvinyl alcohol (PVA) [168]. Hydroxyl radicals generated via cleavage mixture of the Fe(II) ions and H2O2 were used for the DNA degradation [168]. A GCE was modified using a layer-by-layer deposition technique with low molecular weight DNA. For the detection of damage to DNA as well as antioxidant activity, CV measurements were performed within a potential range from
0.4 to 0.8 V at a scan rate of 100 mV/s and a potential step of 2 mV. Five types of biosensors, bare DNA/GCE, polymer membrane-covered electrode, NAF/DNA/GCE, CHIT/DNA/GCE, and PVA/DNA/GCE, were tested against prooxidant hydroxyl radicals. PVA membrane was revealed to be the most suitable membrane for the protection of the biosensor from the interference. Unwanted fouling occurred on DNA/GCE biosensor surface at the end of the incubation while PVA/DNA/GCE biosensor diminished the fouling caused by white wine matrix. It was reported that PVA/DNA/GCE sensing platform increased the CV peak current of the

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redox indicator and decreased the anodic to cathodic peak potential when compared to the other sensors produced with NAF- or CHITbased protective membranes [168]. A similar approach was followed by Hlavata´1 et al. using DNAbased biosensors with outer-sphere. Nafion and chitosan protective membranes were produced for the evaluation of antioxidant properties of beverages (i.e., beer, coffee, and black tea) against prooxidant •OH radicals as the DNA cleavage agent generated in situ with Fenton’s reagent. Using CV and EIS, the ability of the membrane-covered disposable biosensors (NAF/DNA/SPCE and CHIT/DNA/SPCE) with a SPCE as the transducer was studied to detect a deep degradation of the surface-attached DNA at the incubation in the cleavage agent. Cyclic voltammograms were recorded within a potential range from
0.8 to 1.0 V at a scan rate of 50 mV/s and a potential step of 5 mV. Compared to the simple DNA/SPCE, the response of the biosensors having the outer-sphere membranes showed a lower dependence on the time of the beverage incubation. The DNA biosensor coated with nafion (NAF/DNA/SPCE) was found to be the optimum for the detection of a deep degradation of DNA in beer and black tea extract, whereas the chitosan-based sensor (CHIT/DNA/SPCE) was the best for similar measurements in coffee extract [160]. In 2021, Ligaj and colleagues utilized an oxidized guanine derivative, 8-oxo-7,8-dihydro-20 -deoxyguanosine, as an indicator of DNA oxidative damage for measuring the amount of the survived DNA. The mutant genetic material (i.e., 8-oxoguanine, considered as an indicator of oxidative DNA damage) is generated by oxidative stress processes and associated with a growing risk of serious diseases, such as cancer, atherosclerosis, and neurodegenerative or metabolic diseases. To detect this molecule, the biosensor prepared with dsDNA was subjected to oxidative stress induced by •OH radicals generated by Fenton reaction. The protective effectiveness of the oregano extract on nucleic acids against oxidative stress was tested by analyzing the redox signals on the voltammograms from CV and SWV. This recent study revealed that during oxidative stress, 2.5% oregano extract was able to protect guanine from undergoing oxidation to 8-oxoguanine. The results proved that this genoprotective effect of oregano can make it a very efficient protective barrier against oxidative stress. It was also indicated that the functionality of the prepared bread by adding origano extract was not limited to antioxidative activity [166]. Another recent study reported on a DNA-based biosensor using carboxyl functionalized SWCNT-modified SPCEs (SWCNT-COOH/SPCEs) to evaluate the antioxidant activity of six chlorogenic acids (CGAs) isomers (i.e., 5-CQA, 4-CQA, 3-CQA, 3,4-diCQA, 3,5-diCQA, and 4,5diCQA) and extracts of 10 coffees samples. The addition of chlorogenic

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acids and aqueous coffee extracts significantly diminished the degree of DNA degradation determined using CV measurements within the potential range from 0 V to 1 1.4 V and back to 0 V with the scan rate of 0.1 V/s in phosphate buffer solution of pH 7.0. Addition of caffeic acid, caffeoylquinic acids, and dicaffeoylquinic acids affected survival of DNA at different proportions (i.e., 71%, 70%, and 69%, respectively). According to the results, SWCNT-COOH/SPCEs enhanced electrochemical active area and electron transfer properties. Compared to the bare working electrode and SWCNT-COOH/SPCE, the DNA/SWCNTCOOH/SPCE biosensor showed the most sensitive detection for the evaluation of the antioxidant activity [178].

16.4 Conclusion Phenolics act as metal ion chelators and free radical scavengers to reduce dangerous ROS, which lead to various human diseases. These activities take place by donating electrons, and this fact allows some optical and electrochemical techniques to be used in the determination of total phenolic and AOC. The design of novel techniques for the determination of total phenolics and AOC in various foods and beverages is important since antioxidants are able to prevent oxidative stress-related diseases. Knowing AOC can be helpful in the evaluation of food quality as well as realizing beneficial features of the products in the market. As highlighted in this chapter, many attempts have been made to supply uncomplicated, rapid, economical, and user-friendly analytical approaches for AOC determination from foods. This chapter reviewed the recent optical and electrochemical methodologies with their advantages and disadvantages and provided a comparison with the conventional methods. As a general principle, the selection of a method should be based on the compounds of interest, the reactions that the compounds undergo, and the type and complexity of matrix or food product. The assays of choice for determination of AOC should be sensitive, selective, robust, and reproducible. They should also utilize conventionally available reagents and instruments, and measure a wide variety of antioxidant types including both lipophilic and hydrophilic antioxidants. Although there exist many conventional methods, the aforementioned criteria are not met by a single specific assay for desirable measurement of AOC due to the complexity of oxidation processes in food. Most of the conventional assays are based on spectrophotometric detectors and suffer from interference and sample turbidity. On the other hand, the novel methods introduced in this chapter offer the possibility to measure the electron transfer directly and rapidly with exigible sensitivity and reproducibility.

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Electrochemical (e.g., CV, DPV, SWV, and EIS) and optical sensing methods are good alternatives to their conventional counterparts. The performance of such sensing techniques depends on the detection mechanism, the modalities of sensor development, the interaction between the antioxidant molecules and the electrode’s functional groups, as well as the characteristics of the analyzed matrix (e.g., pH value, nature of the electrolyte and presence of interferent compounds). Biosensors are fast, reliable alternatives to classical techniques. Depending on the scope of research, however, biosensors can be used as a complementary technique alongside classical ones, especially when the focus of research is based on understanding the role of each molecule in a matrix. In most electrochemical biosensors, integration of nanomaterial into the sensor resulted in a highly sensitive assay. Among different kinds of probes NPs (e.g., gold, silver, ceria, etc.) have been extensively utilized in antioxidant sensors because of their high surface area, high stability, and high reactivity. The use of NPs as catalytic tools, immobilization platforms, or as optical and electroactive labels improves the sensitivity of biosensors for antioxidant measurement because metal and metal oxide NPs enhance the catalytic and/or electrochemical processes. Considering the economic, nutritional, and human health importance of foods, it is expected that the novel sensing technologies, focusing on optical and electrochemical strategies in combination with smart nanomaterials, will provide essential and highly efficient analytical tools for the determination of food phenolics as well as AOC in the near future.

Acknowledgments Zeynep Altintas thanks the German Research Foundation (DFG, Grant number: 428780268) for the financial support as the principle investigator.

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17 Chemical sensing of pesticides in water Kaiyu He, Liu Wang and Xiahong Xu State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products; Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou, P.R. China

17.1 Introduction Pesticides play an important role in agriculture; they are extensively applied to prevent and inhibit the growth of harmful organisms to agriculture, including insects, weeds, fungi, invasive plants, animals, etc. Approximately one-third of the global agricultural produce will be lost without the application of pesticides [1]. Over the past decades, global applications of pesticides continue to increase because of their importance in agricultural production. Pesticides are used in large amounts, and over 95% of sprayed pesticides reach a destination other than their target species. Repeated applications of pesticides result in their accumulation in farmland, and they can be transported to the aquatic environment through surface runoff. As their toxic actions are not restricted to their intended targets, and some of them are persistent, pesticides in environment also affect biodiversity and human health. The adverse effects of pesticides are widely discussed. It is certain that excessive exposure to some pesticides will cause various acute and chronic symptoms, including asthma, chronic sinusitis, bronchitis, cognitive effects, reproductive dysfunctions, and cancer [2 5]. Organophosphorus and carbamate insecticides, which are structurally similar to the nerve gases soman and sarin, are frequently applied. These neurotoxic substances

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00008-X

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irreversibly inhibit the enzyme acetylcholinesterase (AChE), and subsequently result in the accumulation of the neurotransmitter acetylcholine (ACh) in the synaptic membrane. ACh accumulation causes a marked dysfunction of many autonomic and behavioral systems, eventually leading to respiratory paralysis and death. Generally, fungicides have low-to-moderate mammalian toxicity, but they are potent carcinogens as compared to other pesticides. It was estimated that more than 80% of all oncogenic incidence from the use of pesticides originates from a few fungicides [6]. The adverse effects of pesticides lead to the crucial need for accurate and rapid methods to monitor the level of pesticide residues in water. Modern techniques for the detection of pesticides involve thin-layer chromatography, capillary electrophoresis, gas chromatography (GC), and high-performance liquid chromatography (HPLC) coupled with mass spectrometric detection (GC-MS, HPLC-MS). Currently, chromatographic techniques are the most commonly used methods for analysis of pesticides. In spite of the advantages of accuracy and sensitivity, chromatographic techniques require strict pretreatments and do not easily allow rapid in-field monitoring. The assay procedures are complex and require qualified and experienced operators, prohibiting the use of chromatographic techniques for rapid analysis under field conditions. In addition, chromatographic techniques usually generate waste containing hazardous solvents. Recently, immunoassays, based on antigen-antibody interactions, are emerging as simple and rapid methods for pesticide analysis. However, the preparations of antibodies for pesticides are expensive and challenging due to the low antigenicity of pesticides. Moreover, the storage and application conditions of antibodies including the temperature, pH, and ionic strength are rigidly specified. Due to the harsh reaction conditions and limited types of antibodies, immunoassays are unable to fully meet actual needs. In recent years, the flourishing development of materials science and nanotechnology has opened up windows of opportunity for developing sensing techniques to detect pesticides. Researchers have been motivated to explore sensitive, effective, and portable sensors to reinforce the techniques system for pesticide assays. In this chapter, recent advances and new trends of the main chemical sensing techniques for the detection of various pesticides in water are reviewed. The chemical sensing techniques are classified according to signal types (e.g., colorimetric, fluorescent, Raman, electrochemical, etc.), and the working principles are described. The advantages and disadvantages of each strategy are discussed. Also, challenges for practical implementation and future research directions are highlighted for this active and important research area.

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17.2 Colorimetric sensors for detection of pesticides For rapid, on-site detection, easy-to-use colorimetric detection is generally considered the best because color change is convenient to observe and monitor. During sensing, the change in the color of the reaction, upon interaction of recognition element and target pesticides, indicates the presence and concentration of the target pesticides. The color change can be visible to the naked eye and accurately quantified by spectrophotometer. Therefore colorimetric sensing techniques have been intensively researched in recent years, and applied to real samples assay because of their simple operation, rapid response, and cost-effectiveness. It is wellknown that organophosphorus and carbamate pesticides irreversibly inactivate the activity of AChE in the nervous system, which is actually responsible for their high toxicity and insecticidal activity. AChE catalyze ACh to form choline, which can be catalytically oxidized by choline oxidase (ChOx) to produce H2O2. Similarly, S-ACh (an analog of ACh) can be catalyzed by AChE to generate thiocholine. Based on these classical enzyme catalyzed reactions and different concentration of organophosphorus or carbamates having different inhibition capability to AChE. Lin et al. developed a colorimetric sensor array comprising five inexpensive and commercially available thiocholine and H2O2 sensitive indicators for the simultaneous detection and identification of organophosphorus and carbamates [7]. In their strategy, when there are no target pesticides to inhibit the activity of AChE, S-ACh and ACh are converted into thiocholine and choline, respectively, and choline is further catalyzed by ChOx to produce H2O2. At last, thiocholine and H2O2 induce color changes of the array. However, when organophosphorus and carbamates are present, the activity of AChE is partially or fully inhibited, and thus the generation of thiocholine and H2O2 is prevented, accompanied by weak or no color changes. This array can not only identify organophosphorus and carbamates from other kinds of pesticides, but also discriminate them exactly from each other (Fig. 17.1A). AChE, ChOx, and ACh have long been utilized to develop rapid sensing strategies for organophosphorus and carbamates monitoring. However, coupling multiple natural enzymes and carrying out multistep enzyme-catalyzed reactions always make the detection procedures complex and increases the cost. A few nanomaterials have enzymemimicking activity, which are known as nanozymes, and have attracted extensive interest because of their high activity and low cost compared with natural enzymes [9]. Recently, Han et al. prepared the peroxidaselike GeO2 nanozymes and constructed a novel colorimetric organophosphorus assay platform [10]. In this study, paraoxon was selected as the target pesticide. In the absence of paraoxon, active AChE can catalyze

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FIGURE 17.1 (A) The detection principle of the colorimetric sensor array based on acetylcholinesterase inhibition for organophosphorus and carbamate pesticides. (B) Nanozyme sensor arrays based on heteroatom-doped graphene with peroxidase-like activity for detecting aromatic pesticides [8]. (A) Reprinted with permission from Ref. S. Qian, H. Lin, Colorimetric sensor array for detection and identification of organophosphorus and carbamate pesticides, Anal. Chem. 87 (2015) 5395 5400.Copyright (2015) American Chemical Society. (B) Reprinted with permission from Ref. Y. Zhu, J. Wu, L. Han, X. Wang, W. Li, H. Guo, et al., Nanozyme sensor arrays based on heteroatom-doped graphene for detecting pesticides, Anal. Chem. 92 (2020) 7444 7452. Copyright (2020) American Chemical Society.

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the hydrolysis of ACh into choline, which can trigger the decomposition of GeO2, resulting in the loss of peroxidase-like activity. In the presence of paraoxon, AChE was irreversibly inhibited and choline production was prevented. Therefore the peroxidase-mimicking activity of GeO2 was indirectly affected by organophosphorus, allowing the quantification of organophosphorus based on the subsequent H2O2-TMB color reaction. MnO2 nanoflakes is also a nanozyme with remarkable mimic oxidase activity, which induces the oxidation of TMB. AChE and ChOx catalyzed the hydrolysis of ACh to produce H2O2, which can trigger MnO2 nanoflake decomposition, further blocking the oxidation of TMB. On the basis of these phenomena, Jin et al. constructed a targetresponsive hydrogel kit by encapsulating MnO2 nanoflakes into sodium alginate hydrogel [11]. Taking paraoxon as the model organophosphorus pesticide, the authors showed that a color response of the kit can be observed because the pesticide inhibited AChE and suppressed the generation of H2O2, which reduced the decomposition of MnO2 nanoflakes. Furthermore, they captured the color change of the kit with a smartphone and analyzed images via a self-made application program. This benchtop-size biosensor was capable of screening pesticide within 65 min in a sensitive manner (detection limit of 0.5 ng /mL). This work exhibited a miniaturized sensor with ease of operation for real-time measurement of pesticides. The rapid detection of multiple pesticides with a colorimetric method simultaneously is highly desired. Zhu et al. prepared graphene materials with peroxidase-like activities, and then constructed nanozyme sensor arrays for the rapid detection of multiple aromatic pesticides [8] (Fig. 17.1B). Three graphene materials including nitrogen doped graphene, nitrogen and sulfur codoped graphene, and graphene oxide were used to fabricate the sensor arrays. These graphene-based nanozymes were able to catalyze the oxidation of TMB in the presence of H2O2. Aromatic pesticides can be absorbed on these nanoenzymes, thus the active sites of these nanozymes were masked, followed by the decrease of peroxidase-mimicking activity and weak color change. Five pesticides including lactofen, fluoroxypyrmeptyl, bensulfuron-methyl, fomesafen, and diafenthiuron were used as model targets to investigate the feasibility of the developed strategy. The collected color intensities were transformed to 2D canonical score plots by linear discriminant analysis (LDA) and the five pesticides were well-clustered into five groups and separated from each other. This research demonstrated it is possible to detect multiple pesticides rapidly and simultaneously with novel materials, ingenious design, and excellent data analysis. Among various the nanomaterials employed for colorimetric detection, gold nanoparticles (AuNPs) are appealing nanomaterial for the construction of sensors for on-site and rapid detection. AuNPs are

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widely utilized for rapid detection due to their properties such as easy operation and visualized results [12]. AuNPs tend to aggregate when their surface charges are neutralized. This aggregation changes the color of the solution and is visible to the naked eye and can be quantitated by measuring the absorbance at 650 nm. However, AuNPs can prevent this aggregation when covered by properly charged polymers. This signal transduction is easier and more straightforward than that from catalyzed color reactions. Based on this mechanism, Chen and coworkers modified the surface of AuNPs with different pesticide aptamers and then used them as probes to detect pesticides [13]. When these aptamerfunctionalized AuNPs were mixed with the target pesticide containing samples, the aptamer binded to the pesticide then left the surface of AuNPs rendering them to aggregation in the presence of high concentration of salt, resulting in aggregation of AuNPs and a color change from red to purple-blue. The proposed method detected six kinds of organophosphorous pesticides with good recoveries from 72% to 135% in environmental river water samples. In another work, Bala and coworkers demonstrated a novel sensing strategy for malathion by employing unmodified AuNPs, aptamer, and positively charged polydiallyldimethylammonium chloride (PDDA). In the absence of malathion, the aptamer was free and hybridized to form a duplex with the cationic PDDA owing to the interaction of negatively charged phosphate backbone of aptamer with PDDA. Thus the aggregation of AuNPs was prevented due to the lack of sufficient PDDA. However, in the presence of malathion, the aptamer formed a complex with malathion that therefore left the PDDA free and resulted in the aggregation of AuNPs, thus the remarkable change in the color of the AuNPs from red to blue. The color of the solution is dependent on the concentration of PDDA, which is directly linked to the concentration of malathion. Hence, the developed methodology can be employed for detecting the presence of malathion colorimetrically.

17.3 Fluorescent sensors for detection of pesticides Assays based on fluorescent techniques are usually more sensitive, which is favorable for analyzing low concentrations of highly toxic pesticides. Dasgupta et al. reported a fluorescent approach for the detection of four kinds of pesticides in water [14]. They prepared ZnO QD with highly visible emission as the sensing probe by a s ol-gel technique at room temperature. These ZnO QD were coated with 3-amminopropyltrimethoxysilane (APTES), which played an important role for the interaction with pesticides. Fluorescence intensity at 525 nm is very sensitive to different pesticides (e.g., aldrin,

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FIGURE 17.2 (A) Schematic representation of interaction of APTES coated QD with (a) aldrin and (b) glyphosate. (B) Schematic diagram for synthesis of ZnPO-MOFs and their application for rapidly detecting organophosphorus pesticides. (C) Schematic diagram for synthesis of water-stable Zr-LMOF and its application for organophosphorus pesticide sensing. Insets show the blue florescence of aqueous solution of the Zr-LMOF before and after quenching by target pesticide [15,16]. (A) Rreprinted with permission from Ref. D. Sahoo, A. Mandal, T. Mitra, K. Chakraborty, M. Bardhan, A.K. Dasgupta, nanosensing of pesticides by zinc oxide quantum dot: an optical and electrochemical approach for the detection of pesticides in water, J. Agric. Food Chem. 66 (2018) 414 423. Copyright 2018 American Chemical Society. (B) Rreprinted with permission from Ref. X. Xu, Y. Guo, X. Wang, W. Li, P. Qi, Z. Wang, et al., Sensitive detection of pesticides by a highly luminescent metal-organic framework, Sens. Actuators B: Chem. 260 (2018) 339 345. Copyright 2018 American Chemical Society.(C) Adapted with permission from Ref. K. He, Z. Li, L. Wang, Y. Fu, H. Quan, Y. Li, et al. Luminescent metal organic framework for rapid and visible sensing of organophosphorus pesticides, ACS Appl. Mater. Interfaces 11 (2019) 26250 26260. Copyright 2019 American Chemical Society.

tetradifon, glyphosate and atrazine). After gradual introduction of different pesticides, the fluorescence of QD was quenched gradually without having any shift. The authors revealed that pesticides aldrin and tetradifon can bind more strongly with QD than glyphosate and atrazine. Excited state dynamics of QDs showed the dynamic quenching of QDs in the presence of different pesticides.The presence of good leaving group (-Cl) in aldrin and tetradifon was the probable reason behind their covalent binding with QDs, which can be substituted by primary amine group on QDs. While for glyphosate, the ionic interaction played the key role in binding with QDs, which resulted in less binding affinity (Fig. 17.2A). Silicon quantum dots (SiQDs), nontoxic, stable, and low-cost nanomaterials, are environmentally friendly fluorescent probes and have attracted much interest. The fluorescence of label-free SiQDs can be largely quenched by H2O2 [17]. On the basis of this phenomenon, Yao et al. designed a label-free SiQDs-based sensor for ultrasensitive detection of pesticides [18]. They utilized the hydrolysis of ACh catalyzed by AChE to form choline that was in turn catalytically oxidized by ChOx to produce

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H2O2, which can quench the fluorescence of SiQDs. In the presence of pesticides, the activity of AChE was inhibited, leading to less generation of H2O2, and hence the fluorescence of SiQDs increased. The fluorescence intensity was relevant to the pesticide’s concentration. Thus pesticides, such as carbaryl, parathion, diazinon, and phorate, were determined with the developed sensing strategy based on SiQDs. The lowest detectable concentrations for the abovementioned pesticides were 7.25 3 1029, 3.25 3 1028, 6.76 3 1028, and 1.9 3 1027 g/L, respectively. Parathion and parathion-methyl are organophosphate insecticides and acaricides. They are highly toxic to nontarget organisms. Recently, our group exploited 1,2,4,5-Tetrakis (4-carboxyphenyl) benzene (H4TCPB) with Zn(NO3)2 6H2O to prepare a highly luminescent metalorganic framework (MOF) to detect parathion-methyl in irrigation water [15] (Fig. 17.2B). The porous ZnPO-MOFs exhibited strong fluorescence and excellent adsorption ability. Its specific detection ability toward parathion-methyl, based on obvious fluorescence quenching, was useful, enabling the as-prepared ZnPO-MOFs to be a fluorescent sensor for parathion-methyl with a wide linear detection range (1.0 μg/kg to 10 mg/kg) and a low limit of detection (0.12 μg/kg). This convenient fluorescent method has been reliably used for the determination of parathion-methyl in spiked irrigation water with an excellent recovery ranging from 93.0% to 104.6%. The aforementioned ZnPO-MOFs is facile for chemical sensing of pesticides in water. However, this type of MOFs is susceptible to hydrolysis at their nodes or metal ligand bonds, limiting their extensive applications. Therefore we further synthesized zirconium (Zr41) cluster-based stable MOFs for parathion-methyl and parathion detection in water [16] (Fig. 17.2C). The Zr-MOFs can retain their excellent performance after soaking in water for 24 h due to the strong metal-ligand bond strength and hydrophobicity of the ligand. Using parathion-methyl as the model target, the Zr-MOFs were applied to rapid detection with a wide linear range from 70 μg/kg to 5.0 mg/kg and low limit of detection of 0.12 μg/kg. We speculated that the presence of the -NO2 group caused the transfer of photoexcited electrons from Zr-LMOF to parathion-methyl after a series of investigation including resonance energy transfer, the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital energies.




17.4 Raman sensors for detection of pesticides Raman scattering is the inelastic scattering of photons by the excited molecules that are at higher energy levels. It is also known as the Raman effect, which can be observed only in Raman-active molecules. Some noble metal nanomaterials, like silver and AuNPs, are able to enhance

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the scattering significantly, which is known as surface-enhanced Raman scattering (SERS). This phenomenon effectively improves the sensitivity of Raman sensing techniques. Usually, the SERS method has an enhancement in order of magnitude 106, but in extreme cases an enhancement in order of magnitude B1014 1015 is also possible, which is sufficient for detection of single or a few molecules. Today, SERS is an effective technique for detection of various analytes in low concentration. The sensitivity of the SERS method depends on the type of substrate that can be either a colloidal solution of metal nanoparticles (NPs) or a metal surface with a suitable nanostructured topology [19,20]. Therefore one of the most critical steps for fabricating a SERS sensor is the construction of a SERS substrate. Ivanda and coworkers investigated the application of silver nanospheres and silver nanoprisms as SERS substrates in pesticide detection [21]. They prepared Ag nanoprisms and spherical silver nanoparticles (AgNPs) colloids. The SERS results showed that the shape and charge of a nanoparticle affect the binding of the molecule and accordingly the enhancement of related Raman signal. In this study, it was possible to detect millimolar concentrations of atrazine, simazin, and irgarol. The Han group proposed a gecko-inspired nanotentacle SERS platform for the simultaneous detection of three kinds of pesticides [22]. The SERS platform was obtained by seeding deposition of AgNPs on a 3D PDMS nanotentacle array. Compared with other substrates, this SERS substrate provided outstanding SERS activity with an enhancement factor of 1.2 3 107. Recently, Dai et al. prepared a surface-enhanced Raman scattering-based lateral flow assay test strip by combining SERS nanotags with antibodies, and then detected three pesticides simultaneously [23]. In their strategy, a silver-core, gold-shell nanoprobe was prepared, and the SERS signal molecule 4-nitrothiophenol (4-NTP) was encapsulated between the two metals (Ag4-NTP@Au). Antibodies specific to the three pesticide analytes were coupled with the SERS beacon molecule and sprayed onto a conjugate pad, and three test lines (each sprayed with antigens specific to the three pesticides) were prepared on a nitrocellulose membrane for multiresidue detection. Sample flow from the sample pad to the absorption pad by capillary action was followed by a competitive immune response, whereby the pesticide molecules competed with the Ag42NTP@Au-antibodies for binding to the antigens on the T line, and the intensity of the SERS signal was proportional to the sample concentration. The competitive immune binding between antibodies and antigens ensured that the three pesticides would not interfere with each other, which made it possible to detect the three pesticides simultaneously (Fig. 17.3A). SERS techniques have made Raman detection of trace pesticide residues implementable. However, the main devices for SERS on the market currently are all large-scale equipment, and can hardly be applied to on-site detection. To overcome these disadvantages, Mu et al. integrated

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FIGURE 17.3 (A) Schematic illustration of the surface-enhanced Raman scatteringbased lateral flow assay test strip by combining SERS nanotags with antibodies. (B) Schematic illustration of a microdroplet-captured tape toward rapid SERS screening of pesticides. (C) Schematic illustration of the preparation of Ag/LCP filter membranes based on liquid-crystal polymer (LCP) textile fibers decorated with Ag NPs, and the application of the as-prepared membranes in the extraction of pesticides from water for SERS analysis [23 25]. (A) Reproduced from Ref. E. Sheng, Y. Lu, Y. Xiao, Z. Li, H. Wang, Z. Dai, Simultaneous and ultrasensitive detection of three pesticides using a surface-enhanced Raman scattering-based lateral flow assay test strip, Biosens. Bioelectron. 181 (2021) 113149 with permission from Elsevier). (B) Reproduced from Ref. X. He, S. Yang, T. Xu, Y. Song, X. Zhang, Microdroplet-captured tapes for rapid sampling and SERS detection of food contaminants, Biosens. Bioelectron. 152 (2020) 112013 with permission from Elsevier (C) Reproduced from Ref. S. Fateixa, M. Raposo, H.I.S. Nogueira, T. Trindade, A general strategy to prepare SERS active filter membranes for extraction and detection of pesticides in water, Talanta 182 (2018) 558 566 with permission from Elsevier.

SERS with a cellphone to fabricate a novel system for the detection of pesticide [26]. The Raman optical system was redesigned, where the original planar reflection grating and concave mirror were replaced by volume phase hologram gratings and large numerical aperture lenses, respectively. Raman probes, lasers, and spectrometers were integrated

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together. As a result, the system size was reduced, while the sensitivity of the optical system was greatly improved. The authors constructed the SERS chip, which could be inserted directly onto the accessory linked to the cellphone. The measurement of pesticide residues could be conducted by one-click through the cellphone application. They achieved successful detection of 12 kinds of pesticides with characteristic Raman spectra and the limit of detection was less than 10 ppm. This study showed that rapid and on-site detection of pesticide residues is feasible by assembling portable Raman analyzer. In another work, Zhang and coworkers reported a microdroplet-captured tape toward rapid SERS screening of pesticides [24] (Fig. 17.3B). They prepared functionalized microwell in conductive carbon tapes by physical punching, magnetron sputtering, and electrochemical deposition of Au nanodendrites. The tape-based sensors not only possessed highly branched Au nanodendrites in microwell for promoting SERS activity, but also enabled anchoring the microdroplets via direct dip-pulling from pristine analytes solutions upon sticky incorporated on a glove. By scanning analytes collected in nanodendritic gold-modified microwell by a portable Raman analyzer, rapid and sensitive in-field detection can be easily achieved. Usually, the concentration of pesticide residues in water is not high, and it is necessary to preconcentrate the pesticides in-field to achieve accurate monitoring. A pretreatment method compatible with on-site Raman detection is also essential. Removing impurities and interferent by filtration is a common method for pretreating water samples. To allow the rapid collection and analysis of pesticides in water, Fateixa et al. prepared SERS active substrate filter membranes by combining SERS active substrate and filter membranes [25] (Fig. 17.3C). First, the authors developed a strategy to fabricate SERS active substrates (Ag/ LCP) based on liquid-crystal polymer (LCP) textile fibers decorated with Ag NPs. Then they utilized polyamide filters as support for the Ag/LCP composites to obtain the SERS active substrate filter membranes. The efficiency of Ag/LCP composites coupled with the filter membrane did not change, and consequently, they could be used to fabricate highly and efficient SERS active filter membranes for the extraction and detection of pesticides in water such as thiram. This study showed the possibility to prepare simple, highly efficient, and low-cost SERS active filter membranes for water analysis.

17.5 Electrochemical sensors for detection of pesticides Electrochemical sensors transform the physical, chemical, or biological changes of analytes that take place between the analytes and the

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electrode surface into measurable amperometry, voltammetry, impedance, or other electrochemical signals. Electrochemical sensing techniques have a few outstanding advantages including fast response, quick fabrication, good sensitivity, high selectivity, easy operation, low cost, and portability. Kumar et al. developed a novel 3D flower-like gadolinium molybdate (Gd2MoO6, GdM) and used the GdM as a catalyst for the electrochemical detection of organophosphate pesticide fenitrothion (FNT) [27] (Fig. 17.4A). They prepared the flower-like GdM and found that it could catalyze the electrochemical reduction of FNT. Then, the asprepared GdM was modified on the glassy carbon electrode (GCE) and revealed excellent electrocatalytic activity toward FNT sensing. This GdM-modified GCE sensor had a very low detection limit (5 nM), wide linear ranges (0.02 123; 173 1823 μM), and good sensitivity (1.36 μA/μM/cm2). It was also examined for sensing of FNT in contaminated soil and water samples at very low concentrations. Recently, we developed an effective competitive electrochemical sensor based on hapten-grafted

FIGURE 17.4 (A) Synthesized flower-like gadolinium molybdate (Gd2MoO6) used as a catalyst for the electrochemical detection of organophosphate pesticide fenitrothion (B) The oxidase-like Ti3C2-MXene/BP nanohybrid modified LIPG for the portable wireless smart analysis of ultra-trace α-naphthalene acetic acid in agro-products and farmland environments including water. (C) Schematic representation and photographs of the configuration of the paper-based platform and measurement procedure three-dimensional origami paper-based sensor for the detection of several classes of pesticides [27,29,30]. (A) Reprinted with permission from Ref. J. Vinoth Kumar, R. Karthik, S.-M. Chen, K. Natarajan, C. Karuppiah, C.-C. Yang, et al., 3D Flower-like gadolinium molybdate catalyst for efficient detection and degradation of organophosphate pesticide (Fenitrothion), ACS Appl. Mater. Interfaces 10 (2018) 15652 15664. Copyright (2018) American Chemical Society. (B) Reproduced from Ref. X. Zhu, L. Lin, R. Wu, Y. Zhu, Y. Sheng, P. Nie, et al., Portable wireless intelligent sensing of ultratrace phytoregulator α-naphthalene acetic acid using self-assembled phosphorene/Ti3C2-MXene nanohybrid with high ambient stability on laser induced porous graphene as nanozyme flexible electrode, Biosens. Bioelectron. 179 (2021) 113062 with permission from Elsevier (C) Reproduced from Ref. F. Arduini, S. Cinti, V. Caratelli, L. Amendola, G. Palleschi, D. Moscone, Origami multiple paper-based electrochemical biosensors for pesticide detection, Biosens. Bioelectron. 126 (2019) 346 354 with permission from Elsevier.

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programmed probe (HGPP) as a corecognition element for highly sensitive and selective detection of acetamiprid [28]. Acetamiprid present in samples competes with HGPP to bind with capture antibody on the electrodes by specific recognition interaction. Methylene blue probe (MBP) was used as the electrochemical redox probe to combine with the hybridized HGPP on the electrodes. Thus the more acetamiprid added, the less MBP combined on the electrodes, producing a low current signal. In the optimal conditions, the sensor showed a linear range from 5 to 1 3 105 ng/L for the acetamiprid assay with a detecting limit of 3.2 ng/L. Sensors that can detect multiple kinds of pesticides are highly demanded. Arduini and coworkers proposed a three-dimensional origami paper-based sensor for the detection of several classes of pesticides [30] (Fig. 17.4C). This sensor was developed by integrating two different office paper-based screen-printed electrodes and multiple filter paperbased pads to load enzymes and enzymatic substrates. By exploiting the capability of these different types of pesticides (i.e., organophosphorus insecticides, phenoxy-acid herbicides, and triazine herbicide) to inhibit the catalytic activity of butyrylcholinesterase, alkaline phosphatase, and tyrosinase, respectively, this electrochemical sensing platform was employed to detect paraoxon, 2,4-dichlorophenoxyacetic acid, and atrazine. The versatile analysis of different pesticides was carried out by folding and unfolding the filter paper-based structure, avoiding any addition of reagents and sample treatment (i.e., dilution, filtration, and pH adjustment). To improve the sensitivity, the paper-based electrodes were modified with carbon black alone in the case of platforms for 2,4dichlorophenoxyacetic acid and atrazine detection, or decorated with Prussian blue NPs for the detection of paraoxon. The sensitivity of the sensor for the detection of paraoxon, 2,4-dichlorophenoxyacetic acid, and atrazine was at ppb level in both standard solutions and river water samples. The accuracy of this electrochemical sensor was evaluated in river water by recovery studies, obtaining satisfactory results (e.g., for paraoxon 90 6 1% and 88 6 2%, for 10 and 20 ppb, respectively). Compared with other types of sensors, electrochemical sensors are easy to be miniaturized, intelligentized, and wirelessly transported. Wen and coworkers proposed a strategy for ultra-trace smart analysis of α-naphthalene acetic acid (NAA) residues in agroproducts and farmland environments including water [29] (Fig. 17.4B). They fabricated a nanozyme flexible electrode by coating two-dimensional phosphorene (BP) nanohybrid with graphene-like titanium carbide MXene (Ti3C2MXene) on the flexible substrate surface of laser-induced porous graphene (LIPG). The Ti3C2-MXene/BP/LIPG had the oxidase-like catalytic activity (nanozyme) for the electrocatalytic oxidation of NAA, outputting measurable electrochemical signal. Portable and intelligent sensing was achieved via machine learning (ML) using a portable electrochemical mini-workstation with the wireless intelligent system in comparison 3. Environmental applications

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with the traditional electrochemical large-workstation. The development of the ML model also enabled additional information regarding smart operation and intelligent output of NAA. More interestingly, ML-based calibration improved dynamic range of the sensor. NAA was electrochemically detected in a wide linear range of 0.02 40 μM with a low limit of detection of 1.6 nM using the mini-workstation. The practical application of Ti3C2-MXene/BP/LIPG nanozyme sensor for the detection of NAA was tested by utilizing the standard addition method in real samples. The results obtained by traditional large-scale workstation and portable mini-workstation were comparable. The satisfactory results indicate that the proposed nanozyme sensor for intelligent detection of NAA in real samples was acceptable and applicable, which further highlights the advantage of ML models for improving the performance of the existing sensors and guidance on how to realize the smart analysis of sensor.

17.6 Chemiluminescent sensors for detection of pesticides Chemiluminescence (CL) is defined as the emission of electromagnetic radiation at specific wavelengths in the visible and near infrared region [33]. There are two types of CL emission: direct and indirect CL. In a redox reaction, unstable intermediates release energy in the form of luminescence when they go back to the ground state from their excited states, which is known as direct CL. Indirect CL is based on the energy transfer of the excited species to luminophore molecules leading to emission of light upon energy release when the excited luminophore molecules go back to their ground state. CL has some unique advantages such as ease of operation, low cost, quick response, and high sensitivity. The main advantage of the CL technique is that it does not need an external light source. Consequently, the background signal is minimized, leading to an improvement of sensitivity [33]. In recent years, CL has been studied as one of the rapid sensing techniques for the analysis of pesticides [31 36]. Free DNA aptamer can prevent AuNPs from aggregation, and aggregated AuNPs have catalytic activity for luminolH2O2 CL reaction. Based on these mechanisms, Qi et al. reported an amplified CL sensing platform for ultrasensitive and selective acetamiprid detection [31] (Fig. 17.5A). When acetamiprid was absent, the DNA aptamer possessed high freedom to wrap on AuNPs. Thus the negativecharged phosphate backbone in ssDNA aptamer ensured the AuNPs’ stability against salt-induced aggregation, and there was no catalyst for luminol-H2O2 CL reaction. While acetamiprid was present in the reaction system, the aptamer would specifically bind with it, forming aptamer-acetamiprid complexes, which led to aptamer to be folded into

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FIGURE 17.5 (A) Schematic illustration of the simple and rapid CL assay for acetamiprid detection. (B) Schematic representation of the foldable paper-based biosensor and assay procedure for OPPs [31,32]. (A) Reproduced from Ref. Y. Qi, F.-R. Xiu, M. Zheng, B. Li, A simple and rapid chemiluminescence aptasensor for acetamiprid in contaminated samples: sensitivity, selectivity and mechanism, Biosens. Bioelectron., 83 (2016) 243 249. with permission from Elsevier (B) Reproduced from Ref. L. Montali, M.M. Calabretta, A. Lopreside, M. D’Elia, M. Guardigli, E. Michelini, Multienzyme chemiluminescent foldable biosensor for on-site detection of acetylcholinesterase inhibitors, Biosens. Bioelectron. 162 (2020) 112232 with permission from Elsevier.

certain structure. As the relatively rigid structure of folded aptamer prevented the exposure of DNA bases to the AuNPs, its ability to protect the AuNPs from aggregation was lost. In this situation, the salt-induced aggregation of AuNPs appeared, and then luminol-H2O2 CL reaction was catalyzed to output detection signal. Therefore the proposed sensing platform for pesticide residue exhibited a high sensitivity toward acetamiprid with a detection limit of 62 pM. Paper has its own unique advantages for fabricating simple, low-cost, portable, and disposable sensing devices suitable for on-site detection. Recently, Montali and coworkers proposed a foldable paper-based CL biosensor for OPP detection [32] (Fig. 17.5B). This biosensor was based on the coupled enzymatic reactions involving AChE, ChOx, and horseradish peroxidase (HRP), and the inhibition of AChE by OPPs. In the absence of OPPs, a series of coupled enzymatic reactions based on AChE, ChOx, and HRP started, where H2O2 was produced, triggering CL emission. When there were OPPs, AChE was inhibited, the

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production of H2O2 was decreased or inhibited, and consequently a reduction in light emission. For fabricating the biosensor, wells were delimited on cellulose chromatography paper by wax printing, then enzymes were loaded in the wells via physical adsorption, maintaining their excellent catalytic activity. Addition of sample and reagent solutions (ACh and luminol) and sequential folding of the biosensor allowed solubilization of the enzymes loaded in the biosensor and initiation of the enzymatic reactions. In the end, the biosensor was completely folded to obtain a single well, in which luminol was oxidized by the produced H2O2 to give out CL signal.

17.7 Electrochemiluminescent sensors for detection of pesticides Electrochemiluminescence (ECL) is a light-emission phenomenon produced during an electrochemical redox reaction. Owing to the fast respond, high sensitivity, and low cost as well as easy controllability, the application of ECL in biological analysis, environmental monitoring, and clinical diagnosis have been intensively researched. Gong and coworkers developed a highly sensitive and selective ECL biosensor based on target induced signal on for the detection of OPPs [37]. In their strategy, graphene nanosheets (GNs), CdTe quantum dots (CdTe QDs), and AChE were artfully integrated to yield a biofunctional AChE-GNs-QDs hybrid for ECL signal amplification. On the AChE-GNs-QDs modified GCE, a highly sensitive GNs-anchored-QDs-based signal-on ECL biosensor was developed by combining the enzymatic reactions and the dissolved oxygen as coreactant. Using methyl parathion (MP) as a model OPP, the detection limit was found to be as low as about 0.06 ng/mL under the optimized experimental conditions. In the ECL process, the coreactants of the luminophores are usually required for a high ECL efficiency. However, the employments of coreactants have their own disadvantages. Directly adding coreactants to the detection solution will change the microenvironment of the test system, resulting in poor reproducibility and stability. Also, immobilizing coreactants on the electrode surface might involve a complicated pretreatment. Moreover, the coreactants are easily leaked from the electrode surface. Applying dissolved oxygen (O2) as coreactant also results in poor repeatability and stability problems due to the uncertainty of its concentration. To avoid these drawbacks resulting from the addition of exogenous species or dissolved O2 as coreactant, Chen and coworkers prepared carboxyl groups functionalized poly [(9,9-dioctylfluorenyl-2,7-diyl)-co-(1,4benzo-{2,1’,3}-thiadazole)] polymer NPs (PFBT PNPs), and then used these PFBT PNPs as luminophore to construct coreactant-free ECL

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FIGURE 17.6 The preparation of PFBT PNPs (A); the construction of enzyme-based biosensor and quenching effect of H2O2 on the ECL of PFBT PNPs (B); and the possible ECL emission of PFBT-PNPs (C) [38]. Reproduced from Ref. Y. He, J. Du, J. Luo, S. Chen, R. Yuan, Coreactant-free electrochemiluminescence biosensor for the determination of organophosphorus pesticides, Biosens. Bioelectron. 150 (2020) 111898, with permission from Elsevier.

biosensor for the detection of OPPs [38] (Fig. 17.6). In the absence of OPPs, H2O2 from the AChE-catalyzed reaction quenched the ECL signal of PFBT PNPs, resulting in an ECL signal-off state. In the presence of OPPs, the ECL signal obviously increased because the enzyme AChE activity was inhibited by OPPs. The linear range for OPP detection was from 1.0 3 10212 to 1 3 1027 M and the detection limit was 1.5 3 10213 M.

17.8 Piezoelectric sensors for detection of pesticides Piezoelectric systems have emerged as attractive physical transducers due to their simplicity, low instrumentation costs, possibility for real-time, and generally high sensitivity [39]. In the past few years, various piezoelectric sensors have been reported for the detection of pesticides. The Lorenzo group developed a sensor based on quartz crystal microbalance (QCM) for the determination of organophosphorus and carbamate pesticides [40]. The detection was based on the inhibitory effects of these compounds on the activity of AChE immobilized on one of the faces of the crystal. Exposure of the immobilized enzyme to a solution of the histological substrate, 3-indolyl acetate, gave rise to the formation of an indigo

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pigment insoluble product that deposited (precipitates) on the crystal surface. The rate and extent of the enzymatic reaction were measured through the frequency changes associated with the mass changes at the crystal surface induced by the accumulation of the enzymatic reaction product (indigo pigment). In the presence of paroxon (organophosphorus pesticide) or carbaryl (carbamate pesticide), a diminution of the signal (frequency change) appeared due to the inhibitory effect of pesticides to AChE. Calibration curves were constructed by plotting the percentage of inhibition versus the logarithm of the pesticide concentration. The limits of detection were 5.0 3 1028 and 1.0 3 1027 M for paroxon and (after a 5min preincubation) carbaryl, respectively. In another study, a dual-template molecularly imprinted polymer (MIPs)-QCM sensor was developed for the analysis of dichlorodiphenyltrichloroethane and hexachorobenzene [41] (Fig. 17.7). In this

FIGURE 17.7 Schematic representation of MIPs-QCM sensor for analysis of ultrarace organochlorine pesticides. Reproduced from Ref. B.B. Prasad, D. Jauhari, A dual-template biomimetic molecularly imprinted dendrimer-based piezoelectric sensor for ultratrace analysis of organochlorine pesticides, Sens. Actuators B: Chem. 207 (2015) 542 551; with permission from Elsevier.

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research, 2,5-thiophene dicarbonyl dichloride molecules were initially immobilized on the surface of gold quartz crystal via Au-S links. Then, these molecules were covalently attached to dendron molecules (monomer) followed by the free-radical polymerization. In the presence of both target pesticides, cross-linker, and initiator, self-assembled molecularly imprinted dendrimer nanofibers developed on the gold surface. In the analysis process, one type of pockets (dendritic boxes) was saturated with an authentic amount of respective analyte, allowing the remaining pockets to be accessible for the intended analyte, and vice versa. In order to improve the sensitivity, the surface area of MIPs was substantially enhanced simply by adopting the “grafting from” approach to obtain a homogeneous growth of the nanofibers on the QCM surface. Detection limits of dichlorodiphenyltrichloroethane and hexachorobenzene were realized as low as 0.75 and 0.69 ng/mL, and linearity was observed in the concentration ranges 5.0 150.0 and 5.0 75.0 ng/mL, respectively.

17.9 Conclusion and future perspectives To ensure enough crop yields, the application of pesticides is almost inevitable at present. The detection of pesticide residues in environmental samples like water is an effective strategy to evaluate and regulate pesticide use for safety. The sensitive detection of pesticides has already been well achieved using chromatographic techniques such as GC and HPLC coupled with mass spectrometric detection (GC-MS, HPLC-MS). However, as these methods require complex sample preparation, skilled personnel, and specialized laboratories, there is an intensive need for rapid, portable, and convenient sensing techniques for pesticide residue assays in many areas. Due to their small size, portability, and ease of use, sensors are becoming the most potential technology for pesticide residue assays. The booming development of materials science and nanotechnology has contributed a lot to pesticide sensing methods in terms of targets recognition, signal transduction, and amplification. The response speed, sensitivity, and specificity of pesticide detection are being continuously improved to meet practical requirements. Future efforts should be made in the commercialization of these sensing techniques and the development of accessory instrumentation in order to realize and extend their applications in practice. Further advancements of these sensing techniques can be made by integrating multiple techniques together, thus overcoming the limitations resulting from a single technique. Also, future endeavors should be in the development of technologies with reduced complexity that can be easily used by nonexpert operators. In addition, on-site detections call for more

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robust, convenient, and intelligent sensing technology, which can be effective, intelligent, and convenient under environmental conditions with limited resources for in-field analysis. There have been some good examples. AuNP-based colorimetric techniques were combined with SERS to get enhanced Raman signal, and the excellent photothermal and fluorescent properties of polydopamine NPs were utilized to develop dual-mode assay. Smart phones and ML have also been introduced into sensing strategy. Such integration and interdisciplinarity can lead to improved specificity, high sensitivity, broadened adaptability, and intelligent detection. Hence, sensing techniques are promising for the field of pesticide detection in water, and we will see a large number of efficient products being put into practical use in the near future.

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[31] Y. Qi, F.-R. Xiu, M. Zheng, B. Li, A simple and rapid chemiluminescence aptasensor for acetamiprid in contaminated samples: sensitivity, selectivity and mechanism, Biosens. Bioelectron. 83 (2016) 243 249. [32] L. Montali, M.M. Calabretta, A. Lopreside, M. D’Elia, M. Guardigli, E. Michelini, Multienzyme chemiluminescent foldable biosensor for on-site detection of acetylcholinesterase inhibitors, Biosens. Bioelectron. 162 (2020) 112232. [33] I. Al Yahyai, H.A.J. Al-Lawati, A review of recent developments based on chemiluminescence detection systems for pesticides analysis, Luminescence 36 (2021) 266 277. [34] R.S. Chouhan, K. Vivek Babu, M.A. Kumar, N.S. Neeta, M.S. Thakur, B.E. Amitha Rani, et al., Detection of methyl parathion using immuno-chemiluminescence based image analysis using charge coupled device, Biosens. Bioelectron. 21 (2006) 1264 1271. [35] Y. Ma, Y. Zhao, X. Xu, S. Ding, Y. Li, Magnetic covalent organic framework immobilized gold nanoparticles with high-efficiency catalytic performance for chemiluminescent detection of pesticide triazophos, Talanta 235 (2021) 122798. [36] L. He, Z.W. Jiang, W. Li, C.M. Li, C.Z. Huang, Y.F. Li, In situ synthesis of gold nanoparticles/metal organic gels hybrids with excellent peroxidase-like activity for sensitive chemiluminescence detection of organophosphorus pesticides, ACS Appl. Mater. Interfaces 10 (2018) 28868 28876. [37] H. Liang, D. Song, J. Gong, Signal-on electrochemiluminescence of biofunctional CdTe quantum dots for biosensing of organophosphate pesticides, Biosens. Bioelectron. 53 (2014) 363 369. [38] Y. He, J. Du, J. Luo, S. Chen, R. Yuan, Coreactant-free electrochemiluminescence biosensor for the determination of organophosphorus pesticides, Biosens. Bioelectron. 150 (2020) 111898. [39] G. Marrazza, Piezoelectric biosensors for organophosphate and carbamate pesticides: a review, Biosensors 4 (2014). [40] J.M. Abad, F. Pariente, L. Herna´ndez, H.D. Abrun˜a, E. Lorenzo, Determination of organophosphorus and carbamate pesticides using a piezoelectric biosensor, Anal. Chem. 70 (1998) 2848 2855. [41] B.B. Prasad, D. Jauhari, A dual-template biomimetic molecularly imprinted dendrimer-based piezoelectric sensor for ultratrace analysis of organochlorine pesticides, Sens. Actuators B: Chem. 207 (2015) 542 551.

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C H A P T E R

18 Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications Selma Hamimed1, Yethreb Mahjoubi2, Nissem Abdeljelil1, Afef Gamraoui1, Amina Othmani3, Ahmed Barhoum4,5 and Abdelwaheb Chatti1 1

Laboratory of Biochemistry and Molecular Biology, Faculty of Sciences of Bizerte, University of Carthage, Jarzouna, Tunisia, 2Laboratory of Plant Toxicology and Environmental Microbiology, Faculty of Sciences of Bizerte, University of Carthage, Zarzouna, Tunisia, 3Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia, 4NanoStruc Research Group, Chemistry Department, Faculty of Science, Helwan University, Cairo, Egypt, 5National Centre for Sensor Research, School of Chemical Sciences, Dublin City University, Dublin, Ireland

18.1 Introduction Soil is a complex and heterogeneous environment that has great importance in food production and the maintenance of socioeconomic activities. It is difficult to analyze and assess since the uniqueness of soil lies in its spatial and temporal heterogeneity, as well as the multitude of forming elements that can often affect distinct directions and produce diverse outcomes. Variations in underlying geological rocks, flora, terrain

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changes, animals, and human activity produce geographical heterogeneity. Temporal heterogeneity can manifest itself in brief intervals over long periods, as evidenced by scientific and lyrical metaphors. Soil has fast-changing properties in a few hours or days [1]. There is no doubt that for environmental monitoring of soil, for its economic management, planning measures, the soil protection, reclamation, and rehabilitation, new fast and relatively inexpensive methods have been conducted to measure properly the soil physical characteristics and chemical properties and to collect necessary data for the complicated entity [2]. Soil contamination by pesticides constitutes a threat to the quality, impacting the biodiversity and nutrient cycling, as well as the quality of water bodies and the atmosphere. The use of agrochemicals (e.g., fertilizers, pesticides, etc.) has led to significant deterioration of soil and water resources, thereby increasing levels of pollution in agricultural environments. These changes significantly affect food quality and safety and thus strongly affect human health. The extensive use of pesticides causes poisoning for 26 million people and about 220,000 annual deaths [3]. Furthermore, due to their persistent nature, the residues of pesticides stay in the environment for a prolonged period, thereby contaminating the soil and thus raising concerns about the functioning of the soil, biodiversity, and food safety agrochemicals. Efficient analytical techniques for sample extraction and analysis are needed for soil due to its diversity and complexity. To date, many efforts have been dedicated to the application of chemical sensors and biosensors for soil monitoring analysis [4]. The use of different types of sensors aims to determine the status of soil including its nutrients, availability, salt conditions, pH, texture, organic matter, etc. For example, acoustic sensors are used to measure the soil texture (e.g., sand, silt, and clay), soil bulk density (e.g., compaction), and soil depth variability (e.g., depth of topsoil, depth to hardpan) [5]. A chemical sensor is a device that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytically useful signal. In general, chemical sensors contain two basic functional units: a receptor part and a transducer part. Some sensors may include a separator, which is a membrane [6]. It provides information about the chemical nature of its environment. It typically consists of a physical transducer and a chemically selective layer [7]). Chemical sensors and microbial biosensors gave gained great interest because they are low cost and easy to handle. The ease of fabrication, quick detection, high sensitivity, and selectivity, as well as easy naked-eye sensing of chemical sensors and biosensors also add great potential for the detection of metallic cations, anions, organic dyes, drugs, pesticides, and other toxic pollutants and microbes [8
10]. The combination of the chemical sensors with a transducer able to produce a signal proportional

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18.2 Detection of soil nutrients

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FIGURE 18.1 The most important applications of sensors for determining soil health. From [2]. Copyright 2021, Wiley-VCH.

to target analyte concentration presents a performing analytical device able to analyze soils and wide use in environmental monitoring [11]. Electrochemical sensors are the most versatile and highly developed soil sensors [12]. They use ion-selective membranes that produce a voltage output in response to the activity of selected ions (e.g., H1, K1, NO3 2, Na1, etc.). They are divided into several types: potentiometric (measure voltage), amperometric (measure current), and conductometric (measure conductivity), among others. In all these sensors, metal-based electrodes are typically used as a transducer element in the presence of an analyte. The design of new electrode materials has offered increases in selectivity and sensitivity toward target compounds [13]. According to the literature, the use of sensors for soil analysis has taken an important place due to their ability to determine a set of parameters useful for soil qualification. Fig. 18.1 cites some roles of different types of sensors for soil analysis [14
16].

18.2 Detection of soil nutrients Soil is a major source of nutrients needed by plants for growth. The three main nutrients are nitrogen (N), phosphorus (P), and potassium (K).

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Together they make up the trio known as NPK. Sulfur, calcium, and magnesium are also considered as essential nutrients. Detection of nutrient availability in soil is important for element-deprived soil detection. N, P, and K fertilizers are applied to improve the production of crops as these elements are the most important nutrient for plant growth. However, high amounts have significant side effects economically and environmentally. Total soil nitrogen is measured using NIR spectrophotometry technology. Laser-induced graphene has been used to sense the availability of nitrogen in the soil for plants [17]. In addition, photometric detection of soil nutrients based on optical sensors equipped with a wavelength of a light-emitting diode to fit the absorption band of soil nutrients reacting with chemical reagents was used to identify the amounts of ammonia nitrogen, nitrate nitrogen, available phosphorus, available iron, exchangeable manganese, and exchangeable calcium [18]. With regard to optical sensors, silicon nitride microcantilever and single-walled carbon nanotube-based chemiresistive sensors have been widely used because of their high sensitivity. Indirect methods are also used to detect nutrients such as chlorophyll meters through tracking plant responses and growth. UV and NIR can also be effective cost/timesaving methods to measure phosphorus. NIR with cobalt electrochemical method was proposed to detect phosphorus availability in situ [19], developing a data fusion in a way to measure phosphorus concentration [19]. An on-chip microsensor with planar cobalt microelectrodes and Ag/AgCl reference electrode has also been used for P detection in soil. Planar cobalt showed a selective potential response to phosphorus in an acidic medium for both inorganic and organic phosphate compounds (KH2PO4 and ATP, respectively) (Jochen et al., 2018). This disposal is easier to use and less expensive. Another low-cost sensor, the Raman sensor, is a portable field sensor utilized to determine P concentration [2]. Chemical and electrochemical sensors can be used in a wide range of situations. According to Kumar et al. [20], the majority of electrochemical sensors are used to detect nutrients, poisons, and pollutants in fluids and aqueous solutions. In slurries, unfiltered soil extract, and naturally moist soils, sensor-based ion-selective electrodes have been reported to collect quick measurements. The portable multi-ion measurement instrument, which was invented in 1941, allows for site-specific nutrient monitoring and characterization of surface soil nutrient variability. The wide dynamic range of ion-selective electrodes has been demonstrated, with the ability to discriminate between fluctuations in residual nitrogen (0.1
10,000 ppm N) and nutrients [21]. Ion-selective electrodes are traditionally used in laboratory facilities to obtain nutritional measurements. A common way to use ion-selective electrodes is to integrate them into microfluidic structures at micro- and picolevels for rapid analysis of nutrients. Using flow loop microfluidic devices integrated with

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18.3 Detection of pH

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sensor-based ion-selective electrodes provides precise and rapid detection in situ of phosphate [22]. To date, various membrane materials have been used to chemically and/or electrochemically quantify soil nutrient concentrations. Although there are many recognition elements for nutrition detection, the most popular membranes are based on polyvinyl chloride (PVC) ligand membranes and molecularly imprinted polymers. Glass membrane-based ionselective electrodes are widely used to determine pH, but not often used to detect nutrients of the soil. While another doped polymer was used for sensing micro elements such as tetradecyl ammonium-based o-nitrate for the detection of nitrate, nitrogen-doped polypyrrole (N-doped PPy) for nitrogen detection, electrodes based on valinomycin-bis(2-ethylhexyl) sebacic acid (DOS) for the detection of potassium, electrodes based onchip microsensor with planar cobalt rods for the detection of phosphate [23]. Similarly, Zou et al. [24] developed a miniaturized phosphate sensor that had a sensitive response ranging from 1025 to 1022 M of phosphate (Fig. 18.2). Further, Aravamudhan and Bhansali [25] reported an increase in the sensitivity of ion-selective electrodes by adding polypyrrole nanowires for soil nitrate detection. However, the use of ion-selective electrode sensors has some drawbacks due to the necessity of calibration each time of measurement and the reading measure is affected by different characteristics of soil like moisture, organic content, particle size, etc. [26]. Ammonium is an inorganic compound that is critical for agriculture and soil fertilization. However, when the bioavailable fraction reaches high concentrations, it becomes toxic to exposed plants and organisms. Thus developing methods for rapid and inexpensive assessment of ammonium levels in large areas is important. Dong et al. [27] adapted the technology of bioluminescent bacteria to the sensing of bioavailable ammonium in soils. In their method, Nitrosomonas. europaea ATCC 19718 (pHLUX20) reporter strain successfully detected a range of ammonium concentrations (B20
400 µM) in different conditions including fertilized and unfertilized soils. Bioluminescence was induced through the microbe’s luciferase activity and the emitted light could be registered by luminometry, and concentrations of bioavailable ammonium could consequently be deduced. The biosensor’s accuracy was compared to chemical quantification and showed a near 1:1 correlation making N. europaea ATCC 19718 (pHLUX20) assay a simple, rapid (B10 min), and robust method for the general investigation of bioavailable ammonium in soils.

18.3 Detection of pH Soil pH is a measure of soil alkalinity or acidity, reflecting the comprehensive influence of soil-forming factors, such as organic content,

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FIGURE 18.2 On-chip phosphate sensor with planar Co electrodes on polymer substrates: schematic representation and working principle. The cobased phosphate sensors test for the inorganic phosphate (KH2PO4 and NaH2PO4). In the presence of phosphate ions in the solution, Co3(PO4)2 is formed at the electrode surface and shifts the equilibrium of the net electrochemical reaction [24]. From Z. Zou, J. Han, A. Jang, P.L. Bishop, C.H. Ahn, A disposable on-chip phosphate sensor with planar cobalt microelectrodes on polymer substrate. Biosens. Bioelectron. 22(9
10) (2007) 1902
1907. https://doi.org/10.1016/j.bios.2006.08.004. Copyright 2007. Elsevier B.V.

organisms, and climate. The optimal pH range for soil suitable for the growth of most plants and the availability of nutrients is between 5.5 and 8 [28]. The shift of soil pH outside its range to acidic level may cause toxicity due to the increase of aluminum and manganese concentrations leading to reduce in microorganism activity and decomposition of soil organic content. On the other hand, the shifting to the basidic level decreases the phosphorous content and other basic microelements [29]. Thus various methods have been examined to determine the variation in soil pH. Unfortunately, the usual methods used for soil pH analysis need different procedures of specimen and extraction and can be expensive [30]. For that reason, some sensing technologies have been employed for soil pH evaluation using optical, electrochemical, and chemical sensors such as ion-selective electrodes and ion-selective field-effect transistor (ISFET).

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18.4 Detection of soil moisture

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Optical methods such as colorimetric and photometric indicators naming, dyes, and pH test strips depend on color changes pH-sensitiveorganic pigments, pH-color charts are used to determine the pH level [31]. Compared to colorimetric methods, photometric methods, such as optical fiber pH sensors fluorometric pH sensors, holographic pH sensors, as well as CCD camera full range pH sensors, provide much higher resolution and accuracy. However, none of them is suitable for in-site measurements due to the complicated optical system [2,31]. Lemos et al. [28] developed an ion-selective electrode sensor for pH analysis with a combined glass electrode. Moreover, Zamani and Mohaddeszadeh [32] replaced the proton-selective glass membrane with PVC membrane electrode for dysprosium (III) to a better pH sensitivity. This ion-selective electrode membrane showed high sensitivity of pH ranging from 2 to 10 at 55 Mv. In addition, the performance of ISFET materials can also be an alternative for pH sensing such as the use of silicon nitride (Si3N4), silicon oxide (SiO2), and aluminum oxide, which displayed potentiometric response to hydrogen ion concentration [33,34].

18.4 Detection of soil moisture Soil moisture is one of the most important parameters for measuring the water content in the soil driving weather conditions, plant growth, groundwater storage, etc. [35]. Because direct gravimetric measurement of free soil moisture necessitates the removal, drying, and weighing of a sample, soil moisture sensors indirectly measure the volumetric water and express it in volume or weight. In addition, checking soil moisture level is of extraordinary importance to keep up with reasonable soil conditions like choosing fertilizer rates for crops and plants to maintain their physiological activities [36]. Typically soil moisture sensors are used to determine water content in soil and its related parameters such as dielectric permittivity, matric potential and, volumetric water content [2,37]. Depending on the operational mechanism, many types of soil moisture can be categorized; (1) Capacitive soil moisture sensors are used to measure changes in capacitance to determine the water content of soil [38]; (2) electromagnetic induction -based moisture sensors are offer rapid measurement, high accuracy, and easy access to deep soil layers [39]; (3) ultrasonic-based moisture sensors transmit a sound pulse that reflects from the surface of the water and measures the time it takes for the echo to return, and are usually more expensive due to the requirement of ultrasonic source and integrated detector; and (4) optical fiber sensors are cheap and can be employed in the analysis of moisture content in soils [40]. As shown in Fig. 18.3, any change of the water content will be reflected through the calculated difference between

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FIGURE 18.3 Soil moisture sensors.( A) Schematic illustration of a typical soil moisture sensor with critical components. (B) Photograph of the prototype of a capacitance soil moisture sensor. (C) Photograph of a miniaturized wireless system for sensing soil water content and conductivity. (D) Schematic diagram and test setup for a volumetric soil moisture sensor. (E) Graphene quantum dot (GQD) soil moisture sensor packaged with a filter cap. SEM image of the IDE with dimensions of 1.5 3 2.4 mm. (F) Schematic design of an ultrasonic soil moisture sensor. (G) A miniaturized optical moisture sensor for direct, continuous, in situ monitoring soil water potential variations through the nanoporous ceramic diaphragm. [2]. Copyright 2019, 1 Wiley-VCH.

back-and-forth signals of the ultrasonic soil sensor [41]. Compared to other approaches, Dean et al. [42] designed a miniature sensors based on a microelectromechanical system (MEMS) (Fig. 18.3B) that showed successful water detection and quantity measurement in soil. Other researchers developed a moisture sensor-based fringing electric field related to Zigbee technology (Fig. 18.3D) that is capable of detecting water content in soil from 1% to 80% [43].

18.5 Detection of organic matter Soil organic content includes the remains of plants roots, animals, and microorganisms that decompose at various stages. Also, soil can be contaminated naturally or by human activities with chemical elements and substances that are toxic amounts. A number of undesirable organic pollutants found in soils pose severe danger to human health and the ecosystem. The most common hazardous materials are oil hydrocarbons

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(e.g., alkanes, alkenes, cycloalkanes); chlorinated compounds (e.g., PCBs, PCDDs, and PCDFs); phenolic compounds, monomeric aromatic hydrocarbons (e.g., benzene, toluene, ethylbenzene, and xylene, collectively known as BTEX); PAHs (e.g., benzo [a]pyrene, chrysene, and fluoranthene); pesticides including herbicides such as atrazine, acetochlor, and bifenox, fungicides (lindane, metalaxyl, procymidone, and penconazole); and insecticides (e.g., benomyl, endosulfan, heptachlor, and endrin) [44
48]. Soil contamination from anthropogenic petroleum hydrocarbon spills has become a severe environmental issue; contamination can occur as a result of storage tank and pipeline leaks, or as a result of an accident during transportation. The assessment of hydrocarbon soil pollution is also crucial for determining the bioavailability of contaminated soil. Kaur et al. [49] proposed a method for detecting the level of hydrocarbon contamination in soil. As shown in Fig. 18.4, the amount of CO2 emitted because of biota respiration and soil microbial activity in hydrocarbon-contaminated soils were utilized as contamination indicators. After glucose addition, an affordable nondispersive infrared CO2 sensor was successfully used to distinguish between control and dieselcontaminated soils in terms of CO2 emission.

FIGURE 18.4 Glucose-induced CO2 emission sensing system for soils integrated with NDIR-based K3OCO2 sensor. From J. Kaur, V.I. Adamchuk, J.K. Whalen, A.A. Ismail, Development of an NDIR CO2 sensor-based system for assessing soil toxicity using substrateinduced respiration. Sensors. 15 (3) (2015) 4734
4748. https://doi.org/10.3390/s150304734 [49]. Copyright 2015. MDPI.

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Extensive use of herbicides and pesticides causes significant runoff of many compounds through soil contamination and then water and food. Electrochemical and biosensors sensors are widely used in herbicide detection [50,51]. In this regard, researchers designed a modified sensor based on double-template molecularly imprinted polymer gold nanofilmmodified pencil graphite electrode as an efficient sensor to detect phosphorus-containing amino acid-type herbicides like glyphosate and glufosinate [52]. Dong et al. [53] developed an electrochemical sensor based on multiwalled carbon natotube
CeO2
Au nanocomposite as tool to detect the methyl parathion within the limit of 3.02 3 10
11 M. Otherwise, synergistic or antagonistic interactions between herbicides, their metabolites, and other additives should be considered. As a result, it is critical to take accurate measurements of the analytes. Traditional herbicide estimating methods in soils allow assessment of sorption rates in the laboratory using physicochemical parameters for specific soil types, but they do not correctly reflect natural soil conditions in the field. The combined sensory technique to herbicide analysis in soils appears to be appealing in this regard. Tang et al. [54] developed an electrochemical immunosensor based on conductive chitosan/ gold nanoparticle composite membrane to detect picloram-herbicide. The sensitive device encapsulated the self-synthesized picloram antibody in the immune membrane to form an immunoconjugate that detects picloram in the range from 0.005 to 10 µg/mL. Baskeyfield et al. [55] designed a membrane-based immunosensor using a heterogenous competitive enzyme-linked immunosorbent assay containing an immobilized isoproturon
ovalbumin conjugate to detect herbicide isoproturon within a detection limit of 0.84 ng/mL. Similarly, another voltammetric competitive immunosensor based on the immobilization of conjugate atrazinebovine serum albumin on a nanostructured gold substrate functionalized with poliamidoaminic dendrimers was developed and had high sensitivity of atrazine of 1.2 and 5 ng/mL [56]. On the other hand, nitrogen overfertilization and other synthetic compounds can damage soil and plant growth. For example, [57] developed electrical Point-of-Use measurements to measure NH41 that are easily accessible whether and allow instantaneous prediction of levels of NO32 (Fig. 18.5). Moreover, the detection of dysprosium has been examined using neutral ionophores-poly(vinyl chloride)-based membrane sensors, and results showed that the addition of sodium tetraphenylborate and various plasticizers improve the performance of the sensors [58]. Bojdi et al. [59] designed palladium(II)-selective electrode based on the use of palladium(II) imprinted polymer nanoparticles for high selectivity of palladium(II) with a sensitivity of 16.12 µA/µmol/dm3. A prepared electrode-based carbon paste was used to high detection for 2,4,6-trinitrotoluene (TNT) by the three-step procedure with sample

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679

Gas-phase NH41 sensor cartridge, consisting of a container, 1 mL 15 M NaOH and a disposable chemPEGS that acts as a scrubber of soil-NH41 connected to an integrated circuit (IC) to perform impedance analysis. From Grell et al., 2020. Copyright 2020. BioRxiv. [57]

FIGURE 18.5

extraction in the electrode, washing, and electrochemical analysis of TNT with a limit of 1.5 3 10(29) mol/L [60]. Furthermore, voltammetric and amperometric sensors were used for detecting polyphenols due to their redox behavior. Based on their antioxidant activity and their

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18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

possibility to give electrons [45,61], these sensors are highly sensitive to phenolic compounds detecting 4-phenoxyphenol ranging from 2.5 to 40 mM, phenol with a range of 2.5 to 60 mM, and 4-methoxyphenol from 5.0 to 40 mM [62]. Similarly, Vishnu and Kumar [63] designed an electrochemical sensor based on an ultralow cost 6B grade pencil graphite (6B-PGE*) for the sensitivity of phenols in a range of 40
320 µM.

18.6 Detection of inorganic pollutants During the past few years, researchers have looked at ways to measure different heavy metals (e.g., cadmium, silver, mercury, etc.) in soils due to the increase of their discharge in the environment [64]. The acrylic mold and selective colorimetric method were developed to measure metal content [65]. This method can detect aqueous mercuric, silver, and Pb ions. Electrochemical electrodes were used to detect individual or multiple heavy metals; this method can be improved by modifying the electrode surface to have better sensitivity [2]. Microbial whole-cell sensor bioluminescent detection has been widely used to assess the bioavailability and the risk of toxic elements (e.g., Cd, Pb, As) [2]. Hung et al. [66] designed selective sensor-based label-free gold nanoparticles (AuNPs)-alkanethiols for detecting aqueous mercuric, silver, and Pb. In addition, Wilson et al. [67] developed an enhanced sensor formed of two PVC membrane ion-selective electrodes based on two bis-thioureas: 1,3-bis(N’-benzoylthioureido)benzene and 1,3-bis(N’-furoylthioureido)benzene as ionophores for higher selectivity of Pb(II) ion with a range from 4 3 1026 to 1022 M. Moreover, precise potentiometric analysis of Cu ions in soils has been done using 1-phenyl-2-(2-hydroxyphenylhydrazo) butane-1,3-dione (H2 L) ionophore-based Cu-selective PVC membrane electrode [68]. Similarly, [69] developed an ion-selective electrode sensor based on benzothiazole-modified chelating ionophores (1,3-bis[2-(1,3-benzothiazol2-yl)-phenoxy]propane (L1) and 1,20 -bis[2(1,3-benzothiazol2-yl)-phenoxy]2-ethoxyethane(L2)) for better detection of Cu21. To get better selectivity of the electrochemical and chemical sensors, many studies have used modified and combined methods on the surface of the electrode such as nanoparticle coatings, DNA addition, or ion-selective membrane coatings. DNA-based thrombin-binding aptamer probe with the donor carboxyfluorescein (FAM) and 4-([4(dimethylamino)phenyl]azo)benzoic acid (DABCYL) sensor was used for detecting Pb21 and Hg21 in Montana Soil samples [70]. Brenneman et al. [71] also used DNA aptamer as the molecular recognition for Hg21 ions at the nanomolar level (Fig. 18.6). Moreover, real-time microfluidic paper-based analytical devices using passive capillary force have

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18.7 Soil-borne disease using a microbial biosensor

Hg2

: eFluor® 650NC

: Nanogold

: Hg2+

: TBA

FIGURE 18.6 Sensing mechanism of the QD-TBA nanogold probe for the detection of Hg21 ions. From K. L. Brenneman, B. Sen, M.A. Stroscio, M. Dutta, Aptamer-based optical bionano sensor for mercury(II) ions. in: 2010 IEEE Nanotechnology Materials and Devices Conference. (2010). https://doi.org/10.1109/nmdc.2010.5652331. Copyright 2010. IEEE

been developed for faster combined collection and analysis of samples with high sensitivity to Cr and Pb with a limit of 0.12 µg and 0.25 ng, respectively [72].

18.7 Soil-borne disease using a microbial biosensor Bacterial biosensors are detection tools that use bacterial cells to sense the presence of target molecules such as antibiotics, organic pollutants, or heavy metals but also parameters that are relevant for environmental monitoring such as soil salinity, dissolved oxygen, or bioavailable ammonium. In general, biochemical reactions happening between the bacteria in the biosensor and the investigated molecule translate into a signal that depending on the setting (transducer) can reflect qualitative or quantitative information. Thanks to their natural high sensitivity to changes in the environment, bacteria represent an excellent indicator of levels of presence of numerous organic and inorganic compounds. In addition, the abundant diversity in the bacterial kingdom offers great possibilities for developing large sets of sensing protocols and multiple detection methods based on these microorganisms’ biochemical reactions. Research over the biological production of light or “bioluminescence” has increasingly captured the interest of the scientific community in recent decades and has translated into the development of many applications for such special organisms. Since then, genetically engineered bioluminescent organisms and especially bacteria have been created and applied to the field of biosensing [45]. Studies have shown that bioluminescent bacteria can be used both for qualitative and quantitative

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18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

detection of various compounds including organic pollutants and heavy metals (Table 18.1). For example, Pseudomonas putida can be genetically transformed to host in its chromosomal DNA the bioluminescence-coding mobile element called pTn5attPuLux. The resulting genomic region is further modified to be coupled with the gene xylR and its promoter Pr responsible for the regulation of the response to m-xylene (one of the most common hydrocarbons contaminating soil and water). This genomic construction allows the modified bacteria to glow when m-xylene is present in their environment. The radiance is transformed into a signal by a photon-counting device and the information can be analyzed. In order to allow maximum cell viability in ambient conditions and therefore in situ large-scale application of this sensing technique, the biosensor developed by de las Heras A et al. has also been protected by a capsule of water-soluble gelatin and various combinations of lyoprotectants. It succeeded in demonstrating the presence of diluted m-xylene in fluvisol-type soil [75]. Similarly, Bae et al. [74] developed a biosensing cartridge for the detection of toluene in soil samples based on bioluminescent bacteria of which signals evolve proportionally to the levels of the toxic compound. Three different strains of genetically modified Escherichia coli RFM443 were used: EBSoxS that is bioluminescent in response to oxidative damage; TV1061 specifically responsive to protein damages; and GC2 (harboring a constitutive lac::luxCDABE fusion) of which constitutive bioluminescence decreases in the presence of stress agents. Bacterialoaded optical biosensors successfully detected different levels of toluene in soil samples and at different depths. However, the detection was also influenced by the sample’s thickness and the authors suggested further optimization of the proposed device. Shin et al. [76] used S.paucimobilis EPA505 to produce two fluorescencebased reporter strains: 1. Strain S: hosting the plasmid pBBR1PGEF 2. Strain D: hosting the plasmid pRK415PGFP As shown in Fig. 18.7, both strains react to the presence of a polycyclic aromatic hydrocarbon called phenanthrene and are able of degrading it. However, the response is different. S strain is designed to die proportionally to the biodegradation of the hydrocarbon. The idea here is to monitor the dying rate of cells thanks to LIVE/DEAD fluorescent staining. The D strain behaves in a different way; it is constructed to produce fluorescence proportionally to the biodegradation of the same toxic compound. When applied to soil samples (wet sand), these biosensing bacteria were able to indicate the presence of phenanthrene at levels ranging from 50 to 1000 mg/kg.

3. Environmental applications

TABLE 18.1 Main bioassay techniques used in biosensing of organic compounds. Biosensing of organic compounds Sensed molecules

Sample

Detection

Detection limit

Advantages

Limitations

Reference

Tetracyclinesinducible mCherry in Escherichia coli

Tetracyclines

Soil

Quantitative: Fluorescence reader

5.32 2 10.2 µg/kg


Rapid (6 h for 96 samples)
Cost saving
Direct method

Tetracyclinesspecific

Ma et al. [73]

Stress sensitive bioluminescent immobilized bacteria

Toluene

Soil

Quantitative: optic probe-Luminometer

0.2%

Detection 1 bioavailability and toxicity information
Direct method

Influenced by sample’s thickness

Bae et al. [74]

BTEX-activated bioluminescence in encapsulated engineered Pseudomonas putida CPLUX

Toluene, mxylene, p-xylene, and 4ethyltoluene

Water-satured Soil

Qualitative: Photoncounting chargecoupled device & camera

NA


In situ detection
Suitable for large areas
Direct method


Sensitive to desiccation
Time consuming (82 h)

De las Heras, de Lorenzo [75].

Sphingomonas paucimobilis EPA505 reactive to phenanthrene biodegradation

phenanthrene

Sand/water mixture

Quantitative: Confocal laser scanning microscopy and image analysis/ Fluorometry

50
1000 mg/kg


Direct method


Potentially influenced by the presence of other toxic compounds

Shin et al. [76]

Technique

(Continued)

TABLE 18.1 (Continued) Biosensing of organic compounds Sensed molecules

Sample

Detection

Detection limit

Advantages

Limitations

Reference

Sulfur-oxidizing bacteria (SOB) assay (metabolic activity)

Soil toxicity in general

Soil/water mixture

Quantitative: Oxygen consumption

NA

Relatively rapid (24 h)


Indirect method
Nonspecific

Ahmed et al. [77]

Immobilized cellsurface-expressed Organophosphorus hydrolases

Paraoxon, parathion and methyl parathion

Sewage/water

Quantitative: electrochemical

Paraoxon: 0.05
25 µM Parathion: 0.05
25 µM Methyl parathion 0.08
30 µM


Rapid
Sensitive
In situ

Indirect method

Tang et al. [78]

Technique

18.7 Soil-borne disease using a microbial biosensor

685

FIGURE 18.7 Images of confocal laser scanning microscopic observation (X200 magnification) showing changes in fluorescence intensity in Ottawa sand/water mixture (i.e., ratio of 1e5). Fluorescence was determined by CLSM/image analysis and the values in the parentheses represent the mean values of relative fluorescence intensity (FL/FL0) and standard deviations. From D. Shin, H.S. Moon, C.-C. Lin, T. Barkay, K. Nam, Use of reportergene based bacteria to quantify phenanthrene biodegradation and toxicity in soil. Environ. Pollut. 159 (2) (2011) 509
514. Copyright 2011. Elsevier B.V.

For their whole-cell bacterial biosensor, Ma et al. [73] genetically engineered E. coli to produce fluorescent proteins proportionally to the presence of tetracyclines. E. coli was therefore transfected with reporter plasmids containing tetracycline-inducible genes for the fluorescent proteins GFP and mCherry. These plasmids allowed the engineered cells to respond to variations in intracellular tetracyclines by producing GFP or mCherry proteins of which fluorescence was measured. This biosensor was able to detect tetracyclines with levels of accuracy comparable to routine techniques such as high performance liquid chromatography (HPLC). The proposed technique was time-saving and required just 6 h for 96 samples compared to 40 h for HPLC while consuming lower amounts of chemical and technical resources. Biosensing techniques not based on direct observation or measurement of cells have also been investigated. For example, in order to detect organophosphorous compounds such as paraxon, parathion, and methyl parathion in environmental samples like water and sewage, Tang et al. [78] developed a biosensing setup where a genetically modified E. coli was immobilized on an electrode thanks to ordered mesopore carbons. The bacteria E. coli strain BL21 (DE3) used in this biosensor hosted the plasmid pTInaPb-N/OPH and were able to express on their surface the enzyme “organophosphorus hydrolase,” which serves for the hydrolysis of a large variety of organophosphorus molecules. The hydrolysis produces consequently an oxidation current

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18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

that can be detected by electrochemical equipment. The expression of such an enzyme on the cell’s surface is an advantageous feature of the biosensor as extraction and purification steps are no longer needed. In addition, the cell embedding of the enzyme allows better stability and sensitivity. The proposed biosensor successfully and rapidly detected the investigated pollutants at significantly low levels: 0.05
25 µM for paraxon, 0.05
25 µM for parathion, and 0.08
30 µM for methyl parathion. On the other hand, the use of nongenetically modified bacteria for indirect biosensing is equally possible, especially when unspecific detection is needed. The method described by Ahmed et al. [77] is based on sulfur-oxidizing bacterial (SOB) and senses the toxicity of soil through the metabolic inhibition of SOB (mainly the oxidation of reduced sulfur). A significant decrease in levels of parameters such as oxygen consumption and sulfur production reflects the bacterial inhibition and is proportional to the toxicity of the soil sample presented to the microbial consortium. This simple biosensing technique allows a relatively fast (24 h) identification of contaminated soil and provides information on toxicity levels (EL50). Thanks to their high sensitivity for the composition of their surroundings, bacteria can also be employed to sense the presence of harmful inorganic compounds such as heavy metals (e.g., chrome, lead, arsenic, copper, etc.) or inorganic compounds closely related to their metabolisms such as oxygen and different forms of nitrogen and salts. The research of Adekunle et al. [79] focused on the development of an accurate, fast, and inexpensive biosensor for a continuous and realtime in situ detection of heavy metals in mining regions. The technique uses membrane-less microbial fuel-cells (MFC) inhabited on the anodic side by a diverse community of electroactive eubacteria and archea (Fig. 18.8). The electrogenic microbial consortium has an oxidizing activity and can transfer electrons to the anode of the biosensor. This leads to the creation of an electron flow or in other words a “current” between the cathode (made of manganese oxides) and the anode (made of carbon felts). In general, when heavy metals are present in the media traversing this kind of biosensors, they interfere with the current created by the electroactive bacteria and can consequently be detected. In the aforementioned setup, Adekunle et al. [79] measured the time that takes such a circuit to reach its high voltage limit (Vmax) to assess the toxicity of the influent stream. According to the experiment, the presence of heavy metals in the soil leachate (1095 to 5151 µg/L) translated into longer time for the MFC to reach its Vmax. In another experiment, Eom et al. [80] proposed a simple and portable biosensor based on SOB for the detection of heavy metal-contaminated soils. As shown in Fig. 18.9, their work consisted of exposing the SOB to the investigated soil sample inside a hermetically sealed container where

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18.7 Soil-borne disease using a microbial biosensor

687

FIGURE 18.8 Schematic diagram of (A) setup for obtaining mining rock drainage on the left and (B) MFC biosensor (BIOS-A and BIOS-B) on the right [79]. From A. Adekunle, V. Raghavan, B. Tartakovsky, On-line monitoring of heavy metals—related toxicity with a microbial fuel cell biosensor. Biosens. Bioelectron. (2019). https://doi.org/10.1016/j.bios.2019.03.011. Copyright 2019. Elsevier B.V.

FIGURE 18.9 SOB toxicity test kit uses movement of the syringe plunger as the reading represents oxygen consumption by SOB in the test kit. From H. Eom, E. Ashun, U.A. Toor, S.-E. Oh, A solid-phase direct contact bioassay using sulfur-oxidizing bacteria (SOB) to evaluate toxicity of soil contaminated with heavy metals. Sens. Actuators B: Chem. 305 (2020) 127510. https://doi.org/10.1016/j.snb.2019.127510. Copyright 2020. Elsevier B.V.

the quantity of oxygen consumed by the active microbial community can be easily measured through a syringe. In general, their results showed that oxygen consumption and heavy metal concentration was inversely correlated. The increase in heavy metal concentrations in soil samples

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18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

was associated with an inhibition of the SOB community, expressed by a significant decrease in oxygen uptake by the resident microbes. The biosensor was also able to sense differences between heavy elements as the SOB community could show different oxygen uptake profiles depending on the heavy metal it is exposed as shown in the following equation:   rate of oxygen consumption in test Inhibition rateð%Þ 5 1 2 3 100 rate of oxygen consumption in control Hassan et al. [81] presented another simple SOB-based biosensor for heavy metal detection where variations in the electrical conductivity (EC) and pH reflected the presence of toxic levels of the investigated heavy element (here hexavalent chromium Cr61). In fact, in an active SOB community, the oxidation of sulfur produces sulfuric acid and sulfate. As a result, the pH is decreased and the EC is increased. In an inhibited SOB community, the opposite scenario is expected. This research showed that the presence of Cr61 could be detected by the SOB biosensor in the range of minutes as the microbial consortia was rapidly disturbed by the heavy metal and the decrease in EC was observed a few minutes after the injection of Cr61. However, the effect of SOB inhibition on pH appeared hours after the exposure to the toxic element and was not as fast as the changes in EC. Recently, Hajimohammadi et al. [82] proposed a dual biosensing system for the detection of Cr61 and Zn21 (Fig. 18.10). The setup consisted of a bioreactor where a couple of biosensors hosting two different bioluminescent bacteria were immersed. The light-emitting microbes were element-specific as follows: 1. E. coli grafted with the Gap reporter gene: Chromate-sensitive 2. E. coli grafted with the Gfp reporter gene: Zinc-sensitive These bacterial strains are designed to be natively bioluminescent; the presence of heavy metals they are intended to show sensitivity to is expected to inhibit their metabolism and thus decrease their bioluminescence. This dual biosensing system succeeded in demonstrating a dosedependent reduction in the light detected by the luminometer for both E. coli Gap and E. coli Gfp. However, it is worth noting that for this system, a pretreatment step was required consisting of saponin-based extraction of heavy metals from soil samples before injection into the bioreactor. In a similar experiment, Hou et al. [83] used a recombinant bioluminescent E. coli arsRp::luc to sense the bioavailable fraction of arsenic (Ar) present in contaminated soils (Table 18.2). This E. coli strain was rapidly induced (120 min.) by Ar and emitted light proportionally to the concentrations of the heavy metal (0.05
0.5 µM). When exposed to soil samples from agricultural areas, the biosensors revealed a correlation

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18.7 Soil-borne disease using a microbial biosensor

(A)

Luminometer

Air inlet Injection port Vent Fiber optic probe

Light

Fiber optic probe

Fiber optic probe

(B) Biosensor kit of Zn

Immobilized cell

Biosensor kit of Cr

LB medium

Black rubber

Stirrer

Heavy metals (Cr, Zn)

Cr 6–

Zn 2–

Zn 2–

Cr 6–

Propylene tube

biosurfactant(saponin)

(C)

FIGURE 18.10

Schematic of (A) dual biosensor system; (B) biosensor kit; (C) mechanism of extraction. From R. Hajimohammadi, M. Johari -Ahar, S. Ahmadpour, Designing of dual biosensor system for detection of zinc and chromium from contaminated soil using saponin biosurfactant and bioluminescence bacteria. Int. J. Environ. Anal. Chem. (2020) 1
11. Copyright 2020. Elsevier B.V.

between levels of fertilization and the bioavailability of Ar. Indeed, soils that have been for years treated with manure fertilization showed 7 to 9 times higher concentrations of bioavailable Ar. Using another recombinant light-emitting bacterium, Ivask et al. [84] applied bioluminescence-based biosensing to the monitoring of cadmium (Cd) in a large number of contaminated soils. In order to optimize the method and to investigate the possible influence of soil particulate, the authors exposed Cd sensor B. subtilis BR151 (pTOO24) to soil/water extracts resulting from two different protocols. Interestingly, this bacterial biosensor was not only fast in developing a response to the presence of Cd (150 min.) but was also more sensitive than the analytical reference methods (i.e., atomic absorption spectroscopy and inductively coupled plasma atomic emission spectroscopy). The biosensor demonstrated that bioavailable Cd fraction was 30-fold higher than the total Cd fraction determined by analytical methods and that the bioavailability of Cd was negatively correlated with soil parameters such as pH, organic matter, silt, and clay content. Moreover, fluorescent proteins are widely utilized

3. Environmental applications

TABLE 18.2

Main bioassay techniques used in biosensing of inorganic compounds. Biosensing of inorganic compounds

Technique

Sensed molecules

Sample

Detection

Detection limit

Advantages

Limitations

Reference

Electroactive microbial biofilms (microbial fuel cell)

Heavy metals

Mining rock drainage/soil leachate

Qualitative: current disturbances

NA

Real time, inexpensive, long-term application, continuous measurement


Indirect
Possible accumulation of metals precipitate

Adekunle et al. [79]

Sulfuroxidizing bacteria (SOB) In situ assay (metabolic activity)

Arsenic, nickel, chromium, zinc, mercury, copper, lead, cadmium

Soil, Sand, Silt, and Clay

Qualitative: Oxygen levels through Owen’s method

Undetermined

Inexpensive In situ No pretreatment

Indirect Unspecific

Eom et al. [80]

Sulfuroxidizing bacteria (SOB) (metabolic activity)

Hexavalent chromium (Cr61)

Swine wastewater

Quantitative: EC meter and pH meter

50 ppb ,

Simple Rapid (minutes) Inexpensive Real time

Indirect Unspecific

Hassan et al. [81]

Two strains of immobilized bioluminescent Escherichia coli

Chromium (Cr61) and zinc (Zn21)

Soil

Quantitative: Luminometer

Cr61:5
25 ppm Zn21: 10
20 ppm

Rapid inexpensive direct method dual detection

Pretreatment steps (metal extraction)

Hajimohammadi et al. [82]

Bioluminescent E. coli

Arsenic

Red soil (45.0% clay, 46.3% silt, and 8.7% sand)

Quantitative: GlomaxMuti 1 Microplate Multimode Reader

0.05
5 µM

Rapid (120 min.) direct

Hou et al. [83]

Bioluminescent Bacillus subtilis BR151 (pTOO24)

Cadmium

Naturally polluted soils

Quantitative: microplate luminometer

0.01
0.1 mg/kg

Simple rapid (150 min.)

Ivask et al. [84]

Bioluminescent Nitrosomonas europaea

Ammonium

Alluvial rice paddy soil

Quantitative: Luminometer

B20
400 µM

Simple rapid (10 min.) accurate inexpensive

pH sensitiveinterference of high toxicity

Dong et al. [27]

692

18. Chemical sensors and biosensors for soil analysis: principles, challenges, and emerging applications

FIGURE 18.11

The iEFE/cEFE whole-cell biosensor system was used to study diffusion of mercury ions in soil in 45 min. From B. Liu, J. Zhuang, G. Wei, Recent advances in the design of colorimetric sensors for environmental monitoring, Env. Sci. Nano. 7 (2020) 2195
2213. https://doi.org/10.1039/d0en00449a, Y. Liu, M. Guo, R. Du, J. Chi, X. He, Z. Xie, et al., A gas reporting whole-cell microbial biosensor system for rapid on-site detection of mercury contamination in soils. Biosens. Bioelectron. 170 (2020) 112660. Copyright 2020. Elsevier.B.V.

in whole-cell microbial biosensors for soil monitoring as optical reporting elements. Liu et al. [9,10] described a method that used gas as a reporting signal that could be used to detect mercury in soil quickly on-site. Results showed that mercury ions convert to C2H4 gas signals where they are quantified using C2H4 sensor (Fig. 18.11).

18.8 Challenges and future perspectives This chapter covered the major advancements in soil sensors, as well as their applications as smart devices for detecting various elements in soil. We anticipate that these three goals will be the key priorities for future soil sensing, as outlined in this chapter: (1) develop sufficient low-power consumption chemical and biosensors with powerful data processing and long-range wireless communication capability; (2) improve versatile soil sensing platforms that can be distributed in large-scale to collect real-time soil microenvironment data continuously; and (3) develop versatile soil sensing platforms that can be distributed in largescale to collect real-time soil microenvironment data continuously. Soil wearables will also be useful instruments in the development of precision detection. Soil sensors are predicted to become key driving forces in improving sensing systems for smart and precision agriculture due to their usefulness, low cost, compact size, and environmentally

3. Environmental applications

References

693

friendly production. In addition, the ability to measure many soil characteristics simultaneously using a multisensory approach in combination with combined techniques opens up new possibilities in sensor applications for soil type ranking and classification, as well as quantitative analysis. Due to their high sensitivity and ability to provide fast, reliable, and prior information, smart nanosensors devices have gotten a lot of attention, and they are starting to prove to be an important instrument for advocating soil assessment in near future. The usage of nanosensors has been found to increase agricultural output. These real-time sensors can measure temperature, soil health, soil moisture content, and even soil microbiological/microenvironment and nutritional status. Furthermore, the application of nanotechnology-based biosensors aids in the realization of precision agriculture. The use of nanosensor-based devices needs improvements in sensitivity and specificity to detect a wide range of soil elements and organisms.

18.9 Conclusion Due to increases in areas with damaged soil, as well as the heterogeneity and variability of natural soil, new technologies and methods of soil assessment have gained much interest. A wide range of modern methods for soil environmental monitoring, in particular chemical and biosensors, have been developed. This chapter described high-efficiency and low-cost sensors for the evaluation of main soil nutrient concentrations (organic and mineral compounds) and measurement pollutants, moisture, pH, and other soil parameters. Recently, sensors have been widely employed in on-field soil assessment for soil management, agriculture, horticulture, etc. Thus there is a need to study the effectiveness of the nanoscale-multisensory approach, which can provide easy and fast soil analysis.

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C H A P T E R

19 Recent advances in sensor and biosensor technologies for adulteration detection Narjiss Seddaoui and Aziz Amine Laboratory of Process Engineering and Environment, Faculty of Sciences and Techniques, Hassan II University of Casablanca, Mohammedia, Morocco

19.1 Introduction Fraud is an age-old concern that has a long unimpressive history. Milk, meat, oils, honey, spices, drinks, drugs, cosmetics, and fuels have become increasingly prone to fraud. Fraudulent claims also relate to the geographical and botanical origin of products [1]. While most cases of fraud do not raise a serious risk to public health, other cases have threatened the health of consumers and even resulted in death. One of the most high-profile cases is the 2008 “melamine incident” in China. More than 300,000 Chinese children became ill and 6 infants died after consuming milk containing melamine [2]. In another example, in 2013, the “horsemeat scandal” broke out in Europe [3]. Meat lasagna labeled as containing beef was found to contain undeclared horsemeat, which represented an infringement of consumers’ dietary habits. Even though its exact extent is not known, fraud has already reached shocking levels. A multitude of products associated with fraudulent practices are not as well documented and may never be discovered. According to the US Food and Drug Administration, economically motivated adulteration (EMA) can be defined as fraudulent, intentional substitution or addition of a substance in a product for the purpose of

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increasing the apparent value of the product or reducing the cost of its production, for economic gain. EMA includes dilution of products with increased quantities of an already present ingredient, as well as the addition or substitution of substances in order to mask dilution (https://www.govinfo.gov/content/pkg/FR-2009-04-06/html/E9-7843. htm). In 1820, Frederick Accum demonstrated the presence of toxic metals in beverages. Later, Arthur Hill Hassall was able to detect adulterants in cayenne pepper and custard powder. These studies were published in 1850 in the Lancet and led to the first Food Adulteration Act, which was passed in 1860 [4]. During the Industrial Revolution, the number of cases of adulteration multiplied leading to the establishment of a series of laws in the industrial sectors: The Pure Food and Drugs Act in 1906, the Food, Drugs and Cosmetics Act in 1938, the Food and Agriculture Organization of the United Nations in 1945, the World Health Organization (WHO) in 1948, and the Codex Alimentarius in 1960. Several reasons explain the invasion of global markets with adulteration. Firstly, as the world’s market resources are being depleted by the rapid increase in the world’s population, adulteration has found a way to meet the increased needs of consumers and has therefore spread to all industrial sectors. Secondly, the globalization of markets and the increase in international trade, driven by the reduction of export and import restrictions, as well as outsourcing to foreign producers, have facilitate the occurrence of goods adulteration. EMA can also be explained by the emergence of tricky and sophisticated ways of adulteration. All of these emanate from the greed of profiteers to boost their incomes. In the first section of this chapter, we describe the different types of adulteration, focusing on the most common cases in each industrial sector, as well as the possible consequences on consumer health, economy, and environment. Then, an overview of traditional analytical techniques for the detection of adulterants is given along with recent applications. In the last section, the most prominent sensors and biosensors developed during the last decade for adulteration detection are discussed, including all forms of transduction whether electrochemical, optical, or gravimetric.

19.2 Adulteration: a global scam and health threat To get a quick overview of the number of studies aimed at detecting goods adulteration, the Scopus database was queried in March 2021 using the keyword “adulteration.” The number of articles dealing with

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adulteration increased from 405 in 2011 to 929 in 2020, highlighting the increase in the rate of adulteration practices. In general, EMA occurs in three different ways [5]: 1. Replacement: This is the complete or partial replacement of an authentic product with a less expensive substitute. It also includes the false declaration of geographic or botanic origin, and the misrepresentation of the production process. 2. Addition: This consists of adding small amounts of a nonauthentic substance to mask the poor quality of the ingredients or to enhance the color or flavor of a final product. 3. Removal: This is the intentional omission of a genuine and valuable component from a product without the knowledge of consumers.

19.2.1 Spectrum of adulterants and associated products most vulnerable to adulteration Fig. 19.1 shows a consolidated distribution of EMA ranked by most adulterated products over the last decade, based on the Scopus database. Products frequently affected by adulteration are drugs (25.2%), oils (21%), milk (14%), and meat (12%). Focusing on food products, there is almost no product that has escaped adulteration, whether it is honey, juice, spices, or drinks. Adulteration practices also encompass fuel and cosmetics industries. The presented list of products is not exhaustive and does not include all cases of adulteration. The lack of

FIGURE 19.1 Leading EMA reported studies by sector during the last decade (http:// www.scopus.com).

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data can be explained by the use of tricky methods for counterfeiting products and their harmless effect in most cases, making them go unnoticed. 19.2.1.1 Food Food safety refers to food that meets nutritional requirements and causes no harm to human health [6]. As the profit margins in food production are generally narrow profiteers are constantly looking for new tactics to reduce production costs and maximize their revenues [7,8]. Unfortunately, the true extent of food adulteration may be unknown or perhaps even unrecognizable since the aim of EMA is to go undetected. 19.2.1.1.1 Milk

Melamine is added to milk in order to increase its nonprotein nitrogen content [9,10]. It can be found in milk, infant formula milk powder, and biscuits [11]. The intake of melamine beyond the safe limit (1 mg/kg for infant powder formula and 2.5 mg/kg for other milk foods) leads to kidney failure [12]. The first “melamine incident” was reported in 2007 when a large number of pets died of kidney failure after ingesting products fortified with melamine [13]. In 2008, high levels of melamine in infant formulas caused the poisoning of thousands and the death of at least six Chinese infants [14]. In addition, urea is also found in milk [15]. Beyond 0.4 mg/mL urea, milk becomes unsafe for consumption [16] and may cause gastrointestinal hemorrhages and kidney failure [17]. Milk is often mixed with reconstituted milk powder, sugar, starch, and water. Milk adulteration involves the removal of beneficial fats and substitution with milk from another species. 19.2.1.1.2 Meat

Meat adulteration consists of the substitution of high-quality meat with low-quality species [18]. One high-profile example is the horsemeat scandal of 2013 that shook several European countries. High levels of undeclared horsemeat were found in beef products [19]. Another case deals with the substitution of beef with pork due to its similar red appearance and its low cost [20]. Meat adulteration involves the partial or complete replacement of highly nutritional meat with donkey, rat, dog, cat, and rabbit [21 23]. The intake of undeclared meat species may lead to allergic reactions [7] and zoonotic diseases [24]. In general, it is up to consumers to choose what to eat without worrying about their lifestyle or beliefs. 19.2.1.1.3 Edible oils

Olive oil is the most nutritious and tasty edible oil, with a price that exceeds 3 to 5 times that of ordinary edible oils [25]. Olive oil can

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prevent high blood pressure, diabetes, and obesity [26]. Adulteration of olive oil includes the admixture of cold-pressed oil with refined oil [27] or its substitution with cheap oils [28,29]. On the other hand, argan oil is considered as a luxury food ingredient with a price of about 120h/L. Argan oil is a choleretic and hepatoprotective agent and skin problem corrector [30]. Argan oil adulteration consists of its mixture with cheap vegetable oils such as low-grade olive oil, soybean oil, or sunflower oil [31 33]. Among others, all varieties of edible oils (e.g., coconut oil, sunflower oil, sesame oil, camellia oil) are prone to adulteration without taking into account the potential danger of the consumption of such adulterated oils [34 37]. 19.2.1.1.4 Honey

Honey is a natural sweetener produced by bees from the nectar of flowers [38]. It consists of around 43% fructose, 35% glucose, 2% sucrose, and water [39]. The composition of honey depends on the species of bee, the environmental conditions, and the harvesting technology [40]. Honey is used to treat oral mucositis [41], skin problems, and coughs [42]. Honey adulteration can be indirect by feeding bees with industrial sirup during the nectar flow period [43], or direct by mixing honey with fructose or sucrose sirups. Honey adulteration can provoke weight gain and abnormal kidney function [44]. 19.2.1.1.5 Culinary spices and herbs

Herbs and spices are valuable ingredients extensively used in cuisine, with a growth rate of 6.3% annually [45]. Spices derivate from parts of the plant other than the leaves and stems; they include fruits (cumin), rhizomes (turmeric), bark (cinnamon), unexpanded flowers (cloves), and seeds (nutmeg). Herbs are generally represented by sage, oregano, rosemary, basil, laurel, thyme, and peppermint [46]. The limited production, the increased demand, the availability in powder form, and the long supply chain are excellent opportunities for EMA occurrence [47]. Adulteration involves (1) Adding azo dyes (Sudan I, II, III, IV, and Rhodamine B) to improve the appearance of paprika powder, turmeric powder, saffron powder, and chili powder. Due to their harmful effects (e.g., carcinogenicity, neurotoxicity, and genotoxicity), these dyes are prohibited for use in food products [48]; (2) substituting plant material from a totally different plant with a similar texture: starch in turmeric, sand in chili powder, and coriander in cumin [49]; (3) mixing fresh spices with older ones to increase weight; or (4) adding extenders, which are the inedible parts of the same plant, to increase bulk [50].

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19.2.1.2 Herbal medicines and drugs Medicinal herbs are a diversified category of commodities used to prevent or treat diseases. The herbal medicine industry is one of the fastest growing industries in the world, with a growth rate of 15% annually [51]. According to WHO, over 80% of the world’s population uses herbal medicines, either in developed or developing countries [52]. Several factors explain the increased use of medicinal herbs: the false feeling of safety, the high cost of synthetic drugs, and the ease accessibility via online purchases. However, only 12.5% of these plants have been well studied and have shown pharmacological effects. They are currently used for the synthesis of several drugs such as morphine (pain), aspirin (headaches), paclitaxel (breast cancer), digoxin (heart function), lovastatin (high blood cholesterol), irinotecan (colorectal cancer), etoposide (lung cancer), and metformin (diabetes) [53]. Medicinal plant adulteration ranges from 10% to 80% [54]. For example, Stephania tetrandra roots, used to relieve pain, were substituted with roots of the toxic herb Aristolochia fangchi leading to over 100 cases of renal failure in women [55]. Mesua ferrea, used as an antiseptic and anti-inflammatory, was adulterated with Calophyllum inophyllum causing vomiting and diarrhea [56]. Examples include the adulteration of Saraca asoca bark with Mallotus nudiflorus bark, which is cardio- and immunotoxic [57] and ginseng extracts with Panax subspecies and Eleuthero coccus that do not contain bioactive ginsenosides [58]. Adulteration involves also the illegal addition of pharmaceuticals to herbal preparations such as weight-loss products (e.g., fenfluramine, sibutramine, ephedrine); herbal preparations for rheumatic and inflammatory disease treatment (e.g., acetaminophen, trimethoprim, sulfamethoxazole); herbal preparations for blood sugar regulation (e.g., libenclamide, rosiglitazone, metformin); and herbal preparations for blood pressure regulation (e.g., amlodipine, indapamide, valsartan) [59]. The presence of undeclared drugs in herbal preparations can lead to drug-drug interactions, which can cause serious adverse effects on the whole human body [60]. 19.2.1.3 Cosmetics Cosmetics are personal care products (e.g., creams, toothpastes, deodorants), beauty products (e.g., makeup, nail polish, hair care products), and perfumes [61]. The cosmetics industry is one of the fastest growing industries in the world with a rate of 5% annually [62]. Due to their pleasant scents, essential oils (EO) are incorporated into almost every cosmetic product. They are a mixture of volatile and semivolatile compounds [63], commonly extracted from plants by steam distillation [64]. The low extraction yield makes EOs trade at very high prices and therefore prone to several adulteration practices. EOs are diluted with

3. Environmental applications

19.2 Adulteration: a global scam and health threat

705

nonvolatile ingredients such as edible oils or solvents (e.g., coconut oil or kerosene in lemongrass EO) [65]. Moreover, natural or synthetic components such as citral, benzyl benzoate, synthetic irone, and linalyl acetate have been found in lemon, balsam of Peru, iris, and lavender EOs, respectively, in order to improve their olfactory quality [66]. Another common adulteration is to mix an expensive EO with a lower quality one but with similar olfactory notes. Lavandula angustifolia EO, which costs up to 130 h/kg, was mixed with EOs of other Lavendula species, which cost around h20/kg [67]. On the other hand, skin whitening creams are available at different prices and widely used by men and women. Skin whitening creams have a depigmenting potential that helps in lightening the skin and improving its tone by decreasing the concentration of melanin [68]. Nevertheless, deleterious compounds with skin peeling properties such as heavy metals, hydroquinone, and corticosteroids are added to cosmetics in order to accelerate skin depigmentation [69]. For instance, mercury salt is commonly introduced into skin lightening products as it inhibits melanin synthesis [70]. Mercury is nephrotoxic and neurotoxic and skin exposure can lead to irritation and allergic reactions [62]. To meet the demands of consumers, regardless of their beliefs, cosmetic brands have leaned toward halal cosmetic products [71]. “Halal” and “Toyyib” cosmetics are increasingly in demand due to the high quality offered by these products, as well as the potential to be offered not only to Muslims but also to the whole world. “Halal” cosmetic products do not contain any trace of pig or alcohol derivatives. “Toyyib” refers to the absence of harmful substances in the final product [72]. 19.2.1.4 Fuels Fossil fuels such as gasoline, diesel, and kerosene have been the energy source of choice worldwide [73]. Due to the rapid increase in energy requirements and the divergence in energy policies among countries, fuel adulteration is a persistent concern that seeks to increase the profit margins of fraudsters. Gasoline is commonly adulterated with kerosene along with diesel, naphtha, ethanol, and other solvents available at lower cost [74]. As for diesel, kerosene, vegetable oils, and solvents are added. Kerosene, in particular, is often adulterated in diesel fuel due to the overlapping C9 C19 hydrocarbons in diesel and C6 C16 in kerosene, making its detection almost unfeasible by physicochemical tests [75]. Fossil fuel tampering can have significant economic and environmental impacts including the increase of hydrocarbon, nitrogen oxide, and carbon monoxide content in tailpipe emissions and reducing the durability of vehicle engines [76]. Apart from fossil fuels, bioethanol is one of the most important renewable energy sources due to its low emission of CO [77]. Bioethanol is produced from various

3. Environmental applications

706

19. Recent advances in sensor and biosensor technologies for adulteration detection

biomasses, such as corn, sorghum, and sugarcane. It is generally marketed in anhydrous form or as hydrated ethyl alcohol (HEAF) [78]. The lower price of methanol and the similarity of these alcohols contribute to the ease of adulterating HEAF with methanol, which is extremely toxic and can cause headaches, vomiting, blindness, and even death [79]. 19.2.1.5 Other industrial products Adulteration of products also extends to fruit juices, which can be diluted with water, or mixed with less expensive juice (e.g., grape juice into pomegranate juice). Some juices may be made with only water, dyes, and sweet sirup [80]. On the other hand, potential coffee adulteration cases include the misrepresentation of low-value beans as highvalue beans, the mixing of two beans species, and the incorporation of undeclared plant materials (e.g., chicory and coffee hulls) [81]. Furthermore, valuable seafood species are being replaced by cheaper and more abundant fish: red snapper (actually tilefish), wild Alaskan salmon (actually farmed Atlantic), and caviar (actually catfish roe) [82].

19.2.2 Adulteration: major concern for health, economy, and environment EMA has a serious impact on consumers, producers, and the environment. Human health is fragile and the intake of products containing hazardous substances threatens the health of consumers. Immediate side effects such as diarrhea, vomiting, and headaches may occur. However, long-term adverse effects can also appear including cancer, kidney failure, liver problems, cardiovascular disease, muscle paralysis, brain damage, and respiratory distress [83]. In addition, food adulteration costs the global food industry between $10 billion and $15 billion per year [84]. Unfortunately, the extent of adulteration may be unknown, if not unknowable, as the number of documented incidents is most likely only a fraction of the actual numbers. The sale of an adulterated product is inevitably reported in the media, casting doubt on the reputation of that company and causing consumers to lose confidence. The resulting brand damage can be devastating, and recovery can be expensive and time consuming. The adulteration of products through the introduction of nondegradable and persistent substances such as heavy metals and dyes constitutes a potential ecological hazard as they can accumulate in the human body, and once excreted again become an environmental contaminant that is difficult to eradicate.

3. Environmental applications

19.3 Conventional analytical techniques for adulterants detection

707

19.3 Conventional analytical techniques for adulterants detection The assessment of goods authenticity is a key tool to mitigate adulteration problems and protect public health. The choice of one method among others strongly depends on sample matrix and the type of adulterant to be detected. Chromatographic techniques are among the most commonly used methods for adulterant detection. They are used to separate different molecules of a mixture based on their differential partitioning between a mobile phase and a stationary phase. High-performance liquid chromatography is the most widely used to verify the authenticity of edible oils [85], honey [86], spices [87], and coffee [88]. On the other hand, gas chromatography (GC) is used to separate volatile organic compounds from a mixture. As a nondestructive technique, GC has been successfully applied to determine the presence of solvents in adulterated fuel [74], apple juice addition into orange juice [89], and peanut oil adulteration with rapeseed oil [90]. Spectroscopic methods are based on the interaction between electromagnetic radiation and matter that comprises the product matrix as a function of the wavelength or frequency detected in the emitted or absorbed energy spectrum. Among spectroscopic methods, infra-red, Raman, and nuclear magnetic resonance are the most frequently reported for adulterants identification in oils [91], milk [92], and spices [45]. Isotope ratio mass spectrometry (IRMS) is a powerful technique to measure the relative abundance of different stable isotopes of chemical elements such as C, H, N, O in a given sample. Plants are classified by their metabolic assimilation of atmospheric CO2. Their metabolites do not display the same isotopic fractionation, which results in the distinction of plants by their isotopic ratio. For each element, one or more isotopes are present at different levels, with a specific distribution pattern. Although IRMS requires a significant financial investment, it is practical for the detection of EOs adulteration [93,94], as well as for the authentication of red yeast rice [95], grape must [96], and honey [97]. Molecular biology techniques based on DNA analysis are broadly used to identify adulterants in traded products. DNA barcoding has proven to be very useful in recent years. By comparing a short genetic marker called a “DNA barcode” in an organism’s DNA to a compiled database of barcodes, species identification can be achieved when there is molecular variability between species and high-quality reference sequences are available. Investigations report the use of DNA-barcoding technology for the assessment of adulteration in herbal medicines [50,54] and spices [98,99].

3. Environmental applications

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19. Recent advances in sensor and biosensor technologies for adulteration detection

Among immunological techniques, enzyme-linked immunosorbent assay (ELISA) is the most popular immunoassay for the detection of adulterants. It uses a type of solid phase enzyme immunoassay for a liquid sample using antibodies directed against the substance to be measured. This gold standard method is commonly used in laboratories due to its high throughput, good specificity, robustness, and low cost compared to the abovementioned methods. ELISA has proven successful in detecting pork adulteration in beef samples [100,101].

19.4 Recent trends in adulteration detection EMA continues to concern people around the world, and has led to the development of a variety of analytical methods, as mentioned above. These techniques are undoubtedly reliable and accurate, but unfortunately, they are time-consuming, expensive, and require sophisticated equipment. Therefore sensors and biosensors are increasingly solicited as prominent candidates for rapid and sensitive detection of adulterants in a broad range of sectors including food, cosmetics and fuels, as listed in Fig. 19.2.

19.4.1 Why sensors and biosensors for adulteration detection? Sensors and biosensors are an attractive alternative to conventional methods, with a vast range of applications. A biosensor is an analytical device integrating a biological recognition receptor (e.g., antibody, DNA/RNA,

FIGURE 19.2 Pie chart illustrating the contribution of sensors and biosensors to the detection of various adulterants in industrial sectors. Percentages calculated according to Scopus database (2011 2021).

3. Environmental applications

19.4 Recent trends in adulteration detection

709

enzyme) at the surface of a transducer that generates an electrical or optical signal proportional to the concentration of the analyte to be detected [102]. Unlike biosensors, a sensor is an analytical device that involves an abiotic recognition receptor such as metallic nanoparticles, carbon nanomaterials, conductive polymers, or nanocomposites [103]. The popularity of sensors and biosensors is increasing because they offer accurate, highly selective, and sensitive analysis in addition to their excellent portability, ease of use, and fast response [104]. Based on the transduction mode, we distinguish (1) electrochemical sensors (e.g., amperometric, voltametric, impedimetric, and conductimetric), which measure the electrical signal resulting from the recognition of the analyte; (2) optical sensors (e.g., plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), fluorescence, chemiluminescence, and colorimetric) based on the measurement of light absorbed or emitted in response to the interaction between the recognition receptor and the target molecule; and (3) gravimetric sensors (quartz crystal microbalance (QCM) and surface acoustic wave (SAW)), which measure small changes in mass. According to an extensive query in the Scopus database (Fig. 19.3), the transducers most commonly used for adulterant detection are electrochemical (65.3%), optical (33%), then gravimetric (1.7%).

19.4.2 Sensors for adulterants detection Melamine is one of the most common adulterants used to inflate the protein content of milk. Accordingly, a molecularly imprinted polymer (MIPs) was prepared on the surface of glassy carbon electrode (GCE) by

FIGURE 19.3

Classification of sensors and biosensors developed for adulteration detection over the last decade according to the Scopus database (2011 2021).

3. Environmental applications

710

19. Recent advances in sensor and biosensor technologies for adulteration detection

the electropolymerization of aniline in the presence of melamine as template [105]. Using square-wave voltammetry (SWV), a detection limit of 0.44 nM was obtained. To improve the sensitivity of such a sensor, aniline was coelectropolymerized with acrylic acid in the presence of melamine. As a result, a lower limit of detection (LOD) of 0.017 nM was reached [106]. This high sensitivity can be ascribed to the synergistic effects of the amine of aniline and the carboxyl group of acrylic acid to form multiple noncovalent interactions with melamine. For an easier detection, an electrochemical sensor based on the competitive oxidation of melamine and uric acid on the surface of a preanodized GCE was developed [107]. The decrease in the anodic peak of uric acid at 10.23 V was assigned to the adsorptive behavior of melamine. Under the optimized parameters, 0.6 nM of melamine was detected. Based on the poor electrochemical activity of melamine, a FRET (fluorescence resonance energy transfer) sensor using carbon quantum dots (CQDs) and gold nanoparticles (AuNPs) was developed for melamine detection [108]. CQDs were used as a reductant to synthesize AuNPs, and thus the formation of Au@CQDs nanocomposites. Au@CQDs were nonfluorescent due to fluorescence quenching between CQDs and AuNPs driven by the FRET process. After the addition of melamine, it interacted with AuNPs that underwent aggregation. Consequently, the CQDs were released from AuNPs and the fluorescence increased as melamine concentration increased. The LOD was equal to 3.6 nM. On the other hand, SERS optical sensing of melamine was achieved by incorporating metal nanoparticles into hydrogel micropellets with small pore size to selectively detect melamine [109]. The nanoparticles were uniformly dispersed in the hydrogel micropellets, thereby ensuring high stability and reproducibility of SERS substrate. The LOD was equal to 10 nM with no need for sample pretreatment. For simpler configuration, SPR optical sensor based on polyethylene glycol-modified AuNPs (PEGylated AuNPs) was designed for melamine detection [10]. The interactions between melamine and PEGylated AuNPs disrupted the electrostatic equilibrium of the particles that underwent aggregation, resulting in a shift of the SPR and consequently a change in color solution, which can be monitored by the naked eye and UV-vis spectroscopy. A low detection limit of 1 nM was reached by the proposed sensor. One of the major adulterations in honey and juice industries is the addition of sugars. An enzyme-free electrochemical sensor based on copper nanospheres-modified SPE was developed for glucose oxidation at 10.65 V. By this simple configuration, 0.57 μM of glucose was detected by chronoamperometry [110]. Similarly, a simple electrochemical method for the synthesis of Ag-PANI/rGO nanocomposite on indium tin oxide (ITO) electrode was reported for glucose detection in juices [111]. The AgNPs acted as catalyst for glucose oxidation and the PANI/rGO composite films enhanced the electron transfer rate. A LOD

3. Environmental applications

19.4 Recent trends in adulteration detection

711

of 0.79 μM was obtained within few seconds of amperometric measurements. Recently, a novel electrochemical sensor based on nickel complex was developed for the simultaneous detection of glucose and fructose in honey without pretreatment steps [112]. Schiff Ni-base was electropolymerized onto carbon nanotube-modified-SPE. The prepared complex exhibited interesting catalytic characteristics against the electrooxidation of both sugars, enabling the detection of glucose (12 μM) and fructose (16 μM) by cyclic voltammetry in 0.1 M NaOH, without overlapping responses of the two monosaccharides. Sudan I is a toxic azo dye found in food. GCE was modified with graphene/β-CD/PtNPs nanocomposites [113] and Bi2WO6 nanosheets [114] for the construction of highly sensitive sensors for the detection of Sudan I in ketchup and chili powder. The electrochemical sensors were able to detect Sudan I in a submicromolar range, 1.6 2 nM, measured by DPV. SPEs, as low-cost and mass-production electrodes, were modified with La31-doped Co3O4 nanocubes [115] and ZnFe2O4 nanoparticles [116] to enhance the electro-oxidation of Sudan I. The electrochemical sensors exhibited excellent electrochemical behavior allowing the detection of nanomolar level of Sudan I when conducting DPV in 0.1 M PBS. Very recently, an optical sensor based on fluorescent carbon dots (CDs) was developed for the detection of Sudan I, II, III, and IV in chili powder [117]. The fluorescence of CDs quenched upon the addition of Sudan I IV, which can be attributed to the internal filter effect occurrence. LODs were 0.17, 0.21, 0.53, and 0.62 μM for Sudan I, II, III, and IV, respectively. Sunset yellow (SY) and tartrazine (TZ) are synthetic azo dyes that become toxic when the Admissible Daily Intake value exceeds 1.0 mg/kg body weight/ day when both dyes are mixed in a single ingredient. An electrochemical sensor for the simultaneous detection of SY and TZ in soft drinks was fabricated by the modification of GCE with Multi-Walled Carbon Nanotubes (MWCNTs) [118]. The prepared sensor (MWCNT/GCE) displayed excellent electrocatalytic activity for the oxidation of TZ and SY. LODs were equal to 0.12 and 0.22 μM for SY and TZ, respectively, according to DPV. In addition, by mixing CoC with graphite powder, a carbon paste electrochemical sensor was developed for SY and TZ detection [119]. LODs were 0.9 and 0.3 μM of SY and TZ, respectively, using SWV. Later, SY and TZ were detected in a submicromolar range (0.09 μM SY; 0.02 μM TZ) using a neodymium (III) oxide decorated carbon paste electrode (Ndox-CPE) [120]. The use of Ndox resulted in an increase in the anodic peak currents for TZ and SY by more than 60%, compared to the bare CPE. In order to meet the high demand for cost-effective weight loss formulations, prohibited synthetic substances such as ephedrine and sibutramine are added as they are appetite suppressants. An electrochemical sensor for highly sensitive ephedrine detection was developed using Fe3O4@SiO2@TiO2-MIP nanocomposite (FST-MIP) prepared by

3. Environmental applications

712

19. Recent advances in sensor and biosensor technologies for adulteration detection

microwave-assisted polymerization in the presence of ephedrine as template [121]. The FST-MIP was mixed with graphite powder and then transferred into CPE holder. The oxidation current of ephedrine increased dramatically in BRB (pH 5 10.5) when using the prepared sensor. The LOD was found to be 3.6 nM using DPV. In a similar context, an ephedrine MIP was prepared by sol-gel technique and then dropped onto a GCE modified with Nafion-MWCNTs [122]. The electrochemical sensor displayed a fast response time of less than 180 s and allowed the determination of 72 nM ephedrine based on DPV responses. For more simplicity, a boron-doped diamond electrode (BDD electrode) was used for the direct oxidation of ephedrine at 11.91 V [123]. Using SWV, 0.79 μM ephedrine was detected in 0.1 M acetate buffer. Moreover, a simple and sensitive electrochemical sensor was constructed for the determination of sibutramine in slimming products. A carbon graphite SPE was used for direct electrochemical oxidation of sibutramine at 0.1 V in 0.1 M BRB (pH 7.0) [124]. Using DPV, a low LOD of 0.3 μM was detected by the developed sensor. For ultrasensitive determination of sibutramine, magnetic MIP was combined with silver nanoparticles for the preparation of a label-free SERS for trace sibutramine analysis [125]. The coating of Fe3O4@Ag with a MIP layer improved the affinity and selectivity of the developed sensor, and thus sibutramine was detected in the nanomolar range (LOD 5 1 nM). Heavy metals such as lead (Pb), cadmium (Cd), arsenic (As), copper (Cu), and mercury (Hg) are found in hair dyes, lipsticks, powdered make-up, and whitening creams. An electrochemical sensor for the simultaneous determination of Pb21 and Cd21 in commercial hair dyes was developed [126]. GCE was modified with AuNPs/([Ru(NH3)6]31/ nafion by drop casting technique. Anodic stripping voltammetry (ASV) in 0.1 M NaCl was used to quantify Pb21 and Cd21 in the capillary stains and LODs were 0.21 and 1.77 μM, respectively. The presence of Pb21 and Cd21 was also investigated in the capillary products by a bismuth film modified graphite-epoxy composite electrode [127]. The voltammetric sensor allowed the detection of 70 and 50 nM of Pb21 and Cd21, respectively, based on ASV responses. Recently, a cost-effective gold-sputtered plastic electrode was used for the detection of Pb(II), As (III), and Hg(II) in skin creams [128]. Three effectively separated peaks at 20.1, 0.1, and 0.55 V (vs AgCl) were observed for Pb(II), As(III), and Hg(II), respectively. This sensor allowed simultaneous detection of 9.65, 66.73, and 2.49 nM Pb(II), As(III), and Hg(II), respectively, with no overlapping responses. Copper (Cu21), which is largely added to lipsticks and eye shadows, was simultaneously detected with lead (Pb21) using a GCE modified with EDTA-NQS layer [129]. Complexation of EDTANQS/GCE surface with heavy metal ions resulted in well-separated anodic peaks of Cu21 and Pb21 at 20.795 and 20.285 V (vs Ag/AgCl),

3. Environmental applications

19.4 Recent trends in adulteration detection

713

respectively. LODs for Cu21 and Pb21 were 10.2 and 15.4 nM, respectively, according to LSV. Methanol adulteration in fuel ethanol is still raising concerns worldwide due to its high toxicity. The simple structure of methanol (CH3OH) makes its determination rather difficult. Therefore few sensors dealing with methanol detection have been designed. A copper glass electrode [130] and a gold electrode [131] were used for the direct oxidation of methanol at low potential. LODs were estimated to be 0.9% and 0.045%, respectively. Recently, a trimetallic PtAuAg nanotube-modified GCE was constructed for the ultrasensitive detection of methanol [132] at 6.4 3 1025%. In addition, methanol adulteration in bioethanol was also evaluated using a Tb31-based photoluminescent metal-organic framework (Tb31-MOF) [78]. The luminescence intensity of Tb31 increased with increasing methanol concentration, as it completed the coordination spheres of the lanthanide (Tb31). With this optical sensor, 0.82% (v/v) methanol was detected in bioethanol fuel samples. An overview of recently developed electrochemical and optical sensors for the detection of the main adulterants found in milk, drinks, spices, slimming products, cosmetics, and fuels is provided in Table 19.1.

19.4.3 Biosensors for adulterants detection The presence of undeclared meat species in meat products is a serious problem as consumers are left unaware of what they are ingesting. Since the adulteration of beef with pork is unacceptable for Muslims and Jews, various electrochemical biosensors have been constructed for the detection of several pork biomarkers (e.g., porcine mitochondrial DNA (mtDNA), porcine immunoglobulin G (pig-IgG), and porcine serum albumin (PSA)) in real meat mixtures. Indeed, a label-free electrochemical genosensor was established by immobilizing AuNP porcine ssDNA probe bioconjugates on the surface of goldmodified SPE [157]. The hybridization of target porcine mtDNA to the ssDNA probe was characterized by DPV based on the current response of methylene blue, used as hybridization indicator. The increase in peak current correlates to the number of MB molecules intercalated into the dsDNA, which in turn correlates to the concentration of target porcine mtDNA. The detection limit was 10% (v/v) of the pork DNA in meat mixture extract. Recently, beef authenticity was checked by verifying its adulteration with pork. Graphite electrode modified with reduced graphene oxide (rGO)/poly(3-hydroxybenzoic acid) nanocomposite was used to immobilize cattle ssDNA probe [158]. The hybridization of cattle DNA with DNA probe was revealed by monitoring the SWV signal of

3. Environmental applications

TABLE 19.1 Examples of sensors intended to detect adulteration in food, cosmetics, and fuel. Adulterant

Matrix

Sensing platform

Sensor classification

Analytical method

LOD

Reference

Melamine

Milk

MI-PANI/GCE

Electrochemical

SWV

0.44 nM

[105]

Melamine

Milk

MI-PANI-PAA/GCE

Electrochemical

DPV

0.017 nM

[106]

Melamine

Milk

Preanodized GCE

Electrochemical

DPV

0.6 nM

[107]

Melamine

Milk

Au@CQDs

Optical

Fluorescence

3.6 nM

[108]

Melamine

Milk

Zr-based MOFs

Optical

Fluorescence

90 nM

[133]

Melamine

Milk

AuNP@4-MBN nanopellets

Optical

SERS

10 nM

[109]

Melamine

Milk

PEGylated AuNPs

Optical

SPR

1 nM

[10]

Melamine

Milk

SauNPs

Optical

SPR

1.82 μM

[134]

Melamine

Milk

DNA-AgNC53

Optical

Luminescence

27 nM

[9]

27

Melamine

Milk

Fluorescein/Gold nanoparticles

Optical

Chemiluminescence

3 3 10

Urea

Milk

NiBzimpy/CPE

Electrochemical

LSV

1.5 μM

[136]

Urea

Milk

Au@AgNPs/gold coated slide

Optical

SERS

0.83 mM

[137]

Glucose

Honey

CuNS/SPE

Electrochemical

CA

0.57 μM

[110]

Glucose

Juice

Ag-PANI/rGO/ITO electrode

Electrochemical

Amperometry

0.79 μM

[111]

Glucose

Honey

(Ni(II)-2,3dhS)/CNT/SPE

Electrochemical

CV

12 μM

[112]

16 μM

[112]

26 μM

[138]

Fructose Glucose

Juice

Pt/PVF/Pt electrode

Electrochemical

CA

μM

[135]

Sudan I

Ketchup/ chili powder

graphene/β-CD/PtNPs/ GCE

Electrochemical

DPV

1.6 nM

[113]

Sudan I

Ketchup/ chili powder

Bi2WO6/GCE

Electrochemical

DPV

2 nM

[114]

Sudan I

Ketchup/chili powder

La3 1 doped Co3O4 nanocubes/SPE

Electrochemical

DPV

0.05 μM

[115]

Sudan I

Ketchup/chili powder

ZnFe2O4/Graphite SPE

Electrochemical

DPV

0.03 μM

[116]

Sudan I

Ketchup/chili powder

CuO/ITO electrode

Electrochemical

DPV

0.12 nM

[139]

Optical

Luminescence

0.26 μM

[140]

Optical

Fluorescence

0.17 μM

[117]

Sudan I

Ketchup/chili powder

[Ru(bpy)2(CIP)]

Sudan I II III IV

Ketchup/chili powder

CDs

21

0.21 μM 0.53 μM 0.62 μM

Sunset yellow

Soft drinks

MWCNTs/GCE

Electrochemical

DPV

Drinks

CoC/CPE

Electrochemical

SWV

0.9 μM

[119]

0.3 μM

Tartrazine Sunset yellow

[118]

0.22 μM

Tartrazine Sunset yellow

0.12 μM

Drinks

NdOx/CPE

Electrochemical

SWV

0.09 μM

[120]

0.02 μM

Tartrazine Sunset yellow

Drinks

Polydopamine/MIP/ MWCNT/GCE

Electrochemical

DPV

1.4 nM

[141]

Sunset yellow

Drinks

Fe3O4 MWCNTs/GCE

Electrochemical

DPV

1.4 nM

[142]

Sunset yellow

Drinks

Dy2O3NPs/MWCNTs/ GCE

Electrochemical

SWV

0.35 nM

[143] (Continued)

TABLE 19.1 (Continued) Sensor classification

Analytical method

LOD

Reference

rGO-g-CN/ZnO-AuNPs/ GCE

Electrochemical

SWV

1.34 nM

[144]

Drinks

Gr/PLPA/PGE

Electrochemical

DPV

1.54 μM

[145]

Metanil yellow

Curcumin

calix/Au NPs/GCE

Electrochemical

DPV

9.8 nM

[146]

Metanil yellow

Curcumin

CQDs/GCE

Electrochemical

DPV

0.03 μM

[147]

Rhodamine B

Chili powder

MnO2NRs-ERGO/GCE

Electrochemical

LSV

6 nM

[148]

Rhodamine B

Drinks/juice

Cu2ONPs-ERGO/GCE

Electrochemical

LSV

6 nM

[149]

Rhodamine B

Hair color

MWCNT/CPE

Electrochemical

DPV

20 nM

[150]

Rhodamine B

Drinks/juice

Cu@CS/GCE

Electrochemical

DPV

100 nM

[151]

Ephedrine

Slimming products

Fe3O4@SiO2@TiO2-MIP/ CPE

Electrochemical

DPV

3.6 nM

[121]

Ephedrine

Slimming products

Nafion-MWCNTs/MIP/ GCE

Electrochemical

DPV

72 nM

[122]

Ephedrine

Slimming products

Boron-doped diamond electrode

Electrochemical

SWV

0.79 μM

[123]

Ephedrine

Slimming products

MWCNT/GCE

Electrochemical

ASV

0.75 μM

[152]

Ephedrine

Slimming products

Gold electrode

Electrochemical

SWV

0.33 mM

[153]

Sibutramine

Slimming products

Graphite SPE

Electrochemical

DPV

0.3 μM

[124]

Sibutramine

Slimming products

Fe3O4@Ag/MIP

Optical

SERS

1 nM

[125]

Sibutramine

Slimming products

PGr-ink/GCE

Electrochemical

SWV

17.86 nM

[154]

Adulterant

Matrix

Sensing platform

Sunset yellow

Drinks

Tartrazine

Lead

Hair dyes

AuNPs [Ru(NH3)6]31/GCE

Electrochemical

ASV

Hair shampoo

Cadmium Lead Arsenic

Eye shadow/skin lotion/ talcum powder

Bismuth-graphite epoxy electrode

Electrochemical

Gold-sputtered plastic electrode

Electrochemical

ASV

[127]

70 nM 50 nM

DPV

9.65 nM

[128]

66.73 nM

Mercury Copper

[126]

1.77 μM

Cadmium Lead

0.21 μM

2.49 nM Eyeshadow/lipstick

EDTA-NQS/GCE

Electrochemical

LSV

Lead

10.2 nM

[129]

15.4 nM

Bismuth oxichloride

Eye shadow

CPE

Electrochemical

SWV

8 nM

[155]

Methanol

Bioethanol

Copper-glass electrode

Electrochemical

CV

0.9%

[130]

Methanol

Bioethanol

Gold electrode

Electrochemical

CV

0.045%

Methanol

Bioethanol

PtAuAg NTs/GCE 31

[131] 25

Electrochemical

Chronoamperometry

6.4 3 10 %

[132]

Methanol

Bioethanol

Tb -MOF

Optical

Photoluminescence

0.82%

[78]

Methanol

Gasoline

Quartz surface

Gravimetric

QCM

1%

[156]

Keys: Au@AgNPs, Gold-silver nanoparticles; Au@CQDs, gold nanoparticles@carbon quantum dots nanocomposites; AgNC53, silver clusters; ASV, adsorptive stripping voltammetry; Bi2WO6, bismuth-doped tungsten oxide; calix, calixarene; CoC, cobalt complex; CD, carbon dots; CPE, carbon paste electrode; CQD, carbon quantum dots; CuO, Copper oxide; CuNS, copper nanospheres; CV, cyclic voltammetry; DPV, differential pulse voltammetry; Dy2O3, dysprosium oxide; ERGO, electro-reduced graphene oxide; GCE, glassy carbon electrode; ITO, indium tin oxide; LSV, linear sweep voltammetry; MIP, molecularly imprinted polymer; MI-PANI-PAA, molecularly imprinted poly (aniline-coacrylic acid); MWCNTs, multi-walled carbon nanotubes; NdOx, neodymium (III) oxide; NiBzimpy, 2,6-bis(2-benzimidazoyl)pyridine; NQS, 1,2-napthaquinone-4 sulphonic acid sodium salt; NRs, nanorods; Ni(II)-2,3dhS, NiII-(N,N’-bis(2,3-dihydroxybenzylidene)-1,2-diaminobenzene); PGE, pencil graphite electrode; PGr-ink, porous graphene ink; PLPA, poly(L-phenylalanine); PtNPs, platinum nanoparticles; Pt/PVF/Pt, platinum (Pt) particles on polyvinylferrocene-coated Pt electrode; rGO, reduced graphene oxide; [Ru(bpy)2(CIP)]21, Ru(II) polypyridyl complex; SERS, surface-enhanced Raman spectroscopy; SPE, screen-printed electrode; SPR, surface plasmon resonance; SWV, square wave voltammetry; Tb, terbium; Zr-based MOFs, zirconium-based metal organic frameworks.

718

19. Recent advances in sensor and biosensor technologies for adulteration detection

potassium ferro/ferricyanide, which decreases in the presence of porcine DNA. By this strategy, 1% (v/v) of pork DNA was detected in meat mixture extract. Moreover, electrochemical sensing of porcine mtDNA in fortified solution was performed by hybridizing the target mtDNA with an ssDNA probe immobilized on the surface of a SPE modified with silicon nanowires/platinum nanoparticles (SiNWs/ PtNPs) [159]. The detection of mtDNA was performed using DPV signal of ferrocenylnaphthalene diimide, employed as hybridization indicator. The developed biosensor showed a detection limit of 2.4 nM porcine mtDNA in buffer fortified with pork extract. In another approach, electrochemical immunosensors were fabricated for the detection of pig-IgG as biomarker of beef adulteration. Pig-IgG electrochemically entrapped into polypyrrole film-modified CPE was directly detected using a peroxidase labeled antibody. The developed immunosensor allowed the detection of 0.1% (v/v) pig-IgG in meat mixture extract using chronoamperometric measurements [160]. However, in a competitive format, the developed immunosensor allowed the detection of 0.01% (v/v) pig-IgG in meat mixture extract, within 20 min. In addition, a label free voltametric biosensor was prepared by immobilizing PSA antibody onto SPE using 4-carboxyphenyl diazonium salt [161]. Based on the strong affinity of serum albumins towards [Fe(CN)6]32/42 redox anions, the reported immunosensor allowed the detection of 0.5 pg/mL PSA in buffer solution spiked with porcine albumine. Fig. 19.4 represents a schematic illustration of optical/electrochemical immunosensors aimed at detecting meat adulteration with several species.

FIGURE 19.4 Typical immunosensors based on optical and electrochemical biosensing of meat adulteration.

3. Environmental applications

19.4 Recent trends in adulteration detection

719

Recently, MIPs have emerged as artificial antibodies for pork detection. Cheubong and coworkers developed molecularly imprinted nanogels (MIP-NGs) in the presence of PSA as template [162]. The MIP-NGs were immobilized on a QCM sensor for detecting pork in beef samples extract. The developed sensor enabled the detection of 1% (v/v) PSA in meat mixture extract. For improved sensitivity, the as-prepared MIP was remade in the presence of ATTO 647 N fluorescent reporter molecule [163]. The fluorescence response was measured using a custommade liquid handling robot equipped with a fluorescence microscope. A detection limit of 0.1% (v/v) PSA in meat mixture extract was reached by the developed sensor. Enzymatic biosensors have been increasingly used in recent years to detect urea in milk. The most popular configuration is to immobilize urease on different electrodes such as Fe3O4/MWCNT/PANI-Nafion/ GCE [164] and GNPlts/GNDs/SPE [165]. Although the construction of these biosensors was complicated, they showed high sensitivity with LODs of 67 and 83 μM, respectively. Moreover, urea adulteration in milk was also studied along with melamine based on the uncompetitive inhibition of acetylcholinesterase (AChE) by these two adulterants [166]. Urea and melamine can combine with the active site of AChE and inhibit its catalytic activity, thereby reducing the yield of thiocholine, which in turn leads to a lower electrochemical response. The voltammetric Pt/ZnO/AChE/chitosan biosensor detected urea and melamine at 1 and 3 pM level, respectively. A chronoamperometric biosensor for glucose detection in honey was fabricated by the immobilization of glucose oxidase (GOx) onto SPE modified with bismuth (III) oxide (Bi2O3)-decorated graphene nanoribbon (GNR) composite. The prepared biosensor (GOx/GNR/Bi2O3/SPE) displayed excellent performance toward glucose detection in honey samples with a LOD of 70 μM [167]. In addition, enzymatic biosensors have likewise been advanced for the detection of glucose in adulterated juice. rGO was combined with conductive PtNPs and Zn-MOF-74 for efficient immobilization of GOx on GCE surface [168]. Although the preparation of such a biosensor (GOx-rGO/Pt NPs@Zn-MOF-74/GCE) took several hours, the amperometric responses of the resulting biosensor allowed the determination of 1.8 μM glucose in cherry juice. In addition, an enzyme-composite biosensor was designed for sucrose sensing in juice samples. Invertase (Inv) and glucose oxidase (GOx) were coimmobilized on PtNPs and carbon nanotube (MWCNT)-modified SPE using a bipolymer matrix of gum arabic and corn flour [169]. The developed chronoamperometric biosensor (Inv-GOx-MWCNTs-AgNPs/SPE) showed high sensitivity for sucrose detection with a LOD of 1 nM. An overview of recently developed biosensors for the detection of most common adulterants is given in Table 19.2.

3. Environmental applications

TABLE 19.2

Examples of biosensors aimed at adulteration detection in various samples.

Adulterant

Matrix

Biosensing platform

Biosensor classification

Analytical method

LOD

Reference

Pork (mtDNA)

Beef

AuNPs/ssDNA/gold/SPE

Electrochemical

DPV

10% (v/v) (meat extract)

[157]

Pork (mtDNA)

Beef

ssDNA/RGO/Graphite electrode

Electrochemical

SWV

1% (v/v) (meat extract)

[158]

Pork (mtDNA)

Beef

ssDNA/SiNWs/PtNP/SPE

Electrochemical

DPV

2.4 nM (spiked buffer)

[159]

Pork (pig-IgG)

Beef

Pig-IgG-Ppy/CPE

Electrochemical (non competitive)

Chronoamperometry

0.1% (v/v) (meat extract)

[160]

Pork (pig-IgG)

Beef

Pig-IgG-Ppy/CPE

Electrochemical (competitive)

Chronoamperometry

0.01% (v/v) (Meat extract)

[160]

Pork (PSA)

Beef

CNF/SPE

Electrochemical

SWV

0.5 pg/mL (spiked buffer)

[161]

Pork (PSA)

Beef

MIP-NGs/gold chip

Gravimetric

QCM

1% (v/v) (meat extract)

[162]

Pork (PSA)

Beef

F-MIP-NGs/gold chip

Optical

Fluorescence

0.1% (v/v) (meat extract)

[163]

Horsemeat (mtDNA)

Beef

RNA/Strep-MBs/SPE

Electrochemical

Amperometry

0.5% (v/v) (meat extract)

[170]

Donkey meat (DNA)

Beef

ssDNA/gold electrode

Electrochemical

SWV

1% (v/v) (Meat extract)

[171]

Donkey meat (DNA)

Beef

GNS-ssDNA/gold chip

Optical

SPR

1% (v/v) (meat extract)

[23]

Urea

Milk

Fe3O4/MWCNT/PANI/ urease/GCE

Electrochemical

Chronoamperometry

67 μM

[164]

Urea

Milk

GNPlts/GNDs/urease/CNT/ SPE

Electrochemical

Amperometry

83 μM

[165]

Urea

Milk

ZnO/AchE/Chitosan/ Platine electrode

Electrochemical

CV

1 pM

[166]

Melamine

Milk

ZnO/AchE/Chitosan/Platine electrode

Electrochemical

CV

3 pM

Melamine

Milk

β-CD-Carbon nanoparticles

Optical

Fluorescence

0.054 μM

[172]

Melamine

Milk

TC base-rich ssDNA bio-dots

Optical

Fluorescence

1.4 μM

[173]

Glucose

Honey

GOx/Bi2O3/GNR/SPE

Electrochemical

Chronoamperometry

70 μM

[167]

Glucose

Juice

GOx-rGO/Pt NPs@Zn-MOF74/GCE

Electrochemical

Amperometry

1.8 μM

[168]

Sucrose

Juice

Inv-GOx-MWCNTs-AgNPs/ SPE

Electrochemical

Amperometry

1 nM

[169]

Glucose

Juice

PAN NFs/Cu NPs/Gox/Pt electrode

Electrochemical

Amperometry

5.6 μM

[174]

Sucrose

Juice

INV/MUT/Gox/chitosan/PB/ SPE

Electrochemical

Amperometry

20 μM

[175]

Keys: AchE, Acetylcholinesterase; Bi2O3, bismuth (III) oxide; CNF, carbon nanofiber; CNT, carbon nanotubes; CPE, carbon paste electrode; CV, cyclic voltammetry; DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy; F-MIP-NG, fluorescent molecularly imprinted polymer nanogel; GCE, glassy carbon electrode; GNDs, graphitized nanodiamonds; GNPlts, graphene nanoplatelets; GNR, graphene nanoribbons; GNS, gold nanostar; GOx, glucose oxidase; Inv, invertase; mtDNA, mitochondrial DNA; MUT, mutarotase; PAN, polyacrylonitrile nanofibrous; PB, Prussian blue; pig-IgG, porcine immunoglobulin G; Ppy, polypyrrole; PSA, porcine serum albumin; QCM, quartz crystal microbalance; RGO, reduced graphene oxide; SPE, screen-printed electrode; Strep-MBs, streptavidine-magnetic beads; SWV, square wave voltammetry; TC base-rich ssDNA, thymine-cytosine rich single-stranded DNA; ZnO, Zinc oxide.

722

19. Recent advances in sensor and biosensor technologies for adulteration detection

19.4.4 Electronic noses/tongues for adulterants detection The electronic tongue (E-tongue) and electric nose (E-nose) have proven to be useful in detecting adulterants when the physical aspects (e.g., smell, taste, heat, freshness, opacity, transparency, color, etc.) of an ingredient are required. E-tongue is an analytical gustatory device used to evaluate taste qualities (e.g., sourness, saltiness, sweetness, bitterness, and umami) of food/drinks. It consists of a set of sensing elements including enzymes, lipids, and metallic particles, with limited selectivity [176]. The interaction of the sensing element with an individual or group of closely related analytes results in a change in the potential [177]. As for the E-nose, it includes an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, capable of recognizing smells emanating from different odorant molecules [178]. The most commonly used sensors are (1) metal oxide semiconductor (MOS) films (e.g., SnO2, TiO2, ZnO, ZrO2) that measure the interaction of oxygen species with volatile molecules adsorbed to the semiconductor surface, resulting in a change in sensor resistance; (2) conducting polymers (e.g., polypyrrole, polyaniline) whose electrical conductivity is perturbed by the presence of organic vapors; and (3) gravimetric crystal sensors in which the adsorption of volatile compounds on the sensing membrane increases the mass of the device, resulting in a change in its resonance frequency. Unlike E-tongue, the E-nose is fast and powerful, and does not require any special preparation of the sample to determine its aroma [179,180]. A voltammetric E-tongue consisting of an array of seven platinium electrodes was developed for classifying honey samples from different geographical and botanical origins, as well as to detect glucose and sucrose sirup adulteration in honey samples [40]. The data obtained were analyzed by PCA (principal component analysis), SVMs (support vector machines), and HCA (hierarchical cluster analysis) chemometric tools. These methods enabled the classification of 18 honeys of different geographical origins and 7 honeys of different botanical origins. Moreover, 2% (v/v) of undeclared sucrose and glucose were detected in honey samples. In addition, the aroma profile of several cumin samples from different geographical origins was established by combining an electronic nose with a voltametric electronic tongue, coupled with chemometric tools [181]. Accordingly, 11 volatile compounds were found in cumin powder while only 8 were found in cumin seed, confirming that the cumin powder sold was not authentic. A potentiometric E-tongue using lipid polymeric sensor membranes was used for the detection of extra virgin olive oil adulteration with rancid and winey-vinegary olive oils [182]. Using linear discriminant analysis (LDA) coupled with the simulated annealing (SA) meta-heuristic variable selection algorithm, 2.5% and 5% rancid and winey-vinegary olive oils were detected, respectively.

3. Environmental applications

19.4 Recent trends in adulteration detection

TABLE 19.3

723

Examples of E-nose and E-tongue for adulterant detection.

Sensor application

Sensor configuration

Sensor device

Chemometrics

Reference

Grapes

Geographical origin

GOx, FDH, laccase, tyrosinase

E-tongue

PCA

[183]

Cheese

Authenticity verification

Carboxen/ PDMS fiber

E-nose

PCA, LDA, SIMCA, SVM

[184]

Argan oil

Adulteration with sunflower oil

5 MOS sensors

E-nose

PCA, DFA, SVMs

[185]

Mutton meat

Adulteration with pork

10 MOS sensors

E-nose

CDA, BDA, PLS, MLR, BPNN

[186]

Honey

Confirmation of botanical origin and adulteration with rice and corn sirups

10 MOS sensors

E-nose

PCA, SVM, PLS

[187]

Cherry tomato juice

Adulteration with overripe tomato juice

10 MOS sensors

E-nose

PCA, CA

[188]

Saffron

Adulteration with safflower

6 MOS sensors

E-nose

PCA, ANN

[189]

Product

The involvement of the E-nose and E-tongue in verifying the authenticity of goods has extended to all industrial fields as seen in Table 19.3.

19.4.5 Other sensing strategies Lateral flow sensing assays are suitable for the rapid determination of adulterants. They are based on the biochemical interaction of antigenantibody or probe DNA-target DNA at the surface of a membrane paper. They consist of a sample pad, a conjugation pad, a detection pad with a test line and a control line, and an absorbent pad, all placed with overlapping ends to ensure the flow of the migration buffer. They are sensitive, user-friendly, inexpensive, and convenient for on-site control of commodities authenticity. A AuNP-based DNA lateral flow biosensor was developed for pork detection [190]. Porcine DNA was extracted from meat samples and amplified in the presence of a biotinylated primer. The biotinylated product was then hybridized to poly(dA)-complementary

3. Environmental applications

724

19. Recent advances in sensor and biosensor technologies for adulteration detection

oligonucleotide probe. The hybridized product was deposited on the strip conjugation pad next to streptavidin-AuNPs and the strip was dipped into the migration buffer. The resulting products were captured by poly(dT) sequences immobilized in the test line due to dA-dT hybridization. A red line appeared indicating the presence of porcine DNA, which was detected as 0.02% (w/w). In a different approach, an anti-FITC antibody labeled with AuNPs was used to detect the presence of turkey DNA in meat samples [191]. DNA amplification product was labeled with FITC at one end and biotin at the other end using two labeled primers. Once deposited on the strip, the PCR amplicon moved along the strip to reach the conjugation pad and bound to the AuNPs-labeled anti-FITC antibody conjugate (AuNP-anti-FITC). The complex flowed through the strip and became captured on the test line by the interaction between streptavidin and biotin, forming a red line due to the accumulation of AuNPs. The LOD was about 0.1% (w/w) of turkey in mixed meats. In another format, a mouse antibody immobilized on the test line was used to capture horse dsDNA labeled with biotin, which was used to bind AuNPssstreptavidin (AuNP-SA) [192]. After accumulation of AuNP-SAdsDNA at the test line, a red line appeared indicating the presence of horsemeat in the samples. The detection limit was 0.01% horsemeat. In all cases, the excessive unbound signal reporters move further and were captured at the control line. The extension of lateral flow biosensor for the simultaneous detection of donkey and horse meat [193], chicken meat [194], as well as for the detection of sibutramine [195] and melamine [196] has been reported. In a much simpler form, a strip biosensor based on the immobilization of a whole-cell bacteria was developed for the detection of mercury in whitening products [197]. A modified plasmid carrying MerR (mercury resistance) and RFP (red fluorescent protein) genes was transformed into Escherichia coli DH5α. The increase in the concentration of mercury induced the expression of the RFP gene. Therefore when the fluorescent proteins reached a certain concentration, they displayed a visible color on the paper. 1 μM mercury was detected by this biosensor. Mercury in skin whitening cosmetics was also detected by thioketone-based Al-MOF (TAM) nanorods [198]. Al-MOFs were fabricated using a solvothermal approach and then modified with thioketone, used as a reporter chromophore. The resulting TAM had a typical yellow color that turned green in the presence of mercury, indicating the formation of the [Hg-TAM]n1 complex. The detection limit was found to be 0.8 ppb. Recently, smartphone-based sensing assays have been largely applied in the determination of adulterants thanks to their low cost, ease of use, and portability. In this regard, a smartphone-based colorimetric assay using AuNPs was developed for sibutramine analysis in slimming

3. Environmental applications

19.5 Conclusions and remarks

725

products [199]. Well-dispersed citrate-stabilized AuNPs underwent aggregation in the presence of sibutramine, resulting in a color transition from wine red to blue. The color change of the solution was monitored with a smartphone camera coupled to ColorMeter image processing application. The developed method allowed the detection of 1.15 μM sibutramine in herbal products. More recently, smartphone was used for on-site detection of pork adulteration in meat by a colorimetric immunoassay [200]. The competitive immunoassay coupled with smartphone and image J software allowed the identification of 0.01% pork in beef binary mixtures within 30 min.

19.5 Conclusions and remarks EMA is an age-old concern that has been practiced for centuries around the world. The rapid growth of the world’s population, the globalization of markets and the increase in international trade as well as the greed of some fraudsters to increase their profit margins explain the spread of adulteration in all industrial sectors such as food, drugs, cosmetics, and fuels. A variety of chemical, physical, and biological methods have been successfully used for adulterant detection. While these techniques are reliable and accurate, unfortunately they are time consuming, expensive, and require sophisticated equipment. Accordingly, sensors and biosensors offer a genuine alternative to conventional methods. They are fast and sensitive, highly selective, and cost-effective. Thanks to their excellent portability, they are convenient for decentralized identification of adulterants. With 200 scientific reports, this chapter covered the most prominent sensors and biosensors developed over the last decade for adulteration detection in different industries. Two basic elements of life are often adulterated: food products and medicines. The situation is getting worse daily as many cases of adulteration occur unnoticed and may never be known. Despite considerable efforts to advance sensors and biosensors for adulterant detection, several challenges remain with respect to the application and implementation of these technologies in both research studies and point-of-care commodity authentication control. In addition, the adulteration techniques used are at least as sophisticated as the sensing approaches, making adulteration detection a challenging task. As adulteration continues to spread around the world, collaboration among the various stakeholders in the supply of commodities is essential to circumvent adulteration practices. Additionally, the harmonization of legislation between countries may help to better control the trade of goods and thus mitigate the health, economic, and environmental impacts of adulteration.

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19. Recent advances in sensor and biosensor technologies for adulteration detection

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3. Environmental applications

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[187] Z. Gan, Y. Yang, J. Li, X. Wen, M. Zhu, Y. Jiang, Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey, J. Food Eng. 178 (2016) 151 158. Available from: https://doi.org/10.1016/j.jfoodeng.2016.01.016. [188] X. Hong, J. Wang, S. Qiu, Authenticating cherry tomato juices—discussion of different data standardization and fusion approaches based on electronic nose and tongue, Food Res. Int. J. 60 (2014) 173 179. Available from: https://doi.org/10.1016/j. foodres.2013.10.039. [189] K. Heidarbeigi, S.S. Mohtasebi, A. Foroughirad, S. Rafiee, K. Rezaei, Detection of adulteration in saffron samples using electronic nose, Int. J. Food Prop. 18 (2015) 1391 1401. Available from: https://doi.org/10.1080/10942912.2014.915850. [190] M. Magiati, V.M. Myridaki, T.K. Christopoulos, D.P. Kalogianni, Lateral flow test for meat authentication with visual detection, Food Chem. 274 (2019) 803 807. Available from: https://doi.org/10.1016/j.foodchem.2018.09.063. [191] L. Zhao, K. Wang, C. Yan, J. Xiao, H. Wu, H. Zhang, et al., A PCR-based lateral fl ow assay for the detection of Turkey ingredient in food products, Food Control. 107 (2020) 106774. Available from: https://doi.org/10.1016/j.foodcont.2019.106774. [192] Y. Chen, Y. Wang, M. Xiao, S. Wei, H. Yang, R. Yin, Polymerase chain reaction with lateral flow sensor assay for the identification of horse meat in raw and processed meat products, Food Chem. 345 (2021) 128840. Available from: https://doi.org/ 10.1016/j.foodchem.2020.128840. [193] C. Zhang, X. Zhang, G. Liao, Y. Shang, C. Ge, R. Chen, Species-specific TM-LAMP and Trident-like lateral flow biosensor for on-site authenticity detection of horse and donkey meat, Sens. Actuators B. Chem. 301 (2019) 127039. Available from: https://doi.org/10.1016/j.snb.2019.127039. [194] O.D. Hendrickson, E.A. Zvereva, N.L. Vostrikova, I.M. Chernukha, B.B. Dzantiev, A.V. Zherdev, Lateral flow immunoassay for sensitive detection of undeclared chicken meat in meat products, Food Chem. 344 (2020) 128598. Available from: https://doi.org/10.1016/j.foodchem.2020.128598. [195] S. Zhang, Y. Sun, Y. Sun, H. Wang, Z. Li, Z. Xu, Visual upconversion nanoparticlebased immunochromatographic assay for the semi-quantitative detection of sibutramine, Anal. Bioanal. Chem. 412 (2020) 8135 8144. [196] K. Wu, W. Huang, S. Chang, C. Kao, R. Shyu, Colloidal silver-based lateral flow immunoassay for rapid detection of melamine in milk and animal feed, Mater. Chem. Phys. J. 231 (2019) 121 130. [197] M. Guo, J. Wang, R. Du, Y. Liu, J. Chi, X. He, et al., A test strip platform based on a whole-cell microbial biosensor for simultaneous on-site detection of total inorganic mercury pollutants in cosmetics without the need for predigestion, Biosens. Bioelectron. 150 (2020) 111899. Available from: https://doi.org/10.1016/j. bios.2019.111899. [198] A. Radwan, I.M. El-sewify, A. Shahat, H.M.E. Azzazy, M.M.H. Khalil, Multiuse AlMOF chemosensors for visual detection and removal of mercury ions in water and skin-whitening cosmetics, ACS Sustain. Chem. Eng. 8 (2020) 15097 15107. Available from: https://doi.org/10.1021/acssuschemeng.0c03592. [199] K. Chaisiwamongkhol, S. Labaidae, S. Pon-in, S. Pinsrithong, Smartphone-based colorimetric detection using gold nanoparticles of sibutramine in suspected food supplement products, Microchem. J. 158 (2020) 105273. Available from: https://doi. org/10.1016/j.microc.2020.105273. [200] N. Seddaoui, A. Amine, Smartphone-based competitive immunoassay for quantitative on-site detection of meat adulteration, Talanta. 230 (2021) 122346. Available from: https://doi.org/10.1016/j.talanta.2021.122346.

3. Environmental applications

C H A P T E R

20 Biosensing technology in food production and processing Seyed Mohammad Taghi Gharibzahedi1,2, Francisco J. Barba3, Vahid Mofid4 and Zeynep Altintas1,2 1

Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Technical University of Berlin, Berlin, Germany, 2Institute of Materials Science, Faculty of Engineering, Kiel University, Kiel, Germany, 3 Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de Vale` ncia, Vale` ncia, Spain, 4 Department of Food Sciences & Technology, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran

20.1 Introduction There is growing demand for the rapid and accurate assessment of quality parameters of food products due to safety considerations, adulterations in food supply chains, and the preference of consumers for healthier high-quality products [1]. Food materials undergo several handling, processing, production, storage, and distribution stages, which can significantly affect their final quality and safety. Monitoring the safety and quality parameters using traditional analytical techniques not only is time consuming and tedious but also needs well-trained operators [2]. In recent years, methods such as spectroscopy, machine vision systems, and hyperspectral imaging have replaced conventional techniques to analyze chemical residues (e.g., chromatographic-based techniques like high-performance

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00023-6

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© 2023 Elsevier Inc. All rights reserved.

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20. Biosensing technology in food production and processing

Enzyme

Cell

DNA

Antibody

Receptor

Aptamer

liquid chromatography (HPLC) and gas chromatography mass spectrometry (GC-MS)) and foodborne pathogenic microorganisms and natural toxins such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) kits [3]. Although they are very sensitive and selective, these expensive methods require several pretreatment steps for sample preparation [4]. Therefore there is a necessity to use valid and fast alternatives in modern food industries. Sensors are one of the most important nondestructive substitutes for the inspection of food quality and safety. The sensor is a cheap and rapid tool with an excellent ability for portable and real-time detection as well as in situ monitoring of the risk of food contamination and quality degradation [5,6]. Overall, they can be categorized into two groups including nonflexible (rigid) and flexible types. Nonflexible sensors with superlative signal-to-noise rate and low hysteresis are made of brittle materials such as silicon substrates using complex procedures. However, these sensors compared to flexible ones have higher costs because of the requirement of cleanroom facilities. Other limitations of these sensors include high input power, time-dependent sensitivity degradation, highly stiff and intransigency nature, short-term life cycle, and the development of toxic compounds during fabrication stages [7,8]. Thus much attention was drawn to the sensor development based on flexible raw materials with great electrical and thermomechanical properties. Flexible sensors despite high fragility have shown remarkable advantages such as low cost, lightweight, ideal electromechanical characteristics, and high comfortability, transparency, stretchability, and roll-to-roll production [7,9]. In a biosensor device, a biorecognition element (BRE; e.g., aptamer, enzyme, single-stranded DNA, antibody, cell, etc.) interacts with an analyte, and then this biochemical reaction is transformed into an electrical signal through a transducer [10]. A schematic biosensor is shown in Fig. 20.1. According to the recognition element, they can be bioaffinity, enzyme,

Biorecognition element Interface chemistry

Sensor material Signal transducer

FIGURE 20.1

A schematic illustration of a biosensor. Source: Reproduced with permission from Ref. E.B. Bahadır, M.K. Sezgintu¨rk, Biosensor technologies for analyses of food contaminants, in: Nanobiosensors. Academic Press; 2017, pp. 289
337.

4. Construction and other applications

20.1 Introduction

745

transmembrane, and whole-cell biosensors. In another category based on the used transducer, biosensors can be classified as electrochemical, optical, calorimetric, piezoelectric, and magnetic types. An electrode is the transducer of electrochemical biosensors such as potentiometric, amperometric, and impedimetric modes [11]. A photodetector in optical biosensors measures the concentration, mass, or number of photons during absorption, reflectance, fluorescence, and emission mechanisms [12]. Temperature changes resulting from the biochemical reaction between the analyte bonded by the recognition element can be assessed using calorimetric transducers (thermistor or thermopile). The temperature can be altered with the intensity of consumed reactants or developed products [13]. In piezoelectric biosensors, alterations in the vibration frequency induced by mass changes are recorded after binding the analyte to the BRE on the piezoelectric crystal surface [5]. Magnetic biosensors can detect target bioelements labeled on the stray field of magnetic beads. Thus a stronger signal can be detected as a higher number of magnetic beads are mobilized within microtubes [14,15]. One of the most significant biosensors in improving the quality assessment of food properties is the single and combined use of electronic nose (e-nose), electronic tongue (e-tongue), or electronic eye (e-eye) [16,17]. Table 20.1 summarizes some of the recent applications and advantages of electronic sensors in the food industry [18
62]. The artificial olfactory system of e-nose works closely with analytical techniques for the identification of volatile compounds in GC [63]. This noninvasive, intelligent online instrument by simulating the human nose can rapidly diagnose odors and flavors using a gas sensor array. Hence, the electrical signal of e-nose sensors as a quality control tool can be utilized to recognize food contamination, food fraud, and formation of odors/flavors in food products during processing and storage and their sensory attributes [1,64]. Gas sensors used in e-noses are usually fabricated from a diverse range of sensing materials such as quartz crystal microbalance (QCM), conducting polymers (CP), amperometric electrochemical (AEC), surface acoustic wave (SAW), and metal oxide semiconductor (MOS; such as nickel (Ni2O3) and cobalt (CoO) oxides (p-type) or tin dioxide (SnO2), titanium dioxide (TiO2), zinc oxide (ZnO), and iron(III) oxide (Fe2O3) (n-type)) sensors [4]. However, they are different in terms of sensitivity, selectivity, efficiency under environmental changes with different moisture contents and temperature degrees, response speed or accuracy, and drift over time in embedded phase space [65,66]. The reaction between oxidizing (e.g., O2, NO2, Cl2, etc.) and reducing (e.g., H2, CH4, H2S, CO, etc.) gases with each sensing material leads to reversible electrical characteristics like conductivity, which can be assessed by the sensor’s output voltage (OV). Also, some factors including peak voltage, response time (ResT), and recovery time can elaborate the OV pattern in these sensors [4,67]. Fig. 20.2 illustrates a multiple-step process of sensing, interpreting, and discriminating in an e-nose.

4. Construction and other applications

TABLE 20.1 A summary of recent applications and advantages of e-nose, e-tongue, and e-eye in the food industry. Electronic sensing device

Food material

Utilization purpose(s)

Beef (sousvide meat)

Nonvolatile compounds and sensory attributes

e-tongue

Beef

The diagnosis of pork adulteration in beef

Beef products

Mutton meat

Used sensor type

Classifiersa

Advantageous note(s)

Reference

Commercial etongue (SA402B)

PLS


A robust relationship between equivalent umami concentration (EUC) based on the measurement of umami amino acids and nucleotides, and umami taste evaluated by the e-tongue

[18]

e-nose

MOS (MQ series, gas sensors)

N/A


The efficient assessment of meat adulteration for developing Halal authentication

[19]

Detection of ammonia and putrescine (freshness monitoring)

e-tongue

Voltammetric

PCA, PLSDA


Discrimination and classification of meat products according to their storage time by analyzing the content of ammonia and putrescine in their extract solutions

[20]

Adulteration detection of minced mutton mixed with pork

e-nose, etongue

MOS (PEN 2; enose), Commercial etongue (α-ASTREE)

BPNN, MLR, PLS


BPNN: the most effective method to predict pork proportions (R2 . 0.97) based on the calibration and validation data set
The integration of E-nose and E-tongue: A useful tool to detect mutton adulteration

[16]

(Continued)

TABLE 20.1 Food material

(Continued)

Utilization purpose(s)

Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

Harbin red sausages

Characterization of selected samples with their flavor profiles

e-nose, etongue

MOS (PEN 3; e-nose), Commercial e-tongue (SA402B)

PCA, PLS


A high correlation between the E-nose sensors and the GC/MS data to differentiate traditional and conventional sausages based on the content of aldehydes and phenols

[21]

Golden pompano fillets

The quality differentiation of samples based on volatile compounds

e-nose, etongue

MOS (PEN 3; enose), Potentiometric (TS-5000Z; e-tongue)

PCA


Effectively differentiation of volatile flavor compounds (e.g., hydrocarbons, aldehydes, esters, and alcohols) of fish fillet samples using the e-nose and e-tongue systems

[22]

Chicken meat

Identification of freshchilled and frozen-thawed samples and their shelf life assessment

e-nose

MOS

FuzzyKNN


High accuracy of Fuzzy-KNN algorithm (.94
95%) to prove the potential of e-nose system for online and automated identification of fresh and frozen-thawed chicken meat

[23]

Cooked chicken drumsticks

Assessing the flavor profiles of sugar-smoked chicken drumsticks at different smoking times

e-nose, etongue

MOS (PEN 3.5; e-nose), Potentiometric (TS-5000Z; etongue)

PCA, PLSDA


A guidance for smoked chicken producers to rationally control the formation of flavor compounds

[24]

Sauce spareribs

Discriminating 11 types of sauce spareribs based on the isolated volatile compounds

e-nose

MOS (PEN 3)

PCA


The feasibility of evaluating flavor of braised sauce spareribs using SPMEGC-MS and e-nose

[25]

(Continued)

TABLE 20.1 (Continued)

Food sample

Utilization purpose(s)

Electronic sensing device

Chinese liquors

Sensory attributes

Mushroom extracts

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-tongue

Commercial etongue (SA-402B)

PCA, Fuzzy evaluation


Relatively accurate simulation of sensory evaluation of food liquors by e-tongue similar to human perception systems

[26]

Umami taste assessment

e-tongue

Potentiometric

N/A


A significant correlation between the EUC and the human sensory scores of mushroom extracts

[27]

Vegetable oils (rapeseed-soysunflower)

Evaluating the peroxide and p-anisidine values, and the concentration of total tocopherols

e-tongue

Potentiometric

PCA, PLS


Developing the multisensor system to recognize the types of the raw materials and potential adulteration in edible oils based on quality parameters

[28]

Edible olive oil (extra virgin-olivepomace)

Quality characterization and shelf life assessment

e-nose, etongue, eeye

MOS (e-nose), Spectrophotometer (e-eye), Commercial e-tongue (SA-402B)

PCA, KNN


The high ability of KNN classification model in the esenses to classify edible olive oils in the two classes of freshness based on the analysis of physicochemical and nutritional properties

[29]

Extra virgin olive oil

The oil test with various ripe and green intensities

e-nose

MOS

PCA, LDA


Confirming the olive oils’ intensity complementary commercial labeling using the used e-nose

[30]

(Continued)

TABLE 20.1 (Continued)

Food sample

Utilization purpose(s)

Electronic sensing device

Spicy paneer cheese

Sensory evaluation

e-tongue

Potentiometric

PCA


A good applicability of etongue in qualitative discrimination among paneer samples along with sensory evaluation

[31]

Foods containing spicy compounds

Sensory assessment

e-tongue

Potentiometric

PCA


The high potential of etongue in the development and sensory characterization of spicy products containing capsaicin, thymol, piperine, zingerone, p-cymene, menthol, and eugenol

[32]

Skim milk products

The quality assessment and discrimination of six skim milk products based on volatile flavor components

e-nose

MOS (PEN 2)

PCA, PLS


Based on the PLS results: an integration of HSSPME
GC
MS and e-nose can successfully differentiate and classify volatile flavor profiles in skim milk products

[33]

Plant milks (oat-soy-ricetiger nutalmond)

Sensorial analysis and discrimination

e-tongue

Voltammetric

PCA, PLS


The goodness of predicting models based on the sensory parameters such as body, granularity in the wall of glass, and color homogeneity

[34]

Used sensor type

Classifiersa

Advantageous note(s)

Reference

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

Food sample

Utilization purpose(s)

Sicilian honey pollen

The recognition of honey botanical origins, a rapid verification of the honey acceptability and quality

e-tongue

Potentiometric

PCA, SIMCA


A 100% correlation between the results of e-tongue and melissopalynological analysis
The e-tongue suitability in the diagnosis of honey botanical origins

[35]

Honey

Adulteration detection

e-tongue

Voltammetric

MLR, PLSDA


A 97.5% correct classification between the etongue and physicochemical traits of authentic and adulterated honeys
A useful tool in situ for beekeepers by providing accurate data on electrical conductivity and free acidity

[36]

Sugarcane

The quality assessment based on purity and refined sugar percentage

e-nose

MOS

MLR, PCA, PLS, ANN


An accurate instrument to measure chemical features of sugarcane sirup from enose system signals

[37]

Coffee

Bitterness quantification

e-tongue

Potentiometric

N/A


The actual bitterness assessment of coffee roasted at various temperatures after adding different levels of sweeteners
A high correlation (R2 5 0.91) between the human taste scores and the predicted levels of bitterness by e-tongue

[38]

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-tongue, e-nose

MOS (Fox 3000; enose), Commercial e-tongue (α-ASTREE)

PCA, KNN, PLSDA, BPNN


Improving the quality (i.e., pH, Brix , acidity, total solids) of the predictions with the combined use of an e-nose and an e-tongue (PLS-DA model 5 the best model)

[39]

Better classification of black tea based on the taste and odor resulting in chemical volatile compounds

e-tongue, e-nose

Metal oxide semiconductor (MOS; e-nose), Voltammetric (LAPV; e-tongue)

KNN


A 99.75% accuracy by integrating wavelet energy features of e-nose and etongue to classify black tea from four different origins and grades
Using the feature extraction technique and sensor response fusion to classify other beverages and agroproducts

[40]

Longjing tea

Assessing the chemical components of tea such as amino acids, catechins, polyphenols, and caffeine

e-nose, e-tongue, e-eye

MOS (e-nose), commercial etongue (α-ASTREE), colorimeter (e-eye)

PLS, SVM, RF


Representing the best performance in predicting the concentration of chemical components of tea by jointly utilizing the three technologies using RF-based fusion signals

[17]

Tea

Tea grade identification

e-tongue

Potentiometric

PLS-DA, PLS-DASRD


PLS-DA-SRD: a high efficiency and capability in identifying the grade of tea samples

[41]

Food sample

Utilization purpose(s)

Chinese robusta coffee

Differentiation according to species with different roasting degrees

Black tea

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-tongue, e-nose

MOS (e-nose), Commercial etongue (α-ASTREE)

KNN


Much better classification power for the multilevel fusion system (e-nose/etongue) to recognize the overall acceptability of tea based on odor and taste

[42]

Classification of teas: 79 Brazilian samples (40 green, 39 black tea) and 39 Argentinian samples (20 green, 19 black tea)

e-tongue

Voltammetric

LDA, SPA, GA, SW


SPA/LDA models: A correct classification (B100%) rate for the prediction set

[43]

Tea

Assessing the quality grades by detecting the volatile components of leaves and infusion of tea

e-nose

MOS (PEN 3)

PCA, MDS, LDA, LR, SVM


The feasibility of e-nose for qualitatively and quantitatively analyzing tea quality grades

[44]

Chinese bayberry juice

Organoleptic evaluation of the juice samples

e-tongue

Potentiometric

PLS


Significant contributions of the content of total polyphenols, fructose, sucrose, and quininic, maleic, citric, lactic, and succinic acids with the taste characteristics sensed by the e-tongue

[45]

Food sample

Utilization purpose(s)

Tea

Quality identification based on sensory taste/odor attributes

Tea

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-tongue

Commercial etongue (α-ASTREE)

PLS, DA, LA


More sensitivity of e-tongue than test panel evaluation in detection of the spoilage of Z. rouxii of apple juice at early stage
PLS: an exact correlation between e-tongue data and contamination levels by Z. rouxii

[46]

Predicting the contents of food additives (benzoic acid and chitosan) in fruit juice

e-nose

MOS (PEN 2)

RF, ELM, SVM, PLS


Higher prediction accuracy of RF and ELM than SVM and PLS

[47]

Pure and industrial fruit juice

The fast and nondestructive classification

e-nose

MOS

ANN


A high-efficient recognition of fresh samples with desired sensory properties using the combined e-nose and ANN

[48]

Red wine

Discrimination of eight types of Spanish red wines based on the content of phenolic compounds

e-tongue

Voltammetric

PCA, PLS


Better correlations of etongue with their phenolic content than attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy due to the phenols redox processes

[49]

Food sample

Utilization purpose(s)

Apple juice

The early detection of the Zygosaccharomyces rouxii spoilage

Satsuma mandarins juice

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-tongue, e-nose

Colorimetric (enose), Voltammetric (e-tongue)

PCA, ELM


The potential of rapid quality evaluation of red wine through the multisensor fusion of etongue and e-nose coupled with ELM

[50]

Evaluating the marked ages of wine samples

e-tongue, e-nose

MOS (e-nose), Selfdeveloped by polymer nanocompositemodified electrode (e-tongue)

LPP, PCA, PLS, ELM, SVM


SVM was the best model to predict the marked ages of rice wines
Monitoring feasibility of the flavor changes of rice wines during the aging process

[51]

Beer

Beer classification based on the aroma profiles

e-nose

Portable (9 gas sensors, MQ/MG)

ANN


Beer quality assessment within the production line

[52]

Beer

Beer classification based on the flavor data

e-nose, etongue

MOS (PEN 3; enose), commercial etongue (SA-402B)

PCA, GAPLS, ELM, SVM, RF


The best classification performance: SVM (96.67%), RF (94.44%), and ELM (98.33%) models

[53]

Soy sauce

Analysis of ethanol for halal food certification

e-nose

Commercial nose (Smart Nose300)

DA


A high correlation between the first score of the DA plot and the ethanol concentration
The e-nose-based massspectrometry: an efficient way to assess ethanol for halal certification

[54]

Food sample

Utilization purpose(s)

Red wine

Developing an effective identification approach for red wines with different geographical origins, brands, and grape varieties

Rice wine

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

e-nose

Commercial nose (TGS type)

DA, PCA, RBFNN


The differentiation of vinegar samples with high reliability of DA and RBFNN data (94
97%)

[55]

The rapid detection of aflatoxin B1 and fumonisins

e-nose

MOS

ANN, LR, DA


ANN: the best mode to detect target foodborne mycotoxins with an accuracy of 77
78%

[56]

Rice

The classification of Sitophilus oryzae infestation in stored rice grain

e-nose

MOS

PCA, MLR


The high potential of e-nose to classify rice grains based on the content of protein and uric acid

[57]

Rice (cv. Domsiah)

Characterizing the aging process of rice to classify their storage durations

e-nose

MOS

PCA, PNN, RBFNN, LVQ


Using the e-nose system along with the developing methods to control the rice aging

[58]

Jasmine brown rice

Early detection of fungal spoilage of stored grains with the volatile markers

e-nose

MOS (FOX 3000)

PCA, LDA, SVM, PLS


The early detection of fungal infection on rice grain prior to visible growth

[59]

Milled rice

Early identification of Aspergillus spp. contamination

e-nose

MOS

LDA, ELM, PLS, GASVM


An e-nose coupled with ELM/GA-SVM algorithm: the rapid detection of fungal infection to prevent contaminated rice from entering the food chain

[60]

Food sample

Utilization purpose(s)

Vinegar

The discrimination of six kinds of vinegar samples

Maize

(Continued)

TABLE 20.1 (Continued) Electronic sensing device

Used sensor type

Classifiersa

Advantageous note(s)

Reference

Food sample

Utilization purpose(s)

Dried garlic

Quality assessment of dried and prestorage samples with the aroma analysis

e-nose

MOS

LDA, PCA, BPNN


More accurately LDA and BPNN methods in classifying the aroma of samples based on different drying methods

[61]

Gummy candies

Monitoring the release aroma (apple, strawberry, and grape) in the storage

e-nose

Commercial e-nose

PCA


The contribution to food industries with assessing stability of food matrices through detecting the release rate of volatile compounds

[62]

a ANN, artificial neural networks; BPNN, back propagation neural network; ELM, extreme learning machine; GA-PLS, genetic algorithm-partial least squares; KNN, k-nearest neighbors; LA, loading analysis; LDA, linear discriminant analysis; LPP, locality preserving projections; LR, logistic regression; LVQ, learning vector quantization; MDS, multidimensional scaling; MLR, multiple linear regression; PCA, principal component analysis; PLS, partial least square analysis; PLS-DA, partial least squares-discriminant analysis; RBFNN, radial basis function neural network; RF, random forest; SIMCA, soft independent modeling of class analogy; SRD, sum of ranking difference; SVM, support vector machines; SW, stepwise (formulation); VIP, variable importance of projection.

757

20.1 Introduction

Brain

Signal Transducers

Pattern Recognition Algorithms

PV

Voltage

Olfactory Bulbs

Gas Sensor Array Electronic Nose

Human Olfactory

Olfactory Receptors

Reference Gas ResT

Gas off RecT

Time Discrimination and Classification

Discrimination and Classification

FIGURE 20.2 The triple process of sensing-interpreting-discriminating in an e-nose. Source: Reproduced with permission from Ref. J. Tan, J. Xu, Applications of electronic nose (enose) and electronic tongue (e-tongue) in food quality-related properties determination: a review. Artif. Intell. Agric. 4 (2020) 104
115.

The aim of using an e-tongue in food industries is the evaluation of chemical constituents presenting the main taste qualities (i.e., sweetness, saltiness, sourness, bitterness, and umami) and others (i.e., pungency and astringency). Utilizing the e-tongue technology can be an interesting alternative for sensory panelists to assess the organoleptic characteristics of food products [4,68]. The sensory experiments performed by human panels not only are expensive and time consuming but also require training panelists with consistent, discriminative capabilities to increase the test accuracy with a reduction in the risk of bias. In contrast, the e-tongue system is a rapid, easy-to-use, unbiased, and low-cost technology [31,69]. A group of sensors is typically used in the structure of e-tongues including electrochemical (e.g., potentiometric, voltammetric, amperometric, or impedimetric), optical, and enzymatic sensors. However, electrochemical sensors are often preferred due to their better sensitivity and relatively lower cost than other sensors. On the other hand, the e-eye technology evaluates the color- and aspect-related properties of a sample that are visible by the human eye [17]. As a result, an e-eye system acts with the measurement principle of computer vision, spectrophotometric and colorimetric techniques [70]. Overall, different pattern-recognition algorithms (PRAs) or pattern classifiers (PCs) are usually applied to describe data obtained from output signals of artificial electronic sensing devices (i.e., e-nose, e-tongue, and e-eye).

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20. Biosensing technology in food production and processing

The most common PRAs or PCs are principal component analysis (PCA), artificial neural networks (ANNs), support vector machines (SVMs), extreme learning machines (ELMs), random forest (RF), linear discriminant analysis (LDA), partial least squares regression (PLS) or partial least squares-discriminant analysis (PLS-DA), and the k-nearest neighbors (KNNs) algorithm [4]. Table 20.2 exhibits recently used nanobiosensors in monitoring and promoting the quality and safety of food products. The design of biosensors to control the safety and quality parameters of food products has been revolutionized by developing the knowledge of nanotechnology [10,144,145]. Recently, different types of nanoparticles (NPs, 1
100 nm), including metals (such as Au, Ag, and Pt), carbon-based materials [such as carbon nanotubes (CNTs), graphite, and graphene], and quantum dots (QDs) have been extensively applied to develop biosensors in detecting toxic and microbial contaminants, allergens and drug residues, carcinogenic compounds (e.g., pesticides/ herbicides, and heavy metals), as well as nutrients and bioactive ingredients [146
148]. Nanomaterials have shown excellent biological, optical, electrical, mechanical, magnetic, and catalytic properties. The use of nanoscale particles in sensing platforms also provides potential benefits such as a high surface area with numerous active sites with exceptional biocompatibility [149]. Sensing platforms in the food industry are often used for the analysis of quality parameters, risk assessment of safety management, yield improvement of products, energy input optimization, process monitoring, and implementation of a high-efficient process automation system. Consequently, biosensors in this industry can guarantee consumer health through the quality assurance and safety of crops and food products. The present chapter especially emphasizes the current and future achievements and prospects of using biosensors, electronic sensor arrays, and nanosensing devices to improve quality and safety monitoring.

20.2 Biosensors and food quality Food quality assessment is a complicated process because it involves the determination of all the molecular compositions of food according to biological receptors. Biosensors, as one of the best-emerging monitoring technologies, can facilitate quality control in the food industry by measuring polyphenol indices and antioxidant capacities, tracking bioactive and antinutritional constituents, evaluating raw materials and additives, assessing authenticity, and quality control inspection of food materials.

4. Construction and other applications

TABLE 20.2

Current nanosensing platforms used for maintaining/improving the quality and safety of food products. Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Aptamer, labeled by toluidine blue

Dendritic fibrous nanosilica

1.0 3 1026

1.0 3 1026
1.0 3 102

Monitoring of AFM1contamination in milk

[71]

Melamine

Colorimetric

AgNPs

7.9 3 101

7.9 3 101
1.9 3 103

Detection of melamine in milk

[72]

Milk

Melamine

Fluorescent (turn-on mode)

CsPbBr3 NCs@BaSO4

4.2 3 1021

0.5 3 101
5.0 3 102

Analysis of melamine residues

[73]

Commercial milk/ Infant formula

Melamine

Ratiometric fluorescent

AuNPs

2.8 3 104

1.0 3 106
5.0 3 106

Determination of melamine in dairy products

[74]

Children’s snacks

Tartrazine

Paper-based sandwich type SERS

AgNPs

2.1 3 1027

5.6 3 1027
1.9 3 1025

The real detection of tartrazine in baby food

[75]

Tea

S. aureus, E. coli, B. subtilis, and enterotoxin

Chemometric

AuNCschitosan composite membrane

Accuracy of 98.3%




Differentiating pathogenic bacteria and detection of enterotoxin

[76]

Dianhong black tea

Polyphenols

Turn-off fluorescent

Nitrogencarbon-doped CDs

Accuracy of 98.3%




Discrimination of Dianhong black tea grade

[77]

Green teas

Flavonoids, catechins, amino acids

Turn-off fluorescent

NAC-capped CdTe QDs

Accuracy of 99.9%




Batter grading of tea than the complex LCNC classification system

[78]

Food sample

Analyte type

Transductiona

Milk

Aflatoxin M1 (AFM1)

Milk

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Tea

Metal ions

Sensor array

Nanozymes

Orange juice

Sugar or artificial orange powders

Turn-off fluorescent probe combined with chemometric

Kiwi/Grape juice

Vitamin B1

Spinach, Orange, Broccoli, Tomato

Linear range (nmol/L)c

Application note(s)

Reference

Accuracy of 100%




Discrimination of 17 kinds of tea Recognition potential of tea based on quality and origin

[79]

NAC-capped ZnCdSe and CdTe

Accuracy of 97.8%




Identifying the juice adulterations (sucrose sirup (10%, w/w) and artificial fruit powder (10%, w/w))

[80]

Turn-off fluorescent

e-PNP

2.6

1.0 3 102
2.5 3 104

Nutritional and biological assessment of natural juices

[81]

Folic acid

Ratiometric fluorescence, MIT, visual

CdTe

4.8 3 101

2.3 3 102
1.1 3 105

Detection of low or high levels of folic acid in real samples

[82]

Citrus sinensis/C. limon peels

Iron, Tartrazine

Turn-off-on

Luminescent CDs

2.0 3 102

6.0 3 102
2.4 3 104

The cell imaging and intracellular detection of iron, The diagnosis of tartrazine in foodstuffs

[83]

Fruity carbonated beverage, Brandy cocktail, Hard candy, Chocolate candy, Mung bean cake, Dried blueberry

Brilliant Blue FCF (E 133)

Ratiometric fluorescence, MIT

CdTe/ZnS

8.8

0.0
1.0 3 103

Identifying the synthetic food colorants in solid/ liquid foods

[84]

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Milk/Chicken meat

Multiplex antibiotics: Chloramphenicol (CA), Tetracycline (TC)

Label-free colorimetric aptamer

AuNPs

5.65 (CA), 36.55 (TC)

4.04 3 10
1.45 3 10 (CA), 5.55 3 101
3.33 3 103 (TC)

Detecting antibiotics in real samples with consistent results and desirable recoveries

[85]

Milk/Chicken meat

CA (antibiotic)

Label-free colorimetric aptamer

AuNPs

0.475 3 101

2.5
3.63 3 102

Facilitating on-site detection of CA in food samples

[86]

Milk/Chicken egg

Tobramycin (antibiotic)

Colorimetric aptamer

AuNPs

2.72 3 101

4.67 3 101
2.33 3 102

Similar data with the detected values by spectrofluorimetric

[87]

Milk

CA (antibiotic)

Multiple signal amplified colorimetric aptamer

MNPs, PtNPs

2.42 3 1025

8.0 3 1025
3.0 3 1022

Facilitating in situ detection and further extension to identify other antibiotics in food just by simply replacing cDNA on the sensing system

[88]

Milk

CA (antibiotic)

Aptamer-conjugated magnetic bead

AuNPs

3.25 3 1025

2.5 3 1024
2.5 3 102

A high potential for the CA residue detection in various complicated mediums

[89]

Milk

TC (antibiotic)

Colorimetric aptamer

AuNCs

5.11 3 101

1.11 3 103
1.77 3 104

Accurately and reproducibly detection of TC in drugs and milk

[90]

1

3

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

1.15 3 10

1

2.87 3 10
1.72 3 10

The rapid quantification of antibiotics in biological systems

[91]

1

4

Milk

Oxytetracycline (OTC, antibiotic)

Colorimetric aptamer

Milk

Streptomycin (antibiotic)

Point-of-care testing (POCT) colorimetric aptasensor

AuNPs

2.5 3 1025

7.5 3 1025
5.0 3 101

Rapid detection of antibiotic residues in foods through porous SiO2 beads- enzyme linked aptamer probes and exonuclease-assisted target recycling

[92]

Milk

Kanamycin (KC, antibiotic)

Fluorescent aptamer functionalized with MIP

CdSe QDs

3.25 3 101

1.25 3 102
2.5 3 104

Quickly, sensitively and simply sensing of kanamycin in food, water, and biological samples with satisfied results

[93]

Fish

Enrofloxacin (EF), Feroxacin (FC), Levofloxacin (LF), Ciprofloxacin (CF), Enoxacin (EC) (quinolones)

MIP fluorescent probe

Fe3O4 NPs on NaYF4: Yb31, Er31 upconversion particles (MUCPs@MIP)

2.47 3 1024 (EF), 3.75 3 1024 (FC), 13.25 3 1024 (LF), 10.25 3 1024 (CF), 9.0 3 1024 (EC)

1.025
277.12 (EF), 1.55
203.12 (FC), 6.25
252.95 (LF), 6.25
248.5 (CF), 3.125
200.2 (EC)

The recognition capability for rapid and accurate sensing of multiple chemical residues in the environment and agrifood products

[94]

Fish

TC (antibiotic)

SFS with MIP

CDs

9.975

1.11 3 102
5.55 3 104

Detection of trace TC amounts in fish samples

[95]

Milk

KC (antibiotic)

Aptamer

GO CDs

1.5 3 1023

2.5 3 1023
2.25 3 102

The design of novel sensing platform to achieve rapid, simple, and sensitive biological detection

[96]

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Milk

KC (antibiotic)

Fluorescent aptamer

AuNPs

3.75 3 104

0.6
24.2

Good detection of KC in serum and milk

[97]

Fish

Sulfadimethoxine (SLF, antibiotic)

Aptamer

UCNPs, MNPs

2.75 3 104

2.5
22.5

A successful quantification of SLF in spiked samples of perch and catfish

[98]

Milk

Aminoglycoside antibiotics (e.g., KC)

Fluorescence quenching aptamer

GO

31.5

2.42 3 1023
2.42

Great surface regeneration capability to continuously detect more than 60 times

[99]

Milk, Pork

TC (antibiotic)

Aptamer

UCNPs, MNPs

1.55 3 1022

2.5 3 1022
2.5 3 102

The high detection of TC in food to maintain the safety and quality control

[100]

Milk

TC, OTC, KC (antibiotics)

Chemiluminescence Aptamer

AuNFs

5.0 3 1022

1.25 3 1021
1.25 3 101 (TC, OTC), 1.25 3 1022
1.25 (KC)

The application of designed biosensor in food safety and multiplex nanosensors

[101]

Shrimp

TC (antibiotic)

MIP

AuNPs

7.22 3 102

1.11 3 103
2.22 3 104

The successful detection of TC in real food samples with recovery percentage similar to LCMS assay

[102]

Milk

TC (antibiotic)

Ratiometric electrochemical aptamer

AuNPs, CNFs

8.25 3 1021

2.5 3 1022
2.5 3 103

A great potential in TC detection in food analysis and clinical diagnosis

[103]

(Continued)

TABLE 20.2 (Continued) Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Label-free electrochemical aptasensor

MNPs (Fe3O4), rGO

7.93

1.1
5.5 3 103

A viable platform for TC analysis in food, clinical, and environmental samples

[104]

OTC (antibiotic)

Sandwich-type electrochemical aptamer

AuNPs

12.45 3 1024

12.5 3 1024
5.0 3 103

The determination of OTC and related food safety analysis and clinical diagnosis

[105]

Milk

Ampicillin (antibiotic)

Aptamer

AgNPs

3.46 3 105

3.46 3 102
3.46 3 105

The feasibility of low-cost inkjet-printed biosensors to prevent both antibiotic contaminations and antibiotic resistance development

[106]

Milk

Sulfamethazine (antibiotic)

Ratiometric Fluorescent

CQDs-QDs

9.0 3 103

9.0 3 103
5.4 3 104

Determination of sulfamethazine in milk samples with little interference

[107]

Milk

α-Casein (allergen)

Label-free SPR

MIP NPs

3.175 3 102

1.25 3 103
2.0 3 104

Adequately monitoring the allergen risk in food processing

[108]

Baby foods, Juices, Beers

Gliadin, Casein β-Lactoglobulin, Ovalbumin (allergens)

Colorimetric (receptor: pAb for β-lactoglobulin, mAbs for gliadin, ovalbumin, and casein)

AuNPs (5 nm)

0.04, 0.40, 0.08 and 0.16 mg/L (in terms of EC50), respectively




A portable, easy-to-use, array-based method to quantify food allergens with a LOD below the accepted levels of the international legislations, allowing food safety and quality promotion

[109]

Food sample

Analyte type

Transductiona

Food sample

TC (antibiotic)

Honey

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Food sample

Gliadin (allergen)

Colorimetric, absorbance at 450 nm (receptor: Rabbit antigliadin pAb)

AuNPs (20 nm)

3.04 3 10




The sensitivity increase (at least five times), the lower detection limit than ELISA (at least three times)Remarkably decrease in the assay time

[110]

UHT-milk, Whey, Wheat, Chocolate, Cookie

Bovine βlactoglobulin (BLG; allergen)

Fluorescence (QDs/ H2O2-sensetive at quenching system)

CdTe QDs

1.225

(Dr: 0.48
62.5; 125
4000 ng/mL, no CR with egg, peanut, soy wheat proteins, weak CR with casein)-

Fluorescent sandwich ELISA (sELISA): detecting BLG and its allergenic residues in food with highly sensitivity, reliability, and recovery

[111]

Egg

Lysozyme (allergen)

Aptamer-based SERS

Dendritic AgNPs

0.5 μg/mL (water) 5.0 μg/mL (stainless steel foodhandling surface)

Dr: 0.0
6.0 μg/mL

Extending the developed method to detect other food allergens using specific aptamers

[112]

Milk

Casein (allergen)

Fluorescence (Intensity 5 620 nm)

GMNPs, CdTe QDs

91.475 (EC50)

Dr: 11.54
724.7, no CR with α-La, β-La, BSA and OVA

Providing the practical significance in the identification and quantification of milk allergens

[113]

Biscuit

Ara h 1 peanut (allergen)

Fluorescence (QDsaptamer/GO quenching system)

GO-NPs, QDs

140

RD: 500
5000, no CR with Ara h 2 and Ara h 3)

Extending to the detection of other food allergens with a selection of corresponding aptamers

[114]

4

(Continued)

TABLE 20.2

(Continued) Nanomaterial typea

LOD (nmol/ L)b

Fluorescence (GO/ ssDNA-oliGreen quenching system)

GO-NPs

Peanut Ara h 1 (allergen)

Chronoamperometry (receptor: stem-loop DNA)

Rice, Corn, Barley, Rye, Buckwheat, Oats, Mile, Chestnut Chickpea, Quinoa

Gliadin (allergen)

Mango and orange juices

Orange juice

Food sample

Analyte type

Transductiona

Linear range (nmol/L)c

Application note(s)

Reference

Buffer

Tropomyosin (shrimp allergen)

4.2 nM

Dr: 0.5
50 μg/mL, no CR with BSA, lysozyme, thrombin, myosin, etc.

A high demand in food allergen detection and clinical applications to label-free fluorescent aptamer-based GO sensing platform

[115]

Peanuts

Spongy gold film/CSMWCNT/ GCE

4.1 3 108

3.91 3 1028
1.25 3 1026

Excellent ability to analyze Ara h1 in peanuts

[116]

DPV (receptor: monoclonal antigliadin antibody)

SPCE-AuNPs

20.0

6.25 3 1021
6.25 3 102

The suitability of the immunosensing device for safety assessment of raw materials applied for the formulation of dietary products for celiac disease patients

[117]

Formaldehyde (adulterant)

Electrochemical enzymatic (indium tin oxide (ITO)-coated glass electrodes)

Flowerlike α-Fe2O3 nanostructures

0.02
0.04 mg/L

0.01
0.3 mg/L

The detection of potential food adulterant, formaldehyde (formalin)

[118]

Formaldehyde (adulterant)

Electrochemical enzymatic (CNT and CNT
Fe3O4 bioelectrodes)

CNT
Fe3O4 nanocomposite

0.05 mg/L

0.05
0.5 mg/L

Detecting formaldehyde adulteration in citrus fruit juices and other liquid foods to further resolve food safety concerns Controlling the unethical business practices of adulterationReducing the widespread food borne illness outbreaks

[119]

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Strawberry

Benzothiostrobin (pesticide)

Colorimetric

CdSe/ZnS QDs

6.2 3 10




Visual detection of benzothiostrobin residue in strawberry with the unaided eye under a dark box type UV lamp

[120]

Cabbage leaves

Chlorpyrifos (organophosphate (OP) pesticide)

Turn-on fluorescence probe

Modificationfree CDs

8.6

2.8 3 101
2.8 3 103

The visualization detection of organophosphorus pesticides in vegetables

[121]

Apple/Cabbage

Demeton, Dimethoate (Phosphorothioate insecticides)

Colorimetric (fluorescencevisualized paperbased)

QDsnanoporphyrin

1.9, 4.4

1.9
97, 4.4
220

High selectivity and stability to identify organophosphorus residues in complex systems

[122]

Apple, Pear, Green tea

Diazinon (OP insecticide)

Upconversion fluorescence (CDsenzyme)

CDs

0.125

0.25
125

Detecting OPs was also confirmed in adulterated environmental and agricultural samples

[123]

Apple

Methyl parathion (OP insecticide)

IFE-based fluorescent (turn-off mode)

CDs

7.1

2.8 3 102
5.7 3 104

An optical sensor for real applications in environmental and food safety control

[124]

Pea, Lupin

Glyphosate (herbicide)

Fluorescent probe (turn-off)

GQDs-AgNPs

4.9 3 1022

1.8 3 102
1.2 3 104

An interesting alternative to other existing methods for the analysis of glyphosate in food samples

[125]

1

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Apple

Chlorpyrifos (CP, pesticide), Imidacloprid (IC, insecticide)

SERS

AgNPs

Spiked corn

Zearalenone, Fumonisin B1 (biotoxin)

IFE-based fluorescent/turn-off

Lettuce

Salmonella toxin

Cereals

Linear range (nmol/L)c

Application note(s)

Reference

2.8 3 101 (CP) 2.0 3 102 (IC)

1.4 3 102
1.7 3 103 (CP), 2.0 3 102
2.7 3 103 (IC)

Nonplanar SERS substrate: for practical application in food safety and environmental monitoring

[126]

UCNPs, AuNRs

0.031 (Z), 1.4 3 1025 (FB1)

1.6 3 1021
3.1 3 102(Z), 1.4 3 1025
1.4 3 101 (FB1)

The applicability in determining of biotoxin content in stored samples

[127]

Fluorescence visualization (MIT)

Magnetic Fe3O4

6.93101 CFU/ g

6.9 3 101
6.9 3 108 CFU/g

The rapid detection of Salmonella toxins

[128]

Aflatoxin B1 (biotoxin)

ELISA by fluorescence probe

CdTe/CdS/ ZnS CDs

2 3 1023

1.0 3 1022
1.0 3 103

High potential in immunoassays of small molecules with low toxicity, high stability, and excellent fluorescence properties

[129]

Fruit juice

Ochratoxin A (biotoxin)

Fluorescent probe (turn-on mode)

Silica NPs

5.0 3 1022

5.0 3 101
2.5 3 102

The nanosensor sensitively and selectively to detect Ochratoxin A down to 20 ppb

[130]

Corn

Zearalenone (biotoxin)

MIT (fluorescence quenching particles)

CDs

6.3 3 101

6.3 3 101
3.1 3 103

Sensitive detection of zearalenone in other stored cereals

[131]

(Continued)

TABLE 20.2

(Continued)

Food sample

Analyte type 1

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Rapid detection of trace Ag1 in food packaging materials

[132]

Food packaging

Ag (heavy metal)

Fluorescent probe (turn-off mode)

Nitrogendoped CDs

1.2 3 10

1.0 3 10
3.0 3 10

Vitis vinifera juice

Cu21 (heavy metal)

Fluorescence quenching of CDs (turn-off mode)

Polyaminebased CDs

2.0 3 101

7.0 3 101
6.0 3 103

A suitable system for Cu21 ion detection in environmental liquid, food, and beverage samples

[133]

Black tea

Cd21, Pb21 (heavy metals)

Ratiometric fluorescence

UCNPs

3.7 (Cd21), 8.4 (Pb21)

1
1.0 3 105 (Cd21), 1
2.5
1.0 3 103 (Pb21)

An ideal chemosensor to detect heavy metals in real samples

[134]

Fish, Vegetables

Hg21 (heavy metal)

LSPR-based optical fiber (colorimetric)

AuNPs

0.50

0.50
2.5
2.7 3 103

Detection of Hg21 in food inspection and quality monitoring

[135]

Milk, Chicken breast, Beef

Escherichia coli O157:H7 (pathogen)

Optical (LFS) and SERS dual probe

Au@Ag coreshell NPs

53104 CFU/ mL

5 3 104
5 3 108 CFU/mL

A powerful tool for the quantitative and sensitive screening of E. coli O157: H7 in a food matrix

[136]

Spiked chicken

Salmonella (pathogen)

Thin-film piezoresistor-based pressure

Pt@MnO2 NFs

13 CFU/mL

1.5 3 10
1.5 3 105 CFU/ mL

Sensitive detection of pathogenic bacteria in food samples

[137]

Chicken meat

Campylobacter spp.

Paper-based DNA

Silica NPs

0.003 ng/μL or 600 CFU




The development of a portable and multiplex paper-based platform for point-of-care screening of chicken carcasses for Campylobacter

[138]

4

4

4

(Continued)

TABLE 20.2 (Continued) Food sample

Analyte type

Transductiona

Nanomaterial typea

LOD (nmol/ L)b

Linear range (nmol/L)c

Application note(s)

Reference

Coconut water

E. coli, Pseudomonas aeruginosa (pathogens)

Colorimetric

AuNRs







The optical detection and ablation of foodborne bacteria

[139]

Milk

Salmonella (pathogen)

TD-NMR

SMNP

104 CFU/mL

2.3 3 101
2.3 3 107 CFU/ mL

Sensitive and reliable diagnostic tool for common pathogens

[140]

Milk

Salmonella (pathogen)

NMR

Fe3O4 NCs

105 CFU/mL

105
107 CFU/mL

A rapid detection of foodborne pathogens in food, environmental, and agricultural samples

[141]

Kimchi product

Yersinia enterocolitica (pathogen)

Colorimetric (pAbs)

SWCNT

104 CFU/mL

104
106 CFU/mL

A promising approach for food pathogenic detection in real food products such as Kimchi product

[142]

Animal products

Staphylococcus aureus (pathogen)

Colorimetric (pAbs)

SWCNT

104 CFU/mL




Detection of S. aureus with a concentration , 104 CFU/mL

[143]

a AgNPs, silver nanoparticles; AuNCs, gold nanoclusters; AuNFs, gold nanoflowers; AuNPs, gold nanoparticles; AuNRs, gold nanorods; CDs, carbon quantum dots; CdTe, cadmium telluride; CNFs, carbon nanofibers; CNT, carbon nanotube; CQDs, quantum dots; CS-MWCNT, chitosan-multiwalled carbon nanotubes nanocomposite; CsPbBr3 NCs@BaSO4, barium sulfate-coated CsPbBr3 perovskite nanocrystals; DPV, differential pulse voltammetry; dSe, cadmium selenide; e-PNP, exhibiting polymer nanoparticles; GCE, glassy carbon electrode; GMNPs, gold magnetic nanoparticles; GQDs, graphene quantum dots; IFE, inner filter effect; LFS, lateral flow strip; LSPR, localized surface plasmon resonance; mAbs, monoclonal antibody; MIP, molecularly imprinted polymer; MIT, molecular imprinted technology; MNPs, magnetic nanoparticles; NAC, N-Acetyl-l-cysteine; pAb, polyclonal antibody; PtNPs, platinum nanoparticles; Pt@MnO2 NFs, platinum nanoparticle loaded manganese dioxide nanoflowers; rGO, reduced graphite oxide; SERS, surface-enhanced Raman scattering; SFS, synchronous fluorescence spectroscopy; SMNP, superparamagnetic nanoparticle; SPCE, screen-printed carbon electrode; SPR, surface plasmon resonance; SWCNT, single-walled carbon nanotube; TD-NMR, time domain nuclear magnetic resonance; UCNPs, upconversion nanoparticles; ZnCdSe, zinc cadmium selenide. b LOD, limit of detection. c BSA, bovine serum albumin; CR, cross reactivity; DR, dynamic range; OVA, ovalbumin; α-La, α-lactalbumin; β-La, β-lactalbumin.

20.2 Biosensors and food quality

771

20.2.1 Antioxidant capacity assessment The optical [e.g., (nanobased) colorimetric and (nanobased) fluorescence] and electrochemical (e.g., enzyme-, DNA-, and cell-based) sensors/biosensors are usually applied to determine antioxidant activities of food materials/products. In colorimetric analyses, diverse types of color-generating probes like dyes, enzymes, and nanomaterials are applied to determine the antioxidant capacity. Among them, noble metal nanoparticles such as AuNPs and AgNPs are more common due to their practical advantages [150]. For example, recent colorimetric assays using AuNPs to detect gallic acid, thymol, and carvacrol in edible oils [151
153], chlorogenic and gallic acids, and epicatechin in apples [154], as well as gallic and caffeic acids, and rutin [155] have been reported. The main fluorophores in fluorescence-based optical biosensors are organic dyes, carbon dots (CDs), graphene QDs (GQDs), and semiconductor QDs. The most common nano-based fluorescence biosensors are semiconductor nanocrystals of QDs, whose fluorescence characteristics are highly dependent on their size. Cadmium telluride (CdTe) QD- based fluorescence biosensor could identify the total phenolic compounds in tea and coffee such as catechin, quercetin, rutin, and chlorogenic, gallic, and caffeic acids [156]. Earlier, CdTe-QDs and graphene-QDs were respectively used to detect glutathione and ascorbic acid in beverages and fruit juices [157,158]. Recently, the presence of polyphenols in water, beverages, and foods has been analyzed using enzymatic biosensors [159
162]. The antioxidant capacity in the food industry using electrochemical biosensors is assessed with various kinds of electrodes, transducers, and receptors. However, the sensitivity and efficiency of these biosensors can be increased by integrating nanoparticles/nanomaterials on the working surface of electrodes. Two enzymes of tyrosinase and laccase are commonly applied in the structure of electrochemical biosensors because of their specific substrates, namely, o-diphenols and monophenols [163]. Garcı´a-Guzma´n et al. assessed polyphenol compounds and antioxidant activities of beers and wines using a poly(3,4-ethylenedioxythiophene)-tyrosinase/sonogel-carbon (PEDOT-Tyr/SNGC) electrode. This tyrosinase-based amperometric biosensor with high reproducibility and stability evaluated the index of polyphenols within a linear response range through an innovative sinusoidal current technique [164]. de Oliveira Neto et al. designed a laccase-based modified carbon paste biosensor to measure the total phenolic content (TPC) and antioxidant activity of honey samples collected from several countries. The detection principle in these biosensors is the laccase capability to oxidize a wide range of polyphenolic compounds. The obtained results from this biosensor showed a robust relationship with the TPC determined

4. Construction and other applications

772

20. Biosensing technology in food production and processing

by Folin-Ciocalteu spectrophotometric method [165]. A similar result was reported by Mohtar et al., who assessed polyphenols in propolis using an amperometric biosensor based on laccase immobilized onto an AuNP-structured screen-printed electrode (SPE) [166]. A laccase-based biosensor with PtNPs deposited on the graphite oxide paste electrode surface was also used to differentiate the quality of brewed coffee beverages based on their chlorogenic acid content [167]. Also, Becker et al. developed an amperometric biosensor with fast response and easy automation for antioxidant capacities of Amazonian and nonautochthonous fruits. In this biosensor, the enzyme xanthine oxidase (XOD) via entrapping with the polymer of azide-unit pendant water-soluble photopolymer (PVA-AWP) was immobilized on the surface of Prussian Blue-modified electrodes. The high correlation of biosensor results with the in vitro antioxidant capacity analysis of Amazonian fruits in terms of the inhibition of H2O2 and/or O2•2 radicals with gallic acid as the standard component showed that the fabricated biosensor can be considered to assess the antioxidant activity in natural or processed vegetables [168]. On the other hand, DNA compared to proteins and antibodies is a better BRE in designing biosensors to detect antioxidants in foods because of its smaller size, higher stability, and lower cost. DNA probes can determine the antioxidant activity of bio- and food products based on the DNA damage in the exposure to free radicals (such as OH•) and their ability to scavenge them [169]. The use of these biosensors in assessing antioxidative activities of tea extracts, beers, coffees, white wines, fruit juices, and beverages has been proved [170]. The limit of detection (LOD), sensitivity, and electrochemical properties of DNA-based biosensors were successfully improved using twodimensional nanomaterials in their structure. Glassy carbon electrodes (GCEs) modified with a graphene nanoribbon and AgNPs were used to produce electrochemical DNA-based biosensors in assessing the total antioxidant capacity in fruit juices and green tea infusions [171,172]. When living cells act as a bioreceptor, any physiological change in these cells can be detected using a special transducer. Most cell-based electrochemical biosensors are used to diagnose toxic materials in food materials. Recently, these biosensors were frequently used to assess the antioxidant activity of probiotics isolated from food products. For instance, Ge et al. developed a RAW264.7 macrophage cell-based electrochemical biosensor to assess the antioxidant potential of Lactobacillus plantarum isolated from Chinese dry-cured ham according to the generation of reactive oxygen species by macrophage cells in the cytoplasm, leading to the release of H2O2 [173]. Xing et al. also determined the antioxidant activity of a low-molecular-weight bioactive peptide (Asp-Leu-Glu-Glu) isolated from Chinese dry-cured ham using a Caco-2 cell-based electrochemical biosensor. For this work, they designed a

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platinized gold electrode covered with silver nanowires (AgNWs) to diagnose the release of H2O2 in the cell membrane. Although the use of these biosensors in the food industry is limited, sustaining the viability and activity of cells during embedding into the structure of biosensors should be promoted in future studies [174].

20.2.2 Screening of food-grade ingredients and additives The identification and quantification of food constitutes or adulterants in conventional methods usually require complex treatments on the analyzed samples, laboratory infrastructure, and highly experienced staff. In recent years, different biosensors to overcome problems of classical analysis methods have been utilized for real-time monitoring of food compositions and additives. Enzyme-based potentiometric biosensors based on the use of glucose oxidase (GOx) were used to detect glucose in orange juices and soft drinks. In these biosensors, GOx was immobilized with polymeric membranes made of polyvinyl alcohol (PVA) and chitosan (CS) [175] and CS hydrogel onto highly ordered titanium dioxide nanotube arrays (TiO2-NTAs) [176]. The results showed a precise, valid, and rapid analytical response compared to conventional standard techniques such as HPLC. Gokoglan et al. also developed a sensing platform assembled onto polyethylene terephthalate (PET) substrates to detect glucose in commercial milk, strawberry soda, and orange soda through incorporating a vertically aligned-CNTs (VACNTs) and a conjugated polymer (CP). After casting PFLO (poly (9,9-di-(2-ethylhexyl)-fluorenyl-2,7-diyl)-end capped with 2,5-diphenyl-1,2,4-oxadiazole) onto PET/VACNTs deposited on an aluminum foil, the electrodes as an immobilization medium were used to diagnose GOx with aid of the cross-linker of glutaraldehyde (GA) for designing flexible biosensors for glucose (Fig. 20.3) [177]. Stredansky et al. detected sucrose in green coffee samples by an amperometric biosensor using flavin adenine dinucleotide-dependent glucose dehydrogenase coimmobilized with invertase and mutarotase on a thin-layer gold electrode. This oxygen-independent biosensor not only had a low LOD of 8.4 μM at a 50 s ResT but also revealed excellent processing and storage stability. There was a robust correlation between HPLC and biosensors data, showing the validity and applicability of the developed biosensor for the coffee industry [178]. Bathinapatla et al. also tracked the existence of sucralose in food samples using a laccase immobilized zinc oxide nanoparticles (ZnONPs)/graphene oxide (GO)-based electrochemical enzymatic biosensor. This artificial sweetener as an electroactive constituent can be faced with oxidation at electrodes used in biosensors. Sucralose forms hydrogen bonds with glutamine-228, isoleucine-230, and/or other

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(A)

PET/VACNT-Al foil/PFLO/GOx

GOx/GA immobilization

Attachment of VACNT deposited Al foil

PET electrode

PET/VACNT-Al foil/PFLO

PET/VACNT-Al foil Casting PFLO solution (B) O2 + 2H+ + 2e-

GOx-FAD2

δ-D-Gluconolactone H2O D-Gluconic acid

H 2O 2

GOx-FAD

β-D-Glucose

FIGURE 20.3 (A) A schematic diagram of flexible GOx-based potentiometric biosensor to detect glucose, and (B) the reaction mechanism of the used enzyme. Source: Reproduced with permission from Ref. T.C. Gokoglan, S. Soylemez, M. Kesik, I.B. Dogru, O. Turel, R. Yuksel, et al. A novel approach for the fabrication of a flexible glucose biosensor: the combination of vertically aligned CNTs and a conjugated polymer. Food Chem. 220 (2017) 299
305.

amino acids within the water-insoluble channel of binding sites, contributing to the redox reaction for the diagnosis of this no-calorie artificial sweetener [179]. In addition, screen-printed carbon electrodes (SPCEs) nanostructured with 3,4DHS (N,N0 -Bis(3,4-dihydroxybenzylidene)-1,2-diaminobenzene Schiff)
AuNP assemblies with immobilizing lactate oxidase were used to diagnose lactate in wine, beer, and yogurt [180]. A voltammetric sensor based on vanadium pentoxide nanoparticle (V2O5/NP)-modified HMIHPF6 (n-hexyl-3-methylimidazolium hexafluorophosphate) carbon paste electrode was successfully applied to assess kojic acid in the concentration range of 0.08
500 μM in fresh vegetables with a LOD of 20.0 nM [181]. An innovative yellow-emissive nanoprobe to detect glutathione in vegetables and fruits was developed using nitrogen-doped nano-CDs (Nd-NCDs) prepared through a hydrothermal heating process of o-phenylenediamine. Nd-NCDs as a fluorescent nanoprobe with a good sensitivity and selectivity act to sense glutathione in LOD levels of lower than 0.059 and 5.54 μM [182]. Fluorescent biosensors were also used to detect nisin produced by the mutant strain of Lactococcus lactis in milk [183]. In two distinct studies,

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Hassan et al. detected κ-carrageenan in seaweeds and dairy products using two biosensors (i.e., DNA- and cell-based electrochemical biosensors) [184,185]. They designed a potentiometric whole-cell biosensor to immobilize the marine bacterium, Pseudomonas carrageenovora, which had a specific selectivity for κ-carrageenan than other types of carrageenans like ι- and λ-carrageenans [185]. Another electrochemical biosensor was fabricated by immobilizing calf thymus double-stranded DNA on the carbon-based SPE. This biosensor based on differential pulse voltammetry (DPV) technique was able to monitor κ-carrageenan. DPV analyzed the sample easier and faster than chromatographic and spectrophotometric methods with no further preparation steps [184]. Winiarski et al. recently detected folic acid in dietary supplements and wheat flour using an electrochemical sensor based on a multiwalled carbon nanotube (MWCNT)/nickel hydroxide composite. They reported that the electrochemical oxidation of this vitamin via DPV can attain a linear response in the concentration range of 0.5
26 μmol/L, while the LOD was 0.095 μmol/L. The comparison of the results obtained from the developed sensors and molecular absorption spectrometry proved that the designed sensor can be a promising tool to accurately detect water-soluble vitamins in the food industry [186]. Surface plasmon resonance (SPR) is an optical transducer to conduct the resonance collective oscillation of valence electrons at the interface between the materials with negative and positive permittivity. A research group prepared vitamin B2, B9, and B12 imprinted SPR sensors for their real-time monitoring in milk and infant formula samples. These sensors adequately detected vitamins B2, B9, and B12 in very low LODs (0.00016, 0.00135, and 0.00025 ng/mL, respectively). The storage stability analysis of SPR sensors showed that they maintained their activity (90.03%
91.54%) to diagnose B-group vitamins even after 90 days. The researchers concluded that vitamin B2, B9, and B12 imprinted SPR sensors combined with molecular imprinting technology would be a valid, sensitive, and selective to assess vitamins in liquid- and solidbased food products [187]. Different biosensing platforms have recently been utilized to detect amino acids in food samples, such as L-tryptophan using a molecularly imprinted polymer (MIP)-based QCM biosensor [188], L-arginine using a “turn-off” fluorescent biosensor [189], L-glutamate using a biosensor based on the modified nanocomposite (AuNPs/GO/CS) connected with L-glutamate oxidase (GluOx) [190], glutamate using an AEC biosensor with GluOx-crosslinked CS and AgNWs on a GCE [191], and an amperometric glutamate-sensitive biosensor based on immobilized GluOx and Pt-disk electrode [192]. Different research groups evaluated the detection of lactose in milk types and dairy products using biosensors [193
196] Among these

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studies, Churakova et al. initially determined the lactose content in lactase-treated ultra-high temperature (UHT) milk using a valid analytical technique, namely high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD), and then compared the potential of four other methods such as amperometric biosensor (Biomilk300), nuclear magnetic resonance (NMR), HPLCrefractive index detector, enzymatic commercial kits, and cryoscopy to detect lactose. They reported that the biosensor had comparable efficiency and sensitivity with the HPAEC-PAD in low lactose levels [193]. Lopez et al. successfully detected low amounts of lactose (10
100 mM) in the presence of high doses of glucose using an amperometric polymer multilayer-based biosensor. They developed this biosensing platform in two distinct layers including (1st) the bottom sensing layer based on cellobiose dehydrogenase (CB-DH), bonded with electrostatic interactions to efficiently transfer electrons, and (2nd) the upper layer comprising of a hydrophilic polymer linked with GOx and catalase. This device easily removed the interfering analyte (glucose) using GOx in the second layer and inhibit its sensing on the inner layer. Furthermore, the presence of catalase at the same layer concurrently removed the formed H2O2 [196]. de Brito et al. recently detected lactose (LOD 5 0.15 mmol/L) in skimmed milk with high sensitivity (1.06 μA/cm2) using an electrochemical biosensor by immobilizing lactase in the matrix of MWCNT and carbon paste [194]. However, Kuˇcerova´ et al. reported that the gas chromatography/tandem mass spectrometry (GC/TMS) had higher sensitivity and selectivity to detect lactose in milk and dairy products (e.g., curd cheese and yogurt) compared to a three-enzyme amperometric biosensor having β-galactosidase, GOx, and horseradish peroxidase (HRPO) [195]. Manoj et al. recently compared the detection of triglycerides in coconut milk using two analytical systems including enzyme-based amperometric biosensor and ultra-HPLC. In the designed biosensor, three enzymes of lipase, glycerol kinase, and glycerol-3phosphate oxidase were coimmobilized on the gelatine-based membrane and then coated on the electrode surface. Although a high correlation rate of 97% was observed between findings obtained from the two systems, the electrode activity at 40 C for 4 h and 4 C for 30 days was reduced by 48% and 54.7%, respectively [197]. In another study, they used an SPE biosensor using the three above-mentioned enzymes to detect triglycerides in coconut milk. Results revealed that the optimum response for detecting triolein concentrations (0.1 to 1.5 mM) by the developed biosensor can be found under pH 7.0 and 45 mg of gelatin in a GA solution with a concentration of 2.5% [198]. Jayaprakasan et al. fabricated a glyoxalase 1-based electrochemical biosensor with ZnO nanoflakes interface to detect methylglyoxal formed in foods processed at high temperatures, such as grilled chicken.

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The linear range, sensitivity, LOD, response speed, and storage stability of the established biosensor were 0.6
2.0 μM, 0.281 μA/μM, 9 nM, ,4 s, and 89% for 18 days, respectively [199]. Additionally, the food colorant of indigo carmine in food and beverage products could be detected using a poly (glycine)-modified carbon paste electrode in a low LOD of 11 3 1028 M based on DPV and cyclic pulse voltammetry (CPV) techniques [200]. Biosensor-based monitoring of other colorants such as coccine (C,E 124, ponceau 4 R or cochineal red A) and orange II dyes in soft drink and paprika [201], the synthetic lemon yellow azo dye of tartrazine in soft drinks [202], and sunset yellow in soft drinks [203,204] has been reported. The corresponding biosensors were a surfaceenhanced Raman spectroscopy (SERS) platform based on metal-organic framework [UiO-66(NH2)] fabricated with AuNPs, a poly (p-aminobenzenesulfonic acid)/ZnO-NPs in carbon paste electrode, a laccase-based electrochemical biosensor with a photocured polyacrylamide membrane, and an electrochemical biosensor based on the hybrid of Fe3O4 NP-MWCNT, respectively. The presence of biogenic amines such as tyramine in fermented foods (e.g., Gouda and Brie cheeses) and beverages was detected using different biosensors such as amperometric tyrosinase-based biosensor [205], electrochemical nonenzymatic biosensor based on silver-substituted ZnO nanoflower [206,207], and solidstate potentiometric sensor [208]. In general, the detection priority of tyramine using these sensing platforms compared to chromatographic techniques is due to the rapid measurement process without any timeconsuming preparation steps, and with a considerable savings in consumed chemicals and solvents.

20.2.3 Food authenticity assessment Food adulteration, as a growing problem in the industry, is usually performed aiming at reducing the quality of food products for sale with illegal acts such as the admixture or replacement of low-grade ingredients (such as malachite green and melamine), and the removal of some valued constituents. Hence, food fraud not only can carry irreparable economic problems but also can seriously affect the health of consumers. The use of biosensors as a sensitive, portable, and rapid technology is necessary to detect adulterants to maintain the quality and safety of food products. Malachite green is one of the organic dyes used to give a fresher and greener appearance to raw materials and food products such as green vegetables (e.g., peas, beans, okra, etc.) and ready-to-use foodstuffs (e.g., ice candy, chili sauce, etc.) [209]. Shukla et al. designed a florescence-based sensor using highly photostable CD derived from the leaf extract of

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Ocimum tenuiflorum to detect malachite green in green vegetables. The main advantage of this sensor was a low LOD (18 nM) associated with remarkable antioxidant capacity and negligible cytotoxicity [210]. The addition of water to milk aiming to increase the volume of milk can reduce its protein content (B3.4% w/v). One of the most important frauds in milk processing is the addition of nitrogen-rich chemical compounds such as melamine and urea to milk for inducing high protein content of dairy products like infant formulas. From a nutritional perspective, the intake of melamine can lead to renal problems such as acute kidney failure and stone complications [211,212]. The melamine detection using various biosensors including aptamer-based evanescent wave fiber sensor [213], aptamer-DNAzyme conjugated biosensor [214], fluorescence resonance energy transfer biosensor based on GQDs and protoporphyrin IX [215], paper-based visual sensor using Triton X-100-modified-AuNPs [209], and MIP
SERS [216] has been conducted. The efficient sensing platforms compared to past analysis techniques such as laser Raman spectrometry possessed significant precision and repeatability, less run time, simple sample pretreatment, constant recovery, and ideal linear working range to detect low concentrations of melamine. Singh et al. determined the added concentration level of urea to milk using a GCE that was electrochemically modified with a nanocomposite film made of Fe3O4/MWCNT-polyaniline. They immobilized urease on the nanocomposite surface using GA induced-crosslinking. The fabricated biosensor with 60-day storage stability detected urea in a linear range between 1.0 and 25.0 mM with a LOD of 67 μM [217]. Earlier, Ezhilan et al. successfully developed highly sensitive acetylcholinesterase (AChE) cyclic voltammetric biosensor possessing a ZnO-NP-modified Pt electrode for the concurrent detection of urea (LOD 5 3.0 pM) and melamine (LOD 5 1.0 pM) in cow milk samples [212]. Moreover, polar and nonpolar/ionic adulterants (e.g., melamine, urea, allantoin, starch, baking soda, cyanuric and benzoic acids, and ammonium sulfate) in milk have been detected using a Pt/Teflon/silicon dioxide (SiO2)/silicon (Si) biosensor device according to the impedimetric assessments by electrical impedance spectroscopy. Impedimetric phase angle-modulus diagram interestingly mapped different adulterants in a point-of-care, low-cost, and fast detection pattern [218]. The usage of milk from different species is one of the main adulterants in milk processing. A paper-based DNA biosensor using the receptor of streptavidin (SAV)-AuNP conjugates was applied for the visual detection of particular DNA sequences of yogurt samples prepared from cow, sheep, and goat milk. After DNA amplification of each animal species in a PCR reaction, each biotinylated product was diluted, denatured, and hybridized to complementary DNA probes and followed by prehybridized PCR products in contact with SAV-AuNP conjugates were visualized. The adulteration using this biosensor could be

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detected based on binary mixtures containing 0.01 of cow yogurt in sheep one and 5% of goat yogurt in goat one. This fact showed that the detectability was ten times greater than other analytical techniques [219]. In addition, there is a potential to detect wine authenticity using a long-period grating DNA-based optical biosensor because this sensitive device can discriminate specific grapevine varieties using genomic DNA extracted from the leaf, must, and wine samples [220]. In their earlier study, Gutie´rrez et al. applied a hybrid e-tongue to differentiate wine samples in terms of grape variety and vintage using PCA and PLS-DA algorithms and accordingly predicted chemical factors for the quality control of wine [221]. Using a voltammetric e-tongue, Apetrei and Apetrei diagnosed adulteration in virgin olive oil (VOO). The data integration of e-tongue and chemometrics not only discriminated botanical origin-based pure oils but also identified extra VOO with over 5% adulteration. They also forecasted the composition of the blended oil with extra VOO and oils extracted from other seeds (up to 25%) with high reliability [222]. Zougagh et al. utilized AuNP-based optical sensors to assess VOO authenticity based on the percentage analysis of ferulic acid because it composes 95% of the total phenolic acids of this edible oil [223]. An optical sensor with fluorescence quenching of CdSe/ZnS QDs recognized adulterated vegetable cooking oils with no sample preparation. Accordingly, the illegal use of refined oils in soybean oil at concentrations of 0.4% or more was detected for 120 s using the developed optical sensing tool [224]. Recently, fluorescent “turn-off” sensors based on double water-soluble ZnCdSe-CdTe QDs using the classifier of oneclass PLS have detected illegal additives among pure orange juices (117 samples) and herbal honeys (122 samples) [80,225]. Kundu et al. determined the adulterant traceability of formaldehyde (LOD 5 0.05 mg/L) in orange juice using a formaldehyde dehydrogenase-based electrochemical biosensor with an electrode made of Fe3O4-MWCNT nanocomposite by CPV technique [119]. Kundu et al. also developed a similar electrochemical enzymatic biosensor based on flowerlike α-Fe2O3 NPs to detect formaldehyde in extracted and commercial mango and orange juices (LODs 5 0.02a0.05 mg/L) [118]. Khalil et al. fabricated a SERS biosensor based on a dual nanoplatform consisting of a short-length DNA probe incorporated with a Raman tag (ATTO Rho6G) for the detection of pork DNA in Halal meat products [226]. Hartati et al. successfully detected pork (LOD 5 1.76 μg/mL) in beef and chicken meat samples using an electrochemical DNA biosensor based on an SPC-reduced GO electrode. The DNA probe immobilization of the swine cytochrome b (cytb) gene on the working electrode surface was conducted by passive adsorption, whereas the DPV technique was used to evaluate the oxidation signal of the target DNA’s guanine [227].

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Furthermore, Kuswandi et al. determined the pork adulteration in processed meats using a simple DNA biosensor based on the color change of AuNPs from red to purple in phosphate buffer saline (Fig. 20.4A) [228,229]. Also, an SPR-based biosensor was effectively applied to discriminate gelatin from porcine and bovine sources at concentrations up to 10% [230]. It was previously demonstrated that nucleotide fragments are one of the most efficient markers in diagnosing meat adulteration. Ali et al. [231] detected pork adulteration in meatballs using a speciesspecific nanobiosensor based on citrate/tannate-coated AuNPs, functionalized with a 27-nucleotide AluI fragment of swine cytb gene. The results showed that this sensing platform can diagnose 1% pork in raw and cooked beef meatballs. Mansouri et al. recently fabricated a highly selective SPR-based DNA biosensor coupled with AuNP-SAV conjugates to detect donkey meat in homemade sausage products [232]. In another study by Mansouri et al., the adulteration in cooked beef sausage formulated with donkey meat was recognized using an innovative

FIGURE 20.4 An illustration of the possible mechanism of (A) DNA-based colorimetric biosensor based on AuNPs for pork detection in processed meat, and (B) colorimetric biosensor for glucose detection in wine. Source: (A) Reproduced with permission from Ref. B. Kuswandi, A.A. Gani, N. Kristiningrum, M. Ahmad, Simple colorimetric DNA biosensor based on gold nanoparticles for pork adulteration detection in processed meats. Sens. Transducers. 208 (2017) 7
13 (B) Reproduced from Ref. H. Dai, Y. Li, Q. Zhang, Y. Fu, Y. Li, A colorimetric biosensor based on enzyme-catalysis-induced production of inorganic nanoparticles for sensitive detection of glucose in white grape wine. RSC Adv. 8 (2018) 33960
33967 with permission from the Royal Society of Chemistry.

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species-specific electrochemical DNA probe. There was a high correlation between results obtained from biosensor and quantitative real-time PCR (QRT-PCR) analyses [233]. Zhang et al. developed a trident-like lateral flow biosensor equipped to a species-specific tag-labeled multiplex loop-mediated isothermal amplification (TM-LAMP) to simultaneously diagnose horse and donkey meat (LOD , 40 pg) within 40 min in a homogeneous meat mixture. This sensing platform with notable specificity and sensitivity can be used to identify species authenticity in meat processing [234].

20.2.4 Freshness evaluation of food products The freshness assessment of food materials is one of the most practical applications of biosensors within the food production and consumption chains. In recent years, many studies have been performed to assess the freshness of aquatic (such as fish) and meat products. The freshness of these products not only is an indicator of the physiochemical and organoleptic quality properties but also can indirectly guarantee consumer health. These nutrient-dense food products are highly susceptible to deterioration during storage due to the accelerated enzymatic and microbial activities. The freshness in meat and aquatic products is routinely evaluated with two main criteria including the expert panelists’ sensory assessment and the determined levels of a target biochemical [e.g., pH and total volatile base nitrogen (TVB-N)] and microbial (e.g., spoilage bacterial count) marker using classical analytical methods. Today, optical and electrochemical biosensors by overcoming the shortcomings of traditional techniques can rapidly and accurately assess freshness-related parameters at very low LODs [235]. Beef meat freshness was also assessed using the GCE-modified glucose biosensor by immobilizing GOx on MWCNTs/CS via GA-bovine serum albumin cross-linker. The fabricated biosensor rapidly detected glucose with a LOD of 0.05 mM during the storage. It was pointed out that this biosensor can easily be replaced with the TVB-N method to determine the meat freshness [236]. One of the metabolic intermediates of nucleotides, namely hypoxanthine, is highly prone to spoilage and easily develops through the degradation of adenosine triphosphate. Albelda et al. fabricated a graphene/TiO2 nanocomposite as a platform for the XOD immobilization in an amperometric hypoxanthine sensor for the freshness assessment of pork tenderloins stored for a week at ambient temperature. This highly sensitive biosensor with great antiinterference characteristics in the presence of uric acid, vitamin C, and glucose quantified hypoxanthine with a LOD of 9.5 μM. A high association was found between the results of biosensor response and standard enzymatic colorimetric assay [237]. Moreover, the pork freshness with a

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high correlation of TVB-N levels was assessed using a microsensor constructed by TiO2-based CP [238]. Yazdanparast et al. managed to construct an MWCNT-poly(L-aspartic acid) nanobiocomposite to immobilize XOD on a GCE for sensing xanthine in fish meat with a LOD of 3.5 3 1024 μM based on the DPV method [239]. Dalkıran et al. developed an electrochemical xanthine biosensor based on a nanocomposite made of graphene/cobalt oxide nanoparticles (Co3O4 NPs)/CS to detect fish freshness. However, they immobilized XOD on the working surface of GCE using the cross-linker of GA. This biosensor identified xanthine at a low LOD of 0.0002 mM within 10 s with a high sensitivity of 6.58 μA/mM or 74.8 μA/mMcm2 [240]. Chen et al. evaluated the fish freshness by tracking hypoxanthine through immobilizing XOD on the visual and low-cost gold nanorods (AuNRs)-based multicolor biosensor. Under the XOD catalysis, H2O2 can be formed by reacting hypoxanthine with the dissolved oxygen. The catalytic process of the Fenton reaction can be accelerated in the presence of Fe21 ions to produce OH•. This free radical is much stronger oxidability compared to H2O2 and significantly increases the etching reaction from the AuNRs tips. Changing the aspect ratio of AuNRs is accompanied by intense color alterations in the solution, which can be easily distinguished by the naked eyes. Accordingly, the longitudinalSPR (LSPR) shift of AuNRs had a direct correlation with hypoxanthine concentration and noticeable color changes [241]. The freshness of aquatic products such as fish, shrimp, and squid meats based on the detection of hypoxanthine (LOD 5 2.88 μM) was also monitored using a high-selectivity fluorescence biosensor according to the mimicking activity of peroxidase on PtNPs [242]. Ghanbari and Nejabati measured the freshness of fish meat using a nonenzymatic xanthine biosensor based on a modified GCE made of reduced GO/polypyrrole/CdO nanocomposite. They concluded that this biosensor can be a satisfactory sensing platform to assess xanthine because of the integration of the expanded active surface area, high adsorptive power of the used nanomaterials to form specific interactions with the analyte [243]. Also, a SAW sensor was acceptably used to evaluate the freshness of chicken meat by measuring the generated aldehyde gas (mainly, CH3CHO) during 15 days storage. This sensor was developed on the active surface of lithium niobate (LiNbO3)-based piezoelectric wafer aiming at the crosslinking agent of polydimethylsiloxane [244]. Koskela et al. easily monitored the freshness rate of raw broiler meat using printed copper acetate-based sensors through the release traceability of H2S gas as one of the important end products in the microbial metabolism pathway [245]. The usage of electrochemical immunosensors and genosensors is common to identify a group of spoilage-causing bacteria in meat products, such as Escherichia coli, as well as Listeria and

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Salmonella species [246,247]. Alexi et al. [248] recently predicted the shelf life of yellowfin tuna steaks chilled at 2 C employing an innovative cadaverine biosensor system. According to the results of biosensor response, they found that the obtained findings were comparable with results of other techniques such as quality index method, LC-MS/MS, and microbiological counts (e.g., Pseudomonas spp, Vibrio spp., Enterobacteriaceae, etc.). Creatine is one of the important indicators for fish freshness showing the spoilage extent. Recently, Fazial et al. recognized levels (16
48 mM) of this nitrogenous organic acid using a reflectometric bienzymatic biosensor kit made of covalent grafting of creatinase and urease on the pH chromoionophore ETH 5295-doped poly(styrene-co-acrylic acid) latex microspheres through EDC/NHS coupling chemistry. It is worth recalling that this biosensor detected the target substance within 7.0 min without any interference by biogenic amines, amino acids, and other nitrogen compounds present in the fish surface like histamine, tyrosine, phenylalanine, and taurine [249]. In addition, much attention has been given to the application of e-nose technology in assessing meat freshness [64]. Gu et al. assessed the lipid oxidation of Chinese-style sausages in processing and storage steps using a portable e-nose consisting of an array of ten metal oxide sensors to their flavor fingerprint map [250]. A similar e-nose having ten MOSs was designed to discriminate the odor profile of fresh and grilled eel meats by identifying 93 volatile compounds [251]. In general, over 10% of fruits and vegetables are discarded by food processers due to the low freshness quality according to their appearance and sensory attributes (such as taste, smell, and texture). The quality control of fresh-cut vegetables and fruits is time consuming and expensive. However, the use of biosensors based on the immobilized enzymes (e.g., alcohol peroxidase and alcohol oxidase) or chromogen agents can contribute to the easy diagnosis of injuries caused by less oxygen rate in lightly processed and modified atmosphere packed vegetables (e.g., lettuce, broccoli cauliflower, and cabbage) can improve their quality assurance and control. On the other hand, sugar compositions, total titratable acidity, and total soluble solids (Brix) of fruits have significant effects on the favorable formation of flavors, pigments, and texture characteristics, indicating their freshness and ripeness [2]. A “switch-on” fluorescent sensor based on CD-manganese dioxide (MnO2) was used to detect ascorbic acid in food matrices such as fruit juices, as well as fresh fruits and vegetables [252]. Dey and Sarkar constructed a TiO2-based thin film biosensor incorporated into microgap cavities. Embedding this tool into the fruit surface provides increased sensitivity for responding to any dielectric changes during the rotting phase of fruits. The surface alterations in the microgap cavity were also visualized using transmission and field emission scanning electron

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microscopes. The developed biosensor could well determine the freshness of apple, orange, and guava fruits during storage for 10 days [253]. Maftoonazad and Ramaswamy designed a pH biosensor based on an electrospun nanofiber mat with PVA and an anthocyanin-rich pigment extract from red cabbage to recognize fresh date fruit. They assessed the freshness by placing the sensor film in the packaging so that the film’s anthocyanin-related color changes were easily evaluated based on the pH changes of date fruits [254]. Hashemi Tameh et al. successfully applied an electrochemical impedance immunosensor integrated with a microfluidic module and a microelectrodes array to detect the blackleg and soft rot disease causing-phytopathogen in potato tubers (,104 cfu/mL). Promising results of this research showed that the label-free lab-on-chip platform can potentially be an alternative for the standard techniques of ELISA and PCR [255]. Kiani et al. used a portable e-nose to classify different saffron types according to the release of organic volatile compounds. This system had several major parts including a laptop, microcontroller devices, a sensor chamber, an array of gas sensors, a system for air circulation, and several moisture- and temperature-controlling sensors [256]. In 2018, these researchers concluded that the use of multiarray biosensors based on the online data fusion of e-nose and e-tongue technologies can assess the taste quality authentication of saffron to control processes involved in the related industries [257].

20.2.5 Quality monitoring of wine Wine is comprised of different constituents with various percentages such as ethanol, other aliphatic and aromatic alcohols, sugars, organic acids, glycerol, certain ions, and so on. Recently, a high number of biosensors have been utilized to monitor wine quality by assessing these compounds. Earlier, the usage of nanotechnology-based biosensors to quantify chemical ingredients present in wine was reviewed to evaluate the quality and safety considerations [144]. Monitoring the glucose consumption and ethanol production during fermentation of honey wine was performed using a bienzymatic electrochemical bienzymatic biosensor prepared with GOx, alcohol dehydrogenase, core-shell Fe3O4@Au NPs, MnO2, and carbon paste electrodes. Since the response obtained from this biosensor had a close correlation with the GC and glucose-meter results, the developed biosensor was recommended for use in wine production [258]. Shkotova et al. analyzed lactate concentrations (0.5
5.0 g/L) in different wine types using a highly sensitive lactate oxidase-based amperometric biosensor having carbon electrodes modified with PdNPs and PtNPs (LOD 5 0.1 μM). A strong relationship was obtained between the results obtained from

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biosensor and spectrophotometric analyses [259]. In addition, the glucose content (LOD 5 3.29 μM) in white grape wine was assessed using a colorimetric biosensor based on GOx-catalysis-induced production of Prussian blue NPs, which only had a 1% variation with the obtained data by HPLC. In this procedure, the addition of glucose to the solution containing GOx, FeCl3, and K3Fe(CN)6 led to the color change from light-yellow to blue for 10 min. There were two pathways to produce Prussian blue NPs including (i) the glucose oxidation by GOx in the presence of oxygen and the production of gluconic acid and H2O2. The ion of Fe31 in the vicinity of H2O2 generates Fe21 and Fe(CN)632, causing the production of Prussian blue NPs, and (ii) the oxidation Fe (CN)632 to Fe(CN)642 in the GOx-containing solution and the development of Prussian blue NPs in the presence of Fe31. The applied dualpath method effectively overcame the deficiency of O2 and improve the remarkable formation of Prussian blue, intensifying the detection signal of glucose (Fig. 20.4B) [229]. Stasyuk et al. designed an amperometric nanozyme-based biosensor to detect glucose in wine. Two coimmobilized enzymes of HRPO and GOx were conjugated with Ag-, Au/Ag-, and Ag/Au-NPs on the carbon-electrode surface. A good electrocatalytic response to reduce glucose at a low potential of 250 mV was obtained, while a significant relationship between the results of used biosensors and commercial enzymatic kits was found [260]. A strong association between amounts of carboxylic acids and the process progress of malolactic fermentation has been recorded in wine production. Milovanovic et al. fabricated an amperometric enzymebased biosensor by coimmobilizing lactate oxidase, sarcosine oxidase, and fumarase/sarcosine oxidase in the three sensing channels to detect organic acids of 31 wine samples collected in the Czech Republic. The data analysis by self-organized maps (SOMs) and PCA could well classify organic acids identified by the biosensor and capillary electrophoresis. Based on a good correlation between the instrumental analytical techniques, it was concluded that the combined use of biosensor/SOM can be a suitable tool to classify wines with high quality [261]. On the other hand, acetoin is the main organic compound in the biosynthesis of diacetyl as the wine flavoring in alcoholic and malolactic fermentation processes. The production of acetoin during wine fermentation was assessed using a capacitive electrolyte-insulator-semiconductor (EIS) field-effect biosensor. The EIS sensor was structurally made of Al/porous Si/SiO2/tantalum pentoxide (Ta2O5)/acetoin reductase. Racemic acetoin thanks to the immobilization of acetoin reductase in an H1-consuming reaction is converted to (R,R)-2,3-butanediol and mesobutanediol, respectively. This chemical reaction will be resulted in a change in pH and diagnosed by the used EIS. Accordingly, it can be concluded that the acetoin development in alcoholic beverages such as

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wine and beer can be easily tracked by identifying the immobilization technique (through GA cross-linking) under the optimal pH value (57.1) [262]. Molinnus et al. also developed a Si-based biosensor chip to detect acetoin during wine fermentation using the immobilization of a microbial acetoin reductase. This enzyme reduces acetoin whereas NAD1 is concurrently produced by the oxidation of NADH. The changed pH value after the reaction formation can be diagnosed using a capacitive EIS field-effect biosensor [263]. Guerreiro et al. assessed wine astringency (1a140 μmol/L pentagalloyl glucose) using a biosensor based on integrating LSPR and MIP at AuNPs according to the interactive reaction between saliva and wine. The good sensitivity of this platform demonstrated that the anthocyanin compounds play a pivotal role in the color formation and astringency property [264].

20.3 Biosensors and food safety Biosensors are efficient platforms to identify allergens, antibiotics, pathogens, and chemical contaminants (e.g., insecticides, pesticides, heavy metals, etc.) in the food industry. Table 20.2 reveals some recent nanosensing platforms to maintain or promote the safety of food products by detecting different microorganisms, as well as highly reactive hazardous chemicals.

20.3.1 Food allergens The consumption of allergen-containing foods in pediatric (4%
8%) and adult (1%
2%) populations can cause immune pathogenesis or a type I hypersensitivity immune response. This public health concern has been progressively increasing because immune allergenic response (such as anaphylaxis) is significantly associated with the intake of new food formulations. Thus the development of novel allergen-detecting technologies and methodologies plays a key role in promoting human health [265]. Food allergens are traditionally measured using immunological, genetic, peptidomics, proteomics, and mass spectrometry-based analytical approaches. However, (nano)-biosensors compared to these classical techniques have many advantages to detect allergenic substances via automation and miniaturization such as better sensitivity and selectivity, higher response speed, and lower cost. The most common biosensors to determine food allergens are immunosensors with bioreceptors like allergenic substances, proteins, and antibodies [e.g., monoclonal (MAb) or polyclonal (PAb)], genosensors (DNA-based types), and nanomaterial-based sensors [266].

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Biosensors have currently been applied to quantify food allergens such as histamine in fish [267
269]; β-lactoglobulin in bovine milk [270,271]; α-lactalbumin in UHT skimmed milk, almond milk, and nonfat dry milk [272]; casein (e.g., α-type) in milk [108,273]; allergenic proteins of peanut in processed foods and edible oils (e.g., Ara h 1, Ara h 2, Ara h 3/4, Ara h 5, and Ara h 6) [274
276]; BWp16 protein of buckwheat [277]; tropomyosin in shrimp and shellfish [278,279]; soy protein Gly m Bd 30K in processed meat products [266]; ovomucoid (Gal d 1) and ovalbumin (Gal d 2) proteins of the egg white in red wine as well as fresh and baked foods [280
283]; the actin-binding protein of profilin (Jug r 5) in tomato [284]; and gluten in cereal flour-based foods such as breads [285]. Advanced SPR-based biosensors, such as LSPR, fiberoptic SPR (FOSPR), transmission SPR, and SPR imaging (SPRI), are one of the most useful sensing platforms for food allergens in complex food systems. Conventional SPR biosensors have practical limitations to diagnose food allergens because they were designed on a large-size scale without concurrently detecting multiple allergens. Thanks to the small size and high flexibility in designing FOSPR biosensors, they are fascinating candidates for the long-distance and real-time assessment of allergens in food products. Furthermore, the SPRI employing probe arrays can monitor multiallergens, whereas the multiplexed LSPR biosensor can detect them in several food samples. These biosensing systems not only can be integrated with smartphones and new apps for designing mobile devices for the on-site recognition of food allergens but also can be coupled with other analytical instruments to improve the data accuracy, repeatability, and comparability [286]. Electrochemical immunosensors are another group of efficient biosensors to evaluate food allergens. New trends in designing these SPE-based sensing platforms are (1) improving the effective area and conductivity as well as facilitating the immobilization process of bioreceptors using hybrid nanocomposites (e.g., AuNPs and CNTs); (2) designing the portable smartphone-based immunosensors on small scales; and (3) assembling the immunosensors with low arrays to assess multiallergens [287]. Since there is easy accessibility to DNA-based allergen analyses using PCR and ELISA kits, miniaturization of efficient allergendetecting biosensors into small portable devices using nanomaterial-based sensing elements can overcome the principal challenge of biosensors for monitoring allergens [265,284].

20.3.2 Antibiotics in animal-based food products Antibiotics are common antimicrobial agents to treat infectious diseases in human and veterinary medicine. Since the origin of most antibiotics present in food is livestock and aquaculture industries, the

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residual of these infection-fighting medications can be found in animalbased food products such as meat, fish, chicken, egg, milk, and honey [288]. Antimicrobial resistance is another negative aspect of using antibiotic residues in food products of animal origin, which can potentially intensify the risk of other microbes and infectious diseases. There are three conventional detection methods used to diagnose antibiotics in the food industry: microbiological (based on the microbial sensitivity to antibiotics); immunological (based on the antibody-antigen recognition); and analytical (using HPLC, thin-layer chromatography, LC-MS/MS, etc.) [289]. Today, biosensors with different kinds of bioreceptors (such as antibody, enzyme, cell, MIP, aptamer, the synthetic binding proteins namely affibody molecules, liposome, and nanozyme) and transducers (such as electrochemical, gravimetric (piezoelectric (e.g., QCM and SAW), optical (e.g., classical fluorescence, time-resolved fluoroimmunoassay, flow cytometric immunoassay, SPR, LSPR, etc.), and thermal (such as calorimetry) are used to detect antibiotic residues in animalbased foods [289
291]. Moreover, the use of different types of nanomaterials such as NPs, magnetic NPs (MNPs), NWs, NRs, QDs, and carbon nanomaterials has been recently introduced to detect antibiotics in the food industry [288,289]. Recently, biosensors have been utilized to recognize a high number of antibiotics such as amphenicol in bovine milk [292], ampicillin in raw and spiked milk [106,293], cloxacillin in milk [294], chloramphenicol in milk and shrimp [295], doxycycline in pork [296], furazolidone in milk powder, pork, and shrimp [297], penicillin in milk [298
303], honey, and meat-lysate [298], quinolone in milk, honey, fish, and meat products [304], kanamycin in liquid and powdered milk [305
309] and honey [308], streptomycin in milk [310], kanamycin-streptomycin in milk [311], vancomycin in milk [312], sulfadimethoxine in fish and meat (e.g., beef and chicken) [313], tetracycline in milk [314,315], and ceftiofur in round turkey meat [316]. The results showed that the best transducers to use in biosensors are potentiometric and amperometric. These transducers compared to other ones (e.g., piezoelectric, SPR, chemiluminescence, bioluminescence, flow cytometric immunoassay, and thermal) have many advantages to detect antibiotic residues such as lower cost and LOD, better portability, specificity, response speed, and multiplex potential, easier preparation of samples, and better throughput [289]. Fluorescent biosensors for the semiquantitative assessment of antibiotic residues require a fluorophotometer to deliver the output data. Nonetheless, the sensitivity of these biosensors to detect antibiotics has been significantly improved using nanomaterials (e.g., UCNPs). On the other hand, colorimetric biosensors in the case of using DNAzymes and signal amplification can be good candidates to diagnose antibiotics in food samples with a high analysis

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sensitivity, simplicity, and speed. To sum up, the miniaturization of biosensors (such as electrochemical platforms) in terms of portable and ecofriendly tools would be a hopeful technology in diagnosing antibiotics [288].

20.3.3 Detection of foodborne pathogens Identifying innovative technologies is important to detect pathogens and their toxins in food products to guarantee food safety and security. Although some immunology- or molecular biology-based techniques with good sensitivity (e.g., ELISA, PCR, and LAMP) have been increasingly used in the last decade, time-consuming preparation stages in these methods require well-trained technicians and are associated with false-positive responses. Therefore there is a serious need to develop simple and rapid systems to screen spoilage- and disease-causing bacteria in contaminated foods to ensure safety [317]. Currently, biosensors have been applied to identify the most frequent foodborne bacteria in food products such as Salmonella enterica (serovars Typhimurium, Enteritidis, and Typhi), E. coli, and Listeria monocytogenes. In contrast, biosensors in recent years have been less commonly used to detect molds (e.g., Aspergillus flavus) [318] and other bacterial species such as Staphylococcus aureus [143,319,320], Campylobacter spp. [138,321], Pseudomonas aeruginosa [322,323], Clostridium spp. (e.g., C. perfringens and C. fluorescens) [324], Aeromonas hydrophila [325], Vibrio parahaemolyticus [326], and Yersinia enterocolitica [142]. Salmonella accounted for the largest number of studies on the biosensor-based detection of food pathogens. Electrochemical biosensors are one of the most common biosensors to detect Salmonella. Three bioreceptors of aptamer, antibody, and DNA probe are typically used to diagnose Salmonella spp. in food products. Amperometric biosensors were reported to recognize S. Typhimurium in skimmed/whole milk using an antibody bioreceptor within a LOD range between 5 to 10 cfu/mL [327
329]. Although these biosensors can detect Salmonella in a very low count, their in-field application is limited due to tedious labeling required to escalate the electrochemical reaction on the working electrode surface [330]. Voltammetric biosensors were used to detect Salmonella spp. (mainly S. Typhimurium) with different bioreceptors such as aptamer in chicken meat [331
334], milk [335
339], and bottled mineral water [336] and antibody in milk [340
342], as well as DNA probe in spring water [343]. Although antibodies as bioreceptors to detect Salmonella spp. have high affinity and specificity [344], they have poor stability after production, which is an expensive and difficult process. On the other hand, aptamer bioreceptors only are prone to the hydrolyzing effect of nuclease but

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have more practical benefits including low cost, easy synthesis, and modification, as well as high storage stability, affinity, and specificity [330,345]. The application of impedimetric biosensors was also reported based on the different bioreceptors such as aptamers in apple juice [346], chicken and turkey meats [347], and egg [348], as well as antibodies in orange and apple juices [349,350], and milk [351]. Furthermore, biosensors including the impedance-based microelectromechanical system have recently been used for Salmonella serogroups B and D in ready-to-eat turkey matrix, which provided a LOD of 300 cells/mL [352]. The potential in using nanomaterials in the structure of biosensors for diagnosing Salmonella spp. is evident due to their advantageous roles, such as MNPs (as a catalytic label, mass amplifier, and separation agent); AuNPs (as a mass amplifier, Fo¨rster/fluorescence resonance energy transfer (FRET) quencher, electrode modification, colorimetric probe, and SERS nanoprobe); QDs (as a FRET donor and acceptor, as well as fluorescent and electrochemical probe); upconversion NPs (UCNPs; as a FRET donor and fluorescent probe); and carbon nanomaterials (as a catalytic label, FRET quencher, and electrode modification) [330,345,353
355]. The different serotypes of this microorganism were also detected using label-free or label-based SERS biosensors. A SERSbased lateral flow strip biosensor with AuMBA@Ag core-shell NPs was successfully designed to concurrently assess Salmonella enterica serotype Enteritidis (LOD 5 27 cfu/mL) and L. monocytogenes (LOD 5 19 cfu/mL) in milk, chicken breast, and beef [356]. The modified aptamers on the AgNR array substrates were applied for label-free SERS detection of S. Typhimurium [357]. This bacterium was also detected using a labelbased SERS aptasensor where the pathogen was sandwiched between Au@Ag core/shell NPs and X-rhodamine reporters [358]. Although label-free SERS biosensors compared to label-based SERS ones could diagnose S. Typhimurium at a lower LOD, it is necessary to improve the sensitivity and reproducibility of both SERS biosensors. FRET- and sandwich-format-based biosensing assays were also utilized to identify Salmonella spp. These fluorescence sensing platforms have been developing to detect this foodborne pathogen by emerging UCNPs, fluorescent nanospheres/microspheres, and time-resolved fluorescence NPs. For instance, Cheng et al. reported strong electrostatic interaction between aptamer-modified UCNPs and AuNRs applied in a FRETbased fluorescence biosensor triggered FRET with a reduction in the distance among the nanomaterials. Moreover, the added S. Typhimurium prevented the UCNP-aptamers from the AuNRs, leading to the fluorescence recovery [359]. Recently, considerable attention has been directed to the use of microfluidics-based biosensors in diagnosing Salmonella spp. in the food industry. A microfluidic biosensor equipped to immunomagnetic separation, fluorescent-labeling, and smartphone video

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processing for S. Typhimurium in apple juice (LOD , 58 cfu/mL) [360], and a microfluidic signal-off biosensor using magnetic separation and enzymatic catalysis for S. Typhimurium in spiked milk (LOD , 33 cfu/mL) [361] were acceptably utilized. Smartphone-based biosensors are the newest technology in biosensing Salmonella spp. in a simple and low-cost manner. In an advanced online system, a smartphone-based fluorescent microscopic device was integrated with a microfluidic biosensor to assess the number of Salmonella (LOD 5 58 cfu/mL) in spiked apple juice. This highly sensitive tool consisted of a superficial microscope for optical amplification, a light source for excitation of fluorescent spots, and an APP for video processing. They concluded that this online sensing platform can be used to determine multiple foodborne pathogens employing various fluorescent materials [360]. Various serotypes of E. coli, mainly O157:H7, were detected in mineral water by an interferometric biosensor (LOD 5 103 cells/mL; [362]), in drinking water by a fully automated electrochemical immunosensor (LOD 5 50 cfu/mL; [363]), in orange juice by a fiberoptic SPR (LOD 5 94 cfu/mL; [364]), in ground beef by an impedimetric biosensor (LOD 5 2.05 3 103 cfu/g; [365]), in ground beef by a dielectrophoresisbased microwire biosensor (LOD 5 103 cfu/mL; [366]), in ground beef by an enzyme-linked immunoelectrochemical biosensor (LOD 5 400 cells/mL; [367]), in ground beef by an aptamer-based SERS biosensor (LOD 5 B103 cfu/mL; [368]), in milk by a square-wave voltammetry (SWV)-based biosensor (LOD 5 10 cfu/mL; [369]), in spiked milk by a microfluidic impedance biosensor integrated with the immune magnetic AuNPs (LOD 5 12 cfu/mL; [370]), in milk, beef, and chicken breast by an optical and SERS dual probe based on AuMBA@Ag NPs in a lateral flow strip (LOD 5 5 3 104 cfu/mL; [136]), and in raw pork meats by an aptamer-based MNPs and UCNP-conjugated fluorescence biosensor (LOD 5 10 cfu/mL; [371]). Listeria monocytogenes is one of the most leading pathogens in the environment and food materials. This bacterium can easily survive under harsh conditions such as a broad range of pH and salt concentrations as well as low temperatures. Thus it is essential to rapidly monitor L. monocytogenes in the food industry to maintain the safety of products and the promotion of public health [372]. As an example, listeriosis caused by contaminated foods is one of the most important reasons for premature deaths in pregnant women and infants [373]. Hadjilouka et al. recently identified L. monocytogenes only in 3 min with a very low LOD (0.6 cfu/mL or per g) in ready-toeat lettuce salads, milk, and Halloumi cheese using a portable cell-based biosensor. The data comparison between the findings obtained from the used biosensor and standard ISO methods showed high accuracy (up to 98%) in all the food samples. Accordingly, this portable device may be effectively utilized in food supply chains to track different foodborne

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pathogens for ensuring the safety of food products [374]. Chen et al. conjugated magnetic nanobeads with MAbs through SAV-biotin binding to generate magnetically labeled Listeria cells. Subsequently, AuNPs modified by urease and PAbs were electrostatically adsorbed on the labeled bacteria. After the urea hydrolysis into ammonium carbonate by urease present in the formed biomolecular complexes, the resulted pH increase was monitored by the color change of Bromcresol purple at 588 nm to detect Listeria with a LOD of ,100 cfu/mL in spiked lettuce samples [375]. Bu et al. fabricated a label-free lateral flow strip biosensor to diagnose pathogenic microorganisms (i.e., S. Enteritidis and L. monocytogenes) in drinking water, tomato, and pork based on the Gram one-step staining marked with crystal violet and the direct immunoreaction. They demonstrated that the biological dye tracer in strip biosensors can be a useful tool to detect pathogens [376]. Liu et al. detected L. monocytogenes (LOD 5 8 cfu/mL) in pasteurized milk using a fluorescence aptasensor functionalized by UCNPs [377]. Savas and Altintas recently introduced a GQDs-based electrochemical immunosensor for the highly sensitive and specific detection of Yersinia enterocolitica in milk. This label-free sensor has used GQDs as nanozymes and avoided the use of complex assay procedure requiring enzyme labels. With its cost-effective nature and rapid response-time, the sensor has revealed a LOD of 5 cfu/mL [353]. Such enzymeless sensor platforms can also be fabricated on SPE systems using nanocomposites (i.e., GQDs and AuNPs) for the onsite detection of analytes in complex matrices [378]. In another recent study, Sannigrahi et al. reported an SPE-based sensor, where they extracted the intracellular, bacterial magnetosomes from Magnetospirillum sp. RJS1 and conjugated it with the antilisteriolysin antibody for immobilization on the SPE surface using an external magnet. They detected L. monocytogenes in contaminated milk samples with a LOD of 10 cfu/mL [373]. Wang et al. had earlier detected the count of L. monocytogenes (LOD 5 8 cfu/mL) in spiked lettuce samples using an impedance biosensor using MAbs-MNPs (to separate and concentrate cells), urease (to amplify received weak signals), and a screen-printed interdigitated electrode (to measure the impedance alteration). The reaction between Listeria cells and AuNPs modified with urease and PAbs resulted in the formation of MNPListeria-AuNP sandwich complexes. Resuspending complexes with the urea hydrolyzed the urea into ammonium and carbonate ions, facilitating their assessment by the electrode [379]. Using a multichannel SPR device, Zhang et al. introduced PAb-based immunosensors for the concurrent quantification of E. coli O157:H7, S. Enteritidis, and L. monocytogenes in boiled chickens with the simultaneous enrichment [380]. Recent years have witnessed increasing interest in the development of peptide-based sensors for foodborne pathogens. These sensors are often combined with electrochemical transducers owing to their cost-efficient,

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highly sensitive, and portable characteristics. In this regard, several research groups have recently constructed peptide-based electrochemical biosensors for the detection of L. monocytogenes. Hossein-Nejad-Ariani et al. designed the self-assembled monolayer of leucocin-A (bacteriocin type)-peptide with highly fluorescent Au-nanoclusters to detect L. monocytogenes [381], while Eissa and Zourob simultaneously assessed L. monocytogenes and S. aureus by employing a voltammetric biotinylated-peptide biosensor using an array of AuNP-modified SPCEs [382].

20.3.4 Assessment of biotoxins Different fungi and molds, mostly Aspergillus, Penicillium, and Fusarium, in harvested/stored raw materials can produce mycotoxins triggering serious health hazards. These toxic secondary metabolites can usually be detected using some analytical techniques requiring complicated equipment and well-trained operators such as GC-MS, ELISA, and fluorometer [383]. Biosensors are interesting alternatives to conventional methods for the detection of mycotoxins. Aflatoxin B1 (AFB1) is one of the most toxic mycotoxins produced by A. flavus and A. parasiticus. This potent carcinogen was recently assessed by a label-free microfluidic SPR biosensor by AuNPs in spiked wheat samples (LOD 5 0.19 nM) [384]; a metal-organic (UiO-66-NH2) framework/ TAMRA label aptamer fluorescent biosensor in corn, rice, and milk (LOD 5 0.35 ng/mL) [385]; a fluorescent biosensor based on G-quadruplex oligonucleotide-aptamer chimera and silica NPs in grape juices (LOD 5 8.0 pg/mL) [386]; a wash-free and label-free colorimetric biosensor using G-quadruplex in peanuts (LOD ,1 pM) [387]; an AChE inhibitionbased impedimetric biosensor with the immobilization matrix of sodium alginate in ground rice (LOD 5 0.1 ng/mL) [388]; in pistachio nuts (LOD 5 0.5 ng/mL) [389]; a soybean peroxidase enzyme-based biosensor with the reduction potential of GO in corn (LOD 5 2.3 3 1029 M) [390]; a stimuliresponsive hydrogel containing PtNPs-based aptamer biosensor in peanuts (LOD 5 9.4 ppm) [391]; and an electrochemical AFB1antibodybased biosensor with the deposited film of GQDs@molybdenum disulfide (MoS2) nanosheets on the indium tin oxide-coated surface in maize (LOD 5 0.09 ng/mL) [392]. The detection of AFB1 in fig samples was also studied with a costumed designed fully automated electrochemical sensor, which provided a very good correlation with the HPLC results. The average readings of nine individual fig samples that were spiked with 6 ppb AFB1 resulted in 5.50 6 0.17 ppb and 6.51 6 0.58 ppb concentrations using the sensor and HPLC, respectively. This costume-made sensing device allowed real-time and on-site detection in foods with a rapid, sensitive, and miniaturized system [393].

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AFB1 present in contaminated feeds in the rumen of ruminants is metabolized to aflatoxin M1 (AFM1) through the hydroxylation mechanism. The presence of this 4-hydroxy derivative of AFB1 in milk and its derivatives can have irreversible effects on human health because mycotoxin is not eliminated during milk processing, such as in pasteurization and sterilization. Based on the European standard, the highest allowable levels of AFM1 for newborns and adults are 25 and 50 ng/kg, respectively. Thus it is very important to detect AFM1 as well as its products in milk with high precision [394]. Biosensors have recently been applied to detect AFM1 in powdered and liquid milk samples, including a label-free aptasensor based on ferrocene-modified SiNPs deposited on a polymerfunctionalized SPCE as a transducing platform (LOD 5 1.48 pg/L) [394]; a DNA aptamer-based biosensor on the surface of GQDs-CS coated with dendritic fibrous nanosilica (DNFS) functionalized by amine groups (KCC-1-NH2-Tb) (LOD 5 10 fM) [71]; DNA-aptamer linked to DNFS functionalized by amine groups (KCC-1-nPr-NH2) and AuNP-CS and electrodeposited on the GCE surface (LOD 5 10 fM) [395]; an automated optical biosensor-based immunoassay (LOD 5 0.1 ng/g) [396]; an electrochemical immunosensor using dispense-printed electrodes functionalized with single-WCNTs in (LOD 5 0.02 μg/L) [397]; a structure switching-aptasensor using the quenching-dequenching mechanism (LOD 5 0.5 ng/kg) [398]; an electrochemical DNA-polylactide-modified biosensor (LOD 5 5.0 ng L) [399]; an aptasensor based on AuNP-modified mat of electrospun carbon nanofiber (LOD 5 0.1 μM) [400]; a microfluidic paper-based biosensor (LOD 5 10 nM) [401]; a PdNPbased FRET aptasensor (LOD 5 1.5 pg/mL) [402]; and a white light reflectance spectroscopy immunosensor (LOD 5 6.0 pg/mL) [403]. Ochratoxin A (OTA) is another mycotoxin produced by fungal species of Aspergillus and Penicillium in stored cereals. This carcinogenic toxin has harmful effects on humans because it can be easily absorbed in the small intestine and then excreted into bile and urine. Similar to other biotoxins, monitoring the OTA using ultrasensitive biosensors is an appropriate method to track it in agrifood products. Recently, different types of biosensors have been utilized to detect OTA such as a portable solar-driven ratiometric photoelectrochemical biosensor in corn juice (LOD 5 0.29 ng/mL) [404]; an electrochemical biosensor based on MNP-SPEs in corn and wheat (LOD 5 0.28 ppm) [405]; a bipolar electrode-electrochemiluminescence biosensor in rice, wheat, buckwheat, corn, sorghum, and barley grains (LOD 5 5.0 ng/mL) [406]; a FRET aptasensor between colloidal cerium oxide NPs and GQDs in peanuts (LOD 5 2.5 pg/mL) [407]; and a QCM-based biosensor amplified by the implementation of a secondary antibody (Ab2) labeled with AuNPs through monitoring the energy dissipation loss in red wine (LOD 5 0.16 ng/mL) [408]. Zhu et al. also applied a colorimetric biosensor based on the GO/Fe3O4 and Fe3O4@Au platforms

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in peanuts for the simultaneous detection of AFB1 (LOD 5 5.0 ng/mL) and OTA (LOD 5 0.5 ng/mL), respectively [383]. Qian et al. concurrently diagnosed AFB1 (LOD 5 1.7 pg/mL) and OTA (LOD 5 0.67 pg/mL) in spiked corn samples using a fluorescence aptasensor coupled with the core-shell Fe3O4@Au magnetic beads [409]. A voltammetric alkaline phosphatasebased biosensor using modified gold SPEs was also used to successfully detect AFM1 in milk (LOD 5 0.037 ng/mL) and OTA in red wine (LOD 5 15.0 ng/mL) [410]. Biosensors have been recently applied to assess several other mycotoxins [e.g., zearalenone (ZEN), deoxynivalenol (DON), and fumonisin B1 (FB1)] in food samples. The ZEN (a nonsteroidal, estrogenic mycotoxin), DON (a trichothecene mycotoxin, known as vomitoxin), and FB1 (a particular toxic inhibitor of de novo sphingolipid metabolism) are produced by some Fusarium species and found in many crops. Ren et al. determined the efficiency of one-use aptasensing by combining polydimethylsiloxane film-based microcell and AuNP-electrodeposited SPCE to label-freely detect FB1 in corn samples with a LOD of 3.4 pg/mL [411]. Xia et al. [412] assessed the single and combined toxicity of AFB1, ZEN, and DON on the human hepatoma (Hep G2) cell line by fabricating a cell-based electrochemical biosensor. The sensor sensitivity was significantly improved by AuNPs, cysteamine, and laminin. The synergistic, additive, and antagonism effects using the developed biosensors were detected between the binary mixtures of DON and ZEN, DON and AFB1, as well as ZEN and AFB1, respectively. Pagkali et al. employed a label-free monolithically integrated optoelectronic biosensor to concurrently detect AFB1, FB1, and DON in beer. They found that this biosensor within 12 min can sensitively assess these mycotoxins with a LOD of 0.8, 5.6, and 24.0 ng/mL for AFB1, FB1, and DON, respectively [413]. Biosensors have also been developed to detect bacterial toxins such as Staphylococcal enterotoxin B using a potentiometric nanobiosensor based on the nanostructured molecular framework polymer in animal-based food products [414], and a colorimetric DNAzyme biosensor [415] and an unmodified AuNP-based colorimetric biosensor with a SEB-binding aptamer [416] in milk, cheese, ice cream, chicken, and pastries. Tam et al. developed a portable Clostridium botulinum cell-based biosensor to rapidly detect botulinum neurotoxin serotype A in some food matrixes such as milk (whole, 2% fat, and skimmed types; LOD 5 7.4a7.9 ng/mL); fruit juices (apple, carrot, and orange; LOD 5 32.5a75.0 ng/mL); bufferdiluted liquid egg (171.9 ng/mL); and solid foods (green bean-based baby puree, ground beef, and smoked salmon; LOD 5 14.8a62.5 ng/mL) [417]. In addition, Lefebvre et al. reported the efficiency of an SPR biosensor for the detection of domoic acid-based neurotoxin, which is produced by algal blooms accumulated in shellfish [418].

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20.3.5 Determination of toxic chemicals In recent decades, the use of pesticides in agriculture has led to an increase in the accumulation of their residues and metabolites on crops, which can potentially threaten human health. The pesticide residue analysis in fruits, vegetables, and crops can be sensitively achieved using sensor-based techniques. Recently, diverse biosensor platforms have been applied to detect many pesticide residues in food samples such as acetamiprid in lettuce and green tea by employing bioelectric cell-based and SERS-based biosensors [419,420]; atrazine in apple juice using an MIP-based colorimetric-SERS dual biosensor [421]; captan residue in apple, apricot, cherry, peach, peer, and plum fruits using an high-performance thin-layer chromatography coupled with a luminescent biosensor [422]; carbamate in tomato using a reduced GO and AChE-based biosensor [423]; carbendazim in matcha tea powder, apple, and cucumber using an upconversion-MnO2 luminescent resonance energy transfer biosensor [424]; glyphosate in maize grains using an electrochemical graphite-epoxy electrode modified with MWCNTs and HRPO [425]; and profenofos in milk and cabbage samples using a biosensor based on the self-assembling of DNA-aptamer and polyethylene glycol functionalized GO [426]. Bilal et al. recently detected phosmet in stored wheat grains using the red flour beetle’s AchE-based electrochemical biosensor having a WO3/g-C3N4 nanocomposite-modified Pencil graphite electrode. Fig. 20.5 illustrates the preparation steps of the sensor for the detection of this organophosphate insecticide in the examined food samples [427]. Sabullah et al. recently reviewed cholinesterase-based biosensors to diagnose toxic heavy metals (e.g., Ni, Hg, Cr, Cu, Cd, As, Pb, etc.) in agricultural-based products [428]. Therefore in this chapter, presenting the results of these studies has been omitted, although some findings are summarized in Table 20.2. Acrylamide is one of the most important carcinogenic constitutes formed during the Maillard reaction in carbohydrate-rich foods. This low-molecular-weight neurotoxin with a vinylic structure is produced in fried or baked products such as fried chips and bread. Recently, the use of electrochemical biosensors has become common to recognize acrylamide in certain food products. Navarro et al. successfully fabricated an electrochemical biosensor based on hemoglobin (Hb)-Fe3O4 MNPs
CSmodified carbon paste electrode to detect fried potato slices with a LOD of 0.06 nmol/L [429]. Varmira et al. assessed the acrylamide content (LOD 5 0.01 nM) of two potato crisp samples in less than 8 s by developing an electrochemical biosensor based on Hb-dimethyl-dioctadecyl-ammonium-bromide (Hb-DDAB)/PtAuPd NPs/CS-1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (Ch-IL)/MWCNTs-IL on a simple GCE. The obtained findings were comparable to the GC-MS results [430].

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FIGURE 20.5 A graphic drawing of AChE/WO3/GCN/PGE biosensor to detect phosmet in stored wheat grains. Source: Reproduced with permission from Ref. S. Bilal, M.M. Hassan, M.F. ur Rehman, M. Nasir, A.J. Sami, A. Hayat, An insect acetylcholinesterase biosensor utilizing WO3/g-C3N4 nanocomposite modified pencil graphite electrode for phosmet detection in stored grains. Food Chem. 346 (2021) 128894

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Asnaashari et al. also proved that electrochemical biosensors designed based on Hb/oligonucleotides-ssDNA/Hb-modified screen-printed gold electrode can potentially determine acrylamide levels in potato fries with a LOD of 0.158 μM. The SWV assessed the optimal response according to the alteration of the reduction/oxidation process of Hb-Fe31/Hb-Fe21. In addition, the release of many organic synthetic compounds present in devices involved in food processing can cause toxicity to food consumers [431]. Although few studies on these organic derivatives have been conducted, biosensors have been used to track bisphenol A in food and beverage containers/cans [432
434].

20.4 Future prospectives Due to the diversity of food and the development of different formulations based on natural and synthetic compounds, the global market constantly needs to design innovative analysis methods to recognize the quality and safety of foods. Moreover, the safety and quality of foods are two main health-related concerns of consumers around the world. Biosensors in food processing monitor both food quality and safety issues through online inspection systems. Therefore these targeted tools can be used to track the nutritional pattern of people and to control risk factors of foodborne infectious diseases. The use of advanced e-nose and e-tongue technologies integrated with the optimized pattern recognition algorithms can automatically monitor the formation of volatile compounds during food processing and assess organoleptic characteristics of final food products. However, two serious challenges relevant to the relatively poor repeatability and comparability of data obtained from these electronic-based sensing systems should be addressed. Future trends to overcome these shortcomings should be directed toward preparing or making simple access to the online open libraries and data pool for training, utilizing the optimized multifunctional devices, and developing smartphone sensing platforms using innovative interfaces. Not only biosensors as nondestructive devices can detect many chemical and microbial contaminations at an extremely low concentration but also correlative techniques can be applied with other analytical instruments to increase the assessment precision and accuracy. The combination of different sensing platforms (such as SERS-microfluidic system and MIPsbased colorimetric-SERS) in the future can be a practical solution to overcome the leading challenges in the on situ analysis of contaminants in real food samples. The design and fabrication of immobilization supports with new hybrid nanocomposite materials such as associated with microelectronics techniques can efficiently detect chemobiological molecules in agrofood products. Also, the use of electrochemical enzyme-based biosensors has been progressively increasing in the agrofood industry. Serious attention

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should be paid to utilize novel immobilization platforms to improve the storage stability, repeatability, and reproducibility of enzyme-based biosensors. There will be competition in the future to explore smart nanomaterials and their combination with electrochemistry to induce specifically tailored characteristics. DNA probes were less applied among the bioreceptors used in electrochemical biosensors to detect pathogen bacteria in real food matrixes. In this regard, a low number of investigations have assessed the integration of smartphones or other mobile devices to electrochemical DNA biosensors. Accordingly, more efforts in the future should be made to develop miniaturized and portable DNA-based biosensors for multiplexed detection. Even though this chapter does not cover the use of biosensors in food packaging, some of these sensitive tools as labels or barcodes have recently diagnosed freshness of fruits, vegetables, meats, and seafood in intelligent packaging.

20.5 Conclusion This chapter comprehensively highlighted recently announced biosensor-based studies in the food industry. Biosensors compared to standard instrumental techniques such as chromatographic and spectroscopic ones have lower construction cost, higher selectivity, sensitivity, and response speed, as well as longer stability. The utilized electrochemical, optical, calorimetric, and piezoelectric biosensors are able to successfully detect antioxidant polyphenols, food ingredients, synthetic and natural additives, adulterants, allergens, antibiotics, foodborne pathogens, fungal and bacterial toxins, heavy metals, and pesticides in food processing and production processes. Moreover, the incorporation of nanoparticles/nanomaterials onto the working surface of electrodes can significantly improve the biosensor sensitivity at trace amounts of analytes with high efficiency. Most nanomaterials have been introduced to immobilize biomolecules as signal generators, fluorescent quenchers, or signal amplification. Also, enzyme-based biosensors using bionanocomposites have been mainly used to recognize the freshness of many foods such as meats, aquatic products, fruits, and vegetables. Thus biosensors can be implemented in different parts of the food industry to monitor food quality and safety and subsequently ensure human health.

Acknowledgments Seyed Mohammad Taghi Gharibzahedi (SMTG) and Zeynep Altintas acknowledge the support of the Alexander von Humboldt Foundation for ‘SMTG’ via the Georg Forster Research Fellowship.

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1522. [416] B. Mondal, S. Ramlal, P.S. Lavu, J. Kingston, Highly sensitive colorimetric biosensor for Staphylococcal enterotoxin B by a label-free aptamer and gold nanoparticles, Front. Microbiol. 9 (2018) 179. [417] C.C. Tam, A.R. Flannery, L.W. Cheng, A rapid, sensitive, and portable biosensor assay for the detection of botulinum neurotoxin serotype A in complex food matrices, Toxins. 10 (2018) 476.

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[418] K.A. Lefebvre, B.J. Yakes, E. Frame, P. Kendrick, S. Shum, N. Isoherranen, et al., Discovery of a potential human serum biomarker for chronic seafood toxin exposure using an SPR biosensor, Toxins. 11 (2019) 293. [419] T. Apostolou, K. Loizou, A. Hadjilouka, A. Inglezakis, S. Kintzios, Newly developed system for acetamiprid residue screening in the lettuce samples based on a bioelectric cell biosensor, Biosensors. 10 (2020) 8. [420] H. Li, W. Hu, M.M. Hassan, Z. Zhang, Q. Chen, A facile and sensitive SERS-based biosensor for colorimetric detection of acetamiprid in green tea based on unmodified gold nanoparticles, J. Food Meas. Charact. 13 (2019) 259
268. [421] Zhao, B. Molecularly imprinted polymers-based colorimetric-SERS dual biosensor for the detection of atrazine in apple juice, Doctoral dissertation, University of British Columbia, 2017. [422] Y. Chen, C. Huang, B. Hellmann, Z. Jin, X. Xu, G. Xiao, A new HPTLC platformed luminescent biosensor system for facile screening of captan residue in fruits, Food Chem. 309 (2020) 125691. [423] M.K. da Silva, H.C. Vanzela, L.M. Defavari, I. Cesarino, Determination of carbamate pesticide in food using a biosensor based on reduced graphene oxide and acetylcholinesterase enzyme, Sensor. Actuat. B Chem. 277 (2018) 555
561. [424] Q. Ouyang, L. Wang, W. Ahmad, Y. Rong, H. Li, Y. Hu, et al., A highly sensitive detection of carbendazim pesticide in food based on the upconversion-MnO2 luminescent resonance energy transfer biosensor, Food Chem. 349 (2021) 129157. [425] S.L. Cahuantzi-Mun˜oz, M.A. Gonza´lez-Fuentes, L.A. Ortiz-Frade, E. Torres, S¸ . T˘ ¸ alu, G. Trejo, et al., Electrochemical biosensor for sensitive quantification of glyphosate in maize kernels, Electroanalysis. 31 (2019) 927
935. [426] J. Xiong, S. Li, Y. Li, Y. Chen, Y. Liu, J. Gan, et al., Fluorescent aptamerpolyethylene glycol functionalized graphene oxide biosensor for profenofos detection in food, Chem. Res. Chin. Univ. 36 (2020) 787
794. [427] S. Bilal, M.M. Hassan, M.F. ur Rehman, M. Nasir, A.J. Sami, A. Hayat, An insect acetylcholinesterase biosensor utilizing WO3/g-C3N4 nanocomposite modified pencil graphite electrode for phosmet detection in stored grains, Food Chem. 346 (2021) 128894. [428] M.K. Sabullah, S.A.M. Khalidi, R. Abdullah, S.A. Sani, J.A. Gansau, S.A. Ahmad, et al., Cholinesterase-based biosensor for preliminary detection of toxic heavy metals in the environment and agricultural-based products, Int. Food Res. J. 27 (2020) 597
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chitosan modified carbon paste electrode, Anal. Lett. 54 (2021) 1180. 119. [430] K. Varmira, O. Abdi, M.B. Gholivand, H.C. Goicoechea, A.R. Jalalvand, Intellectual modifying a bare glassy carbon electrode to fabricate a novel and ultrasensitive electrochemical biosensor: application to determination of acrylamide in food samples, Talanta. 176 (2018) 509
517. [431] M. Asnaashari, R.E. Kenari, R. Farahmandfar, K. Abnous, S.M. Taghdisi, An electrochemical biosensor based on hemoglobin-oligonucleotides-modified electrode for detection of acrylamide in potato fries, Food Chem. 271 (2019) 54
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7151.

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C H A P T E R

21 Sensors for aerial, automotive, and robotic applications Ivan Petrunin and Gilbert Tang School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, United Kingdom

21.1 Introduction Present-day applications of aerial, automotive, and robotic systems rely significantly on perception information to accomplish missions that require higher autonomy, improved situational awareness, and more informed decision making. Emerging use cases where such systems are involved are presented by collaborative autonomous and robotic systems and systems that are intended to work along with humans. In other use cases, the emphasis is made on distributed sensing, object tracking, surveillance, search and rescue, where multiagent systems are required to collect synchronized information from multiple heterogeneous sensors. There is also a growing interest in the scenarios where autonomous systems are intended for operation in complex environments and require redundant sensing solutions supporting communication and navigation performance at the necessary level. In all these cases sensors are playing an important role either as a part of the onboard systems that are looking after required platform performance during the mission or as a payload, providing the user with the mission-specific information. Continuing progress in sensor technologies makes it possible to plan the missions that were not possible previously and collect the information in the amounts and quality we could not think of before. Therefore knowledge of the current state in sensor technologies and examples of the emerging applications is key to further improving and enhancing platforms and systems that are benefitting from sensor information.

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00022-4

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This chapter considers advances and applications in both proprioceptive (internal state) and exteroceptive (external state) sensors such as cameras, lidars, gyroscopes, mmWave radars along with the sources of accurate time information. Comparative analysis of characteristics performed for different sensor technologies allows for the sensor (and sensor technology) selection aligned with the critical requirements of an application. Examples of typical applications are given as well. Particular attention is paid to precise timekeeping in aerial, automotive, and robotic systems, which is becoming increasingly important with the advent of collaborative (swarm) operations and distributed sensing, which require accurate synchronization for fusion and achieving the best performance. A brief overview of future trends in sensor technologies for aerial, automotive, and robotics concludes the chapter.

21.2 Optical sensors Optical sensors are playing an increasing role in modern perception systems due to significant progress made in sensor technologies, enabling weight and power consumption reduction of the sensors while maintaining or improving their user specifications and also in signal processing, which relies increasingly on advanced machine learning methods and high-performance computing solutions. Optical sensors that are becoming a de-facto standard for the majority of the aerial, automotive, and robotic applications are cameras, which are not only using part of the spectrum that belongs to visible light but span beyond this, especially toward longer wavelengths that correspond to so-called infra-red part of the spectrum.

21.2.1 Visual cameras Cameras are an important source of the information about the environment for many platforms that are expected to perform with a certain degree of autonomy and for this reason to be able to perceive the environment for making intelligent decisions or supply information that can be easily interpreted by humans either directly or after preprocessing. Recent advances in the computer vision and machine learning domains emphasize the role and importance of the cameras as the source of the information about the environment that can be used for environmental characterization, static and dynamic object detection and classification, extraction of the information supporting localization and navigation, etc. Using miniature cameras supported by energy-efficient hardware and efficiency-optimized algorithms it is possible to implement perception

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systems on a very small platform, such as micro- and nanodrones, which may not be able to carry other types of sensor payload for perception purposes [1]. Cameras can be categorized according to their ability to provide natively depth information: monocular and binocular (or stereo) cameras. The main characteristic of monocular cameras (i.e., cameras with a single optical sensor) are presented by the sensor technology, specification of the image produced by the camera, parameters of the optical system (if available), as well as size, weight, power, and cost (SWaPC) specifications. Considering the large variety of applications where cameras are used in this work we discuss the main features of the camerabased systems that underpin their suitability for particular tasks. In cases when cameras provide situational awareness the field of view (FOV) of the camera and its low-light capability are frequently of the highest priority. Wide-angle or fisheye cameras are typically utilized in such cases with common examples for aerial mapping, photogrammetry, and automotive adaptive driver assistance systems (ADAS) [2]. An advantage of fisheye cameras is in a very wide coverage that allows capturing information about the entire environment around the platform or vehicle with very few cameras installed. In many cases requirements to the minimum illumination become important (e.g., for safety-critical tasks of automotive and aerial applications). While these characteristics are not always provided, good cameras can operate at or below 0.01 lux illumination (without additional sources of light). Another group of important camera characteristics is related to the resolution of the image that is produced by the camera. The current state of the art of robotic and automotive cameras is around 1 2 megapixels (MP) with an observed tendency to increase the resolution toward 5 8 MP [3]. A different tendency is observed for surveillance and remote sensing tasks, where low-resolution cameras do not provide cost- and timeefficient solutions. Additional constraints can be found from the resolution requirements for the resulting imagery, especially critical when large areas should be covered. In such cases, a 20 50 MP camera payload is becoming an application standard. One of the disadvantages of monocular cameras is in the complexity of acquiring depth information, which is beneficial in cases where application (e.g., collision avoidance for drones, or robotic arm operation) requires estimation of distances, understanding of shapes, and general scene analysis [4,5]. Stereo cameras are able to bring essential improvements in these areas, however, it comes at a cost of increased computational complexity. The principle that allows depth recognition in stereovision (also referred to as 3D vision sometimes) is similar to what we observe in nature having two image sensors at a distance (known as a baseline) and observing the same scene for obtaining so-called disparity information [5].

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The main characteristics of stereo cameras are similar to those of monocular cameras mentioned previously with the addition of depthrelated parameters: baseline, depth range, and depth FOV. While it is possible to arrange two cameras in the custom configuration and get full control over the aspects of the depth information retrieval there is a good choice of the complete solutions available that can be also supplied with onboard inertial sensors and the corresponding software, such as robot operating system drivers, API/SDK, etc. [6]. Typical stereo camera configurations are providing depth information from a few centimeters to tens of meters range, covering a sector of 60 70 degrees. This is expected to be sufficient for static or low-medium speed applications, providing a few seconds’ time interval for making actions, if required. One of the most essential disadvantages of the cameras used for perception or general data collection purposes is limited sensitivity in lowlight conditions or challenging for visual sensors environments (e.g., in poor weather conditions, fog, or smoke). In such conditions, it may become difficult or impossible to achieve planned outcomes in several applications, such as vision-based navigation, collision avoidance, mapping, surveillance, search and rescue. In these cases, improvement can be achieved using sensors with sensitivity in other parts of the electromagnetic spectrum, such as infrared light. Some of the solutions with extended spectrum range cover the ultraviolet part of the spectrum (i.e., above the visible light part), however, these solutions are less widespread and therefore are not included in the scope of this work.

21.2.2 Infrared cameras Infrared cameras are designed to capture information from a longerwavelength part of the spectrum, which is presented by “near-infrared” (NIR) and “far infrared” (FIR) parts. The NIR part of the spectrum covers a wavelength range from 0.8 to 2.5 µm and is directly adjacent to the visible light. Other classifications of the spectrum exist too. Information capture in this wavelength range is available to a newer generation of image sensors manufactured using complementary metal-oxide-silicon (CMOS) technology with high efficiency and therefore improved sensitivity in low light conditions. CMOS cameras with NIR-enabled sensors are offering relatively high resolutions of 2 4 MP that are generally suitable for many perception and surveillance applications, where improved contrast in low light conditions, as well as better classification results, can be achieved [7,8]. FIR or thermal sensors provide high sensitivity to wavelength carrying information about an object’s temperature and cover wavelength

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range 7 15 µm. This becomes especially important for applications that either benefit from or require the identification of objects with a temperature different from the background. Typical applications include ADAS vision systems [9], surveillance of assets for faults or heat leakage, night vision systems without infrared light sources, search and rescue surveillance systems [10]. FIR camera solutions can be categorized into cooled and uncooled types [11]. Uncooled sensors typically represent an array of microbolometers (heat-sensitive elements) that change their resistance if they are heated or cooled. Cooled sensors use more sensitive technologies, such as photon counting and integration, which require low temperatures for their operation. Cooled sensors offer undoubtedly higher performance in terms of spatial and temperature resolution, sensitivity, and update rate. However, due to the presence of the cryogenic system, they may have limitations in terms of use in mobile applications and will require periodic maintenance of the cooling system. Both types of thermal sensors are characterized by lower resolution (usually below 1 MP) and high price, which limit the range of their applications by higher budget solutions. Infrared sensors are playing an important role in multispectral and hyperspectral imaging applications either as a main sensor or a part of the sensing system. Multispectral and hyperspectral imaging is actively used for surveillance, mapping in agriculture and environmental sciences (for moisture or contamination analysis), remote sensing for geological and mineral analysis, and for biomedical imaging and food processing [12].

21.2.3 Laser-based sensors Laser sensors have been used in a variety of robotics applications for their robustness and high performance when compared with other sensors. The use of laser-based sensors in robotic systems is also becoming increasingly common with decreasing sensor cost, reduced power consumption, improved sensor compactness, increased ease of integration, and availability of high-performance processors. Popular robot applications include safety monitoring, localization and mapping, measurement, quality inspection, and calibration. There are different types of laser sensors used in robotics applications. These include laser distance sensors, displacement sensors, laser light curtains, laser photoelectric sensors, laser edge detection sensors, laser radars, and laser scanners. Each of these sensors has unique applications in robotics. In robotic welding, laser vision systems are used for seam-tracking where a laser beam is projected on the surface of the component being welded and the reflected scattered light is imaged

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FIGURE 21.1 A Turtlebot mobile robot equipped with a LiDAR sensor.

back onto a CCD and CMOS sensor. The resulting image can be used for tracking, joint preparation, and inspection purposes. In collaborative robot cells, laser light curtains and laser scanners are used as safety barriers and safety monitoring systems for human-robot interactive work cells through detection of objects and people within the sensors’ FOV. Light detection and ranging (LiDAR) have gained significant prominence in robot applications in the last decade. LiDAR sensors in general have relatively high data acquisition rates, high accuracy, and good low light performance when compare with vision-based systems, which make them a suitable sensor choice for acquiring 3D data in a range of working conditions and requirements. They are used primarily in mobile robots for mapping, localization, and navigation (Fig. 21.1). LiDAR is often used in combination with other sensors including inertial measurement unit (IMU), servo-encoder, 3D camera, and GPS [13 15]. However, LiDAR sensors have also been used on their own for mobile robot navigation [16 18]. There are four commonly used scanning mechanisms in LiDAR systems: optomechanical, electromechanical, microelectromechanical systems (MEMS), and solid-state scanning. Electromechanical scanning is currently the most popular technology being used while MEMS is the more suitable option for applications where low size, weight, and power are paramount. In technology maturity, MEMS is significantly more matured than solid-state LiDAR systems. However, despite the relatively low technology readiness, solid-state scanning is often the preferred technology solution for autonomous ground vehicles due to their superior robustness, the FOV, and the potential to achieve high data acquisition. Solid-state LiDAR technology is being improved and further developed by academics and industries, and it could become more widely used on mobile robots as the cost of the device has decreased to a similar level to other competitive technologies [19].

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21.3 Inertial sensors Inertial sensors are used in many robotics applications due to their low cost, compact size, and low processing power requirements. These sensors are primarily used in robots to measure velocity, orientation, and gravitational force. They were originally developed and used in aircraft navigation in the 1930s, but were limited to large-scale applications at the time due to their size, cost, and power consumption. The demand for inertial sensors increased significantly with the introduction of MEMS since the size, cost, and processing requirement were reduced, which expanded their applications. Inertial sensing is comprised of two types of sensors: accelerometers and gyroscopes. These sensors attach directly to robots and provide output signals proportional to their motion concerning an inertial frame of reference. Each sensor has up to three degrees of freedom (DOF) and combining the two types of sensors could provide up to six DOF. An accelerometer provides acceleration values and a gyroscope gives angular velocity [20,21]. For navigation and positioning, distance data can be extracted through double integration of acceleration with time; integration of angular velocity with time produces angle data. Inertial sensors are internally referenced, and thus are not affected by problems found in externally referenced devices such as signal strength, friction, winds, direction, dimensions, and other external references. This advantage makes them suitable for use in general motion sensing in a variety of environments. Furthermore, the data acquisition rate of inertial sensors can be much higher than other sensors used in motion sensing [22].

21.3.1 Accelerometers Accelerometers are commonly used in robotics applications for their ability to sense accelerations for specific tasks; these include navigation, positioning, humanoid control, hand gesture control, robot arm control, and robot learning. An accelerometer is used for inertial navigation of mobile robots through dead reckoning. The main drawback of inertial sensors is the bias drift problem where errors are accumulated, and the accuracy deteriorates with time due to integration. The bias drift errors can be reduced using methods such as the Kalman filter [22 25]. There are other problems associated with accelerometers that should be considered. For instance, readings from MEMS accelerometers lose data precision over time, and there are scale factor and bias issues. A scale factor is the ratio of the variation at the sensor output to the variation at its input that is intended to be measured. Accelerometer bias can be described as a nonzero offset of the sensor output signal from the

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expected true value measured over a certain time at specified operating conditions, which does not correlate with the input. The bias and sensitivity are affected by factors related to the material and construction of the sensors as well as conditions of operation. These issues could be remediated with a proper calibration procedure [24]. A popular robotics application using an accelerometer is hand gesture control. Hand gesture is a natural way for humans to express themselves and interact with others. It has been identified as an intuitive way for human operators to control and communicate with robots in the last decade. Data captured using a 3D accelerometer can be used to train a gesture recognition system to identify gestures from a predefine gesture set or personalized gestures for interacting with robots [26]. Another example is presented in Ref. [27] of a robotic system that allows nonexpert users to instruct and program a robot through hand gestures captured using an accelerometer embedded in a gaming remote controller, which is recognized using a statistical approach and artificial neural networks. The gradual decrease in robot costs has broadened robotics application areas and variety. A soccer robot is one application that requires proximate interaction between the human team and the robot where they are colocated. A mobile phone accelerometer can be used to receive data for performing gesture recognition [28]. The data received using a handheld accelerometer can be mapped to basic mobile robot control commands based on the acceleration values of each axis, such as move in the forward direction, move in the reverse direction, change in speed, and turn to left or right [29 31]. Apart from mobile robot control, gesture control can also be used to control a robotic arm. An example is a gesture control system for operating an underwater vehicle with a robot arm where the user’s input is sensed using three sets of accelerometer and gyroscope sensors embedded in a wearable bracelet. Each sensor measures the pitch, roll, and yaw angle of the elbow, forearm, and wrist. The sensor’s values are converted to 3D positions by using forward kinematic, which are sent to the underwater robot for teleoperation [32]. Accelerometers have been extensively used as part of the control system of robot arms in various applications. For instance, accelerometers can be integrated into servo motors on a robot arm to provide rotational information of servo motors as feedback to the human operator [33]. Accelerometers have also been used as state observers for elastic joint robots with accelerometers mounted on the links together with motor position measurements. This type of state observer is independent of the dynamic parameters of the robot, relatively predictable, and easy to tune [34]. Apart from the control of robot arms, accelerometers are also useful for pose estimation in robots such as the legged robot, mobile manipulator, and prosthetics where joint sensors are inadequate. However, considerations should be given during system design to parameters that could affect the accuracy

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or postestimation; these include accelerometers, sensor size, and geometric placement [35]. For applications in legged robotics, accelerometers have been used in the control of legged humanoids for climbing upstairs [36] and in the detection of floor surface type for a quadruped robot where sensor data is processed with machine learning algorithms [37].

21.3.2 Gyroscopes The early generation of gyroscopes is known as macroscopic gyroscopes, which are generally larger. The large volume of these gyroscopes results in poor portability and improvement of sensitivity. A microgyroscope is a combination of gyroscope technology and MEMS technology. The emergence of microgyroscope technology has encouraged the use of gyroscopes for a variety of purposes due to its advantages in miniaturization, lower cost, and low power in applications such as robotics and consumer electronics. There are various types and grades of gyroscopes available on the market, and one of the most popular gyroscopes used in robotics applications is the fiberoptic gyroscope, which is a type of optical gyroscope based on the Sagnac effect. In general, optical gyroscopes have demonstrated excellent stability and dynamic range [38]. Gyroscopes have found applications in ground mobile robots, underwater robots, and aerial robots for various purposes; these include navigation and localization [39 44], flight stabilization [45], and balancing [46]. For these applications, a highly reliable sensor should be selected to ensure the data acquisition is accurate and robust. MEMS gyroscopes have been improved over the years for increased reliability and accuracy. However, faults that occur in any gyroscope sensor will affect the feedback signal of the sensor that could disturb the stability, movement quality, and positioning of a robot [41]. Thus most robot system integrations with gyroscopes have been carried out with sensor fusion to reduce information uncertainty and improve overall system robustness. For navigation and positioning, gyroscopes are often used in conjunction with odometry to compensate for estimation errors and angular errors [42 44,47,48]. Gyroscopes have also been used in other sensor combinations including GPS [49], LiDAR [50], magnetometer [51], and photogrammetry [52]. Nonetheless, gyroscopes have been an integral part of many mobile robot navigation systems in existing applications.

21.4 Radio frequency sensors Significant progress in autonomous and robotic systems has stimulated developments of sensors used in these systems for the perception

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of both external to the system environment and internal system states. Ever-increasing demands to performance of the sensors, frequently contradicting their size, weight, and power (and cost) characteristics force developers to look for careful sensor payload optimization with multiple trade-offs. Recent developments in sensor technologies stimulate wider applications of state-of-the-art autonomous and robotic systems, such as drones of different classes and form factors, autonomous ground vehicles, and robotic systems.

21.4.1 Antennas Communication technologies are of paramount importance in today’s aerial, automotive, and robotic applications, where one of the most commonly used ways to communicate is utilizing the radio frequency (RF) spectrum. In order to be able to send and receive sensor information, the system utilizing RF communication requires a suitable antenna that can be characterized from the user perspective by several main parameters [53]: frequency, bandwidth, gain, radiation pattern, matching impedance, polarization, dimensions, and weight. These parameters define how well the antenna is suitable for the application (e.g., for the particular data link type utilized; common examples are Wi-Fi, 4G/5G, Bluetooth, etc.) or general application area (e.g., satellite navigation for small aerial systems). Antenna parameters to a significant extent are defined by the utilized antenna technology. With the growing demands in higher communication bandwidth and miniaturization antenna frequencies are migrating toward higher frequencies of L-band (390 1550 MHz), S-band (1550 3900 MHz), C-band (3900 6200 MHz), and further up in frequencies into the millimeter waves (such as 24 GHz and 60 GHz bands) for numerous applications such as automatic dependent surveillance-broadcast (ADS-B) [54], satellite communication and navigation [55], for command-and-control applications, communication using Wi-Fi, 4G and 5G cellular networks, radars, wireless sensor networks, and Internet of Things (IoT). The antenna design in these applications is as important as the selection of the good receiver itself. The antenna configuration is defining, to a significant extent, a Quality of Service achievable, such as bandwidth and error rate in the communication channel or range and resolution of a radar-based detection system. Active development of antenna technologies results in a reduction of antenna element dimensions enabling a vast number of new and emerging applications, such as IoT and other wireless networks, wearable systems, etc. Miniaturization of antennas also allows building antenna arrays (or phased arrays) of compact sizes suitable for applications in robotics, ground, and aerial autonomous systems [56].

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TABLE 21.1

Antenna types and specifications.

Antenna type

Matching impedance, Ohm

Frequency, GHz

Gain, dB

Size to wavelength ratio

Microstrip “patch”

50 180

0.5 90

3 5

1/4 1/2

UWB crossed dipoles

50 300

0.5 90

3

1/2

Conic and planar spirals

120

3.0 8.0

#3

1/2 3/2

TEM wave launchers

50 80

0.5 4.0

3 15

1/2 5

Some of the key parameters for common candidate antenna types suitable for building compact antenna arrays are listed in Table 21.1. Antenna arrays enable electronic manipulation of a radiation pattern, such as beamforming, beam steering, and nulling, underpinning thus their use in 3D radar applications on mobile platforms (with one of the examples being the Echodyne UAV radar EcoFlight), MIMO radar, and communication systems [57], and GNSS controllable reception pattern antennas with improved resilience to jamming, spoofing, and multipath effects in urban environments [58,59]. Improvements in the antenna performance are also supported by active developments of metamaterials. With metamaterials, such as high-permittivity dielectrics, doublenegative materials, or split-ring resonators [60], it is possible to significantly improve the performance of small antennas, such as microstrip antennas [61,62] for numerous applications that require miniature highperformance antennas and antenna arrays, such as drone-based radars, medical implants [63], etc.

21.4.2 Receivers Receivers are a key part of several key subsystems in aerial, automotive, and robotic applications. Needless to say, present-day platforms rely heavily on communication capabilities (typically, it is RF-based communication) to exchange sensor data or safety-critical messages, provide command and control capabilities, receive navigation signals, retrieve mission and operation-related information, and support data-rich infotainment needs. While receivers are widely available as commercial off-the-shelf products for numerous applications their capabilities are limited to the manufacturer-offered set of functionalities. Recent developments in software-defined receivers (SDR) can offer upgradeable solutions; however, the platform remains typically close to the user. These restrictions can be avoided by considering customizable solutions, such as USRP from Ettus, KiwiSDR, and several others.

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Antenna

RF front-end

A/D conversion

Processing unit

User Output

FIGURE 21.2 Simplified block diagram of the software-defined receiver.

Combined with open software development tools, such as GNU Radio, they offer capabilities for creating flexible and cost-effective RF-based solutions for sensing, measurement, communication, navigation, and networking in aerial, automotive, and robotic applications. Many complex RF-based systems are software-defined, such as radars, where the majority of the signal processing and analysis of the received data are performed in a discrete form. The generic architecture of the SDR-based receiver is simple and includes four functional units as shown in Fig. 21.2. The first element of the SDR receiver antenna was discussed in the previous section. An antenna may have an active design and therefore may include some of the amplification and filtering options that are typically found in the RF front-end, the second functional element of the SDR receiver. RF front-end implements analog preprocessing of the received signal and besides amplification and filtering includes also downconversion functionality that transforms the frequency of the input signal to the value that is suitable for selected analog to digital (A/D) conversion hardware. Key parameters of the front-end may include (depending on particular design) frequency range, the bandwidth of the implemented filters, gain and noise figure. The functional elements of the SDR receiver is shown in Fig. 21.1. RF front-end implements analog preprocessing of the received signal and besides amplification and filtering includes also downconversion functionality that transforms the frequency of the input signal to the value that is suitable for selected A/D conversion hardware. Key parameters of the front-end may include (depending on particular design) frequency range, a bandwidth of the implemented filters, gain and noise figure. Analog-to-digital conversion block is responsible for transferring analog signal into the discrete domain that allows for further digital processing, such as demodulation, message retrieval, detection, and classification the list may greatly vary depending on the application. The key parameters of the A/D converter are resolution and sampling rate. A/D conversion resolution, expressed in bits, defines the level of details in the converted discrete signal and its dynamic range. Common resolution parameter values include 8 bits (e.g., for devices based on RTL chips [64]), 12, 14, and 16 bits for common applications. Higher-resolution A/D

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converters in SDR systems are possible too; however, they are more suitable for specialized systems that are typically intended for use in applications requiring narrow bandwidths (in a range of a few kHz) and high dynamic range (.100 dB). Resolution of A/D converter defines dynamic range (or signal-toquantization noise ratio, SQNR) of the discretized signal, which can be approximated at the output of the D/A convertor as dB, where is the resolution of A/D converter in bits. The higher-resolution A/D conversion is desirable since it is important to ensure that the dynamic range of the converter is also supported by the front-end of the receiver. The sampling rate of the A/D converter is expressed in samples per second and defines the bandwidth of the discretized signal. In communication, navigation and radar applications it is common to see sampling rates of 20 200 Ms/s, resulting in bandwidth values of up to 100 MHz, and resolutions of 8 14 bits [65]. Due to hardware complexity, the higher bandwidth frequently corresponds to lower resolution and vice versa. The processing unit of the SDR receiver supports the implementation of digital signal processing functionality, such as signal demodulation, filtering, and downsampling. It can be implemented using dedicated onboard FPGAs, system on chip, or even more common CPUs. As an example, USRP N-series products use Xilinx FPGAs [66] for the implementation of baseband processing functionality and dual ARM cores with embedded Linux core for standalone operation. In many cases, however, SDR processing is realized in a separate unit, such as a host computer with an operating system, necessary software, and processing algorithms. Examples of applications of SDR for aerial, automotive, and robotic applications include GNSS navigation [67], resilient and secure communication systems [68], drone defense solutions [69], where the software-defined nature of the implemented functionality allows easy improvement of the functionality, resilience, and security of the products and platforms through a simple software update.

21.4.3 Radars Radar, standing for radio detection and ranging, is a system comprising transmitter of RF signals, transmitting and receiving antenna, and receiver with the special processing unit for detection and identification of the objects based on analysis of parameters of the reflected RF signal. Radars have become the de-facto standard sensor system for automotive applications, where advanced driver assistance systems are implemented aiming for improving safety on roads: detection of pedestrians and cyclists and collision avoidance [70,71]. Radars are being actively used in other emerging applications, such as robot guidance systems [72],

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Transmitter

Transmitted radio signal

Target Modulator

Amplitude (pulse) modulation Frequency modulation

Receiver

Reflected radio signal

Range, azimuth, elevation, velocity

FIGURE 21.3 Principle of the radar operation.

safety helmets for motorsport [73], drone collision avoidance [70], radar imaging and aerial mapping [74,75], etc. Basic operational principles of a radar [76] include transmission of either amplitude modulated (pulsed) or frequency modulated continuous wave with the following estimation of range, angular information and velocity from analysis of the time of flight, the direction of arrival, and Doppler shift of the received echoes (Fig. 21.3). In contrast to traditional applications in aviation and defense, radars in aerial, automotive, and robotic applications are typically rangeconstraint. For example, International Telecommunication Union categorizes automotive radars according to their ranging capabilities, generally being within 50 250 m depending on the required functionality [77]. Drone radars may have longer ranges for implementation of detect and avoid capabilities due to the higher dynamics of airborne targets. For example, the Echodyne UAV radar can detect small Cessnasized aircraft at 2 km, mid-size drones, like Phantom 4, from 750 m, and microsized drones from 200 m [78]. For detection of smaller obstacles, such as static ones, wires, branches, etc., the range can be reduced as the only speed of the platform itself is accounted for. In such a case the required range could be limited to 20 30 m [72]. Range requirements and constraints can also be imposed by needs in interference mitigation due to the operation of multiple radars [79] from one side and the size, weight, and power limitations of the platform carrying radar from another. It is observed that radar development tends to go toward higher frequencies, such as 60 GHz and 70 GHz in contrast to the older designs,

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where frequencies of a few GHz could be used. The increase in the frequency, while usually causing a higher attenuation in the atmosphere, supports other advanced capabilities of the radar: higher range resolution, use of compact phased array systems, and an ability to generate dense point clouds, similar to those of a laser-based system. The range resolution of UWB systems where 4 GHz bandwidth can be achieved reaches 3 4 mm and is suitable for high-accuracy applications for robotic sensing and perception applications, such as ranging, scanning and object detection. Considering that, the number of applications utilizing radars for 3D sensing is growing, with phased arrays of antennas also becoming an important part of modern radar. While eliminating issues related to the reliability of mechanical scanning systems, antenna array performance is subject to fundamental constraints that limit its performance if individual elements are placed closer than half of the wavelength distance from each other. Therefore higher radar frequencies allow to place more antenna elements in the same unit of space or make antenna array with the same number of elements more compact. Both higher resolution and antenna arrays with an increased number of elements allow for the generation of more dense point clouds from the radars. This highly desired feature enables new types of radar applications for imaging, mapping that is unlike the LiDARs and is independent of weather and lighting conditions, and support detection of small objects and advanced object recognition capabilities. With typical power consumption profiles between 5 30 watts, advance sensing capabilities and perspectives of further improvements due to the development of new technologies, radars are seen as an important part of perception systems in the modern aerial, automotive, and robotic applications.

21.5 Magnetic and acoustic sensors Magnetic and acoustic sensors constitute an important part of the sensing system of a present-day mobile platform. They provide important information about the external environment that can be effectively utilized in conjunction with the measurements from other sensors for localization and mapping purposes.

21.5.1 Magnetometers The magnetometer, which can also be referred to as a magnetic sensor or compass, is a sensor for measuring the strength of the magnetic field as well as its direction. One of the most important applications of magnetometers is the measurement of the geomagnetic field that, after the

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transformation, provides information on the pose of a mobile system body in a form of an angle concerning the magnetic north of the Earth. Since this information can be obtained at any measurement step independently from the previous states of the mobile system, magnetometers play an important role in the inertial navigation system and are therefore present in virtually all mobile systems, where localization in any form might be required. Multiple types of magnetometers exist, which can be used for measurements of the magnetic field of low, medium, or high strength with representative boundaries between these categories 1 mGs (or 0.1 nT) and 10 Gs (1 mT) [80]. The most commonly used types of sensors are those based on the Hall effect and magnetoresistivity phenomenon. However, many other types of magnetometers exist [80] reflecting the long history of development and scientific and commercial applications. Three sensitivity grades can be also introduced (high, medium, and low), and each of these can be found onboard an aerial, automotive, or robotic system. The typical applications for these three categories of magnetic sensors can be seen as follows: Low sensitivity sensors are frequently used within the onboard electronic systems for noncontact switching and current sensing. Medium sensitivity sensors support magnetic direction finding (most commonly as triaxial magnetometers within the inertial navigation system) and mineral prospecting [81]. High sensitivity magnetic sensors commonly support magnetic field mapping [82], which finds applications in surveying, Earth and space exploration and navigation. The highest sensitivity so far can be achieved by using a so-called “quantum” magnetometer, where measurement is based on the interaction of atomic spins, such as from nitrogen-vacancy defect or atomic vapor cells and the atomic spins in the measurement medium [83]. The latest iterations of the quantum-based magnetometers can achieve a subnanotesla level of sensitivity. However, magnetic sensors of all kinds are sensitive to manmade interferences, such as that originating from electronic devices or power lines. Therefore special care should be taken when interpreting measurements from magnetometers as there is a chance that interference can lead to critical malfunction of the systems that rely on magnetic field measurements and consequent failures in the mobile platform operation. One of the examples of such a problem was illustrated in the recent report of a survey drone crash in the UK [84].

21.5.2 Active acoustic sensors Active acoustic sensing approaches have been used as early as the 1910s when the first underwater echo-ranging device was created in response to the sinking of the Titanic to provide the ship with the

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FIGURE 21.4 A mobile robot equipped with SONAR sensors for navigation.

ability to detect large objects in the water. The Sound Navigation and Ranging (SONAR) system was developed during World War 1 by the military to detect submarines. A SONAR measures its distance to an object by creating and sending out a ping signal and listening for reflections of the pulse; the range can then be calculated using the measured time from transmission of a pulse to reception and the speed of sound. Active acoustic sensors have been widely used in robotics applications in recent decades. For example, SONAR has been used extensively in mapping and localization in unknown and unstructured environments [85 88]. Although active acoustic sensors such as SONAR are generally lower in resolutions and provide fewer readings per second, these sensors are still appealing among roboticists due to their low cost and power consumption when compared to high-performance sensors such as laser scanners [88]. They are being used in a range of robots including professional service robots and small hobbyist robots (Fig. 21.4). SONARs have been used in robotics for the detection and identification of features and objects. Barshan et al. and Khodabandeh et al. used SONAR to differentiate commonly met features in indoor robot operating environments by using neural networks to process SONAR data after being trained to learn to identify their parameter relations [89]. Similarly, Kroh et al. [90] used SONAR for the identification and classification of targets that could be used as landmarks for navigation. They have been able to distinguish different target geometries with a high rate of success. Active acoustic sensors have also found application in soft robotics. An example is a soft robotic system that uses an active acoustic sensor to turn soft pneumatic actuators into contact sensors where the entire surface of the actuator becomes a sensor. It works by embedding a speaker inside the soft actuator that emits a frequency sweep that travels through the actuator before it is recorded with an embedded microphone. The specific contact state is mapped to the changes in how the sound is modulated while traversing the structure [91].

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21.5.3 Passive acoustic sensors In the majority of robotics applications, SONAR systems are based on active acoustic sensing. However, acoustic sensing could also be performed using a passive acoustic system that only receives sound signals without emitting signals [92]. The main benefit of passive acoustic sensors, when compared with active acoustic sensors, is the significantly reduced power consumption, which makes this type of sensor suitable for battery-powered robots. The collected data can be used for target identification as well as tracking through locating the emitting sound source using triangulation algorithms. Passive acoustic sensors can be used in a number of robotics applications. These include target detection and tracking [92 96], localization [96 102], navigation [103], and surveillance [93,104]. One of the prominent robotics-related applications of passive acoustic sensors is an underwater acoustic positioning system where the range and azimuth of signals emitted by one or more transponders are estimated [96,102]. Underwater acoustic positioning systems are categorized into three categories: long-baseline (LBL) systems, ultra-short-baseline (USBL) systems, and short-baseline (SBL) systems. These techniques rely on underwater infrastructures that emit sound signals that are received by the passive acoustic sensors fitted on a robot. LBL systems usually have transponders installed at each corner of the test site. USBL systems use a single sound signal-emitting device fitted with tightly integrated transponders. SBL systems typically consist of three or more transponders with varied spacing and mounting methods. Research teams are also developing new signaling methods based on existing techniques to improve tracking accuracy as well as expand the range of detection zones.

21.6 Timing sources Availability of exact timing information becomes an important requirement in modern mobile systems, where the use and significance of multisensor systems, high-speed communication, networking, and related synchronization requirements become very prominent. Requirements to the perception of mobile platforms are growing with increasing speed, where perception often requires multisensor systems. For example, those under the umbrella of the IoT concept, which is often distributed over a large area [105]. Communication means are frequently used for synchronization of activities of multiple mobile systems, which require accurate synchronization of clocks (sources of time information) onboard of these platforms [106]. While GNSS is seen to be an important and convenient source of high-quality time information,

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which can also be used for synchronization purposes, it becomes more evident that excessive reliance on this system poses significant risks to operations of multiple mobile platforms within aerial, automotive, and robotic applications. Solutions to the problem can be generally split into software solutions, such as synchronization algorithms and clock disciplining, and hardware solutions, where the design measures are considered (i.e., implementation of high-performance clocks with improved absolute accuracy and stability). The latter direction is the subject of the discussion here. Since the performance of the clocks is underpinned by the performance of the oscillators employed inside we will use the terms clock and oscillator interchangeably. Stable clocks are seen as the most straightforward solution to the synchronization problem. However, the requirements for the timing and synchronization performance in different applications may significantly vary. In general, the performance of the clocks is described by accuracy, stability, jitter, and aging characteristics [107]. Accuracy is frequently considered as an initial offset at a constant temperature of 25 C. This value can be improved through the calibration process. Stability is defined as a random deviation from the initial value at the constant temperature. The effects underlying this deviation are random and attributed to different types of noises, such as random walk, flicker noise, white noise, and phase noises. The effects of these noises are usually analyzed using Allan Variance [108], which allows optimizing the performance of the clocks at different time scales. Stability can be defined at different time scales, such as short-term stability (over 1 s interval), longer-term (over 1 day or 1 month), or long-term stability (over 1 year). Jitter and its lower frequency variant, wander, describe the short-term and long-term variations of the signal from its ideal position on the time axis. For example, when observing a square-wave signal on the oscilloscope jitter will look like smearing of the signal edges due to appearing at slightly different positions on the screen when the observation period is repeated. Aging describes drift in the clocks over long periods, typically over 1 year. The drift is related to the age effect (hence the name) and is normally nonrevertable. The definitions and their importance are linked to the application requirements, which will be discussed later. Since clocks use oscillators for time information generation, specifications of clocks sometimes may include frequency-related characteristics, such as phase noise, which is out of the scope of this chapter. The time sources in their hardware implementation can be based on a range of technologies. Crystal oscillators perhaps dominate here, covering a wide range of parameters as summarized in Table 21.2 regarding their type. Three types are presented. XO denote clocks based on the “usual” crystal oscillator, where stability is not

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21. Sensors for aerial, automotive, and robotic applications

Main parameters of the crystal oscillators of different types.

Oscillator type\ performance metric XO TCXO OCXO

Accuracy, ppm

Temperature drift, ppm/ C (0. . .50 C)

Ageing rate, ppm/year

6 104 6 50

6 54 6 20

6 14 6 5

29

6 24 6 10

6 0.24 6 2

6 0.54 6 2

12

6 0.24 6 2

6 0.0014 6 0.1

6 0.024 6 0.2

Stability (short term, 1 s)


1
10 1
10


4 1
10 45
10

1 1027 4 1 1029 28 29

modified in any way and parameter variability is attributed to changes in the temperature and quality of the integrated circuitry, including the quality of the quartz crystal. TCXO is the temperature compensated clock, where the temperature coefficient of the internal oscillator is compensated electronically. It presents a significant improvement in terms of the temperature stability over the uncompensated clocks. OCXO presents oven-controlled oscillators, which use so-called oven (heater) to minimize temperature variation seen by the clock through maintaining the internal temperature of the device stabilized at the level above the environmental temperature. This solution enables further improvement of the temperature stability of the clocks, which is particularly important for mobile solutions (e.g., in automotive applications, which see temperature variations as big as 255 C to 1125 C above the environmental temperature). Often manufacturers do not show individual parameters, but only a combined value that integrates initial offset, temperature effects, aging over a year as well as some load and power variations. It should be noted that some of these solutions may not be well suited for small robotic or aerial applications due to power consumption. Typical power demands are 0.02 0.04 W for XO- and TCXO-based clocks and 1.5 3 W for OCXO. Other technologies that could be utilized for timekeeping are MEMSbased oscillators and chip-scale atomic clocks (CSAC). The advantages of the MEMS solution are underpinned by the technology implemented, which provides lower dependency on temperature and vibration, lower size and weight. Due to a large variety of clock oscillators (both MEMS and XO quartz types) available on the market, it is difficult to make a simple conclusion of the advantages in power consumption and stability they offer as these will depend on the functionality offered and overall quality grade of the device. CSAC clock oscillators use the benefits of higher stability resonant oscillations in cesium-filled cells, which allows them to significantly outperform TCXO technology and exceed most of the parameters from the

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21.7 Final remarks

best of the OCXO devices while requiring only a fraction of their power consumption. As a quick example, MicroSemi’s CSAC SA.45 s [109] provides the user with short-term stability (over 1 s) better than 3 10210 and temperature instability of 6 0.0004 ppm in the range from 210 to 170 C, which is comparable with the best OCXOs but consumes only a fraction of the power they consume (120 mW for CSAC vs. 2 3 W of OCXOs). Newer developments promise to double the performance of CSACs with the same or better weight and power consumption. There are other, more accurate technologies for generating accurate time referring to rubidium, cesium and hydrogen maser technologies, which are utilized for applications with high requirements to the time accuracy, drift, and stability, such as communication and navigation satellites, space exploration, master clocks for cellular networks, etc. Due to excessive power consumption, weight and dimensions, these solutions are rarely seen in any of the robotic, automotive, or aerial applications. The highest requirements for the performance of the clocks are set from the applications, where communication between multiple systems is required. Examples of such applications are UWB precision localization [110]; collaborative localization and navigation in multiagent systems (e.g., UAV swarms) [111]; and multisensor data and information fusion, for emerging applications, such as airborne distributed surveillance [112]. In these applications, the requirements for the synchronization can be very high and for localization with an uncertainty of tens of centimeters, relative time accuracy requirements may reach 0.1 0.2 ns. Pure hardware solutions cannot satisfy the requirements in these cases; therefore complex synchronization algorithms, frequently based on using tracking loops, are used. Requirements to time accuracy can be lower. For example, 0.01 1 ms is common in other applications, such as time stamping for traffic control, location-based services in mobile applications, and heterogeneous sensor fusion [113]. In these cases, hardware solutions presented by high-stability clocks can be utilized to maintain short time stability or holdover of the time information if synchronization with the master clocks is only performed once in a while to minimize communication overhead or not available over a defined period of time. In these cases the stability of the clocks along with the synchronization accuracy will define the frequency of the synchronization events, such as using the concept defined in Ref. [114].




21.7 Final remarks Sensor technologies are evolving rapidly in response to growing demands from new and emerging applications, such as autonomous deliveries, personal aerial transport, UAV-based emergency services, and

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human-robot collaborative systems. Requirements for the resilience, safety, and security in these applications are increasing in anticipation of their wide adoption in the not-so-distant future, stimulating thus the development of new fundamental principles for sensing, such as quantum sensing, and forcing researchers and engineers to explore more actively robust multisensor solutions. These new developments are also underpinned by progress in the related areas of computational platforms and signal processing, led now by AI technologies. As a result of these coordinated efforts we are witnessing the fast evolution of a new generation of safer, but also more capable, energy-efficient and affordable sensor-based solutions in aerial, automotive, and robotic applications.

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22 Challenges and future aspects of sensor technology Richard Luxton Institute of Bio-Sensing Technology, University of the West of England, Bristol, United Kingdom

22.1 Introduction The pages and chapters of this book show the breadth of sensor technologies that have been invented, developed, and evaluated for use across a wide range of applications. The focus of this chapter will be on sensors used for analytical purposes such as chemical sensors and biosensors. The chemical sensor interacting with a particular target or groups of targets to produce a response. In the case of the biosensor, a specific biological interaction occurs on the surface of a transducer producing a signal that is measured and translated into useful information for the user. The biological interactions result from the target molecule in the sample binding specifically to an immobilized biological molecule such as an enzyme, antibody, or single-stranded DNA on the sensor surface. But why, given the extensive range of biosensor of technologies, are there so few commercial successes? The most obvious success story being the glucose biosensor. As a result of this great commercial success several other biosensor technologies have been commercialized. Many of these are simple adaptations of the glucose biosensor being able to measure other metabolic molecules such as cholesterol, urea, and lactate. We shall see in this chapter that by integrating new technologies with sensor design many significant challenges to exploitation and commercialization of sensor and biosensor technology can be overcome. Opportunities afforded by these technologies allow the development of new sensor surfaces and methods of integrating with existing and

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00005-4

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Devices and hardware

Applicaons and soware

novel transduction technologies. As always, the need for cheaper, quicker, smaller, and more robust technologies for deployment of sensors is a major driver for sensor development. One important feature of the analytical sensor is that of integration. Fig. 22.1 shows a diagram of a “sensor stack” that represents different levels of technology that are required for the integration of a sensor with other components required to produce an output that can be analyzed and interpreted to generate information and knowledge for the user. A combination of hardware and software are required for a complete, usable device or system. The sensor itself is it the front end of the sensor stack and produces a signal as a result of an interaction on the sensor surface. The sensor must be integrated with the necessary electronic circuitry associated with a transducer which are able to modify the signal being generated through suitable amplification and signal processing which is applied in order that the signal is as free from noise as possible. A communications module within the device is required to send the signal for further analysis, transferring the processed signal to a software element for further data processing. This can be archived using a hard wire or via wireless transfer using protocols such as Bluetooth or Wi-Fi.

Visualisaon and presentaon

Informaon

IoT

Analysis and postprocessing

Arficial intelligence Smart phones

Data repository

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Communicaons

Low power and energy harvesng Flexible electronics

Transducon

Sensor matrix and fabricaon Sensor Element

Raw data

Nanotechnology

FIGURE 22.1 Diagram to show the sensor stack incorporating the sensor element which produces the raw data and the various stages of signal and data processing to enable information to be generated from the sensor. Areas of technology developments that have been influential in developing new sensor technology are shown on the righthand side.

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The signal generated by the sensor needs to be converted into meaningful data which can be interpreted by the sensor user. This involves software applications for data processing, statistical analysis, data presentation and visualization. Depending on the application and the user, different types of data presentation are required. For example, the patient might only need to know whether the measured substance is in the reference range or whether it is elevated whereas a clinician or researcher would need to know detailed outputs from the sensor to understand how the system is performing and maybe nuances in the information given. Sensing devices require a sample to be presented to the sensing surface which in itself may prove to be a significant challenge. This may be a simple direct method such as dropping the sample directly onto the sensor element or it may involve the integration of a fluidic system which takes a sample and applies several processes such as dilution and addition of reagents to enable the target within the sample to interact with the sensor surface in an optimal manner. Other challenges are associated with the design and operability of the sensing device being used in harsh environments. The design features will depend on the application which may, in turn, impose additional challenges on the device such as the need to minimize power consumption, prevent sensor fouling, include additional durability and enhance the sensor life span. The challenges for any type of sensor development can be categorized as those associated with the analytical capabilities of the sensor and those challenges associated with the development and commercialization of a useful and viable sensor system. Often these are interlinked, for example methods used to enhance analytical performance such as the detection limit or reproducibility need to be translated to robust manufacturing protocols that are employed to fabricate the sensor.

22.2 Technology drivers 22.2.1 Nanotechnology Certainly, one of the most exciting technology developments in the last decade or so, that has influenced the development of innovative sensor development is the utilization of nanomaterials. The term refers to objects at the nanometer scale often between 10 and 100 nanometers although some so-called nanomaterials maybe up to 300 nanometers in size. These materials are often of a similar scale to biological molecules and other biological entities such as virus particles There have been many thousands of papers published highlighting the application of

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FIGURE 22.2 The number of papers in Scopus that have the word sensor/biosensor in the article title in conjunction with one of the three sensor surfaces, graphene, ZnO and carbon nanotubes. Panel A shows the results for sensors and panel B shows results for biosensor.

different types of material including carbon nanotubes, graphene, metal oxide nanocrystals, quantum dots and many more [1]. Fig. 22.2A shows the number of papers published between the year 2000 and 2020 for three important nanomaterials used in sensor construction. The data was generated from the Scopus data base by putting the combination of the word “sensor” along with name the of the technology in the title of the article. This method gave very similar distributions to other methods such as applying the search term to the general content of the paper. Although only isolated in 2004 it can be seen from the figure that the number of research papers integrating graphene with the sensor, or using it as the sensor, has grown rapidly over the last 10 years with the first mention in the title of a paper in 2007. Fig. 22.2B shows the number of papers where these materials have been used in the development of a biosensor where the search term “sensor” was replaced with “biosensor.” This shows that research into of graphene biosensors also show a dramatic increase in popularity. The application of carbon nanotubes in biosensor research peaked around 2011 and since then has dropped in popularity whereas the development of zinc oxide biosensors has shown a steady growth since 2004. In recent years there have been interesting developments in the use of nanomaterials whereby multiple materials have been integrated to enhance their properties in terms of sensitivity or selectivity. In 2010 over 90% of the papers, where graphene was used to enhance sensor capability, the graphene was used in isolation. In 2020 over 90% of the papers developing graphene-based sensors described graphene being used in conjunction with other nanomaterials. This is also true about other, nongraphene, nanomaterial-based sensors which show the integration of multiple types of nanomaterials.

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One of the main reasons for incorporating nanomaterials in the sensor was to enhance sensitivity and selectivity, particularly in the development of gas sensors and biosensors. Sensitivity is only a challenge when trying to measure biomarkers or other target molecules at very low concentrations but the much bigger challenge for sensors and biosensors is that of reproducibility. For many sensors that rely on physical changes, such as temperature and pressure, the reproducibility is linked with the materials used and manufacturing processes. In the case of a biosensor there are additional factors that affect sensitivity and reproducibility that are related to the biological capture element. Biological molecules can be attached to the sensor surface noncovalently, often through electrostatic interactions, or through a covalent chemical bond. There are many methodologies for chemically coupling a biological molecule to the sensor surface and many have the advantage that the molecule can be oriented to allow the part of the molecule that interact with the target to be accessible. Noncovalent, electrostatic binding results in the biological molecules being captured on the sensor surface in a haphazard, random orientation in which only a fraction of the binding sites is avail to the target molecule. Production and manufacture of the biosensor surface needs to be carefully controlled to achieve consistent immobilization of the biological capture molecule and small errors can lead to reproducibility as poor as 15% 20%. In a similar manner, the use of nanomaterials in the production of a sensor surface can also create some challenges in terms of ensuring that the surface is equivalent in all individual sensors. Some methods of producing the nanomaterial surface do not lend themselves to mass production such as growing crystalline structure which are very difficult to control reproducibly. The surface of nano materials has been shown to stabilize and enhance the sensitivity of DNA amplification reactions. Su et. al. reviews the different types of materials that have been used in DNA amplification reactions and proposes a number of mechanisms to explain the enhanced sensitivity observed, including stabilization of molecules on the nanomaterial surface and clustering of molecules so that they are readily available for amplification reactions [2]. The use of nanomaterials on the sensor surface creates a far greater surface area of the sensor allowing a greater number of interactions to take place, thereby enhancing the signal. In other circumstances mixtures of nanomaterials can enhance the signal through other physical effects such as the formation of np-junctions between different material such as ZnO and CuO nanocrystal. There are many examples of many different types of nanomaterials being used to enhance sensitivity of gas sensors such as BiFeO3 used in the detection of acetone gas [3], carbon nanodots for the detection of trinitrotoluene [4] and iron oxide nanopowder for the detection of midodrine [5]. The nanomaterials are not

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only confined to the improvements of gas sensors but also optical sensors, magnetic sensors, radiation sensors and biosensors. Within the biomedical field and the need for measuring biomarkers at an everdecreasing concentration has driven need for increased sensitivity and reduction of the detection limit. One important factor limiting the sensitivity of electrical measurements on a biosensor surface is the electric double layer which is associated with high concentrations of mobile ions found in the sample or reagents. The thickness of the electric double layer is related to the Debye length, which is the distance of potential decay in electrolyte solutions and under physiological conditions this is less than 1 nm. The fact that the distance an antibody protrudes from a sensor surface is approximately 10 15 nm means that antibody interactions with the target analyte are not easily detected (if at all) because of the presence of high concentrations of ions. Traditionally several techniques have been used to overcome this problem including measurement of surface potential, measuring changing impedance and measuring electrochemical signals generated by a reporter molecule. Kesler in 2020 proposes that shape of nanomaterials, particularly concave surfaces, can increase the electric double layer so that it extends far beyond the Debye length allowing the antibody binding reactions to occur within the electric double layer [6]. This explains why the application of nanomaterials on biosensor surfaces can measure changes associated with antibody/antigen interactions which would not be possible on a planar electrode surface. Another use of nanomaterial in sensor development has been to exploit catalytic properties of these materials to replace enzymes which are a biological catalyst used in many biosensors. The use of physical materials overcome the stability and manufacturing issues that exist when using biological molecules. The development of enzyme free glucose sensors has been an important application of these materials. The first papers describing enzyme free glucose biosensors sensors appeared around 2008 and the numbers of papers have steadily increased year on year. Various materials have been reported in the fabrication of these sensors including graphene oxide and copper oxide nanomaterials, carbon micro and nano structured materials, cobalt oxide nano-arrays but many other materials have been described [7 10]. In the literature there are many thousands of papers describing the development and use of nanomaterials to enhance the sensitivity of sensors and biosensors. Table 22.1 gives a number of examples of nanomaterials and demonstrates the wide range of applications for which these sensors have been developed. The targets include nucleic acids, viruses, bacteria, small molecules, large molecules, proteins, gases, radiation and moisture. It is interesting to note that many sensors for measuring glucose have also been developed that utilize the nonenzymatic catalysis of

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TABLE 22.1 Examples of nanomaterials, their applications and mode of enhancement. Nanomaterial

Measurand

Nano-enhancement

References

CoFe2O4

miroRNA

Enhanced DNA amplification

[2,11]

ZnO nanoflowers

Canine borne pathogen

Enhanced DNA amplification

[12]

Silicon nanoparticles

Bacteria

Enhanced surface area with optical detection

[13]

MnWO4/reduced graphene oxide

Tryptophan in milk

Enhanced electrochemistry

[14]

Silver nanoparticles

Paracetamol

Enhanced electrochemistry

[15]

FeO nanopowder

Midodrine

Enhanced electrochemistry

[5]

CuO nanoclusters

Carcinoembryonic antigen

Nonenzyme catalyst with electroluminescence

[16]

ZnO/CuO nanocrystals

C-reactive protein

Increased electric double layer with impedance

[17]

Carbon nanodots

2;4;6trinitrotoluene

Fluorescent properties

[4]

ZnO nanosheets/ gold particles

Acetone in breath

Sensitive resistance changes due to semiconductor properties

[18]

SnS2 nanoflowers/ gold nanoparticles

NO2 gas

Schottky contact formed between AuNP and SnS2

[19]

WO3CoO nanohybryds

Hydrogen gas

Resistance of p-n hetrojunction

[20]

CuxO/NiO

Carbon dioxide gas

Resistance of p-n hetrojunction

[21]

Gold nanoparticles

Gamma radiation

Increase in grain size of AuNP

[22]

Cellulose nanofibers

Moisture

Water bonding with OH groups changing resistance

[23]

Co3O4 nano needles

Glucose and phosphate

Enhanced surface area and nonenzyme catalyst with amperometry

[10]

Co-Fe nanoparticles

Glucose

Nonenzyme catalyst and amperometry

[24]

CuO with carbon nanostructures

Glucose

Nonenzyme catalyst and amperometry

[25] (Continued)

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22. Challenges and future aspects of sensor technology

(Continued)

Nanomaterial

Measurand

Nano-enhancement

References

CuO/graphene oxide nanofibers

Glucose

Nonenzyme catalyst and electrochemistry

[9]

Ni nanoparticle/ carbon nanodot

Glucose

Nonenzyme catalyst with electrochemistry

[7]

BiVO4/ gold nanoparticles/ indium tin oxide

Glucose

Nonenzyme catalyst with photoelectrochemical effect

[8]

glucose; this reflects the dominant market position of glucose biosensors sensors and potential exploitation in this area.

22.2.2 Sensor matrix and fabrication Fig. 22.3 shows three materials that have been described in research papers in the development of sensor technology between the years 2000 and 2020. The three matrices, chosen because of their apparent popularity, a gold electrode, screen printed electrodes and those using a paper matrix. Both sensor and biosensor applications show an increase in the number of papers reporting developments in the use of paper-based sensors. For screen printed sensors there is a continuing steady increase in in the number of papers reporting development using this technology whereas there appears to be a decline in the number of sensors being developed that use gold electrodes alone, which is much more apparent in the application of gold electrodes in the development of biosensors. This in some way reflects the increased adoption of nanomaterials. Also, it is interesting to note that for biosensor applications, the rapid increase in the number of papers using a paper matrix in 2012 results in the number of papers describing paper-based biosensor exceeding the numbers for screen printed electrodes or gold electrodes from 2016. One of the greatest challenges in the implementation of new technology is the production of the new sensing devices at scale and at a reasonable cost. What appears to be a simple process in the laboratory, where sensors are often made by hand and assays are performed manually, can prove to be extremely difficult to develop a manufacturing process to make large numbers of sensing devices that replicate the assay protocol. The assay protocol will often need to be adapted for use in a fluidic device in which a sample is placed and any necessary dilutions and reagent additions is performed on the chip without the need of any manual intervention. There are many ways of producing a fluidic chips

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FIGURE 22.3 The number of papers in Scopus that have the word sensor/biosensor in the article title in conjunction with one of the three sensor matrices, paper, SPE and gold particles. Panel A shows the results for sensors and panel B shows results for biosensor.

and early stage companies and research groups should team up with a medical device product designer experienced in the development of fluidic systems. There has much development in the area of microfluidics which take nanolitre volumes of fluid across a sensor surface. The major challenge in exploiting these microfluidic systems is that of sample introduction, particularly complex samples like blood. In order to make a practical device it is often necessary to use higher volumes of sample and reagents creating what might be called a milli-fluidic system. Fluidic devices often have a high level of complexity but in contrast the paper-based sensors use simple capillary flow materials. These devices can be manufactured at scale and designed to be simple to use, one shot and disposable. Many paper devices have been integrated with electrochemical or optical detection technologies. Impregnating the paper with wax can create complex fluidic tracks within the paper allowing multiple tests to be perform on the same device. The recent pandemic has seen an explosion in the use of paper-based devices with millions being produced in plastic housing and with the sustainability agenda being high in the minds of manufacturers there is the need for biodegradable materials which can be incorporated into the sensor device particularly for the disposable one-shot applications. A number of materials have been described for developing biodegradable sensors including porcine skin gelatin, cellulose, glycine/chitosan, plant materials and poly-lactic-co-glycolic acid, a biopolymer approved by the FDA [26 32]. Since 2005 there has been huge increase in the manufacture of nanomaterials using an eco-friendly, green synthesis technology. In 2000 there were 24 papers with reference to green synthesis in their title, in 2020 this had risen to 1798. Green synthesis refers to the use of biological systems in the synthesis of nanomaterials without the need for toxic

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22. Challenges and future aspects of sensor technology

chemical processes. Bacteria are commonly used to synthesis the nanomaterials where the growing conditions can control their shape, size and composition. In other green-manufacturing methods plant extracts are used in the synthesis of the nanomaterials removing the need for a bioreactor and again reducing the need for toxic chemical processes. A wide range of sensor have been described using green synthesis, all based on nanomaterials for example the development of a paracetamol sensor, a tartrazine sensor and a sensor for the detection of nitrophenols [15,33 37].

22.2.3 Flexible electronics Flexible electronics refers to a technology that allows printing of electrical circuits onto a flexible plastic substrate such as polyimide or PEEK. Other electrical components such as semiconductor chips can be added to the flexible substrates as in traditional electronic assemblies. The flexible nature of the matrix allows the electronic circuitry to be fitted into unusually shaped devices saving space. The technology has been particularly useful in the development of wearable sensors where the devices can be manufactured to follow contours of the body or made to be wrapped round parts of the body such as the wrist. There are now many devices that have been developed for monitoring health incorporating flexible electronics which are often linked via Bluetooth to a smartphone. Sensors integrated with flexible substrates have been used for human motion detection, for the production of flexible strain sensors, the measurement of temperature and in gas sensors [38 43].

22.2.4 Low power electronics and energy harvesting Sensors not connected to the mains power supply, such as those in distributed networks used in remote monitoring most often derive their power using a battery. The obvious limitation is that the battery has only a finite life and in applications where it would be difficult to replace batteries, such as sensors in remote locations, this is especially problematic. Energy harvesting technologies have been developed to supply power to remote sensors. These technologies aim to derive power from the environment surrounding the sensor deriving energy from light, heat, mechanical movement or radio frequencies. Energy generated from mechanical movements is often associated with walking for body worn sensors and from the wind for sensors in remote locations. In some instances, hybrid energy harvesting systems are developed which incorporate two or more and sources to improve the efficiency of the system. The energy harvesting devices rely on

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well-known physical effects such as the thermoelectric, photovoltaic and piezoelectric effects. Energy can also be generated through a microbial fuel cells which utilizes bacteria breaking down a food source and in doing so generates the small current. The typical energy output is from 0.1 microW to 100 W. Associated with energy harvesting devices is the requirement to store energy whether this is through the use of rechargeable batteries or supercapacitors. In order to maximize the efficiency energy harvesting technologies are often used in conjunction with low power electronic devices which will prolong the operational lifetime of the sensor [44 46].

22.2.5 Sensor networks It can be seen from Fig. 22.1 that’s a high degree of integration this required for a device to be commercially successful and another key challenge needing to be addressed if the sensor technology is to be adopted in the wider world is that of interoperability. This is critical for the integration of sensor devices into the new world of smart technology and the Internet of Things (IoT) where multiple devices interact with each other and sophisticated algorithms to process data creating new information. There are many applications associated with sensor networks, but two areas stand out, that of environmental monitoring and in body worn sensor networks. Examples of where wireless sensor networks have been employed include precision agriculture, underwater applications, detecting nuclear materials in urban environments as well as the more traditional application of monitoring air and water quality. The body sensor network takes data from a number of sensors distributed around a body such as ECG glucose humidity temperature blood pressure and accelerometers. The sensors can be placed on the body, incorporated into clothing or may even be implanted inside the body. Body worn sensor networks may be useful in monitoring elderly and sick people but there has been much interest in how they can be exploited by the military and athletes to measure their performance [47 52].

22.2.6 Smart phones The smartphone is considered to be the most ubiquitous sensing device that is accessible to a person with the average smartphone having over 20 different sensors built in which can give information on many personal activities such as physical activity, location and behavioral patterns. Much research has gone into the use of smart phones to determine an individual’s wellbeing through assessment of physical, psychological

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22. Challenges and future aspects of sensor technology

and physiological behavior. Used in conjunction with chemical and biosensor devices the smartphone becomes an analytical platform that is portable and has access to powerful computing capabilities and connectivity. This is the ideal platform for the integration of sensing devices for points-of-test applications. This is for this reason the smartphone, along with dedicated apps, has become so important in the delivery of digital health with over 340,000 apps for health and well-being on the market. Despite the great number of apps available there are only very small numbers that have been validated to show patient benefit [53 57].

22.2.7 Artificial intelligence With the advent of artificial intelligence, the accumulation of data from multiple sensing sources can be analyzed to produce meaningful information to the user. Many different terms are used for the types of data analysis such as neural networks, deep learning, and machine learning. In fact, the term deep learning is actually the application of neural networks with three or more hidden layers in the algorithm. Simple algorithms will accumulate data and make comparisons and search for hidden correlations. More complex algorithms will give feedback and can autonomously adjust a system through a series of actuators. For example, several sensors, such as pH, temperature, glucose, and lactate, used to monitor a bioreactor, send separate data streams to a central control node. The combined data will be interpreted and the system will respond to any deviation from the normal operating envelope by initiating corrective feedback operations, such as increasing the amount of CO2 delivered to the bioreactor. It is only through the application of artificial intelligence that nonspecific data such as movement data coming from a wearable motion sensor can be combined with more specific data to give a deeper understanding of the situation being monitored (e.g., the use of movement and gait date to predict the onset of neurological disease) [58 62].

22.2.8 Internet of things The Internet of Things (IoT) enables physical objects to connect with each other and exchange data over the Internet and forms the basis of sensor applications in smart technology applications. These are known as the smart home, smart health, smart transport, smart cities and many other smart . . .’s. The common factor in all the smart technologies is the need for sensors to collect data. For example, in IoT applications in agriculture data such as rainfall, temperature, humidity, and pest infection can be autonomously collected and help to automate farming processes.

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22.3 Commercialization

In fact, we have reached a point where the IoT is all pervasive in our society even linking domestic appliances to the Internet, which can be controlled through our smartphone. The implications for sensor technologies and integrating multiple types of sensors is tremendous, creating a great number of opportunities for future sensor research. The whole premise of the IoT is sensor connectivity and data collection, which is then used to feedback into particular systems enabling control that previously was not possible [63 67].

22.3 Commercialization In addition to the analytical challenges posed by the development of new innovative technologies a further hurdle to the exploitation of a usable system is the commercialization of the technology. This can be considered in a number of stages, which include the validation, regulatory, manufacturing, scale-up, and pathways to adoption challenges. Fig. 22.4 shows a simplified commercialization pathway of a medical device from the concept and development of the innovation through repeated cycles of design, fabrication, testing, validating results, and then redesigning the system based on the results of the studies. The preclinical studies are usually performed in a laboratory and define the analytical profile of the device in terms of its sensitivity, selectivity, reproducibility, dynamic range, stability, etc. Throughout these studies detailed design files and technical files need to be kept that show any changes and modifications made to the sensing system. These are required for regulatory approval. Following the laboratory-based studies the technology is then tested in a real-world situation. For medical devices this is the role of the clinical trial, which involves a detailed study with patient volunteers. The design of the study is critically important incorporating a number of control groups. When the clinical trials have been successfully completed all the evidence is presented to the regulatory bodies who award the certification for the product. Once

Concept

Innovaon

Pre clinical

Design Fabricaon Bench test Validate Redesign

FIGURE 22.4

Clinical

Regulatory

Design study Collect data Analyse Redesign

Simplified commercialization pathway.

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Product launch

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22. Challenges and future aspects of sensor technology

regulatory approval has been achieved the technology is ready to be marketed. For a successful uptake of the product an in-depth understanding of the market is required along with robust routes into the market defined, often through other companies or known individuals. This can be particularly challenging for new companies spinning out of research institutions.

22.3.1 Regulatory issues Over the last decade or so there has been a great deal of change in relation to medical device regulations in Europe and the UK. Even now these are not defined and will still undergo further refinements. This is problematic to companies looking to commercialize their technology insomuch as the regulatory standards are often redefined. For small companies, spin-outs and start-ups seeking to commercialize technology coming from research establishments, the business environment is often not well understood and the requirements of keeping necessary technical files may have been overlooked. These files need to include design refinement, analytical validation, and then clinical testing for medical and diagnostic devices. There is also a need to understand what standards need to be addressed, and this would be different for each type of device being developed. Consequently, there is a need for a clear commercialization pathway to be produced for the technology with clear responsibilities defined within the innovation team. Running clinical trials can be a challenge in the development of new sensor and biosensor technologies for use in the healthcare environments. Clinical trials are expensive and need careful organization and are often undertaken by a clinical research organization on behalf of the company. Results of the clinical trial are made available to regulators and if successful regulatory approval is given. In developing a business case for the commercialization of sensing technologies it is vitally important that the markets are clearly identified, and strategies must be developed to ensure that the technologies are adopted within that market. This requires specialist marketing skills not usually found in the typical academic or researcher. It is important that the academic group recognizes that a diverse set of skills are required to ensure that any technology is successfully commercialized. This includes marketing and business development skills.

22.3.2 Markets Traditionally chemical and biosensors have been used extensively in the healthcare, biomedical, and environmental sectors. There has

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22.3 Commercialization

been much effort into identifying new areas where these sensors could be exploited, transferring the technology from one sector to another with minimal adaptation of the sensor itself, usually only the sensor housing and sample handling being modified. But with the use of newer technologies and the ability to produce or sensitive sensing systems the range of application areas has grown. There is still the need for innovative solutions and the application of sensor technology in many sectors. Fig. 22.5 shows the increase in the application of wearable sensors relative to other important areas of sensor deployment, agriculture, point of care, and continuous monitoring, which present new market opportunities for sensors. The market for continuous glucose monitoring being of great commercial interest. The new technologies such the enzyme-free glucose sensors would have a great impact in this space. Continuous monitoring requires a signal that can be measured and recorded overtime and is most suitable to sensing technologies where there is no saturation of the sensing surface from the target analytes and where there is no sensor fouling. The great interest in wearable sensors is driven by the application of the technologies with continuous monitoring capability and in particular connectivity with the Internet. It is interesting to note that there was also a huge increase in the number of papers associated with sensor networks generally. In the year 2000 there were 79 papers with the word sensor network in the title, and this peaked in the year 2010 with 6318 papers, but after 2010 there was a steady decline to approximately half that number in 2019.

Number of papers

800

600 Application Wearable Agriculture Point of care Continuous monitoring

400

200

0 2000

2005

2010 Year

2015

2020

FIGURE 22.5 The number of papers in Scopus that have the word sensor in the article title in conjunction with one other terms related to an application area: wearable, agriculture, point of care, and continuous monitoring.

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Opportunities exist for sensor technology in the application of environmental sensor networks and the development of smart sensing and the ability to connect with many other devices. The development of sensor networks started in the mid-1990s and has now reached a stage of maturity with the development of viable sensor networks that are realistic. Networks to monitor water quality across wild and remote catchment areas are now being implemented focusing on defined area or regions and involving new algorithms and security protocols. The complexity and time consideration in obtaining regulatory approval for sensing devices used for diagnostics and other medical applications issues have motivated many companies and researchers to apply their sensor technology to the veterinary markets rather than human health. These technologies are often very similar to those that would be used in monitoring human health but with adaptations to the technology for application for use with various animals. Within the wider agricultural sector many sensors are being produced to monitor crops in terms of health of plants and detection of disease and pests and to monitor the state of soil. In other areas sensing technologies have been developed to measure the ripeness of fruit and to detect spoilage in food storage. There are strong opportunities for connected sensor technology within the food supply chain form food production and ensuring food safety and quality at the retail outlets [68 71]. Other niche markets for sensors might include the bioprocessing industry looking to monitor novel drug production such as biologics and other vaccines where sensors provide information for a feedback loop to automate production of cell products [72,73]. As we have seen there are technology drivers and new opportunities in various markets that are driving an increase in the manufacture of sensors. The growth in sensor manufacturers is reflected in the size of the global market for sensor sales, which is very buoyant as reflected in the numerous market reports that can be found on the web that give predictions of market growth for all types of sensors. The estimated market size and rates of growth identified in the various reports show some variability in their predictions of market size and growth over different time scales but generally all show a significant growth in the markets. In one report the global market size was valued at $166.69 billion in 2019, and is projected to reach $345.77 billion by 2028, with a compound annual growth rate (CAGR) of 8.9% from 2021 to 2028 [74]. According to Next Move Strategy Consulting, the global sensor market is projected to more than double in size between 2019 and 2030. The market was valued at $163.84 billion in 2019 and is expected to reach the size of around 426.2 billion US dollars in 2030 [75]. In another report the global sensor market was valued at $151.40 billion in 2020, and is expected to reach USD 431.21 billion by 2030, showing a CAGR of

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11.3% between 2021 2030 [76]. The most optimistic reports showed a CAGR of 14.9% between 2021 and 2026, with the market Estimated to be $205.2 billion in 2021 growing to $411.2 billion in 2026 [77]. Biosensors have 12 15% of the sensor market with electrochemical biosensors making up 80% of this figure. The biosensor market is valued at $25.5 billion in 2021 and projected to be $36.7 billion by 2026 with a CAGR of 7.5% [78]. Another report assessed the global biosensor market size to be worth $22.4 billion in 2020 and have a CAGR of 7.9% between 2021 to 2028 [79]. In contrast, although a smaller market ($612 million in 2019), it is predicted that the market for wearable sensors will grow at a CAGR of 26.7% to reach a market size of $2.5 billion in 2025 [80]. This is also seen in the trend shown in Fig. 22.5.

22.4 In conclusion The main challenges to the development of usable sensor technologies for use across many sectors relates to performance and connectivity. In many ways these challenges have been addressed by employing technologies from other technology spheres such as the use of nanomaterials, flexible substrates, and enhanced connectivity allowing the development of sensor networks. Certainly, the advent of IoT and the Internet has enabled sensor placement in many areas to be more efficient, to gain more data, and offer smart solutions. The future of sensor and biosensor development can be broadly divided into two main areas. The first area focuses on the production of inexpensive, rapid, disposable tests with an emphasis on sustainability probably based paper technology, which can be exploited in third world countries. The second area focuses on the connectivity and networking of sensors, with great opportunities in the development of wearable technology that will monitor health and wellbeing, with growing interest in applying these to large markets such as the military and the sporting world. Sensors for health monitoring to be used in the community and in home offer major life improvements for people managing their own long-term conditions. Again, the enabling technology is that of connectivity and linking up information collected from point-of-care devices with patients’ records. Networking environmental sensors and collecting large amounts of data has been supported through the application of AI algorithms. Despite overcoming many challenges there still lies many more hurdles to be jumped in terms of having sensor technology accepted. In society there still remains a significant fear of technology that will need to be overcome. The advantages of the technologies in terms of enhancing quality of life while being safe and ensuring the continuing

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production of safe food and water will send a strong positive message to society and for the needs of validated sensor systems for monitoring health and the environment. Importantly, the development of this innovation requires a multidisciplinary approach in designing and executing development projects. The necessary expertise needed to form the development team may include physicists, surface chemists, cell biologists, immunologists, biochemists, electrical engineers, and software engineers along with many others. Recognizing this and building interdisciplinary teams to develop the technologies can lead to exciting opportunities that have not yet been explored. It is the gray area between existing disciplines where new innovation can be found.

References [1] T. Liyanage, A.Z. Qamar, G. Slaughter, Application of nanomaterials for chemical and biological sensors: a review, IEEE Sens. J. 21 (2021) 12407 12425. [2] Y. Su, H. Chu, J. Tian, Z. Du, W. Xu, Insight into the nanomaterials enhancement mechanism of nucleic acid amplification reactions, TrAC.—Trends Anal. Chem. 137 (2021). [3] S. Neogi, N. Mandal, R. Ghosh, Self-assembled Nano-BiFeO3 chemi-resistive VOC sensor: a non-conventional MOS sensor highly selective toward acetone, J. Inst. Eng. (India): Ser. C. (2021). Available from: https://doi.org/10.1007/s40032-021-00684-w. [4] Y. Liu, et al., Sensitive detection of 2,4,6-trinitrotoluene utilizing fluorescent sensor from carbon dots and reusable magnetic core-shell nanomaterial, Talanta 233 (2021). [5] S.D. Al-Qahtani, et al., Iron oxide nanopowder based electrochemical sensor for sensitive voltammetric quantification of midodrine, Arab. J. Chem. 14 (2021). [6] V. Kesler, B. Murmann, H.T. Soh, Going beyond the debye length: overcoming charge screening limitations in next-generation bioelectronic, Sensors. (2020). Available from: https://doi.org/10.1021/acsnano.0c08622. [7] X. Yang, et al., An enzyme-free glucose electrochemical sensor for detection of the glucose in serum based on nickel nanoparticle/carbon quantum dots, Int. J. Electrochem. Sci. 16 (2021) 1 14. [8] L. Chen, L. Miao, Y. Chen, Y. Gao, J. Di, An enzyme-free photoelectrochemical glucose sensor based on coupling BiVO4 with gold nanoparticles, Mater. Sci. Semiconductor Process. 125 (2021). [9] M. Gijare, S. Chaudhari, S. Ekar, A. Garje, A facile synthesis of GO/CuO-blended nanofiber sensor electrode for efficient enzyme-free amperometric determination of glucose, J. Anal. Sci. Technol. 12 (2021). [10] J. Xu, Z. Gao, X. Dou, Y.-Y. Song, Needle-like Co3O4 nanoarrays as a dual-responsive amperometric sensor for enzyme-free detection of glucose and phosphate anion, J. Electroanal. Chem. 897 (2021). [11] J. Han, Z. Wang, C. Wang, J. Han, H. Bu, Combining padlock exponential rolling circle amplification with CoFe2O4 magnetic nanoparticles for microRNA detection by nanoelectrocatalysis without a substrate, Anal. Chim. Acta 962 (2017) 24 31. [12] A. Upadhyay, et al., ZnO nanolower-based nanoPCR as an efficient diagnostic tool for quick diagnosis of canine vector-borne pathogens, Pathogens 9 (2020). [13] H. Yu, G. Zhao, W. Dou, Simultaneous detection of pathogenic bacteria using agglutination test based on colored silica nanoparticles, Curr. Pharm. Biotechnol. 16 (2015) 716 723.

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Further reading Nanomaterials and sensors B. Bra¨uer, C. Unger, M. Werner, P.A. Lieberzeit, Biomimetic sensors to detect bioanalytes in real-life samples using molecularly imprinted polymers: a review, Sensors 21 (16) (2021). Available from: https://doi.org/10.3390/s21165550. S.Z.N. Demon, A.I. Kamisan, N. Abdullah, S.A.M. Noor, O.K. Khim, N.A.M. Kasim, et al., Graphene-based materials in gas sensor applications: a review, Sens. Mater. 32 (2) (2020) 759 777. Available from: https://doi.org/10.18494/SAM0.2020.2492. N.K. Hassan, M.A. Fakhri, E.T. Salim, M.A. Hassan, Gold nano particles based optical fibers for a different sensor in a review, Mater. Today: Proc. 42 (2021) 2769 2772. Available from: https://doi.org/10.1016/j.matpr.2020.12.719. R.A.B. John, A. Ruban Kumar, A review on resistive-based gas sensors for the detection of volatile organic compounds using metal-oxide nanostructures, Inorg. Chem. Commun. (2021) 133. Available from: https://doi.org/10.1016/j.inoche.2021.108893. Y. Kang, F. Yu, L. Zhang, W. Wang, L. Chen, Y. Li, Review of ZnO-based nanomaterials in gas sensors, Solid. State Ion. (2021) 360. Available from: https://doi.org/10.1016/j. ssi.2020.115544. T. Liyanage, A.Z. Qamar, G. Slaughter, Application of nanomaterials for chemical and biological sensors: a review, IEEE Sens. J. 21 (11) (2021) 12407 12425. Available from: https://doi.org/10.1109/JSEN0.2020.3032952. G.A. Naikoo, T. Awan, I.U. Hassan, H. Salim, F. Arshad, W. Ahmed, et al., Nanomaterialsbased sensors for respiratory viral detection: a review, IEEE Sens. J. 21 (16) (2021) 17643 17656. Available from: https://doi.org/10.1109/JSEN.2021.3085084. A. Nyabadza, M. Va´zquez, S. Coyle, B. Fitzpatrick, D. Brabazon, Review of materials and fabrication methods for flexible nano and micro-scale physical and chemical property

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sensors, Appl. Sci. (Switz.) 11 (18) (2021). Available from: https://doi.org/10.3390/ app11188563. S. Patel, R. Jamunkar, D. Sinha, Monisha, T.K. Patle, T. Kant, et al., Recent development in nanomaterials fabricated paper-based colorimetric and fluorescent sensors: a review, Trends Environ. Anal. Chem. (2021) 31. Available from: https://doi.org/10.1016/j. teac.2021.e00136. I. Raya, H.H. Kzar, Z.H. Mahmoud, A. al Ayub Ahmed, A.Z. Ibatova, E. Kianfar, A review of gas sensors based on carbon nanomaterial, Carbon Lett. (2021). Available from: https://doi.org/10.1007/s42823-021-00276-9. P. Sharma, S. Kumar, A. Patel, B. Datta, R.K. DeLong, Nanomaterials for agricultural and ecological defense applications: active agents and sensors, Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 13 (5) (2021). Available from: https://doi.org/10.1002/ wnan.1713. M. Umar, H.H. Nawaz, I. Nawaz, Y. Li, A review on advanced smart material based nano sensors for viral detections, in: Textile Bioengineering and Informatics Symposium Proceedings 2020—13th Textile Bioengineering and Informatics Symposium, TBIS 2020, pp. 37 48.

Sensor networks R.R. Flanagan, L.J. Brandt, A.G. Osborne, M.R. Deinert, Detecting nuclear materials in urban environments using mobile sensor networks, Sensors 21 (6) (2021) 1 10. Available from: https://doi.org/10.3390/s21062196. T. Kim, L.F. Vecchietti, K. Choi, S. Lee, D. Har, Machine learning for advanced wireless sensor networks: a review, IEEE Sens. J. 21 (11) (2021) 12379 12397. Available from: https://doi.org/10.1109/JSEN.2020.3035846. K. Kumari, P.H. Chandankhede, A.S. Titarmare, Design of human activity recognition system using body sensor networks, in: Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, 2021, pp. 1011 1016. https://doi.org/10.1109/ICCES51350.2021.9488958. X. Luo, J. Yang, A survey on pollution monitoring using sensor networks in environment protection, J. Sens. (2019) 2019. Available from: https://doi.org/10.1155/2019/6271206. C. Pandey, S. Sharma, P. Matta, Body sensor network architectures in healthcare Internetof-Things (HIoT): a survey, in: Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021, 2021, pp. 494 499. https://doi. org/10.1109/ICCES51350.2021.9489205. G. Xu, W. Shen, X. Wang, Applications of wireless sensor networks in marine environment monitoring: a survey, Sens. (Switz.) 14 (9) (2014) 16932 16954. Available from: https:// doi.org/10.3390/s140916932. R. Yadav, S. Indu, D. Gupta, Review of evolutionary algorithms for energy efficient and secure wireless sensor networks, Lect. Notes Data Eng. Commun. Technol. 73 (2022). Available from: https://doi.org/10.1007/978-981-16-3961-6_49. E. Yundra, Suyanti, U.T. Kartini, Heart detection system using a wireless body area network for hybrid monitoring system based on pulse sensor, J. Eng. Sci. Technol. 16 (3) (2021) 2763 2775.

Paper-based sensors M. Chandramouli, V.K. Boraiah, R.P. Shivalingappa, V. Basavanna, S. Doddamani, S. Ningaiah, Paper-based carbon dioxide sensors: past, present, and future perspectives, Biointerface Res. Appl. Chem. 12 (2) (2022) 2353 2360. Available from: https://doi. org/10.33263/BRIAC122.23532360.

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N. Colozza, V. Caratelli, D. Moscone, F. Arduini, Origami paper-based electrochemical (bio)sensors: state of the art and perspective, Biosensors 11 (9) (2021). Available from: https://doi.org/10.3390/BIOS11090328. T. Gebretsadik, T. Belayneh, S. Gebremichael, W. Linert, M. Thomas, T. Berhanu, Recent advances in and potential utilities of paper-based electrochemical sensors: beyond qualitative analysis, Analyst 144 (8) (2019) 2467 2479. Available from: https://doi.org/ 10.1039/c8an02463d. Y.-Q. Li, L. Feng, Progress in paper-based colorimetric sensor array, Chin. J. Anal. Chem. 48 (11) (2020) 1448 1457. Available from: https://doi.org/10.1016/S1872-2040(20) 60057-3. ´ ˙ W. Mazurkiewicz, M. Podrazka, E. Jarosinska, K. Kappalakandy Valapil, M. Wiloch, M. Jo¨nsson-Niedzio´łka, et al., Paper-based electrochemical sensors and how to make them (work), ChemElectroChem 7 (14) (2020) 2939 2956. Available from: https://doi.org/ 10.1002/celc.202000512. H. Park, W. Kim, S.W. Lee, J. Park, G. Lee, D.S. Yoon, et al., Flexible and disposable paperbased gas sensor using reduced graphene oxide/chitosan composite, J. Mater. Sci. Technol. 101 (2022) 165 172. Available from: https://doi.org/10.1016/j.jmst.2021. 06.018. S. Patel, R. Jamunkar, D. Sinha, Monisha, T.K. Patle, T. Kant, et al., Recent development in nanomaterials fabricated paper-based colorimetric and fluorescent sensors: a review, Trends Environ. Anal. Chem. (2021) 31. Available from: https://doi.org/10.1016/j. teac.2021.e00136. S. Patel, R. Jamunkar, D. Sinha, Monisha, T.K. Patle, T. Kant, et al., Recent development in nanomaterials fabricated paper-based colorimetric and fluorescent sensors: a review, Trends Environ. Anal. Chem. (2021) 31. Available from: https://doi.org/10.1016/j. teac.2021.e00136. ´ . Torrinha, S. Morais, Electrochemical (bio)sensors based on carbon cloth and carbon A paper: an overview, TrAC.—Trends Anal. Chem. (2021) 142. Available from: https:// doi.org/10.1016/j.trac.2021.116324. H. Zhang, C. Xia, G. Feng, J. Fang, Hospitals and laboratories on paper-based sensors: a mini review, Sensors 21 (18) (2021). Available from: https://doi.org/10.3390/ s21185998.

Wearable sensors S. Aaryashree Sahoo, P. Walke, S.K. Nayak, C.S. Rout, D.J. Late, Recent developments in self-powered smart chemical sensors for wearable electronics, Nano Res. 14 (11) (2021) 3669 3689. Available from: https://doi.org/10.1007/s12274-021-3330-8. I. Boukhennoufa, X. Zhai, V. Utti, J. Jackson, K.D. McDonald-Maier, Wearable sensors and machine learning in post-stroke rehabilitation assessment: a systematic review, Biomed. Signal. Process. Control. (2022) 71. Available from: https://doi.org/10.1016/j. bspc.2021.103197. Y. Cheng, K. Wang, H. Xu, T. Li, Q. Jin, D. Cui, Recent developments in sensors for wearable device applications, Anal. Bioanal. Chem. 413 (24) (2021) 6037 6057. Available from: https://doi.org/10.1007/s00216-021-03602-2. R. Ghaffari, J.A. Rogers, T.R. Ray, Recent progress, challenges, and opportunities for wearable biochemical sensors for sweat analysis, Sens. Actuators, B: Chem. (2021) 332. Available from: https://doi.org/10.1016/j.snb.2021.129447. S. Lee, H. Kim, M.J. Park, H.J. Jeon, Current advances in wearable devices and their sensors in patients with depression, Front. Psychiatry 12 (2021). Available from: https:// doi.org/10.3389/fpsyt.2021.672347. G. Li, D. Wen, Sensing nanomaterials of wearable glucose sensors, Chin. Chem. Lett. 32 (1) (2021) 221 228. Available from: https://doi.org/10.1016/j.cclet.2020.10.028.

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C. Liu, B. Zhang, W. Chen, W. Liu, S. Zhang, Current development of wearable sensors based on nanosheets and applications, TrAC.—Trends Anal. Chem. (2021) 143. Available from: https://doi.org/10.1016/j.trac.2021.116334. Y. Li, W. Chen, L. Lu, Wearable and biodegradable sensors for human health monitoring, ACS Appl. Bio Mater. 4 (1) (2021) 122 139. Available from: https://doi.org/10.1021/ acsabm.0c00859. H. Prasanth, M. Caban, U. Keller, G. Courtine, A. Ijspeert, H. Vallery, et al., Wearable sensor-based real-time gait detection: a systematic review, Sensors 21 (8) (2021). Available from: https://doi.org/10.3390/s21082727. N. Promphet, S. Ummartyotin, W. Ngeontae, P. Puthongkham, N. Rodthongkum, Noninvasive wearable chemical sensors in real-life applications, Anal. Chim. Acta (2021) 1179. Available from: https://doi.org/10.1016/j.aca.2021.338643. E. Ramanujam, T. Perumal, S. Padmavathi, Human activity recognition with smartphone and wearable sensors using deep learning techniques: a review, IEEE Sens. J. 21 (12) (2021) 1309 13040. Available from: https://doi.org/10.1109/JSEN.2021.3069927. H. Teymourian, M. Parrilla, J.R. Sempionatto, N.F. Montiel, A. Barfidokht, R. van Echelpoel, et al., Wearable electrochemical sensors for the monitoring and screening of drugs, ACS Sensors 5 (9) (2020) 2679 2700. Available from: https://doi.org/10.1021/ acssensors0.0c01318. K.K. Yeung, T. Huang, Y. Hua, K. Zhang, M.M.F. Yuen, Z. Gao, Recent advances in electrochemical sensors for wearable sweat monitoring: a review, IEEE Sens. J. 21 (13) (2021) 14522 14539. Available from: https://doi.org/10.1109/JSEN.2021.307431.

4. Construction and other applications

C H A P T E R

23 Sensor commercialization and global market Fulden Ulucan-Karnak, Cansu ˙Ilke Kuru and Sinan Akgo¨l Ege University, Faculty of Science, Department of Biochemistry, Izmir, Turkey

23.1 Introduction Generally, a sensor comprises a measuring window, a transducer, and a power supply that can measure the sensation of physiological, chemical, or biological quantities. It becomes an integrated sensor when a signal conditioning mechanism is added to it. Intelligent special electronics such as processors, integrated circuits, and micro- or nanoelectronics are create smart sensors [1]. In recent years, the sensor market has been exponentially growing due to applications in cars and mobile devices, and has considerably exceeded many industry projections. This is related to the increasing world’s population from 7.126 billion to 10.9 billion in 2050. To develop a trillion-dollar sensor business new applications will be required [2,3]. Most of the commonly used sensors types can be categorized as temperature, pressure, sound, light, infrared, ultrasonic, gas, humidity, chemical, and biosensors. They have unique designs and associated advantages and disadvantages (Table 23.1). There are other sensors for use in labs, cars, homes, smartphones, and even in coffee machines. They are commonly used in almost all electronics, automative, textile, aviation, marine, agriculture, food, medical, and other industries. In every production unit, if you need to control temperature, pressure, light, chemical substance, etc., you must use

Advanced Sensor Technology DOI: https://doi.org/10.1016/B978-0-323-90222-9.00002-9

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© 2023 Elsevier Inc. All rights reserved.

880 TABLE 23.1

23. Sensor commercialization and global market

Most common sensor types, advantages and disadvantages [4 6].

Sensors

Advantages

Disadvantages

Temperature

• Not required for reference • Wide response time range • Easy display • Long lasting

• Self-heating • Calibration difficulty

Pressure

• High signal output • Low cost • Stabile

• High lagging • Sensitive to vibrations

Sound

• Easy to manipulation • Wireless

• Big file sizes • Interference cancelation is needed • Limited area of coverage

Light

• Needed in low power and voltage current • Low cost and fast response • Many shape and size alternatives

• Nonlinear • In high voltage, photo resistor can be damaged • Sensitive to temperature

Infrared

• Needed low power • Able to detect of light in existence • Contact-free • Corrosion or oxidation is ineffective

• • • •

Ultrasonic

• • • •

• Temperature sensitive • Reading of reflections from soft, curved, thin and small objects is hard • Cannot work under vacuum

Smoke and gas

• Simple and low-cost • High sensitivity and reliability • Low power needed • Can detect flammability of gases • Linear and wide measurement range

• Air or oxygen needed • Limited range of temperature

Humidity

• Flexibility and easy to use • Durable

• Limited accuracy and measurement range

Chemical

• Linear output • Low power needed • Excellent repeatability and accuracy

• Temperature range limited • Short or limited life • Cross-sensitive for other gases

Wide sensing capability Stability Works in any conditions High range of sensing

Signals can be blocked by objects Limited range Sensitive to environmental conditions Transmission data rate is slow

(Continued)

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TABLE 23.1

881

(Continued)

Sensors

Advantages

Disadvantages

Biological

• Wide and linear range of detection • High stability • Low cost • Small size • Portability • Rapid response • High selectivity • Modifiable to different applications • Easy to manufacturing

• pH and temperature are affected the results • Sample preparation needed

a specific sensor. Sensors can be in different shapes, forms, or sizes according to their design [7].

23.2 Trends in sensing technologies Technological advances such as microfabrication techniques, smartphones, and nanotechnology have changed the design, production, and applications of sensors. Today’s sensors incorporate wireless, machine learning, artificial intelligence (AI), and Internet of Things (IoT). Portability, implantability, and wearability are also part of modern-day sensor design. All of these properties broaden the sensor application field but also enhance the fragility. Fig. 23.1 illustrates important sensor developments. It is better to consider in the years 2012 2022 to understand the sensor related article publishing trends. When we search “sensors” and “biosensors” keywords in the Scopus database, the increasing trend of article publishing in the literature can be seen in Fig. 23.2. There were more sensor-related publications than there were in biosensor-related publications. Patents numbers are also increased according to Espace.net for "sensor" and "biosensor" keywords. In Fig. 23.3, the increasing trend in filed sensor patents can be seen. However, filed biosensors patent numbers remain almost the same. In order to emphasize the article publishing trend in the sensor field, we also searched “sensors” keyword in the Web of Science database. As shown in Fig. 23.4, the most active five subfields were electric electronical engineering, instrumentation, applied physics, material science, and telecommunications. With this viewpoint, we also searched “biosensors”

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FIGURE 23.1

Timeline of sensor evolution including developing technologies and

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Sensors

100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0

Arcle numbers

milestones.

59509 64384 65809 67816 70961 77759 84946 94102 88680 87051 6988

Biosensors 3640 3575 3443 3709 3948 4151 4316 4625 4708 5138 555

Years Sensors

Biosensors

FIGURE 23.2 Articles about sensors and biosensors in Scopus in 2012 22.

keyword in the Web of Science database in order to understand the trend on biosensors in terms of subfields. Fig. 23.5 shows the five most popular subfields: analytical chemistry, nanotechnology, electrochemistry, biotechnology, and biophysics. Smart technologies may be responsible for these trends by enabling sensors to collect data remotely. Technological advances will expand these sensor application fields in many countries [8 10].

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23.2 Trends in sensing technologies 700,000 600,000

Patent numbers

500,000 400,000 300,000 200,000 100,000 0

2012

sensors

2013

2014

2015

2016

2017

2018

2019

2020

2021

253642 290176 349762 398426 443340 496360 562767 567261 580015 526679

biosensors

5986

6228

6749

7335

7555

8782

8912

9494

8671

7496

2022 4835 91

Years sensors

FIGURE 23.3

biosensors

Sensor and biosensor patents.

Arcle numbers

250,000 200,000 150,000 100,000 50,000 0

Subfields

FIGURE 23.4 Subfields of sensors according to Web of Science in 2012 22.

During COVID-19, wearable, wireless, multiplex, AI-based, and smart-phone integrated sensor systems gained more importance [11 13]. During the pandemic crisis, smart devices helped both patients and health professionals communicate and control the disease remotely. With this viewpoint, we also searched “biosensors” in the Web of Science database in order to understand the trend on biosensors in terms of subfields. Fig. 23.5 shows the five most popular subfields: analytical chemistry, nanotechnology, electrochemistry, biotechnology, and biophysics.

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20,000 18,000 16,000 14,000 12,000 10,000 8000 6000 4000 2000 0

Subfields

FIGURE 23.5 Subfields of biosensors according to Web of Science in 2012 22.

23.2.1 Microsystem technology and application Microsystem technology started with the invention of integrated circuits and continued with lithography-based manufacturing of actuators with a silicon material. After that, piezoelectric, electro-optic, magnetic, and biosensitive featured materials were developed. Microsystems found applications in cochlear implants, smart cards, portable navigation systems, air bag accelerometers, micromirrors, thermalcontrol devices, environmental monitoring, drug carrier systems, etc. [14 17]. The incredible advancements in microelectronic technology seen in recent years are strongly linked to much greater advancements in technological instruments. It is worth noting, however, that these new tools can also be utilized to create a variety of multifunctional constructions [18]. MEMS (microelectromechanical system) and MOEMS (microelectrooptomechanical system) devices are known as combinations of microelectronic and micromechanical structures in one construct, allowing for multidisciplinary applications, with biomedical research being the most exciting and promising. However, the development of these applications necessitates the collaboration of a diverse team of experts from a variety of fields, including physics, chemistry, biology, and electronics, not to mention medical professionalists. As a result, it is critical to disseminate information regarding available processing capabilities [19,20]. Flexible materials with high surface strength and elasticity have contributed to enormous innovation in the creation of MEMS devices,

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where a material’s electrical and mechanical capabilities are the most critical qualities of the technology and include materials such as metals, polymers, ceramics, glasses, and composite materials [21,22]. The flexibility of polymer is more advantageous in achieving extensive and controlled structure deformation of moveable elements. The diaphragm (thin membrane), pillars, cantilevers, or a combination of pillars and movable structures are examples of movable parts. This type of functional material is crucial in the development of MEMS electromagnetic (EM) actuators, such as the microfluidic delivery system used in medication delivery, biocell preparation, and lab-on-a-chip [23]. Also polymeric materials have many advantages, such as lower cost, stiffness, transparency, higher thermal expansion coefficient, biocompatibility, and being environmentally friendly [24]. As an example of microsystem sensor technology in clinic, Spizz et al. described a modular microfluidic platform that used Rheonix CARD (chemistry and reagent device) technology to test a wide range of clinical samples at a low cost. All assay stages were conducted automatically once an untreated clinical material was supplied. The STI (sexually transmitted infection) CARD could identify four sexually transmitted infectious agents at the same time (i.e., Neisseria gonorrhoeae, Chlamydia trachomatis, Treponema pallidum, and Trichomonas vaginalis). It was able to successfully detect a minimum of 10 copies of each of the four diseases using multiplex PCR and microarray detection. Also, the HPV (Human Papillomavirus) CARD was capable of determining and discriminating 20 different clinically relevant HPV strains. Furthermore, utilizing a currently commercially accessible testing method, the HPV CARD detecting HPV types in vaginal samples previously proved to include high- or low-risk HPV. Finally, by analyzing human buccal swabs, the Warfarin Genotyping CARD was able to detect specific single nucleotide polymorphisms related to warfarin dose sensitivity [25]. Another medical application of microsystem technologies is related to a synthetic micropump for insulin. Glycemic control with improved insulin pump therapy has resulted in modest improvements in nephropathy and retinopathy in type 1 diabetes mellitus patients. Artificial pancreas, which consists of an insulin pump connected to a continuous glucose meter and a control algorithm, has recently emerged in diabetes management. The delivery precision of rapid-acting insulin is closely related to patient safety and therapy efficiency. A unique precision-oriented micropump design was created for this purpose. Two check valves and a pumping membrane were included in the device, which is made up of a stack of three silicon wafers and is activated against stop limiters by a piezo actuator. Because of their high sensitivity, they can check pumping accuracy with a tolerance of 5% for each 200 nL stroke [26].

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Water pollution has also been monitored with miniaturized and microsized sensing technologies in terms of total nitrogen (TN), phosphorus (TP), and heavy metal ions (Cu21, Pb21, Zn21, Hg21). Highly sensitive and cost-effective sensors are always demanded all over the world for on-site detection and monitoring. Electrochemical microsensors are great candidates for monitoring of trace pollution targets from freshwater to maintain water quality [27]. Another example of microsensors include a portable multisensor fusion system designed for the automated analysis of multiple beverages from Margarit-Taule et al. The system was constructed of low-powerconsumption electronic equipment, a conductivity sensor, a redox potential sensor, six ion-selective field-effect transistors, and two amperometric microelectrodes. Phyton was used as a software that involves a graphical user interface to envision readouts. With this developed system, significant parameters of mineral water and wine were measured [28]. Micro- and nanosystem technologies have started to use more technology in fabrication and also in commercial realization such as machine learning methods. Also fifth-generation cellular network technology (5G), augmented reality technology, and virtual reality technology are being integrated into these systems in 3D design. It is easy to predict that the future of these sensors will be a great tool for simulation of medical monitoring, sports training, automative, entertainment, and so on [29].

23.2.2 Multisensing technology and applications Multisensing or multiplexing is a term used to indicate the ability the analyze multiple specific molecules at the same time [30]. Due to rapid technological advancements, a large amount of research has recently been conducted working on multimode and multifunctional sensors. Despite the fact that sensor failure risk and multifunctional sensor reliability is always present, researchers are developing new strategies to fabricate sensor fault tolerance [31]. The main advantage of multiplexed analysis is its ability to detect multiple markers or signals qualitatively or quantitatively from a single sample with reduced costs and errors. Also by combining with the nanomaterials for multiplexing analysis, selectivity is also increased [32]. Multiplex technology is preferred for microbiological, genomic, and medical applications, and common applications include bead arrays, real-time PCR, PCR amplifications, and DNA microarrays [30,33]. Werley et al. reported on Multiplexed Optical Sensors in Arrayed Islands of Cells (MOSAIC) that included fluorescent sensor-encoding lentiviral vectors with a microarray printer for multiple analysis of cells. They exhibited cell responses from 20 sensors under multiple modalities. Their results emphasize that MOSAIC can obtain multimodal data from

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complex physiological responses such as calcium ions, ATP, and pH in multiple cell types [34]. In healthcare monitoring, multiplexed sensor systems have been adapted for noninvasive sweat metabolite analysis. These systems are made of mechanical flexible material and are performed with embedded and integrated signal processing circuitry and wireless data transmission [35]. In recent years, wearable and multiplexed sensor devices have become popular due to their wireless, real-time, and quantitative measurement abilities. Gao et al. designed a fully integrated and flexible sensor device to analyze lactate, glucose, sodium, and potassium ion and skin temperature at the same time [36]. Terse-Thakoor reported a study on a wearable band form with a fully integrated multiplexed platform for to real-time measurement of ammonium, sodium, pH, and lactate directly from sweat [37]. To mimic the human somatosensory system, artificial electronic skin is created from flexible and elastic sensor networks that can tolerate uneven surfaces and measure diverse stimuli. For very sensitive contact/pressure/strain detections, a flexible/wearable multifunctional sensor array is created and built using basic fabrication processes in a cost-effective way. The sensor array, which is made up of PET-based Ag serpentine-shaped electrodes, is used to quantify spatial contact/pressure/strain distributions in large-scale static and dynamically. The detection limit was 6 Pa. (corresponding to 0.5 mg). These developed sensor arrays are great candidates for detection of force, controlling the gesture and imaging of spatial pressure distributions with high sensitivity, which could implement next-generation prosthetics combined with advanced robotics technology and human-machine interfaces [38]. Multisensing sensors are gaining popularity in every field. In today’s world, the demands for gas detector functionality and accuracy are increasing, and lots of gas sensors include temperature and cross sensitivity built in. Sensor data fusion technology is becoming more popular and essential in industrial applications. A multifunctional hand-held gas detector that can detect concentrations of O2, CO, CH4, and H2S has been developed. The temperature coverage for a linear assumption of temperature impact was carried out in this design, and the procedures for nonlinear assumption were elucidated. The trials demonstrated that the instrument can successfully decrease cross-sensitivity effects using the surface fitting algorithm and improve accuracy of measurement using one to two orders of magnitude by surface fitting algorithm. It can be concluded that the instrument is stable and well meets the accuracy standards based on these findings [39]. Another important application of multiplexed sensors is simultaneous on-site detection of pathogens, antibiotics, and mycotoxins in food by aptasensors including electrochemical, fluorescent, microfluidic chip, surface-enhanced Raman scattering, and paper-based sensors.

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When contaminated food products are eaten, foodborne diseases can occur. Therefore detection of these contamination sources is important for community health [40]. With the aforementioned advantages of multiplexity and multimodality, these type of sensors can be utilized to analyze many markers and signals with high sensitivity and accuracy.

23.2.3 Wireless systems and applications Wireless sensing has grown throughout the last decade and can be integrated into new approaches sensing and actuating applications in different environments [41,42]. Wireless sensor systems, wireless underground sensor networks, wireless sensor and actuator networks, wireless smart intelligent sensing, wireless body sensor mesh networks, unmanaged aerial vehicle sensor networks, and industrial wireless sensor network systems are now known [43]. But wireless broadband, ultra-band, and new unstructured networking could be combined to create new interconnected devices, such as radiofrequency identification, MEMS, IoT, and nanotechnological devices, all contributing to widening the range of interconnected devices [41,44]. Wireless sensor networks (WSNs) have become popular in healthcare in the last few decades. Patients, particularly those suffering from chronic conditions, can benefit from this technology since sensors are attached to a patient via wires. In a study by Ali et al., a real-time heart pulse (HP) monitoring system using an electronic circuit design to measure HP for patients and display HP measurements via the network in real-time settings using a smartphone and computer was developed. Using Arduino microcontroller with Ethernet shield enabled to connect and transmit data of HP to the Internet and send results to the web server and receive them anywhere. Not only did the developed system provide usability for the final user, it also provided usability for the specialist and provided speed and accuracy in findings at a low cost [45]. It is also possible to use these systems for environmental applications and monitoring of various parameters in the field of agriculture. Ye et al. proposed an environmental monitoring-based WSN system. The system measured a variety of environmental parameters such as barometric pressure, atmospheric humidity, wind direction and speed, ambient temperature, and rainfall, as well as suggested a variety of convenient services for end users via a website or console term-based application. This paper discusses WSN architecture, node hardware, data gathering, data processing with a gateway, and data visualization. It is feasible to change traditional environmental monitoring approaches for individuals by using WSN in environmental monitoring [46]. Cao-Hoang et al.’s study presents an IoT system architecture for agricultural applications based on WSN. The system is made up of sensor

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23.3 Sensing research and development

889

nodes and a gateway that grants users the ability to measure agricultural environmental data via a web browser. The sensor node was an Arduino-based microcontroller with a wireless module and attached sensors including temperature, relative humidity, luminance, air pressure, and other sensors. To transfer environmental data from sensor nodes to the Linux-based gateway, a WiFi interface was used. The gateway controlled and sent data to the cloud, where it was stored and shown as graphs [47]. WSNs are one of the most important technologies of the twenty-first century thanks to advancements in microsensor technology and lowpower circuits in recent years [46,48]. WSNs are one of the most extensively used information technologies in today’s networking and computing platforms. Powerful network features are in high demand in today’s network computing applications. However, due to supplementary issues, effective management of WSNs are still a challenge. Software-defined networking is a new technology that promises to improve computing networks [49,50].

23.3 Sensing research and development There has been tremendous growth in the research and development of sensors and associated signal processing systems during the past few decades. Some of the sensors mentioned have yet to be commercialized and may never be. Sensor development is critical for a country’s instrumentation industry to stay competitive in global markets. The global market for all sorts of sensors is projected to be worth between h15 and h30 billion per year. Traditional sensor sales are currently growing at roughly 4% per year, whereas innovative sensor sales are growing at 10% per year. Aside from the general technological advancement of traditional sensors, a number of new disciplines centered on innovative sensing techniques and materials have evolved over the last two decades. Commercial pressures frequently shift, making previously uneconomic products more appealing. The discovery of new materials or manufacturing technologies can also trigger this [51]. Naghdi et al. developed chitin nanofiber (ChNF) paper-based optical biosensing platform. They used the ChNF paper’s advantageous properties to generate efficient, flexible, biocompatible, and transparent optical sensing bioplatforms by immobilizing various photoluminescent nanoparticles, plasmonic nanoparticles, and colorimetric reagents in the ChNF paper’s 3D nanonetwork scaffold. Laser printing technology was used to develop different configurations including 2D multiwall and 2D cuvette patterns with combination of smartphone technology in order to implement it on Internet of Nano Things/Internet of Medical Things

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concepts. ChNF was regarded as a very important and promising material that could be used in the development of (bio)sensing/monitoring devices [52]. Urinary tract infection (UTI) is a common bacterial illness that can lead to organ failure and even death if left untreated, thus diagnosing UTI is critical for public health. Using a mix of molecular imprinting ¨ zgu¨r et al. attempted to construct a biomiand SPR-based sensors, O metic sensor for diagnosing UTI. Tailor-made Escherichia coli receptors were generated using the microcontact imprinting approach, and E. coli binding events were monitored in real time using a surface plasmon resonance (SPR) sensor in aqueous solution and a urine mimic. Due to its selectivity, quicker processing time, and lower LOD, this biomimetic sensor could be utilized to diagnose UTI [53]. Cancer is a worldwide problem whose early detection is critical for better and successful therapy, follow-up, and patient survival. Therefore more sensitive and specific approaches are always required. Mucin 1 (MUC1) is a clinically approved biomarker for detecting cancer. Nanopolymers functionalized with a lectin affinity-based recognition strategy were constructed as a bioactive layer. This developed layer was used on electrochemical biosensor glassy carbon electrode surface for MUC1 detection. With a linear range of 0.1 100 U/mL and a reaction time of 20 min, the new-generation nanopolymeric material-based biosensor proved to be a reliable, economical, sensitive, and fast technology [54]. For the early identification of substances, novel sensing technologies with smart detection capabilities are needed in various applications. A functional and commercially appealing sensor system for the accurate and quick detection of substances must meet a number of requirements. Sensors are suitable solutions because they can solve many problems and challenges in a variety of fields, such as homeland security, industry, military, medicine, pharmacology, agriculture, environment, and food safety [55]. In the earliest scientific research groups, sensitive, selective, rapid, reliable, and cost-effective determination of biomolecules received a lot of attention [56]. Although such studies were designed with excellent chemical and biological infrastructure, they generally remain at the level of articles or patents, and more cooperation with other engineering disciplines is needed to reach commercialization.

23.4 Commercialization pathway Commercialization is the implementation of a novel idea or productbased intellectual property and technology in order to gain a commercial profit. A technology-based creation starts with an ideal and continued research and development using technology. In other words, using an

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23.4 Commercialization pathway

FIGURE 23.6

891

Typical commercialization process step by step.

existing technology for a novel idea with a new demand to develop a new technology and provide new product to the market on demand [57,58]. To commercialize a novel idea well-conceived constructs, definitions, conceptualizations, and processes are needed. A typical commercialization process consists of idea, design, prototype design, data analysis and validation, approval and commercialization. Until the approval step, research and commercial production have similar steps [59,60]. Fig. 23.6 shows a typical commercialization process step by step for all types of products. Technological maturity is assessed by the TRL scale, which was designed and performed at NASA in the 1970s. This scale is used to determine the level of technological maturity from 1 to 9 scale (Fig. 23.7). The scale initiates with TRL 1 at the bottom, which includes a very basic theoretical form of research and continues with increasing developments of technology. The TRL scale is also called the technology readiness levels. The TRL assessment can be useful for project management and risk management and to monitor progress of a developing technology. TRL may help in choosing between multiple alternative technologies that provide the same function [61,62]. Any business environment is known as an ecosystem, and with the addition of digital components such as software, database components, and hardware it is known as a “digital business ecosystem” (DBE). If there is a problem in the technology transfer, the DBE is affected. All layers are related and affect each other [63]. The sensor development life cycle consists of research and development, testing, calibration, and evaluation. Environmental, health, and

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FIGURE 23.7 Technology readiness level scheme.

safety-based regulations are included in life cycle analysis and are crucial for commercialization of all type of sensors [64]. There are three forms of commercialization of sensors: 1. With new materials or sensing strategies 2. Packaging of existing sensors with a new combination. 3. Measuring of a new unit to measure new utility for a sensor [65,66]. To translate these sensors from opportunity to commercialization accuracy, stability, repeatability, and reproducibility should be considered. All of these parameters are a part of achieving the goals of developed sensors [67]. There are also mandatory principles related to commercial sensor production including: 1. Identification of the market requirements for a specific analyte, application area, or geography.

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2. Defining a new sensor’s advantages over existing methods. 3. Testing and validation of the performance of the new sensor both in use and after storage. For being any practical commercial application, sensor’s response after 6 months of storage should be the absolute minimum. 4. Determining sensor fabrication costs and ease of manufacturing of each component. 5. Hazards and ethics also should be considered [68]. Poor or inproper fabrication technologies, high limits of detection, low specificity, low reproducibility, nonefficient sample flow, low stability, requirement of multiplexing, and subjective data interpretation are some of the main challenges in the commercialization of sensors [69].

23.4.1 Design and modeling Designing and modeling a sensor is the first step after idea creation. For the design process, safety, efficacy, monitoring, sensitivity, and specificity parameters should be considered. Alsom market requirements should be considered [70,71]. The efficient design of biosensors, lab on a chip, and microelectromechanical and nanoelectromechanical systems requires computer-aided design (CAD) tools (Fig. 23.8) [72,73]. Some commonly used software includes AutoCAD, MathCAD, Vectorworks, and Layout Editor for creating geometry and COMSOL, Ansys, Sugar, Intellisuite and Coventor, for simulation [74,75]. Some

FIGURE 23.8

Schematical representation of sensor 3D design using CAD tools.

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software is used for common purposes such as industrial design, while others were generated for only MEMS design and modeling. Some of them must be used after buying a license, while some are free. MathCAD is a design software for industrial or R&D research. It is a type of engineering notebook for solving mathematical equations and performing calculations [76]. AutoCAD is an another common design software for the design of mechanical aparatus, power plants, sensors, and so on. Drawing, editing, and organizing objects with 2D and 3D structure are possible [77]. Vectorworks is another option to sketch flexible designs of architecture, landscape, and industry [78]. Layout Editor is software used for layout design and schematics of chemical sensors, microelectronics, MEMS, and nanoelectronics. It started as an open source project, then became commercialized, but still can be used freely [79]. Ansys is a finite element analysis software used to mimic structural computer models, electronics, or machine components to define elasticity, strength, electromagnetism, distribution of temperature, and other parameters. Autonomous systems are not possible without simulation [80]. COMSOL is another general-purpose simulation software that relies on advanced numerical methods for multiphysics and singlephysics modeling. Using COMSOL, it is possible to make complete modeling workflows. It has also MEMS module, so it is very useful software for sensor design [81]. SUGAR is a free simulation tool for MEMS devices, which is created by Berkeley University. It is used for integrated circuit simulations, electrostatic gaps, beams, circuit elements, etc. [82]. A coventor is an alternative of a software to provide advanced semiconductor fabrication and MEMS design automation with 3D models [83]. IntelliSuite is another CAD tool for MEMS layout design, process and system simulation, etc. It is used by MEMS professionals [84]. Simulation software commonly uses finite element analysis based on the finite element method (FEM) [85]. The FEM method is generally used for solving engineering problems with several steps as seen in Fig. 23.9 [86,87].

23.4.2 Prototyping Prototyping is used to practice the process of building, testing and analyzing. A prototype can be virtual (rapid) or physical. Physical protoyping includes proof-of-concept and experimental models. It aims to provide learning effects and understanding of budget and time. Physical prototyping is useful for visualizing the ideal and final design, and generally improves project performance and product concept [88,89].

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FIGURE 23.9

895

Flowchart of the finite elemental analysis.

A virtual prototype is based on computer simulation of a physical product due to demanded design. Virtual protyping has several advantages such as reduced cost and time and increased quality, productivity, and competiveness [90,91]. Moving from physical to virtual prototyping also reduces the steps of production. Virtual prototyping includes designing, measurements, data analysis, and validations steps (Fig. 23.10) [92,93]. Virtual prototypes can be implemented in the environment under various conditions with the aid of vision-based technologies. This technology helps to realize a virtual prototype through a virtual sensor, which simulates the properties of the corresponding real sensor. Here, the virtual sensor is important because of the ability of applying every design stage. Then data acquisition of real sensors can be performed.

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FIGURE 23.10 Virtual prototyping.

Simulation of environmental conditions to evaluate their effects is performed only on free and open C/C11 libraries: boost::iostreams, boost::asio, OpenCV, and ffmpeg. This application allows the end user to experience many different sets on the same construction [94].

23.4.3 Testing and reliability Test methods are carried out to identify performance and conditions of developed sensors and specify them in terms of energy requirement and characterization. For this purpose fully described test methods, adhoc test methods, and gap test methods are utilized. Every test is used for different purposes. For example, ad-hoc test methods are useful for characterizing sensor performance in indoor spaces, while fully described test methods involve step-by-step instructions for setting up the test, performing test procedures, obtaining data, and evaluating results [95,96]. Testing is also applied for calibration of new developed sensors for defining sensitivity [97 99]. Testing procedures are defined in TRL 5 to TRL 8 levels with different environments and procedures for assessment of sensor performance [100,101]. The reliability of sensor systems may be suggested in three different levels: material, component, and system reliability [102,103]. Reliability of the electromechanic systems can be calculated with complex approaches or estimations such as Markov models or fault tree analysis

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(FTA). FTA is a method that explains all possibilities of causes in a specified system with all components. It is usually used for identification of circumstances the system should be in at any time [104]. Systemic failures of developed electromechanical, computer or electronical systems and their interrelationships can be understood via FTA [105]. Markov model or Markov chains suggest a calculation of the probability of a system being in a particular state over time [106]. A Markovian model is a kind of jump complex network that is commonly used to solve many problems in networks as approximation, synchronization, and stabilization [107]. Also, for smart sensors, experimental tests for proper reliability measurements are used. Thermal stress is the performed technique for reliability assessment [108,109]. Sensor validation should also be carried out due to the development process. The reliability and accuracy of results could be impressed due to various types of sensor faults. Sensors faults can be detected, isolated, and reconstructed using a three-stage process: (1) detection of sensor fault, (2) isolation of sensor fault, and (3) reconstruction of sensor fault. All of these stages include many mathematical models, equations, and statistical evaluations. It must not be forgotten that sensor validation should be performed before using developed sensors in the field in order to avoid false alarms or missed detections [110,111].

23.4.4 Final product realization and marketing Final product realization consists of determining customer needs, scoping, product development, production, sale, and distribution. In the whole procedure the most essential concepts are decisions. To achieve successful transition to commercialization, the best decisions and traceability must be communicated to the product quality management system [112,113]. Quality management systems may be improved by SWOT analysis, which is known as a faster and simple way to determine effective parameters and finding the best solutions. It stands for the strengths (S), weaknesses (W), opportunities (O), and threats (T) of a product or an organization (Fig. 23.11) [114,115]. Product realization can also be managed by international standards such as ISO 9001:2000, ISO 13407 (Human-centered design), and ISO 13485 (medical devices), which are guidance of products in terms of field [116 118]. In order to perform a sustainable product realization, a seven-step systematic approach of methodology has been suggested in the literature. It provides a relationship between the knowledge and its implementation as a product and market requirements. The most crucial parameters include: 1. Process and control that is proper to demanded purposes

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FIGURE 23.11 SWOT analysis of a product.

2. Customer feedback and critical analysis in order to improve the final product 3. Decision of the best improvement actions All of these can be implemented to generate a novel product or to improve existing ones. The aforementioned methodology’s steps are started from decision of parameters and continue with identification, planning, evaluating, and revising processes [119,120]. If someone has a new idea for a product realization, business models are also useful to explain how this business will create, manage, and result [121]. The Canvas is a suitable competitive, value cost, and production strategy [122]. It is known as a graphic presentation of a number of variables that show the values of an organization and help to make clear decisions. It creates the customers-products-market triangle [123]. The Canvas business model also focuses on creation, proposition, and delivery (Fig. 23.12). In order to maintain sustainability and transfer the ideas to intellectual property, the Canvas business model implementation has been widely recognized [124]. In the Canvas model, the customer relationships part includes marketing [125]. For effective and competitive business management, common marketing is used as a tool for increasing market share. Modern marketing strategies include simple good products development, price determination, granting to buyers, and improvement of products according to customer needs [126].

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23.5 Sensors in various industrial areas and global market shares

FIGURE 23.12

899

Business model Canvas [124].

23.5 Sensors in various industrial areas and global market shares Sensors play an essential role in industry in order to automatize a system. There are various types of sensors available in every field of industry in terms of intended use and affordable cost. Standard sensor types can be categorized as position, temperature, flow, pressure, and force sensors in automative, agriculture, environmental, medical, aerospace, and daily life, respectively. These sensors are also vital parameters of Industry 4.0 evolution with the aid of their sustainability by monitoring real-time output and automated control system abilities. It is important to highlight that digitalization processes may help to improve production mobility, which allows automated production lines, process management, and digital supply chains [127]. Remote monitoring and controlling of processes in many industries such as power generation, energy, oil and gas, chemicals, and water are utilized to direct many process parameters. With the increasing rate of viral diseases, especially COVID-19, remote monitoring and controlling systems have made increasing demands on the market in every field. These systems make lives easier for users [128]. In transportation and traffic monitoring systems, the need for realtime information can not be denied. Therefore sensors play a crucial role in those fields with rapid urbanization, focusing on road safety norms and pollution. Governments are increasingly using cameras and sensors to collect data about environmental and traffic conditions [129]. According to the market reports, the most commonly used sensors

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900

23. Sensor commercialization and global market

include piezoelectric, bending plate, acoustic, image, radar, lidar, inductive loop, magnetic, acoustic, and thermal sensors [130]. Integration of renewable energy in modern life and increasing sustainable energy transitions and thus mitigating climate change are vital. For improving energy efficiency and saving existing sources IoT approaches and sensors could be used. IoT applies sensors and communication systems in order to sense and transmit real-time data that allows fast computations and optimal decision-making processes. These changes could include a new threshold of a new transition era [131]. According to market research, the most common sensors being used are lightning, daylight, wireless, infrared, solar panel, automatic door, etc [132,133]. The current global sensor market is predicted to increase from USD 2.3 billion in 2021 to USD 3.8 billion by 2026. It is also predicted to grow at a compound annual growth rate (CAGR) of 10.8% during 2021 26. This market’s growing factors are mainly affected by battery-powered systems and renewable energy consumption and increasing electronics requirements. Integration of IoT, smart electronics, and PoCs with existing sensors increases the demand of sensors [134]. The sensor market has been categorized as optical, touch, pressure, radar, biosensor, image, humidity, color, temperature, proximity and displacement, level, motion and position, accelerometer and speed sensor, and others. Based on other technology, they have also been segmented as MEMS, NEMS, CMOS, and others [135]. According to an industrial sensor marker report published in June 2021, the most growing sensors in the market were image sensors and their implementations and wireless sensor technologies [136]. However, due to COVID-19, some sectors such as automative and industry have stopped and the market dynamics have changed in the direction of MEMS-based sensors and IoT platforms [137]. Within the scope of water monitoring, in compliance with water framework directives and national water legislation, surface waters are monitored by public institutions’ continuous monitoring stations. Water businesses also keep an eye on surface or ground water near the drinking water treatment plant’s entrance, as well as drinking and wastewater. Sensors measure basic physicochemical factors such flow rate, turbidity, pH, temperature, conductivity, and pressure. The five most widely used sensors, pH, conductivity (EC), oxidation-reduction potential, dissolved oxygen, and turbidity, were evaluated. Early warning systems have become a viable option for water management as a result of contamination reaching consumers through water and rising public health and environmental concerns. Businesses are putting new sensors on the market because integrated systems for early warning systems, online monitoring, data gathering, analysis, interpretation, and transfer of monitored data are needed. The detection of chemical and

4. Construction and other applications

23.5 Sensors in various industrial areas and global market shares

901

microbiological components, as well as the simultaneous measurement of a single or a combination of parameters, is their primary function [138]. Optical sensors based on fluorescent light are at the forefront of water quality sensor development. In situ fluorescent dissolved organic matter sensors have been used in a variety of settings to provide a low-cost, high-resolution proxy for dissolved organic carbon (DOC) concentrations and, in some cases, other biogeochemical variables like trihalomethane precursors and methyl mercury concentrations These sensors in particular have commercialization potential [139]. The SecurEau initiative led to the development of the KaptaTM 3000 AC4 sensor. Free chlorine, pressure, temperature, and conductivity are all measured. Two European drinking water systems were outfitted with 80 of these sensors as part of the research, which measured water quality online and communicated results to operational control centers every two hours. S::CAN invented the Spectro::lyserTM probe. S::CAN, an online UV-Vis probe, measures TSS, turbidity, NO3-N, COD, BOD, TOC, DOC, UV254, color, BTX, O3, H2S, AOC, fingerprints and spectral alarms, temperature, and pressure, depending on the application for which it is installed. There is another sensor designed in S::CAN. It is called I::scan, and it is a revolutionary inpipe LED-based spectrometer probe that measures color, UV254, organics (TOC, DOC, COD, BOD), turbidity, and UV-Vis spectral alarm, among other things [138]. Optiqua Technologies, a SME that provides the water industry with unique solutions for both online and sample-based water-quality monitoring, developed EventLab. Optiqua’s award-winning and proprietary lab on-chip sensor technology is used in all of their products. They serve worldwide drinking water corporations from Singapore and the Netherlands, ensuring the provision of safe drinking water [140]. Optisense is a small business established in the Netherlands that creates unique sensor technologies for sensitive, real-time, and on-site water contamination detection. Optisense was developed with the goal of commercializing a refractive index measurement optical lab-on-achip sensor [139]. Although sensors are becoming more widely available, successful deployment in water utilities has yet to be achieved for a variety of reasons. The industry sector is working on developing innovative water quality sensors, conducting market testing with a variety of real-world end customers, and allowing them to demonstrate their benefits before scaling up production. Within the scope of the food and beverage industry, food processing, preservation, and packing techniques include salting, curing, freezing, drying, pickling, and fumigation, all of which are subject to stringent regulatory requirements in terms of quality control, safety, and

4. Construction and other applications

902

23. Sensor commercialization and global market

traceability. Thus there is a great need for dependable methods to assess food quality in a timely, cost-effective, and repeatable manner [141]. Continuous, sensitive, selective, and accurate monitoring of a wide range of different chemicals in diverse food and beverage samples is becoming increasingly important in order to ensure high-quality and determine any probable source of food and beverage contamination. Most currently utilized traditional analytical methods necessitate costly instrumentation, lengthy analysis durations, and well-trained personnel [142]. According to market research of the global food and beverage industry between 2008 and 2018, the most commonly used sensors are in the photoelectric, inductive, capacitive, ultrasonic, level, pressure, flow, temperature, and biosensor categories [143]. With comprehensive integration into the control level, a focus on the decentralization of certain automation functions into the sensors is given, both to reduce the stress on the control and to boost machine output. For packaging, the sector provides sensors, sensor systems, and solutions that are customized to complicated, frequently changing activities while satisfying the increasingly stringent criteria for trademark protection, safety, and documentability. Different sized items and client requirements necessitate flexible machines and a wide range of sophisticated sensors to identify objects and measure physical sizes in terms of detecting and measuring. Smart sensors have automated teach-in and diagnostic capabilities, and they contribute significantly to addressing these issues. Monitoring and controlling are two aspects of quality control. Whether it is pharmaceuticals, cosmetics, food and beverages, or hygiene, quality is one of the most important company goals in the packaging industry. A quality control system that satisfies the highest requirements is required to provide consistent high quality with high throughput speeds on packaging machines. Whether it is standard sensor technology, entire systems, or services, all sections of the packaging business require cutting-edge intelligent sensor solutions [144]. Significant advancements in these procedures have recently been made. Taking the needed qualities of novel unique materials in terms of electrical and chemical aspects of sensor applications has brought key steps in food processing technologies. Electrochemical sensors, which are utilized in conjunction with this sensor technology, are widely used in food-processing plants today. Another key application is in food and beverage manufacturing facilities [144,145]. Within the scope of environmental monitoring, to safeguard the population and the environment from harmful chemicals and diseases that can be released into a range of media such as air, soil, and water, environmental monitoring is essential. Sulfur dioxide, carbon monoxide, nitrogen dioxide, and volatile organic compounds are examples of air pollutants that come from a variety of sources, including automobile

4. Construction and other applications

23.5 Sensors in various industrial areas and global market shares

903

emissions, power plants, refineries, and industrial and laboratory procedures. Microbiological, radioactive, inorganic, synthetic organic, and volatile organic chemicals are all types of pollutants found in soil and water. Pesticides and herbicides are sprayed directly on to plants and soils, and spills, leaking pipes, underground storage tanks, waste dumps, and waste repositories can all result in accidental releases of other contaminants. Some of these contaminants can last for years and travel through extensive areas of soil until they reach water sources, where they might pose a harm to the environment or human health [146]. Sensirion SHT 11 for temperature and relative humidity, Intersema Ms5534 for barometric pressure and temperature, Hamamatsu S1087 for outdoor light, and Taos TSL2550 for ambient light are the main sensors used for environmental monitoring [147]. Emerging sensor technologies are examined in order to find technologies that are compatible with diverse environmental monitoring applications. The development of sensors for long-term groundwater monitoring could cover a gap in long-term environmental monitoring that could have a wide range of applications [146]. In the literature and industry, sensors based on very different working principles and designs and that can be used in environmental monitoring are being developed. Due to their ease of manufacture, quick detection, high sensitivity and selectivity, as well as easy naked-eye sensing, colorimetric sensors and biosensors show promise in the detection of metallic cations, anions, organic dyes, medicines, pesticides, and other harmful contaminants [148]. Due to advantages such as resistance to EM interference, endurance under extreme temperatures and pressures, high transmission rate, light weight, small size, and flexibility, optical fiber sensors are a potential scheme for environmental monitoring of this type [149]. SPR sensors are widely employed in the detection of chemical and biological components, and are particularly useful in domains such as environmental monitoring, food safety, and medical diagnostics. They have a high sensitivity and do not require the usage of molecular labels. These sensors are commercially accessible, but due to their high cost and large size, they are only utilized in laboratories. As a result, the demand for smaller and less expensive SPR sensors has grown critical, which can be met by adopting optical fiber-based SPR sensors [150]. With a different perspective, stretchable, skin-like wearable sensors, in particular, are ideal for a wide range of applications, including individualized health monitoring, human-machine interfaces, and environmental monitoring [151]. The performance of these sensors in each of the field applications is a major concern that has yet to be addressed. Sensitivity, stability, selectivity, speed, size, and cost must all be tested and evaluated under realworld circumstances. Many of these sensors can be harmed by harsh

4. Construction and other applications

904

23. Sensor commercialization and global market

and variable environmental conditions. Nonetheless, a market analysis presented at the beginning of this paper indicates that effective development and use of these sensors in environmental monitoring applications can meet a wide-ranging (and commercially feasible) need [146]. Within the scope of health sector, portable medical devices are noninvasive electronic components used primarily for patient monitoring. Wireless technology improvements in healthcare have expanded patient mobility in medical settings and at home. CPAP machines, sleep apnea monitors, blood glucose monitors, pulse oximeters, ultrasonography devices, and blood pressure monitors are just a few examples of portable medical gadgets. The medical sensors market is expected to be worth USD 1.8 billion in 2021, growing at a CAGR of 10.3% to USD 3.0 billion by 2026. The impact of COVID-19 on the entire world was difficult for the medical sensors market, but it began to show signs of recovery in the third quarter of 2020. The recovery of the medical sensors market was mostly due to increased demand for ventilators, breathing systems, patient monitoring systems, and imaging systems. The growing elderly population and rising life expectancy, rising demand for wearable medical devices, surging adoption of IoT-based medical devices, burgeoning healthcare expenditure, and accelerating demand for ventilators due to COVID 19 are some of the major factors driving the global medical sensors market [152]. The disposable medical sensor industry is already large, with forecasted growth from 5.1$ billion in 2013 to 12.3$ billion by 2025 at a CAGR of 10.2% [153]. Using sensors to track chronic disease patients is crucial to maintain their quality of life while also lowering healthcare costs by keeping them away from hospitals. Early intervention monitoring is also critical for those who are at risk of developing chronic diseases [3,154,155]. Pressure detectors, temperature sensors, image sensors, and other disposable medical sensors are employed in the healthcare industry. These sensors help in disease monitoring, therapy, and diagnosis. Furthermore, they play an important role in making medical devices safer and more effective while also making their operation easier [3]. These sensors are used to monitor numerous health parameters and to identify disease using the values sensed. Each sensor has the ability to measure or monitor the user’s health. Even in a mobile and wireless context, these sensors are used for a range of applications. Smart health management systems can be established in the future employing a variety of sensors, and new sensors are continually being developed to meet society’s present needs [156]. Sensors continue to be developed on the basis of different approaches for use in the healthcare industry. The need for WSNs in real-world applications like mobile multimedia for healthcare organizations is skyrocketing. However, one of the major impediments to increased investment in this technology is the energy issue [157]. Lack of proper medical information, preventable errors, data threat, misdiagnosis, and

4. Construction and other applications

23.5 Sensors in various industrial areas and global market shares

905

delayed transmission are all issues that health-support systems confront. To address this issue, we propose a wearable sensor that is connected to IoT-based big data in healthcare (i.e., data mining analysis) [158]. Mobile-wireless multimedia sensor networks (M-WMSNs) are a better alternative for a smart healthcare environment, since they consist of a number of mobile sensor nodes with multimedia devices that can gather patient data. M-WMSNs are more adaptable than typical WSNs, which require sensor nodes to be deployed in a variety of circumstances and deal with frequent topology changes. The processing unit, sensor array (e.g., temperature, humidity, pressure, movement, etc.), and communication unit of the mobile sensor node are all powered by batteries. Additional elements for recording multimedia data, such as cameras and microphones, are also accessible [159]. The use of healthcare monitoring systems in hospitals and other health institutions has increased significantly, and portable healthcare monitoring systems based on developing technologies are becoming a major issue for many governments across the world [160]. In the future, a ubiquitous and smart healthcare system with P4 (personalized, participative, preventative, and predictive) capabilities to execute diagnostics, monitoring, and treatment functions in a seamless and intelligent manner is desired [161]. The development of novel sensing technologies in the context of healthcare holds a lot of promise for realizing this aim. Biosensor applications are commonly used in healthcare and disease monitoring and the requirement of novel biosensors is always increasing in clinical care and medicine because of increasing diseases and their mortality. COVID-19 affected the global biosensor market positively because diagnosis of SARS-CoV-2 was made possible by userfriendly, low-cost, reliable, sensitive, portable, and rapid kits in real time. These biosensors are also important for screening of the disease and healthcare management [162 164]. The biosensor market size was at over USD 25 billion in 2020 and is expected to grow around 7.4% from 2021 to 2027 [165]. The biosensor market can be segmented into technology, type, product, application, and region. Fig. 23.13 shows these segmentations [166]. PoC testing is known as the essential and the dominant market of biosensors and had the largest revenue share of around 49.6% in 2020. Its segment is predicted to grow with a CAGR of 10.4% from 2021 to 2028 [167]. Specific to the biosensor subfield market, the first commercial biosensor according to the literature was YSI, Ohio, United States, in 1972 and in Europe (CSE Berlin, Germany/PGW Medingen/ENH Hamburg, Germany) in 1982 [168]. In clinical analysis, the first lab-scale glucose analyzer was developed in 1973, which evolved to a pen-sized, single-use version in 1996. A noninvasive version of glucose testers have also been created. Other types of commercial biosensors in clinical analysis can be

4. Construction and other applications

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23. Sensor commercialization and global market

Technology

Type

Product

Applicaon

Region

POC

North America

Sensor Patch

Wearable

Home diagnoscs

South America

Research

Asia Pacific

Defense

Europe

Environmental

Middle East

Food

Africa

Electrochemical Opcal Piezoelectrical Thermal

Embedded Devices

Non-wearable

Nanomechanical

FIGURE 23.13 Biosensor market segmentations.

listed as human chorionic gonadotrophin, E. coli O157, Helicobacter pylori, influenza A and B, human immunodeficiency virus, hepatit C, prostatespecific antigen, tuberculosis, malaria, adenovirus, rotavirus, narcotic drugs such as methamphetamine, etc. They all have different forms such as strip, card, and cassette and can diagnose disease from different body fluids [169]. However, the success gained in glucose biosensors has still not been recorded in the clinical analysis of hormones or biomarkers, which are key elements in early detection of diseases. It is crucial to adapt possible developments for analytes as hormones to be able to involve biosensors and lead the market [170]. For cancer detection, there are four examples of commercial biosensors: “FRENDt PSA” and “Claros Diagnostics” are specific to PSA, ZedScan is for cervical cancer, and iBreastExam is for breast cancer. For cardiovascular disease detection, troponin-, creatine kinase-, and myoglobin-based biosensors are available such as Triager cartridge, RAMP tests, Stratus, Vidas CK-MB, iSTAT, AQT90, Elecsys, Cobas h232, LABGEO IB10, and Minicare with very short response times from different manufacturers. There are also some commercial biosensor examples on the market for detection of diseases such as diabetes, anemia, sepsis, and kidney disorders [171]. All of these examples highlight the importance of development of biosensors, and their application in disease diagnosis with high specificity, selectivity, and short response time.

23.6 Conclusion Sensors have been evolving since the first temperature sensors were invented in the 1600s with the increasing demand in many fields. Technological developments are the key parameters in the improvement of design and manufacturing processes. New and novel technologies are adding to the cumulative existing knowledge and meeting the

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References

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increasing requirements of new sensors due to the increasing demands in industrial production, food and water supplies, and health services. Smart sensors can enhance life quality of people all over the world, and will continue to grow more intelligent and be able to measure more sensitively and accurately in the form of implantable, wearable, and digestible sensors. Commercialization is the main process to be realized to ensure these smart sensors become our future.

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4. Construction and other applications

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Absorption spectrometry (AAS), 566 ABTS methods, 601 Abused drugs, 125
126, 129
130 aptamers to detect abused drug levels, 147t real-time analysis of, 146
160 Accelerometers, 831
833, 863 Acetamiprid, 796 Acetic acid, 632
633 Acetochlor, 676
677 Acetoin, 785
786 Acetylcholine (ACh), 647
651 Acetylcholinesterase (AChE), 647
651, 719, 777
778 Acetylene black NPs, 628 Acids, 632
633 Acinetobacter baumannii (AB), 115 Acoustic biosensors, 28 Acoustic positioning systems, 842 Acoustic sensors, 839
842 Acoustic transduction, 403
404 Acrylamide, 796
798 Action potentials (AP), 474
476 Activated chemisorption model, 237 Active acoustic sensing approaches, 840
841 Active acoustic sensors, 840
841 Active sensors, 23 Active transducers, 21, 23 Actuators, 18, 538 Ad-hoc test methods, 896 Adaptive driver assistance systems (ADAS), 827 Additives, screening of, 773
777 Adenine, 190
191, 632 Adiponectin, 273 Adrenal cortical hormone (ACH), 277 Adrenaline, 273 Adrenocorticotropic hormone (ACTH), 269
270

Adulterants and associated products most vulnerable to adulteration, spectrum of, 701
706 Adulteration detection conventional analytical techniques for, 707
708 global scam and health threat, 700
706 major concern for health, economy, and environment, 706 spectrum of adulterants and associated products most vulnerable to adulteration, 701
706 recent trends in, 708
725 biosensors for, 713
721 electronic noses/tongues for, 722
723 sensing strategies, 723
725 sensors and biosensors for, 708
709 sensors for, 709
713 Aerial systems, 825 Aeromonas hydrophila, 106
110, 789 Affordable, Sensitive, Specific, Userfriendly, Rapid and robust, Equipmentfree and Deliverable (ASSURED), 4 Aflatoxin B1 (AFB1), 793 Aflatoxin M1 (AFM1), 794 Aflatoxins, 297
298 Aggregation-induced emission (AIE), 94 Albumin, 358 Alcohol oxidase (ALOx), 130 Alcohol’s oxidation reaction (AOR), 435 Alcoholic beverages, 785
786 Alkaline phosphatase (ALP), 60, 94, 313
314 Alpha-fetoprotein (AFP), 303
304 α-azidoether quenched probe (Q-STAR probe), 207
208 α-naphthalene acetic acid (α-NAA), 659
660 1,3-alternate calix[4]arenes, 37
38 Aluminum oxide, 675 Amino acid derivative hormones, 261
262

917

918

Index

2-amino-5,6,7-trimethyl-1,8-naphthyridine (ATMND), 577
578 4-aminophenol, 60 3-amminopropyltrimethoxysilane (APTES), 652
653 Ammonium, 673 Amperometric biosensors, 101
102, 269
276, 775
776 Amperometric electrochemical sensors (AEC sensors), 29, 745 Amperometric techniques, 534 Amperometric tyrosinase-based biosensor, 776
777 Amphetamine (AMP), 152, 158 Anabolism, 340 Analog to digital conversion (A/D conversion), 836 block, 836
837 hardware, 836 resolution, 836
837 Analog-to-digital converter (ADC), 22 Analysis process, 664
665 Analyte, 390 Analytical methods, 708 for drug analysis, 388 Androsterone, 274 Aniline functionalized graphene quantum dots (a-GQDs), 249
250 Animal-based food products, antibiotics in, 787
789 Anion exchange membrane (AEM), 419 Anodic stripping voltammetry (ASV), 712
713 Ansys (finite element analysis software), 894 Antennas, 834
835 arrays, 835 types and specifications, 835t 6-(2-(anthracen-9-ylmethylene) hydrazinyl)N2, 209 Anthraquinone labeled PNA probe (AQPNA), 68 Antibiotics, 681, 788 in animal-based food products, 787
789 Antibody immobilized nitrogen and sulfurdoped graphene quantum dots (Ab-N, SGQD), 58
59 Antibody/antibodies (Abs), 57
58, 142, 271, 787
788, 853 antibody-based biosensors for virus detection, 57
60 in electrochemical approaches, 142
143

Antigen-antibody immuncomplexes, 263 Antimicrobial peptides (AMPs), 103
104 Antimicrobial resistance, 787
788 Antioncogenes, 300
301 Antioxidant capacity (AOC), 594
595 assays, 594
595 assessment, 771
773 chemical sensing of conventional methods for determination of total phenolics and antioxidant capacity, 596
597 novel sensing methods of total phenolics and antioxidant capacity, 597
635 Antioxidants, 611
612 activity electrochemical sensing of, 607
624 optical sensing of, 599
607 Antisense oligonucleotides (ASOs), 62 Aptamers, 63
64, 263
264, 481, 787
788 in colorimetric approaches, 130
135 colorimetric-based biosensors for abuse drug detection, 135t detection of MDPV, 134f simultaneous colorimetric detection of METH and cocaine, 133f smart functional DNA hairpin, 135f 3D-printed holder and biosensor, 131f visual methamphetamine detection, 132f in electrochemical approaches, 144 in fluorescence approaches, 136
140 strategies of aptamer-based fluorescence abuse drug biosensing, 139
140 fluorophore displacement upon aptamer binding, 140 messenger activation upon aptamer binding, 139
140 repositioning of quencher upon aptamer binding, 140 Aquatic products, 782 Argan oil adulteration, 702
703 Aristolochia fangchi, 704 Arsenic (As), 688
689, 712
713 Artificial intelligence (AI), 864, 881 Artificial neural networks (ANN), 757
758 Artificial pancreas, 885 Asbestos, 297
298 Ascorbic acid, 603, 633
634 Aspergillus, 793
795 Aspirin, 704 Atherosclerosis, 634

Advanced Sensor Technology

Index

Atomic absorption spectroscopy (AAS), 536 Atomic emission spectrometry (AES), 566 Atomic spectroscopy, 536
537 atomic absorption spectroscopy, 536 inductively coupled plasma spectroscopy, 536
537 Atrazine, 676
677 AutoCAD (design software), 894 Automotive systems, 825 Autonomous systems, 894 Avian influenza virus (AIV), 179
180 Azacyanine 5 (Aza5), 208
209 Azide-unit pendant water-soluble photopolymer (PVA-AWP), 771 2,20 -Azino-bis-(3-ethylbenzothiazoline-6sulfonic acid) (ABTS), 596
597

B 6B grade pencil graphite (6B-PGE*), 678
680 B-type natriuretic peptide (BNP), 271 Bacillus subtilis, 112
113 Bacteria, 81, 861
862 Bacteria detection, 81
82. See also Virus detection biosensors for, 90
112 integrated biosensing platforms for multiplexed, 112
115 nanomaterials-based biosensors for, 85
90 whole-cell biosensors for, 82
85 Bacterial biosensors, 681 Bacterial species, 789 Bacterial toxins, 795 Bacteriophages, 549 Baked products, 796
798 Barrier models, biosensors in, 488
490 Beam steering, 835 Beamforming, 835 Beer, 785
786 Beer-Lambert equation, 536 Benzene, 297
298 Benzyl benzoate, 704
705 β-D-glucuronidase enzyme (GUS), 105 17β-estradiol, 275
276 Bifenox, 676
677 BiFeO3, 857
858 Bimodal waveguide (BiMW), 96 Binocular cameras, 827 Bioavailable ammonium, 681 Biofuel cell (BFC), 415 Biogenic amines, 776
777

919

Biolayer interferometry (BLI), 96 Biologic fluids, compatibility with kinds of, 388 Biological entities, 855
856 Biological fluids, 349
350, 608
609 Biological indicators of water pollution, 527
528 Biological monitoring index, 528 Biological oxygen demand (BOD), 522 Biological processes, 524 Bioluminescence, 681
682 bioluminescence-coding mobile element, 682 Biomarkers, 298
300, 302 biomarker-based biosensors economic burden, 338
339 glucose as diabetes biomarker, 345
355 glycated hemoglobin and glycated albumin as diabetes biomarkers, 355
363 health issues related to diabetes, 338 micro RNA, 363
365 novel biomarkers/metabolites in diabetes and associated complications, 363
372 pathophysiology of diabetes, 339
345 peptides/proteins, 366
370 prevalence, 338 prevalence of diabetes, 338f proteomic biomarkers involved in T1DM and T2DM, 367t cancer progress and, 300
306 Biomasses, 705
706 Biomolecules, 5 Biopharmaceutical analysis, 383
384 Bioreceptors, 390, 473, 787
790 Biorecognition elements (BRE), 263, 744
745 Biorecognition process, 390 Biosensing, 114
115 platforms, 775 for multiplexed bacteria detection, 112
115 for NAs detection, 202
205 process, 632 technologies for monitoring organotypic models, 476
488 biosensors for cell behavior, 477
478 cytokines, 481
483 electrical activity, 485
488 mechanical activity, 483
485

Advanced Sensor Technology

920

Index

Biosensing (Continued) metabolic activity, 478
483 oxygen, 478
479 small molecules of energy metabolism, 479
481 technology in food production and processing antibiotics in animal-based food products, 787
789 antioxidant capacity assessment, 771
773 applications and advantages of e-nose, e-tongue, and e-eye in food industry, 746t assessment of biotoxins, 793
795 biosensors and food quality, 758
786 biosensors and food safety, 786
798 detection of foodborne pathogens, 787
789 determination of toxic chemicals, 796
798 food allergens, 786
787 food authenticity assessment, 777
781 freshness evaluation of food products, 781
784 nanosensing platforms used for maintaining/improving quality and safety of food products, 759t quality monitoring of wine, 784
786 screening of food-grade ingredients and additives, 773
777 Biosensors, 3
4, 8, 27
28, 82, 94, 125
126, 262
263, 286
287, 298
300, 352
353, 386
387, 548, 758
786, 795, 853, 856, 881, 883 for adulterants detection, 713
721 for adulteration detection, 708
709 aimed at adulteration detection in various samples, 720t applications based on NAs structure, 205
212 for bacteria detection, 90
112 electrochemical biosensors, 98
105 mechanical biosensors, 105
112 optical biosensors, 91
97 based on transducers in hormone detection, 264
285 for cancer biomarker detection, 319
320 for cell behavior, 477
478 design, 389
392 basic characteristics of, 390
391 nanobiosensors, 391
392

device, 744
745 for drug detection, 392
406 electrochemical biosensors, 393
395 mass biosensors, 402
404 microfluidic-based biosensors, 404
406 optical biosensors, 396
402 photoelectrochemical biosensors, 402 recent trends in, 406
407 and food safety, 786
798 for hormone, 285 in organotypic models, 473
476 cell-based biosensors, 474
476 molecular biosensors, 473
474 tissue-based biosensors, 476 for organs-on-a-chip and organoids, 471 applications of biosensors in in vitro culture platforms of organotypic models, 488
499 biosensors in organotypic models, 473
476 types based on biorecognition elements, 263
264 antibody, 263 enzymes, 263 MIPs, 264 nucleic acid and aptamers, 263
264 types of, 27
28 Biotechnology, 82, 114
115 Biotoxins, assessment of, 793
795 20,70-bis(2-carboxyethyl)5,6carboxyfluorescein (BCECF-AM), 110 1,3-bis(N’-benzoylthioureido)benzene, 680 1,3-bis(N’-furoylthioureido)benzene, 680 Bisphenol A (BPA), 541
542 Blood glucose levels, 345 Blood
brain barriers (BBB), 490, 492
493 Blue-green bacteria, 523 Bluetooth, 854 Boron doped diamond (BDD), 58, 711
712 Boronic acid, 249
250 Bottom-up approach, 125
126 Bovine serum albumin (BSA), 157
158 Brain heart perfusion (BHI), 106
110 Brassica oilseeds, 602 Bread, 796
798 Business environment, 891 Butyl-rhodamine B (BRB), 581
582

C Cadmium (Cd), 689
692, 712
713 Cadmium telluride QDs (CdTe QDs), 200
201, 771

Advanced Sensor Technology

Index

Caffeic acid, 611
612, 771 Calendula officinalis, 601 Calophyllum inophyllum, 704 Calorimetric sensors, 319
320 Cameras, 826, 904
905 Campylobacter spp., 789 C. jejuni, 91 infection, 565 Cancer, 297, 593
594, 634, 890 cells, 301
302, 405 risk, 305 Cancer antigen (CA), 303
304 Cancer biomarkers, 316 biosensors for, 319
320 cancer progress and biomarkers, 300
306 cancer initiation due to DNA alterations, 301f molecular biology of cancer occurrence and progress, 300
302 detection, 302
306 different types of cancer, 298f electrochemical biosensors for, 306
311 extremely lethal cancer types, 299t genetic biomarkers, 304
306 optical biosensors for, 311
316 piezoelectric biosensors for, 316
319 protein biomarkers, 302
304 Cantilever biosensors, 110 Canvas business model, 898 Capacitive soil moisture sensors, 675
676 Capture DNA probes (CP-DNA), 132
133 Capture probe (CP), 132
133 Carbamate insecticides, 647
648 Carbon based conductive inks, 610 carbon-based nanomaterials, 86
89, 196
199, 351
352, 708
709, 787
788 carbon-based nanomaterials for bacteria detection, 88t carbon-based sensors for water monitoring, 38
39 materials, 628, 758 paste, 617 Carbon aerogel (CA), 448
449 Carbon dots (CDs), 247
248, 711, 771 Carbon nanofoam (CNF), 449 Carbon nanoparticles (CNPs), 94
95 Carbon nanotubes (CNTs), 86
87, 271, 285, 391
392, 426, 597
598, 610
611, 758 Carbon nanotube
screen-printed carbon electrodes (CNT/SPCEs), 270

921

Carbon paper (CP), 425 Carbon paste electrodes (CPE), 141
142, 570
571 Carbon quantum dots (CQDs), 200
201, 246
247, 709
710 Carbonized silk fabric (CSF), 239 Carboxyl graphene, 104 Carboxymethylcellulose (CMC), 272 Carcinoembryonic antigen (CEA), 302
303 Cardiac models, biosensors in, 493
497 Cardiomyocytes (CMs), 474
476 Cardiovascular diseases (CVDs), 494, 593
594 Cas12aVDet method, 202
205 Catabolism, 340 Catalytic process, 782 Catechin, 611
612, 771 Catechol oxidase, 624
628 CD-manganese dioxide (CD-MnO2), 783
784 Cellobiose dehydrogenase (CB-DH), 775
776 Cells, 305, 787
788 adhesions, 477 biosensors for cell behavior, 477
478 cell-based biosensors, 82, 474
476 cell-based impedance biosensor, 485 cytoplasm, 54
55 populations, 82 Cellular electrophysiology, 485
486 Cellulose acetate (CA), 246
247, 632
633 Central nervous system (CNS), 492
493 Ceramics, 884
885 Ceramides, 372 Cerium oxide nanoparticle (Ce-NPs), 130 Cerium system (Ce system), 599
600 Cerium-based MOFs (Ce-MOFs), 201
202 Cetyl trimethyl ammonium bromide (CTMAB), 581
582 Challenges of LSPR-based techniques, 180 Chemical antibodies, 136 Chemical indicators of water quality, 522
525 influences of different metal ions and limit values, 525t Chemical oxygen demand (COD), 522 Chemical pollutants, 517 Chemical sensors, 24
26, 539
541, 670
673, 853 chemosensors for water pollutant detection, 540t

Advanced Sensor Technology

922

Index

Chemical sensors (Continued) different sensors and materials development, 25t types of chemosensors, 25
26 in water pollutant detection, 537
541 sensors and transducers, 538
539 Chemically modified electrodes (CMEs), 541 Chemiluminescence (CL), 91, 392, 660
661 assays, 400
401 chemiluminescent sensors for detection of pesticides, 660
662 technique, 552
553 Chemiluminescence resonance energy transfer (CRET), 400
401 Chemoprotective agents, 594 Chemosensors, 540
541 sensor technology and biotechnology, 26t types of, 25
26 Chip-scale atomic clocks (CSAC), 844 clock oscillators, 844
845 CHIT. See Chitosan (CS) Chitin nanofiber (ChNF), 889
890 Chitosan (CS), 271, 610
611, 632
634, 773
774 Chlamydia trachomatis, 113
114 4-chloro-1-naphthol (4CN), 248
249 Chlorogenic acids (CGA), 611
612, 634
635, 771 Chlorophyll meters, 672 Cholesterol, 853 Choline oxidase (ChOx), 649
651 Chromatographic/chromatography, 535
536 gas chromatography, 535 high-performance liquid chromatography, 536 paper, 432
433 separation techniques, 597 techniques, 384
385, 529, 648, 707 Chronic inflammation, 593
594 Chronoamperometry (CA), 269, 308
309 chronoamperometric biosensor, 719 Chrysanthemum, 602 Citral, 704
705 Clark-type sensor, 478
479 Classical swine fever virus (CSFV), 60 Clock disciplining, 842
843 Clostridium spp., 789 C. botulinum, 795 C. perfringens, 106 C. tetani, 106

Clustered regularly interspaced short palindromic repeats (CRISPR), 202
205 Co coordination polymer spheres (Co CPSs), 243 Cobalt oxides (CoO), 745 Cocaine binding aptamer (CBA), 140 Codex Alimentarius, 699
700 Collision-induced dissociation (CID), 158
159 Colorants, 776
777 Colorimetric/colorimetry, 94 approach, 126
135 aptamers in, 130
135 aptasensor, 132
133 assays, 400 biosensors, 127
128 colorimetric biosensor design, 127f colorimetric-based optical biosensors, 400 detection methods, 60 enzymes in, 128
130 indicators, 674
675 methods, 400, 674
675 sensors, 313
314 for detection of pesticides, 649
652 Commercialization, 584, 890
898 Communication technologies, 834
835 Competitive volumetric-bar-chart chip (CVChip), 156 Complementary metal-oxide-silicon (CMOS), 828 Composite materials, 884
885 Compound annual growth rate (CAGR), 868
869 Computer-aided design (CAD), 893
894 COMSOL (general-purpose simulation software), 894 Concanavalin A (Con A), 250
251 Conducting polymers (CP), 708
709, 745 Conductometric biosensors for hormones, 282
283 Conductometric electrochemical sensors, 29 Conjugated polymer (CP), 773
774 Continuous glucose monitoring (CGM), 240
241, 348 Continuous glucose monitoring systems (CGMS), 348 Contraction, 483 Controlled current techniques. See Galvanostatic techniques Conventional analytical techniques for adulterants detection, 707
708 Conventional characterization methods, 472

Advanced Sensor Technology

Index

Conventional methods, 594 for determination of total phenolics and antioxidant capacity, 596
597 Conventional SPR biosensors, 787 Conventional standard techniques, 773
774 Cooled sensors, 828
829 Copper (Cu), 599
600, 712
713 Corn, 705
706 Corona phase molecular recognition (CoPhMoRe), 285 Coronavirus disease (COVID-19), 9, 883 Corticosteroids, 704
705 Corticotropin-releasing hormone (CRH), 278 Cosmetics, 704
705 Counter electrodes (CE), 569
570 Covalent organic frameworks (COFs), 128 Covalently closed circular DNA (cccDNA), 55
56 Coventor, 894 CRISPR-associated proteins systems (Cas systems), 202
205 CRISPR/Cas-assisted biosensing platforms for NAs detection, 202
205 Cryoscopy, 775
776 Cryptosporidium parvum, 520 Crystalline silica, 297
298 Culinary herbs, 703 Culinary spices, 703 Cyanine dyes, 209 Cyclic pulse voltammetry (CPV), 776
777 Cyclic voltammetry (CV), 32, 145
146, 239
240, 269, 306
307, 542, 595, 608
612 electrochemical sensors based on nanomaterials for determination of food polyphenols by, 613t Cymbopogon citratus, 601 Cynara scolymus, 601 Cysteine, 606 Cytochrome b (cytb), 779 Cytokines, 481
483

D Data analysis, 864 Database components, 891 Deep learning, 864 Degradation, 419
420 Degrees of freedom (DOF), 831 Dendritic fibrous nanosilica (DNFS), 794 Dengue virus, 55 Deoxynivalenol (DON), 795

923

Deoxyribonucleic acid (DNA), 9, 173
174, 297
298, 600
601 addition, 680
681 barcode, 707 DNA-aptamer, 794 methylation, 210
212 20 -deoxyribose and adenine, 173
174 Detection of foodborne pathogens, 787
789 of inorganic pollutants, 680
681 of organic matter, 676
680 of pesticides chemiluminescent sensors for, 660
662 colorimetric sensors for, 649
652 electrochemical sensors for, 657
660 electrochemiluminescent sensors for, 662
663 fluorescent sensors for, 652
654 piezoelectric sensors for, 663
665 Raman sensors for detection of, 654
657 of pH, 673
675 of soil moisture, 675
676 of soil nutrients, 671
673 Detection limit (LOD), 31 DETECTR method, 202
205 Diabetes, 235, 337
338, 593
594 biomarkers glucose as, 345
355 glycated hemoglobin and glycated albumin as, 355
363 health issues related to, 338 pathophysiology of, 339
345 differential diagnosis of T1DM vs. T2DM, 343
344 gestational diabetes mellitus, 344
345 type 1 diabetes mellitus, 343 type 2 diabetes mellitus, 339
343 prevalence, 338
339 Diazepam (DZP), 155 2,4-dichlorophenoxyacetic acid (2,4-D), 285 Diesel, 705
706 Diethylthiatricarbocyyanine (Cy7), 133
134 Differential pulse voltammetry (DPV), 269, 306
307, 595, 612
618, 774
775 electrochemical sensors based on nanomaterial for determination of food polyphenols by, 619t Diffusion technology, 404
405 Digital business ecosystem (DBE), 891 Digital components, 891 Digital processing, 836
837

Advanced Sensor Technology

924

Index

Digoxin, 704 2,5-Dimercapto-1,3,4-thiadiazole (DMcT), 579
580 Dimethyl sulfoxide (DMSO), 602
603 4-([4-(dimethylamino)phenyl]azo)benzoic acid (DABCYL), 680
681 2,2-diphenyl-1-picrylhydrazyl-based assay (DPPH-based assay), 594, 596, 601 Direct analysis in real-time (DART), 158
159 Direct analysis in real-time time-off light mass spectrometer (DART-TOF-Ms), 158
159 Direct CL, 660
661 Direct electrochemical oxidation, 595
596 Disease models, 472 Disparity information, 827 Dissolved organic carbon (DOC), 900
901 Dissolved oxygen (DO), 522, 662
663, 681 Dithiothreitol (DTT), 581 Dithydroxybenzoate derivatives, 605
606 DNAzyme-copper nanoclusters (CuNCs), 95
96 Domestic sewage, 520 Dopamine, 270 Dopant-assisted positive photoionization ion mobility spectrometry (DAPP-IMS), 158
159 Doped polymer, 673 Double negative materials, 835 Double-layer stable gold nanostructures (D-AuNS-SSMB), 111 Double-stranded DNA (ds-DNA), 140, 206 Double-stranded NA (ds NA), 174 Downsampling, 837 DPV-cupric reducing antioxidant capacity method (DPV-CUPRAC method), 623 Drone-based radars, 835, 838 Dropping method, 855 Drugs, 704 analysis, 384 detection, 383
384 advantages and disadvantage of commercial methods, 386t biosensor design, 389
392 biosensors for, 392
406 criteria of ideal method for drug analysis, 387
389 recent trends in biosensors for, 406
407 discovery, 11 drug biosensing, 126
160

colorimetric approach, 126
135 electrochemical approaches, 141
146 fluorescence approaches, 136
140 real-time analysis of abused drugs, 146
160 monitoring, 385
386 screening, 481 Dual-template molecularly imprinted polymer-QCM sensor (Dual-template MIPs-QCM sensor), 664
665 Dyes, 706 Dynamic characteristics, 17 Dynamic light scattering (DLS), 193
196

E E-textiles, 41 ECG glucose humidity temperature blood pressure, 863 Echinacea purpurea, 610
611 Echodyne UAV radar, 838 Economically motivated adulteration (EMA), 699
700 Ecosystem, 891 Edible oils, 702
705 Edible plants, 608
609 Egg, 787
788 Eicosanoids, 261
262 Electric nose (E-nose), 722 Electrical cell-substrate impedance sensor (ECIS), 477
478 Electrical conductivity (EC), 527, 688 Electrical impedance sensor (EIS), 485 Electrical transduction methods, 477 Electrically excitable cells, 474
476 Electroanalytical technique, 536 Electrochem-luminescence, 534 Electrochemical activity, 393 Electrochemical biosensors, 28, 89, 98
105, 141
142, 306, 393
395, 395t for bacteria detection, 99t, 101f for cancer biomarker detection, 306
311 electrochemical biosensor-integrated liver models, 498 for hormone detection, 265
283 amperometric biosensors, 269
276 conductometric biosensors for hormones, 282
283 hormone biosensors based on transducers, 266t impedimetric biosensors, 277
282 potentiometric biosensors, 276
277

Advanced Sensor Technology

Index

schematic diagram representing different classes, 265f impedometric biosensors, 394 potentiometric technique, 394
395 Electrochemical chemiluminescence (ECL), 270
271 Electrochemical electrodes, 680 Electrochemical enzyme-based glucose sensors, 239 Electrochemical glucose biosensors, 237
244. See also Optical glucose biosensors activated chemisorption model, 237f enzymatic electrochemical glucose biosensors, 238
241 nonenzymatic electrochemical glucose biosensors, 241
244 Electrochemical immunosensors, 718, 787 Electrochemical impedance spectroscopy (EIS), 32, 155, 309
310, 393, 575
576, 632 Electrochemical methods (EC methods), 141
146, 174, 529
535, 595, 608, 610 amperometric techniques, 534 antibodies in, 142
143 aptamers in, 144 electrochemical techniques applied in pollution detection of metal cations, 530t molecularly imprinted polymers in, 144
146 voltammetric techniques, 534
535 Electrochemical NAs biosensors, 188
193 Electrochemical nonenzymatic biosensor based on silver-substituted ZnO nanoflower, 776
777 Electrochemical oxidation of iodide, 612 Electrochemical sensing, 306
307. See also Optical sensing of polyphenols and antioxidant activity, 607
624 cyclic voltammetry, 608
612 differential pulse voltammetry, 612
618 square-wave voltammetry, 618
624 technologies, 407 Electrochemical sensors, 25
30, 235, 350
352, 481, 535, 569
577, 624, 671
673, 902 applications of, 31
36 EIS Nyquist plots, 35f food, healthcare, environmental, animal, and agricultural fields, 32t

925

selected applications of, 33t SWV signatures, 35f for detection of pesticides, 657
660 electrochemical methods for heavy metal ions monitoring in water, 572t identification of heavy metals, 571f potentiometric sensor for Pb21ions based on copolyaniline nanoparticles, 575f principle of electrochemical detection for heavy metal ions, 570f SEM images of gold nanoparticle, 575f types of, 29
30 in water pollutant detection, 541
546 electrochemical transducers, 541
544 piezoelectric transducers, 544
546 Electrochemical techniques, 352, 608 Electrochemical transducers, 541
544, 544f Electrochemical workstation, 571 Electrochemical-based real-time analysis of abused drugs, 155
156 Electrochemically reduced graphene oxide (ERGO), 38 Electrochemiluminescence (ECL), 62
63, 104, 283, 576
577, 662 biosensors, 402 electrochemiluminescent sensors for detection of pesticides, 662
663 Electrochemistry-based transducers, 237 Electrodes, 236
237, 265
269, 719, 744
745 Electrogenic microbial consortium, 686 Electrolyte-insulator-semiconductor (EIS), 785
786 Electromagnetic (EM), 86 induction-based moisture sensors, 675
676 Electromechanical scanning, 830 Electronic epidermal wearables, 41 Electronic eye (e-eye), 745 Electronic nose (e-nose), 745 and E-tongue for adulterant detection, 723t electronic noses/tongues for adulterant detection, 722
723 Electronic technologies, 82, 114
115 Electronic tongue (E-tongue), 722, 745 Electronics, 390 electronics-based systems, 22
23 Electrons, 313 Electrophysiology, 491
492 Electropolymerization, 145
146 Elemental hydrogen, 603

Advanced Sensor Technology

926

Index

Eleuthero coccus, 704 Endocrine disruptors, 297
298 Endocrine-disrupting chemical (EDC), 275
276 Endogenous food polyphenols, 599
600 Endothelial cells (ECs), 483
484 Energy harvesting, 862
863 Energy metabolism, insulin role in, 340 Enterobacter cloacae, 112
113 Entromedial nuclei, 343 Environmental sensing, 413 Enzymatic biofuel cell-based glucose sensor (EBFC-based glucose sensor), 240
241 Enzymatic biosensors, 241, 263, 719 Enzymatic commercial kits, 775
776 Enzymatic electrochemical glucose biosensors, 238
241 Enzymatic glucose sensors, 235
236 Enzymatic optical glucose biosensors, 246
249 Enzymatic sensors, 347
348 Enzyme-linked boronated immunoassay (ELBIA), 358
359 Enzyme-linked immunosorbent assay (ELISA), 53
54, 56
57, 83, 128, 271, 298
300, 358
359, 708, 743
744 Enzymes, 235
236, 263, 787
788, 853 conjugation, 628
629 enzyme-based biosensors, 128 enzyme-based colorimetric biosensors, 129
130 enzyme-based potentiometric biosensors, 773
774 in fluorescence approaches, 140 mimicking activity, 649
651 Ephedrine, 711
712 Epigallocatechin gallate (EGCG), 604
605 Epinephrine, 273 Epstein-Barr virus, 297
298 Escherichia coli, 83, 520, 782
783, 789, 890 RFM443, 682 Essential oils (EO), 704
705 Estradiol, 275
276 Estrogen, 275 Ethanol (EtOH), 429
430, 784 Ethylene oxide, 297
298 Etoposide, 704 European Association for the Study of Diabetes (EASD), 357 Evanescent wave (EW), 581 Excited-stated intramolecular proton transfer (ESIPT), 94

Exteroceptive sensors, 826 Extra virgin olive oil (EVOO), 601 Extraction, 597 Extreme learning machines (ELM), 757
758

F Far infrared (FIR), 828
829 Fating plasma glucose (FPG), 355 Fault tree analysis (FTA), 896
897 Fenitrothion (FNT), 657
659 Ferric reducing antioxidant power (FRAP), 594 Fiber optic SPR (FOSPR), 246, 787 Fiber-optic localized surface plasmon resonance (FOLSPR), 97 Fiber-optic particle plasmon resonance (FOPPR), 156
157 Field of view (FOV), 827 Field-effect transistors (FETs), 485
488, 570
571 Fifth-generation cellular network technology (5G cellular network technology), 886 Film bulk acoustic resonator (FBAR), 316 Filtering, 837 Final product realization and marketing, 897
898 Finite element method (FEM), 894 Fish, 782, 787
788 Flaviviridae, 55 Flaviviruses, 55 Flexible electronics, 862 Flexible plastic substrate, 862 Flint River, 565 Flow injection assay (FIA), 102 Flow-injection spectroscopy, 392 Fluidic devices, 861 Fluorescein (FAM), 179
180 Fluorescein amidite (FAM), 201
202 Fluorescence approaches, 136
140 aptamers in, 136
140 label-free aptamers in, 139 labeled aptamers in, 136
139 strategies of aptamer-based fluorescence abuse drug biosensing, 139
140 enzymes in, 140 Fluorescence assays, 401 Fluorescence biosensors, 183
184 Fluorescence immunoassay (FIA), 357

Advanced Sensor Technology

Index

Fluorescence in situ hybridization (FISH), 95
96 Fluorescence polarization (FP), 137
139 Fluorescence resonance energy transfer (FRET), 62
63, 94
95, 174
175, 552, 581
582, 709
710, 790
791 Fluorescence spectroscopy (FL spectroscopy), 178 Fluorescence-based NAs detection methods, 183
188, 185f Fluorescent “turn-off” sensors, 779 Fluorescent biosensors, 94
95 Fluorescent immunochromatographic test (FICT), 67
68 Fluorescent linked immunosorbent assay (FLISA), 67
68 Fluorescent microscope, 95
96 Fluorescent sensors for detection of pesticides, 652
654 Fluorimetry spectroscopy, 392 Fluorine-doped tin oxide (FTO), 141
142 Fluoroimmunoassay techniques, 271 Fluorometer, 793 Fluorometric sensor, 316 Folin-Ciocalteu (FC), 594 Follicle stimulating hormone (FSH), 282 Food adulteration, 777 allergens, 786
787 authenticity assessment, 777
781 chemical sensing of food phenolics conventional methods for determination of total phenolics and antioxidant capacity, 596
597 novel sensing methods of total phenolics and antioxidant capacity, 597
635 matrices, 783
784, 795 products, 777
778, 789 freshness evaluation of, 781
784 quality, 758
786 assessment, 758 safety, 702 biosensors and, 786
798 Food, Drugs and Cosmetics Act (1938), 699
700 Food Adulteration Act, 699
700 Food and Agriculture Organization (1945), 699
700 Food and Drug Administration, 384
385 Food-and-mouth-disease virus (FMDV), 71
72

927

Food-grade ingredients, screening of, 773
777 Foodborne pathogenic microorganisms, 743
744 Foodborne pathogens, detection of, 787
789 Formaldehyde, 297
298 Fo¨rster resonance energy transfer (FRET), 136
137, 175, 581 Fossil fuels, 705
706 Francisella tularensis, 96 Fraud, 699 Free radical scavengers, 594 Free radical scavenging activity, 596 FRENDt PSA, 905
906 Fried chips, 796
798 Fried products, 796
798 Fruits, 608
609 juices, 783
784 Fuel adulteration, 705
706 Fuels, 705
706 electrooxidation, 435
452 oxidation process, 416
418 Fumonisin B1 (FB1), 795 Functional CMs, 495 Functional core-shell hybrid nanoparticle, 628
629 Functional DNAs, 134
135 Functional groups (FGs), 142 Fungi, 793 Fungicides, 676
677 Fusarium species, 793, 795

G G-quadruplex structures (G4), 186
188 G-quadruplexes (G4s), 208
209 Gallic acid, 611
612, 771 Galvanostatic techniques, 575 γ-glutamyl transpeptidase (GGT), 499 Gardia lamlia, 520 Gas chromatography (GC), 384
385, 535, 597, 648, 707 Gas chromatography-mass spectrometry (GC-MS), 262, 319, 743
744, 793 Gas chromatography/tandem mass spectrometry (GC/TMS), 775
776 Gas diffusion layer (GDL), 427 Gas sensors, 857
858 Gasoline, 705
706 Genetic biomarkers, 304
306 telomere activity between healthy and cancerous cells, 305f

Advanced Sensor Technology

928

Index

Genetic tumor markers, 304 Genomic biosensors, 60
62 Gestational diabetes mellitus (GDM), 337
338, 344
345 glucose biosensors, 345t plasma glucose concentration, 346t Gesture control system, 832 Glass-carbon electrodes (GCE), 141
142 Glasses, 884
885 Glassy carbon, 617 Glassy carbon electrode (GCE), 31, 238
239, 271, 610
611, 657
659, 709
710, 771 Global market shares, sensors in various industrial areas and, 899
906 Global sensor market, 900 Glucono-δ-lactone, 237 Glucose, 272, 479 biosensing, 237 biosensors, 251
254, 355 for point-of-care testing, 354 detection electrochemical glucose biosensors, 237
244 glucose biosensors, 251
254 optical glucose biosensors, 244
251 as diabetes biomarker, 345
355 imprinted polymers, 243
244 oxidation on transition metals, 237
238 perspective and glucose sensor developments, 355 role of nanomaterials in glucose biosensors, 354 sensing, 346
347 sensors in clinical practice, 347
353 CGMS, 348 electrochemical sensors, 350
352 enzymatic and nonenzymatic sensors, 347
348 invasive continuous glucose sensors, 348
349 noninvasive glucose monitoring system, 349 optical sensors, 349
350 wearable biosensing, 352
353 Glucose dehydrogenase flavin adenine dinucleotide (GDH-FAD), 346
347 Glucose dehydrogenase nicotin-amide adenine dinucleotide (GDH-NAD), 346
347 Glucose dehydrogenase pyrroloquinoline quinone (GDH-PQQ), 346
347

Glucose oxidase (GOx), 235
237, 346
347, 480, 719, 773
774 Glutaraldehyde (GA), 773
774 Glycated albumin (GA), 355 current GA biosensors in clinical practice, 358
359 as diabetes biomarkers, 355
363 perspective and GA sensors in development, 359
363 designed sensors based on HbA1c and GA biomarkers, 360t Glycated hemoglobin (HbA1c), 366 current hemoglobin sensors in clinical practice, 357 designed sensors based on HbA1c and GA biomarkers, 360t as diabetes biomarkers, 355
363 Glycation process, 355 Glycerol, 450
451 Glycine-hydrochloric acid (Gly-HCl), 277 Glycosylated hemoglobin (HbA1c), 363 GNU Radio, 835
836 Gold (Au), 599
600, 628, 771 Gold nanocluster (AuNC), 577 Gold nanocluster-decorated polycaprolactone nanofibers (AuNC*PCL-NF), 577 Gold nanoislands (AuNIs), 179
180 Gold nanoparticle-decorated carbon nanofiber (AuNPs/CNFs), 571 Gold nanoparticle-embedded polyaniline (AuNP-PAni), 58
59 Gold nanoparticle-streptavidin (AuNP-SA), 723
724 Gold nanoparticles (AuNPs), 38, 86, 193
196, 277, 280, 306
307, 482
483, 599
603, 651
652, 709
710 AuNP-based colorimetric assay, 601 formation, 600
601 Gold nanostar (GNS), 580
581 Gold-standard assay, 356
357 GQD photoluminescence (GQD PL), 606 Grafting from approach, 664
665 Grapheme oxide quantum dot (GOQD), 578
579 Graphene, 196
199, 351
352, 406, 599, 606 graphene-based sensors, 856 graphene-modified GCE, 271 Graphene aerogel (GA), 427 Graphene nanoribbon (GNR), 719 Graphene nanosheets (GNs), 628, 662

Advanced Sensor Technology

Index

Graphene oxide (GO), 38, 65
67, 101, 136, 176
177, 246
247 Graphene quantum dots (GQDs), 94
95, 212, 597
598, 771 Graphene-like titanium carbide MXene (Ti3C2-MXene), 659
660 Graphite paste electrode (GPE), 31 Gravimetric chemosensors, 25
27 Green synthesis, 861
862 Green vegetables, 777
778 Green-manufacturing methods, 861
862 Guanine (G), 173
174, 190
191, 632 Guide RNA sequence (gRNA), 202
205 Gyroscopes, 826, 833

H Hairpin DNA probes (HDPs), 134
135 Hairpin probe (HP1), 577
578 Halal cosmetics, 704
705 Hand gesture, 832 Hapten-grafted programmed probe (HGPP), 657
659 Hardware, 891 solutions, 842
843 Hazardous chemical waste, 566 Hb-dimethyl-dioctadecyl-ammoniumbromide (Hb-DDAB), 796
798 Health and Human Services, 298
300 Health issues related to diabetes, 338 Healthcare monitoring systems, 413, 887, 905 Heavy metals (HMs), 31, 517, 566, 681, 686, 704
706, 712
713 current trends in heavy metal monitoring, 569
577 schematic illustration of synthesis GA and MOF, 569
577 detection of, 551
552 ions, 525, 886 measurement methods in water and performance, 569
582 electrochemical sensors, 569
577 optical sensors, 577
579 sensors, 581
582 SERS sensors, 579
581 toxicity ranges and mechanism in living cells, 567
568 maximum level of heavy metals permitted in ground and drink water, 568t in water current limitations and future prospective, 584
585

929

current trends in heavy metal monitoring, 569
577 heavy metal measurement methods in water and performance, 569
582 heavy metal toxicity ranges and mechanism in living cells, 567
568 Hemagglutinin (HA), 54
55 Hemoglobin sensors in clinical practice, 357 Hepadnaviridae, 55
56 Heparan sulfate proteoglycans (HSPGs), 55
56 Hepatic cells, 405 Hepatitis A viruses, 528 Hepatitis B virus (HBV), 55
56 Hepatitis viruses, 297
298 Herbal drugs, 704 Herbal medicines, 704 Herbal preparations, 704 Herbicides, 676
677 Herbs, 703 Hierarchical cluster analysis (HCA), 722 High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD), 775
776 High-performance liquid chromatography (HPLC), 262, 384
385, 536, 597, 648, 743
744 HPLC-refractive index detector, 775
776 High-performance liquid chromatography/ mass spectrometry (HPLC
Ms), 262 High-performance sensors, 841 High-permittivity dielectrics, 835 Highest occupied molecular orbital (HOMO), 654 Histamine in fish, 787 Hollow Au spike-like nanoparticles (hAuSNs), 179
180 Holographic pH sensors, 674
675 Honey, 703, 787
788 Hormones, 261 biosensor types based on biorecognition elements, 263
264 biosensors based on transducers in, 264
285 biosensors for hormone, 285 electrochemical biosensors for, 265
283 microbial screening technique for, 284 optical biosensors for hormones, 283
284 wearable sensors for, 284
285

Advanced Sensor Technology

930

Index

Hormones (Continued) conductometric biosensors for, 282
283 schematic diagram of biosensor, 262f Horseradish peroxidase (HRP), 58
59, 102, 248
249, 270
271, 308
309, 661
662, 775
776 Housing box, 17 Human biological fluids, 383
384 Human body fluids, 396 Human chorionic gonadotropin (hCG), 279, 303
304 Human growth hormone (hGH), 270 Human immunodeficiency viruses, 297
298 Human mesenchymal stem cells (hMSCs), 477
478 Human papillomavirus (HPV), 68, 297
298 CARD, 885 Human pathogenic viruses, 54 Human serum albumin (HSA), 404 Human serum chorionic gonadotropin (hCG), 271 Human somatosensory system, 887 Human T-cell, 297
298 Human T-lymphotropic virus type 1 (HTLV-1), 62
63 Human telomeric G4 DNA (H-telo G4 DNA), 201
202 Human-IPSCs-derived cardiomyocytes (hiPSC-CMs), 495 Hybrid chain reaction (HCR), 91 Hydrated ethyl alcohol (HEAF), 705
706 Hydrochloric acid, 632
633 Hydrogels, 128 Hydrogen, 413
414 Hydrogen atom transfer (HAT), 594 mechanisms, 596 reaction-based assays, 596 Hydrogen peroxide (H2O2), 235
236, 422
423, 568 Hydroquinone (HQ), 272, 704
705 Hydrothermal heating process, 774
775 Hydrous oxides, 237 Hydroxyl radicals (•OH), 568, 593
594, 596
597, 632
634 8-hydroxyquinoline glucuronic (8-HQG), 105 8-hydroxyquinoline (8-HQ), 105 Hyperbranched rolling circle amplification (HRCA), 186
188 Hyperoxia, 478 Hyperspectral imaging, 743
744

I Ideal method for drug analysis, 387
389 compatibility with different kinds of biologic fluids, 388 cost, 388
389 ease of operation, 388 using minimum amount of biological sample, 388 reproducibility, reliability, and accuracy of method, 387
388 speed of analytical process, 388 for drug assay, 388 Idiopathic T1DM, 343 Illicit drugs, 125, 136 Image sensors, 904 1,3-imidazolidine-2-thione, 209 Immobilization process, 632 of enzymes, 628 Immobilized biological molecule, 853 Immobilizing enzymes, 595
596 Immunoassays, 385
388, 648 Immunochromatographic test strips (ICTS), 150
151 based on real-time analysis of abused drugs, 149
154 Immunochromatography, 150
151 Immunoglobulin G (IgG), 104, 142 Immunological techniques, 708 Impedimetric biosensors, 277
282, 394 Imprinted polymer-based biosensors for virus detection, 69
73 In situ polymerization, 145
146 In vitro culture platforms of organotypic models, applications of biosensors in, 488
499 biosensors in barrier models, 488
490 cardiac models, 493
497 kidney models, 499 liver models, 497
498 neural models, 490
493 Incipient hydrous oxide adatom mediator model (IHOAM), 237 Incretins, 341
342 Indirect chemiluminescence, 660
661 Indium tin oxide (ITO), 36, 141
142, 628, 709
710 Induced pluripotent stem cells (IPSC), 477
478 Inductively coupled plasma mass spectroscopy (ICPMS), 566

Advanced Sensor Technology

Index

Inductively coupled plasma spectroscopy (ICP spectroscopy), 536
537 Industrial products, 706 Inertial measurement unit (IMU), 830 Inertial sensors, 831
833. See also Radio frequency sensors; Optical sensors accelerometers, 831
833 gyroscopes, 833 Influenza A viruses (IAV), 54
55 Infrared cameras, 828
829 Infrared light, 828 Infrared sensors, 829 Innovative sensor technologies, 39
41 broad categories of current and novel sensor technologies, 40t Inorganic ions, 600
601 Inorganic pollutants, 519 detection of, 680
681 Insecticides, 676
677 Insulin, 272 pump therapy, 885 resistance, 339 role of Insulin in energy metabolism, 340 Insulin-like growth factor 1 (IGF-1), 270 Integration of renewable energy, 900 IntelliSuite (CAD tool), 894 Interdigitated electrodes (IDEs), 495 Interface, 390 Interferometry, 96 Interferon-γ (IFN-γ), 313 Interleukin-6 (IL-6), 481 Internal transcribed spacer (ITS), 106 International Diabetes Federation (IDF), 338
339 International Telecommunication Union, 838 Internet of Things (IoT), 413, 834
835, 863
865, 881 Interpenetrating polymer network (IPN), 248
249 Interstitial fluid (ISF), 348 Invasive continuous glucose sensors, 348
349 Invertase (Inv), 719 Ion chromatography, ultraviolet visible spectroscopy (IC-UV-vis), 566 Ion-selective electrode (ISE), 155, 570
571, 672
675 Ion-selective field-effect transistor (ISFET), 674
675 Ion-selective membrane coatings, 680
681 Ionic liquids (IL), 438
439

931

Irinotecan, 704 Iron oxide nanoparticles (IONP), 605 IONP-based method, 605 Iron(III) oxide (Fe2O3) (n-type) sensors, 745 Isotope ratio mass spectrometry (IRMS), 707

J Japanese encephalitis virus (JEV), 55 Juvenile diabetes, 343

K K-nearest neighbors algorithm (KNN algorithm), 757
758 K-Ras, 300
301 genes, 305 Kalman filter, 831
832 Kerosene, 705
706 Ketone bodies, 342 Kidneys, 342 biosensors in kidney models, 499

L L-glutamate

oxidase (GluOx), 775 775 Lab-on-a-chip-based optical biosensor (LOC-based optical biosensor), 248
249 Lab-on-a-patch (LOP), 284
285 Label-free aptamers in fluorescence approaches, 139 Label-free fluorescent biosensors, 136 Laccase, 595
596, 624
628 laccase-based biosensor, 771 Laccase immobilized onto gold nanoparticle/graphene nanoplateletmodified screen-printed carbon electrode (LACC/AuNP/GNPl/SPCE), 629 Lactate, 853 Lactate oxidase, 480 Lactobacillus acidophilus, 97 Lactobacillus plantarum, 110
111, 771 Lactococcus lactis, 774
775 Laminar flow-based FCs, 420 Lamination, 418
419 Laser ablation, 418
419 Laser Raman spectrometry, 777
778 Laser scanners, 841 Laser scanning confocal imaging-surface Plasmon resonance (LSCI-SPR), 96
97 Laser sensors, 829
830 Laser vision systems, 829
830 L-tryptophan,

Advanced Sensor Technology

932

Index

Laser-induced graphene (LIG), 239
240 Laser-induced porous graphene (LIPG), 659
660 Lateral flow sensing assays, 723
724 Lateral flow-based immunoassay (LFA), 150
151 Lavandula angustifolia EO, 704
705 Layer-by-layer (LbL), 247
248, 272 Layout Editor software, 894 Lead (Pb), 712
713 Lectins, 112
113 Legged robot, 832
833 Leptin, 280 Lidars, 826 Ligand, 606 Light detection and ranging (LiDAR), 830 Light-emitting diode (LED), 453 Light-emitting microbes, 688 Limit of detection (LOD), 18, 57
58, 83
84, 125
126, 175
176, 271, 307
308, 390
391, 576
577, 709
710, 771 Limit of qualification (LOQ), 125
126 Linalyl acetate, 704
705 Linear discriminant analysis (LDA), 651, 722, 757
758 Linear sweep voltammetry (LSV), 104
105 Lipidomics, 371
372 Lipids, 372 Lipopolysaccharides (LPS), 83
84 Liquid chromatography-tandem mass spectrometry (LC-MS), 577
578 Liquid-crystal polymer (LCP), 657 Liquid
liquid extraction (LLE), 597 Listeria, 782
783 Listeria monocytogenes, 91, 789, 791
792 Lithium niobate-based piezoelectric wafer (LiNbO3-based piezoelectric wafer), 782 Liver fibrosis-on-a-chip model, 497
498 Liver models, biosensors in, 497
498 Liver organotypic models, 497 Living cells, 474
476 Localized surface plasmon resonance (LSPR), 97, 156
157, 178, 312, 396, 600, 787 LSPR-based NAs biosensors, 178
180 LSPR-based techniques, 180 Long period fiber grating (LPFG), 246
247 Long-baseline systems (LBL systems), 842 Longitudinal-SPR (LSPR), 782 Loop mediated isothermal amplification (LAMP), 81
82 Lovastatin, 704

Love wave type surface acoustic wave (LWSAW), 318
319 Low limit of detection (LOD), 350
351 Low power electronics, 862
863 Low sensitivity sensors, 840 Lymphotropic virus, 297
298 Lysogeny broth (LB), 105

M Machine learning (ML), 659
660, 864, 886 Machine vision systems, 743
744 Macroscopic gyroscopes, 833 Macroscopic pollutants, 520 Magnetic bead (MB), 91 Magnetic multiwall carbon nanotubes (MMWCNTs), 274 Magnetic nanobeads (MNBs), 58
59 Magnetic NPs (MNPs), 787
788 Magnetic resonance imaging (MRI), 343 Magnetic sensors, 839
842 Magnetometers, 839
840 Malachite green, 777
778 Mallotus nudiflorus, 704 Manganese dioxide, 314
315 Manufacturing process, 860
861 Markets, 866
869 Markov models, 896
897 Mass biosensors, 402
404 Mass production, 585 Mass sensors, 403
404 Mass spectrometry (MS), 158
159, 385
386, 577
578 Mass transfer (MT), 128 Mass-sensitive chemosensors, 26 Materials, 884
885 MathCAD (design software), 894 Matrix-assisted laser desorption/ ionization time of flight Ms (MALDI-TOF), 404 Maximum power density (MPD), 415 Meat, 702, 787
788 adulteration, 702 lasagna, 699 Mechanical biosensors, 105
112, 107t Medical implants, 835 Medicinal herbs, 704 Medium sensitivity sensors, 840 Melamine, 702, 709
710, 777
778 incident, 699, 702 Membrane fouling, 419
420 Membranes, 419
420 Mentha piperita, 601

Advanced Sensor Technology

Index

12-mercapto dodecanoic (12MDDA), 278
279 4-mercaptobenzoic acid (4-MBA), 579 Mercaptododecanoic acid (MUA), 111 6-mercaptohexanol (6-MHL), 278
279 4-mercaptophenylboronic acid (4-MPBA), 249
250 Mercaptopropionic acid, 606 Mercury (Hg), 519, 712
713 Mercury resistance (MerR), 724 Mesoporous silica nanoparticles (MSNs), 91
94 Mesua ferrea, 704 Metabolic diseases, 634 Metabolic molecules, 853 Metabolites in diabetes and associated complications, 363
372 biomarkers in diabetes and associated complications, 371
372 micro RNA, 363
365 peptides/proteins, 366
370 proteomic biomarkers involved in T1DM and T2DM, 367t Metabolomics, 371
372 Metal nanoparticles (MNPs), 85 Metal oxide semiconductor (MOS), 722, 745 Metal oxides, 243, 350
351 Metal-organic frameworks (MOFs), 71, 128, 201
202, 577, 654 Metallic electrocatalysts, 243 Metallic nanoparticles (MNPs), 354, 552
553, 597
598, 604
606, 708
709 Metals, 758, 884
885 ions, 31
32 metal-based nanomaterials, 392 metal-based sensors for water monitoring, 38
39 Metamaterials, 835 Metformin, 704 Methamphetamine (METH), 132
133, 157
158, 905
906 Methanol (MeOH), 429
430 adulteration in fuel ethanol, 713 Methanol oxidation reaction (MOR), 422 Methyl parathion (MP), 540
541, 662, 685
686 Methylation-specific QD fluorescence resonance energy transfer (Ms-qFRET), 210
212 Methylene blue (MB), 393 Methylene blue probe (MBP), 657
659

933

3,4-methylenedioxypyrovalerone (MPDV), 133
134 Micro alcohol fuel cells design and flow considerations, 423
434 alcohol fueled MM-FCs types and features, 433t examples toward sensing applications, 452
457 fuels electrooxidation and micropower generation, 435
452 fundamentals, 418
423 fundamental advantages and disadvantages of MM-FCs, 423t Micro fuel cells (Micro FCs), 413 Micro RNA (miRNAs), 363
365 in T1DM and T2DM, 365t Micro-drones, 826
827 Micro-system technologies, 886 Microbial activity, 520 Microbial biosensors, 670
671 soil-borne disease using, 681
692 Microbial fuel-cells (MFC), 686 Microbial pathogens, 519
520 Microbial screening technique for hormone detection, 284 Microbial whole-cell sensor bioluminescent detection, 680 Microbiological counts, 782
783 Microcantilevers biosensor, 110
111 sensors, 582 Microelectrode array with comb structure (MACS), 65 Microelectromechanical system (MEMS), 402
403, 419, 675
676, 830, 884 Microelectronics, 407 Microelectrooptomechanical system (MOEMS), 884 Microencapsulation methods, 128 Microfabrication techniques, 881 Microfluidic chamber. See Organ-on-a-chip technology (OoC technology) Microfluidic membrane-less FCs (MM-FCs), 418 Microfluidics, 82, 114
115, 407 biosensor, 95
96 delivery system, 884
885 devices, 240, 404
405 fabrication, 418
419 microfluidic-based biosensors, 404
406 microfluidic-integrated biosensors, 404
406

Advanced Sensor Technology

934

Index

Microfluidics (Continued) platforms, 404
405 systems, 9, 582
584 technology, 4, 113 Microimpedance tomography system (MITO system), 490 Microorganisms, 519
520 detection of, 552
553 Microphones, 904
905 Micropillar arrays (mPAs), 483 Micropower generation, 435
452 Microprocessor, 22
23 Microribonucleic acids (miRNA), 305
306 Microsensors, 886 Microstrip antennas, 835 Microsystem sensor technology, 885 Microsystem technology and application, 884
886 Mid-infrared spectroscopy (MIR), 349
350 Middle East respiratory syndrome coronavirus (MERS-CoV), 60 Milk, 702, 787
788 Milli-fluidic system, 860
861 Mitoxantrone (MTX), 200
201 Mobile manipulator, 832
833 Mobile-wireless multimedia sensor networks (M-WMSNs), 904
905 Modern sensors, 3
4 Modified glassy carbon, 617 Molds, 793 Molecular beacons (MB), 186 Molecular biology techniques, 707 Molecular biosensors, 473
474 Molecular imprinted polymer nanoparticles, 401 Molecular imprinting, 5 Molecularly imprinted nanogels (MIPNGs), 719 Molecularly imprinted polymers (MIPs), 5, 69
71, 125
126, 243
244, 264, 306, 540
541, 624, 709
710 MIP-based QCM biosensor, 775 molecularly imprinted polymer-based biosensors for virus detection, 69
73 Molecules, 600
601 Molography, 10
11 Monocular cameras, 827 Morphine, 704 Mucin 1 (MUC1), 303, 890 Multielectrode array (MEAs), 485
486 Multiplex technology, 886
887

Multiplexed bacteria detection, integrated biosensing platforms for, 112
115 Multiplexed Optical Sensors in Arrayed Islands of Cells (MOSAIC), 886
887 Multiplexed sensor systems, 887 Multiplexing, 886 Multisensing, 886 sensors, 887 technology and applications, 886
888 Multiwalled carbon nanotubes (MWCNTs), 541, 610
611, 711, 774
775 Multiwalled carbon nanotubes-Screenprinted carbon electrode (MWCNTsSPCE), 623 Murine norovirus (MNV), 65 Muscle cells, 474
476 Myoglobin-based biosensors, 905
906

N

N,N0 -Bis(3,4-dihydroxybenzylidene)-1,2diaminobenzene Schiff
AuNP assemblies (3,4DHS), 773
774 N,N0 -diethyl-p-phenylenediamine (DEPDA), 248
249 N-acylhomoserine lactones (AHLs), 84 N-ethyl-N0 -(3-dimethylaminopropyl) carbodiimide, 281 n-hexyl-3-methylimidazolium hexafluorophosphate (HMIHPF6), 774
775 N-hydroxysuccinimide, 281 N4-diphenyl-1,3,5-triazine-2,4-diamine (9CI), 209 Nafion (NAF), 632
634 Nanobiosensors, 391
392 Nanocarbon materials, 87 Nanocomposites, 708
709 Nanodiamond, 597
598 Nanodrones, 826
827 Nanoelectromechanical systems (NEMS), 402
403 Nanofibers (NF), 577 Nanofiltration membranes, 554 Nanomaterials, 7
8, 62
63, 82, 85, 90, 193
196, 314
317, 391
392, 570, 595, 597
599, 758, 787
788, 855
856 in glucose biosensors, role of, 354 nanomaterial-based DNA electrodes, 632
635 nanomaterial-based enzyme electrodes, 624
631

Advanced Sensor Technology

Index

enzyme-based electrochemical sensors for determination of food polyphenols, 630t nanomaterial-based-sensors for water monitoring, applications of, 38
39 metal and carbon-based sensors for water monitoring, 38
39 polymer-based sensors for water monitoring, 39 nanomaterials-based biosensors for bacteria detection, 85
90 carbon-based nanomaterials, 86
89 noble metal nanoparticles, 85
86 semiconductor nanocrystals, 89
90 for water quality monitoring, 554
555 Nanoparticles (NPs), 178, 314
315, 349
350, 422, 598
599, 758 coatings, 680
681 nanoparticle-based spectrophotometric antioxidant assays, 599 Nanoporous gold (NPG), 272 Nanorods, 597
598 Nanostructures (NSs), 178 Nanosystem technologies, 886 Nanotechnology, 7
8, 18
20, 114
115, 552, 855
860, 881 examples of nanomaterials, applications and mode of enhancement, 859t nanotechnology-based biosensors, 693 Nanowire clusters (NWc), 251
252 Nanozymes, 649
651 1-naphthalene acetic acid (NAA), 285 National Institute on Drug Abuse (NIDA), 125 Natural antioxidants, 602 Natural bioreceptors, 125
126 Natural toxins, 743
744 Near-infrared (NIR), 828 NIR-QDs, 283 parts, 828 spectroscopy, 349
350 Neisseria gonorrhoeae, 113
114 Neural models, biosensors in, 490
493 Neural networks, 864 Neural stem cells (NSC), 490
491 Neuraminidase (NA), 54
55 Neurodegenerative diseases, 634 Neurodegenerative disorders, 593
594 Neurons, 474
476 Neurotransmitters, 490
491 dysfunction, 343

935

Neutral ionophores-poly(vinyl chloride)based membrane sensors, 678
680 Nitric acid, 632
633 Nitrogen (N), 671
672 nitrogen-rich chemical compounds, 777
778 overfertilization, 678
680 Nitrogen-doped graphene quantum dots (N-GQDs), 104 Nitrogen-doped nano-CDs (Nd-NCD), 774
775 Nitrosomonas europaea ATCC 19718, 673 Noble metals, 241
243 nanomaterials, 654
655 nanoparticles, 85
86, 599
600, 771 for bacteria detection, 87t salts, 599 Noise, 22 Non-faradaic EIS systems, 309
310 Noncoding RNAs (ncRNAs), 173
174 Nonenzymatic electrochemical glucose biosensors, 241
244 electrochemical enzymatic glucose biosensors, 242t Nonenzymatic glucose sensors, 241 Nonenzymatic optical glucose biosensors, 249
251 Nonenzymatic sensors, 347
348 Nonflexible sensors, 744 Nonimprinted polymer (NIP), 624 Noninvasive chemical sensors, 352
353 Noninvasive glucose monitoring system, 349 Noninvasive self-powered EBFC, 240
241 Noninvasive wearable electrochemical sensors, 352
353 Nonstructural protein 1 (NS1), 55 Nonvolatile ingredients, 704
705 Noradrenaline, 273 Norepinephrine, 273 19-nortestosterone, 274 Novel AgNP-based spectrophotometric method, 603
604 Novel sensing methods of total phenolics and antioxidant capacity, 597
635 electrochemical sensing of polyphenols and antioxidant activity, 607
624 nanomaterial-based DNA electrodes, 632
635 nanomaterial-based enzyme electrodes, 624
631 optical sensing of polyphenols and antioxidant activity, 599
607

Advanced Sensor Technology

936

Index

Novel TP, 594
595 Nuclear estrogen receptors (nERs), 283 Nuclear magnetic resonance (NMR), 775
776 nuclear magnetic resonance-assisted biosensors, 320 spectroscopy, 392 Nucleic acids (NAs), 8, 60
62, 173
174, 305
306, 548 and aptamers, 263
264 biosensor applications based on NAs structure, 205
212 CRISPR/Cas-assisted biosensing platforms for NAs detection, 202
205 electrochemical nucleic acid biosensors, 188
193 nucleic acid-based biosensing platforms, 62 nucleic acid-based biosensors for virus detection, 60
65 antibody-based biosensors for viruses, 61t nucleic acid-based biosensors for viruses, 66t optical NAs biosensors, 175
188 strategies for improving sensitivity of NAs biosensors, 193
202, 194t nanostructure-based NA biosensors, 197t Nulling, 835

O O-aminophenol (o-AP), 71
72 O-phenylenediamine-aniline (o-PDA-ANI), 623 Ochratoxin A (OTA), 794
795 Ocimum tenuiflorum, 777
778 OCXO, 843
844 Oligonucleotide-silver nanoparticle (OSN), 193
196 Olive oil, 702
703 Ominous octet, 340
343 On-chip FC, 429
430 Open software development tools, 835
836 Open-circuit voltage (OCV), 415 Operational mechanism, 675
676 Optical biosensors, 27
28, 91
97, 396
402, 550 applications of, 550
553 detection of heavy metals, 551
552 detection of microorganisms, 552
553 detection of organic materials, 550
551

for bacteria detection, 92t for cancer biomarker detection, 311
316 chemiluminescence assays, 400
401 colorimetric assays, 400 fluorescence assays, 401 for hormones, 283
284 new trends in optical biosensors sensing and monitoring, 553
554 schematic diagram of, 399f SPR assays, 401
402 surface enhanced Raman scattering spectroscopy, 396
400 types of, 397t uses of nanomaterials for water quality monitoring, 554
555 for water pollution detection, 546
555 advantages and disadvantages of, 550 recognition elements for chemical sensors and biosensors, 548
550 wireless sensor networks, 555 Optical chemosensors, 25
27 Optical colorimetry, 599
600 Optical detection, 311 Optical fibers, 244
247 Optical glucose and lactate sensors, 480
481 Optical glucose biosensors, 244
251. See also Electrochemical glucose biosensors electrochemical nonenzymatic glucose biosensors, 245t enzymatic optical glucose biosensors, 246
249 examples of, 253t nonenzymatic optical glucose biosensors, 249
251 Optical methods, 674
675 Optical NAs biosensors, 175
188 fluorescence-based NAs detection methods, 183
188 localized surface plasmon resonancebased NAs biosensors, 178
180 surface plasmon resonance-based NAs biosensors, 175
178 surface-enhanced Raman scattering NAs biosensors, 180
183 Optical sensing. See also Electrochemical sensing mechanism, 599 of polyphenols and antioxidant activity, 599
607 gold nanoparticles, 600
603

Advanced Sensor Technology

Index

metallic nanoparticles, 604
606 silver nanoparticles, 603
604 quantum dots, 606
607 Optical sensors (OSs), 30
31, 311, 349
350, 577
579, 826
830. See also Radio frequency sensors; Inertial sensors applications of, 36
38 infrared cameras, 828
829 laser-based sensors, 829
830 types of, 30
31 visual cameras, 826
828 Optical technologies, 352 Optical transducers, 91 Optiqua Technologies, 901 Optisense, 901 Oral glucose tolerance test (OGTT), 356
357 Organ models, 476 Organ-on-a-chip technology (OoC technology), 406
407, 471
472 Organic electrochemical transistor arrays (OECT), 494
495 Organic materials applications of optical biosensor-based technologies in different fields, 551t detection of, 550
551 Organic matter, detection of, 676
680 Organic molecules, 600
601 Organic pollutants, 519, 681 Organic polymers, 128 Organic substances, 610
611 Organoids, 471
472 tissue, 406
407 Organophosphorous compounds, 685
686 Organophosphorus hydrolase, 685
686 Organotypic models, biosensing technologies for monitoring, 476
488 Orthomyxoviridae, 54
55 Osmosis, 454
455 Output voltage (OV), 745 Ovarian cancer, 303
304 Oxidation process, 594 Oxidized carbon nanoparticles (OCNPs), 283 8-oxo-7,8-dihydro-20 -deoxyguanosine, 634 8-oxoguanine, 632 Oxygen, 478
479 depletion, 520 Oxygen radical absorbance capacity (ORAC), 594 Oxygen reduction reaction (ORR), 427 Oxytetracycline (OTC), 31

937

P p16 protein, 304
305 p53 protein, 304 Paclitaxel, 704 Palladium oxide (Pd oxide), 599
600 Palladium(II)-selective electrode, 678
680 Panax subspecies, 704 Paper, 610, 661
662 paper-based DNA biosensor, 778
779 Parathion, 654, 685
686 parathion-methyl, 654 Parathyroid hormone (PTH), 278
279 Paraventricular nuclei, 343 Paraxon, 685
686 Partial least square-discriminant analysis (PLS-DA), 158, 757
758 Partial least squares regression (PLS), 757
758 Partially oxidized dextran (PO-Dex), 247
248 Particle plasmon resonance (PPR), 156
157 Passive acoustic sensors, 842 Passive adsorption process, 142 Passive sensors, 23 Passive transducers, 21, 23 Pathogen bacteria, 520 Pattern classifiers (PCs), 757
758 Pattern-recognition algorithms (PRAs), 757
758 Peak currents (Ip), 608 Peak potentials (Ep), 608 Pencil graphite electrode (PGE), 280 Penicillium, 793
795 Pentraxin-related protein (PTX3), 312 Peptide nucleic acids (PNAs), 68, 177
178 Peptides, 65, 261
262, 366
370 peptide-based biosensors for virus detection, 65
68, 70t Perception, 842
843 systems, 826
827 Perchloric acid, 632
633 Peroxyl radical (ROO•), 593
594 Persistent substances, 706 Personalized, participative, preventative, and predictive (P4), 905 3,4,9,10-perylenetetracar-boxylic acid (PTCA), 283 Pesticides, 647
648 in water chemiluminescent sensors for detection of, 660
662

Advanced Sensor Technology

938

Index

Pesticides (Continued) electrochemical sensors for detection of, 657
660 electrochemiluminescent sensors for detection of, 662
663 fluorescent sensors for detection of, 652
654 piezoelectric sensors for detection of, 663
665 Raman sensors for detection of pesticides, 654
657 sensors for detection of, 649
652 pH, detection of, 673
675 Pharmaceutical industry, 406 Phenanthrene, 682 Phenolics, 596 compounds, 605
606 Phenylboronic acid (PBA), 249
250 Phosphate buffer saline (PBS), 130
131 Phosphatidylinositol-4,5-bisphosphate 3kinase, catalytic subunit, alpha (PIK3CA), 302 Phospholipids, 632
633 Phosphoric acid, 632
633 Phosphorus (P), 524, 671
672 Photoelectrochemical biosensors (PEC biosensors), 251
252, 402 Photolithography, 418
419 Photometric indicators, 674
675 Photometric methods, 674
675 Photon counting, 828
829 Photon integration, 828
829 Photonic solid-state cholesteric liquid crystals (CLCsolid), 248
249 Physical indicators of water pollution, 526
527 Physical pollutants, 517 Piezoelectric biosensors for cancer biomarker detection, 316
319 Piezoelectric chemosensors, 25
27 Piezoelectric sensors for detection of pesticides, 663
665 Piezoelectric thin film, 317
318 Piezoelectric transducers, 544
546 piezogravimetric sensors for water pollutant detection, 545t Pillar arrays, 485
486 Pioneering biosensors, 39 Placeable sensors, 41 Plasma, 608
609 Plasma glucose (PG), 356
357 Plasmonic biosensors, 482
483

Plasmonic nanomaterials, 311
312 Plastic optical fiber (POF), 96
97 Platinium, 628 Platinum (Pt), 416
418, 612 Point-of care testing (POCT), 4, 82, 125
126, 146
149, 173
174, 286
287, 306, 354, 905 glucose biosensors for, 354 virus diagnosis, 57 Poisonous materials, 517
518 Poly (9,9-di-(2-ethylhexyl)-fluorenyl-2,7diyl)-end capped with 2,5-diphenyl-1,2,4oxadiazole (PFLO), 773
774 Poly [(9,9-dioctylfluorenyl-2,7-diyl)-co-(1,4benzo-{2,1’,3}-thiadazole)] polymer NPs (PFBT PNPs), 662
663 Poly ethylene glycol (PEG), 193
196 Poly-2-hydroxyethyl methacrylate (pHEMA), 482
483 Poly-modified carbon paste electrode (glycine-modified carbon paste electrode), 776
777 Poly(2-hydroxyethyl methacrylate), 316
317 Poly(3-octylthiophene) (POT), 155 Poly(3,4-ethylenedioxythiophene)tyrosinase/sonogel-carbon (PEDOT-Tyr/ SNGC), 771
773 Poly(diallyldimethylammonium chloride), 176
177 Poly(dimethylsiloxane), 404
405 Poly(ethylene-dioxythiophene) (PEDOT), 270 Poly(ethyleneimine) (PEI), 130 Polyaniline (PANI), 39, 102
103, 279 Polycaprolactone (PCL), 577 Polycarbonate, 420
421 Polycarbonate chips (PC), 152 Polycyclic aromatic hydrocarbons (PAH), 628, 682 Polydiacetylene (PDA), 67 Polydiallyldimethylammonium chloride (PDDA), 651
652 Polydimethylsiloxane (PDMS), 111, 418
419 Polydopamine (PDA), 250
251 Polydopamine-polyethyleneimine (PDAPEI), 95
96 Polyethylene glycol (PEG), 114
115 Polyethylene glycol-modified AuNPs (PEGylated AuNPs), 709
710 Polyethylene terephthalate (PET), 773
774

Advanced Sensor Technology

Index

Polyethyleneimine (PEI), 62
63 Polyimide, 862 Polymerase chain reaction (PCR), 53
54, 81
82, 202
205, 263
264, 743
744 Polymeric sensors, 39 Polymers, 632
633, 884
885 molding, 418
419 polymer-based sensors for water monitoring, 39 Polymethylmethacrylate (PMMA), 418
419 Polyphenol oxidase (PPO), 624
628 Polyphenols, 593, 602 electrochemical sensing of, 607
624 optical sensing of, 599
607 Polysilicon (PS), 285 Polyvinyl alcohol (PVA), 238
239, 633
634, 773
774 Polyvinyl chloride (PVC), 416
418, 673 Porcine serum albumin (PSA), 713
718 Porous rhodium nanoplates (pRhNPs), 280
281 Porous silicon (PS), 551
552 Potassium (K), 671
672 Potential step voltammetry (PSV), 104
105 Potentiometric/potentiometry, 570
571 biosensors, 98, 276
277 electrochemical sensors, 29 sensors, 310
311 technique, 394
395 Potentiostatic techniques, 571 Pressure detectors, 904 Principal component analysis (PCA), 722, 757
758 Procalcitonin, 271 Progesterone, 274
275 Prolactin, 270 Propagating SPR (PSPR), 312 2-propanol (2-PrOH), 429
430 Proprioceptive sensors, 826 Prostate-specific antigen (PSA), 303 Prosthetics, 832
833 Proteins, 366
370, 600
601 biomarkers, 302
304 hormones, 261
262 Proteomics, 366 Proto-oncogenes, 300
301 Protocols, 854 Proton exchange membrane (PEM), 419 Prototyping, 894
896 Protozoa, 528 Proximal tubule-derived cells (PTCs), 499 Prussian blue (PB), 239
240

939

Pseudomonas P. aeruginosa, 95
96, 789 P. carrageenovora, 774
775 P. putida, 682 pTn5attPuLux, 682 Pulse techniques, 612
617 Pulsed amperometry (PA), 270
271 Pure Food and Drugs Act (1906), 699
700 Pure water, 522 Pyruvate dehydrogenase kinase 1 (PDK1), 364

Q Qualitative analysis, 384 Qualitative identification, 383
384 Quality criterion index, 522 Quality index method, 782
783 Quality management systems, 897 Quality monitoring of wine, 784
786 Quality of Service, 834
835 Quantification process, 390 Quantitative measurement, 383
384 Quantitative real-time PCR (QRT-PCR), 779
781 Quantum dots (QDs), 143, 186, 354, 534, 576
577, 599, 606
607, 758, 787
788 Quantum magnetometer, 840 Quartz crystal microbalance (QCM), 105
106, 252, 316, 403
404, 663
664, 708
709, 745 biosensors, 106 Quartz crystal microbalance comprising impedance variations (QCM-I), 544
545 Quartz crystal microbalance with dissipation monitoring (QCM-D), 544
545 Quartz crystal microbalance-dissipation monitoring (QCM-D), 483, 485 Quenchers compound, 184
186 Quercetin, 611
612, 771

R (R,R)-2,3-butanediol, 785
786 Radars, 837
839 Radio frequency (RF), 834
835 Radio frequency sensors, 833
839. See also Inertial sensors; Optical sensors antennas, 834
835 radars, 837
839 receivers, 835
837 Radioimmunoassay techniques, 271

Advanced Sensor Technology

940

Index

Raman effect, 654
655 Raman optical system, 655
657 Raman scattering, 579, 654
655 Raman sensors, 672 for detection of pesticides, 654
657 Raman spectroscopy, 178, 349
350 Random forest (RF), 757
758 Ras genes, 305 Reactive nitrogen species (RNS), 594 Reactive oxygen species (ROS), 129
130, 497, 568, 593 Ready-to-use foodstuffs, 777
778 Real-time analysis of abused drugs, 146
160 electrochemical-based, 155
156 immunochromatographic test strips based on, 149
154 spectroscopic based, 156
160 point-of-care devices, 160t Real-time heart pulse monitoring system (Real-time HP monitoring system), 888 Real-time reverse transcription-polymerase chain reaction (RT-PCR), 57 Receivers, 835
837 Receptor, 24 Recognition elements, 744
745 for chemical sensors and biosensors, 548
550 types of, 549f Recombinant light-emitting bacterium, 689
692 Red fluorescent protein (RFP), 724 Redox probes, 265
269 Reduced graphene oxide (RGO), 31
32, 101, 578
579, 713
718 Reduced graphene oxide-tetraethylene pentamine (rGO-TEPA/PB), 239
240 Reduced graphene sheets (RGSs), 103 Reducing agents, 603 Reduction of metal ions, 596 reduction/oxidation process, 796
798 Reference electrodes (RE), 569
570 Refractive index (RI), 91 Refractometric optical sensors, 30 Relaxed circular DNA (rcDNA), 55
56 Reliability method, 387
388, 896
897 Reporter probe (RP), 132
133 Reporter probe-DNA (RP-DNA), 132
133 Reproducibility method, 387
388, 391 Reproducible 3D in vitro models, 472 Response time (RT), 537

Retinoblastoma protein (Rb), 300
301 Rheonix chemistry and reagent device technology (Rheonix CARD technology), 885 Rhodamine 6G (Rh6G), 247
248 Rhodium nanoparticles (RhNPs), 605
606 RhNPs-based photometric methods, 605
606 Ribonucleic acid (RNA), 9, 173
174 biosensors, 209
210 Robot guidance systems, 837
838 Robot operating system drivers, 828 Robotic systems, 825 Robotic welding, 829
830 Ruthenium complex (Ru1), 104 Rutin, 611
612, 771

S Saccharomyces cerevisiae, 112
113 Salivary cortisol, 285 Salmonella, 520, 782
783, 789
790 S. enteritidis, 94
95 S. typhi, 112 S. typhimurium, 94, 552
553 Salmonella enterica (SE), 83
84, 789 Saraca asoca, 704 Saturated calomel electrode (SCE), 271 Scanning electron microscopy, 276 Screen-printed carbon electrode (SPCE), 102, 623, 773
774 Screen-printed electrode (SPE), 31, 141
143, 239
240, 542, 610, 771 Screen-printing, 610 Screening of food-grade ingredients and additives, 773
777 Secondary antibody (Ab2), 794
795 Secondary building blocks (SBUs), 201
202 SecurEau initiative, 901 Selective sensor-based label-free gold nanoparticles (selective sensor-based label-free AuNPs-alkanethiols), 680 Selectivity, 241
243 of biosensor, 391 Self-assembled monolayers (SAMs), 110
113, 270
271, 310
311 Self-organized maps (SOM), 785
786 Semiautomatic biosensor, 105 Semiconductor chips, 862 nanocrystals, 89
90 quantum dots for bacteria detection, 90t

Advanced Sensor Technology

Index

Sensing devices, 855 element, 23 research and development, 889
890 strategies, 723
725 trends in sensing technologies, 881
889 microsystem technology and application, 884
886 multisensing technology and applications, 886
888 wireless systems and applications, 888
889 Sensitive technologies, 828
829 Sensitivity, 390
391 Sensors, 5
7, 538
539, 744, 881 for adulterants detection, 708
713 sensors intended to detect adulteration in food, cosmetics, and fuel, 714t applications, 31
39 applications of electrochemical sensors, 31
36 applications of nanomaterial-basedsensors for water monitoring, 38
39 applications of optical sensors, 36
38 array, 887 characteristics, 18
20 metrological parameters of, 19t classification, 23
31 biosensors, 27
28 chemical sensors, 24
26 electrochemical sensors, 28
30 optical sensors, 30
31 commercialization, 865
869 design and modeling, 893
894 final product realization and marketing, 897
898 markets, 866
869 pathway, 890
898 prototyping, 894
896 regulatory issues, 866 testing and reliability, 896
897 development, 855 ELISA, 6f life cycle, 891
892 milestones in, 5
7 devices, 863 drivers, 855
865 artificial intelligence, 864 flexible electronics, 862 IoT, 864
865 low power electronics and energy harvesting, 862
863

941

nanotechnology, 855
860 sensor matrix and fabrication, 860
862 sensor networks, 863 smart phones, 863
864 fabrication, 860
862 in industrial areas and global market shares, 899
906 inertial sensors, 831
833 innovative sensor technologies, 39
41 magnetic and acoustic sensors, 839
842 active acoustic sensors, 840
841 magnetometers, 839
840 passive acoustic sensors, 842 market, 879 matrix, 860
862 milestones in sensor development, 5
7 networks, 863 optical sensors, 826
830 radio frequency sensors, 833
839 research works on sensors, 4f sensing opportunities, 9
11 sensing research and development, 889
890 sensor, actuator, and transducer fundamentals, 17
23 working principle and different components of, 18f sensor types, advantages and disadvantages, 880t signal conditioning of, 22
23 signal processing of, 21
23 stack, 854 state-of-the-art in, 7
9 technology, 4, 584 timing sources, 842
845 main parameters of crystal oscillators of different types, 844t trends in sensing technologies, 881
889 validation, 897 Sensory systems, 476 Sequential Evolution of Ligands by Exponential Enrichment (SELEX), 63
64 Severe acute respiratory syndrome coronavirus (SARS-CoV), 53
54 SARS-CoV-2, 176
177 Sexually transmitted infection (STI), 113
114 Sheer horizontal surface acoustic wave (SHSAW), 318 Short-baseline systems (SBL systems), 842 Shrimp, 782 Sialic acids, 54
55

Advanced Sensor Technology

942

Index

Sibutramine, 711
712 Signal conditioning of sensors, 22
23 Signal demodulation, 837 Signal processing of sensors, 21
23 signal conditioning of sensors, 22
23 and transducers, 21 Signal processor, 390 Signal-to quantization noise ratio (SQNR), 837 Signalization process, 390 Silicon nanowires/platinum nanoparticles (SiNWs/PtNPs), 713
718 Silicon nitride (Si3N4), 675 Silicon oxide (SiO2), 675 Silicon quantum dots (SiQDs), 653
654 Silicone rhodamine (SiR), 209 Silver (Ag), 105, 599
600 Silver chloride (AgCl), 105 Silver nanoparticles (AgNPs), 86, 193
196, 577, 599, 603
604, 654
655, 771 Silver nanowires (AgNW), 771
773 Simulated annealing meta-heuristic variable selection algorithm (SA metaheuristic variable selection algorithm), 722 Simulation software, 894 Single electron transfer (SET), 594 Single-stranded DNA (ss-DNA), 158, 280, 549, 577, 853 Single-stranded NA (ss NA), 174 Single-stranded probe DNA (ss-pDNA), 62 Single-walled carbon nanotubes (SWCNTs), 98
101, 273 Single-walled carbon nanotubes-Screenprinted carbon electrode (SWCNTsSPCE), 623 Singlet oxygen (O2), 568 SiSo cells, 304 Situational awareness, 825, 827 Size, weight, power, and cost (SWaPC), 827 Slow potentials (SP), 474
476 Small molecules of energy metabolism, 479
481 Smart cities, 864
865 Smart health, 864
865 management systems, 904 Smart home, 864
865 Smart phones, 863
864 Smart technologies, 882 Smart textiles, 41 Smart transport, 864
865 Smartphone-based biosensors, 790
791

Smartphone-based sensing assays, 724
725 Smartphones, 10
11, 881 Sodium borohydride, 603 Sodium citrate, 603 Sodium taurocholate cotransporting polypeptide (NTCP), 55
56 Soft lithography, 418
419 Soft robotic system, 841 Software, 891 software-defined networking technology, 889 solutions, 842
843 Software-defined receivers (SDR), 835
836 Soil, 669
672 chemical sensors and biosensors for soil analysis detection of inorganic pollutants, 680
681 detection of organic matter, 676
680 detection of pH, 673
675 detection of soil moisture, 675
676 detection of soil nutrients, 671
673 soil-borne disease using microbial biosensor, 681
692 contamination, 670, 677 moisture detection of, 675
676 sensors, 675 organic content, 676
677 pH, 673
674 salinity, 681 soil-borne disease using microbial biosensor, 681
692 bioassay techniques used in biosensing of inorganic compounds, 690t bioassay techniques used in biosensing of organic compounds, 683t wearables, 692
693 Solid phase extraction (SPE), 597 Solid phase synthesis method, 73 Solid state potentiometric sensor, 776
777 Solid-phase micro extraction spectroscopy, 392 Solvents, 704
705 Sorghum, 705
706 Sound Navigation and Ranging system (SONAR system), 840
841 Spectrophotometric/spectrophotometry assays, 599 methods, 596 spectroscopy, 392

Advanced Sensor Technology

Index

Spectroscopic/spectroscopy, 743
744 based real-time analysis of abused drugs, 156
160 methods, 707 Spectrum of adulterants and associated products most vulnerable to adulteration, 701
706 cosmetics, 704
705 food, 702
703 culinary spices and herbs, 703 edible oils, 702
703 honey, 703 meat, 702 milk, 702 fuels, 705
706 herbal medicines and drugs, 704 industrial products, 706 Sphingomonas paucimobilis EPA505, 682 Spices, 703 Split-ring resonators, 835 Square-wave anodic stripping voltammetry (SWASV), 571 Square-wave voltammetry (SWV), 32, 104
105, 189
190, 269, 306
307, 542, 595, 618
624, 709
710 electrochemical sensors based on nanomaterials for determination of food polyphenols by, 625t Squid meats, 782 Stability, 391 Stable clocks, 843 Staphylococcal enterotoxin B, 795 Staphylococcus aureus, 789 State-of-the-art in sensor technology, 7
9 Static characteristics, 17 Static techniques, 570
571 Stephania tetrandra roots, 704 Stereo cameras, 828 Steroid hormones, 261
262 Streptavidin-AuNP conjugates (SAV-AuNP conjugates), 778
779 Streptococcus agalactiae, 102 Streptococcus pneumonia, 106 Sudan I, 711 SUGAR (free simulation tool), 894 Sugarcane, 705
706 Sulfate-reducing bacteria (SRB), 103 Sulfonated graphene oxide-based solid electrolyte (Sulfonated GO-based solid electrolyte), 416
418 Sulfur-oxidizing bacterial (SOB), 686 Sunset yellow (SY), 711

943

Superoxide anion radical (•O2
), 593
594 Superoxide radical (O2
), 568 Support vector machines (SVM), 722, 757
758 Surface acoustic wave (SAW), 708
709, 745 Surface enhanced Raman scattering spectroscopy, 396
400, 399f Surface Enhanced Raman Spectroscopy (SERS), 63
64, 86, 569, 579
581, 776
777 Surface imprinted polymer (SIP), 104 Surface Plasmon polaritons, 311
312 Surface plasmon resonance (SPR), 30, 96
97, 130
131, 246, 270
271, 311, 551, 581, 600, 775, 890 assays, 401
402 optical sensors, 30
31 SPR-based biosensors, 787 SPR-based technology, 401 surface plasmon resonance-based nucleic acid biosensors, 175
178 Surface plasmon resonance imaging (SPRI), 787 Surface plasmon waves (SPW), 246 Surface-enhanced Raman scattering NAs biosensors, 180
183 Surface-enhanced Raman spectroscopy (SERS), 175, 315
316, 654
655, 708
709 Synchronization algorithms, 842
843 Synthetic auxins, 285 Synthetic irone, 704
705 Systematic evolution of ligands by exponential enrichment (SELEX), 9, 263
264, 307
308

T T-cell immunoglobulin and mucin domain (TIM), 55 Tag-labeled multiplex loop-mediated isothermal amplification (TM-LAMP), 779
781 Tannic acid (TA), 607 Target DNA (tDNA), 193
196 Tartrazine (TZ), 711 TCXO, 843
844 Technological maturity, 891 Telomeres, 305 Temperature sensors, 904 Terminator region of nopaline synthase from transgenic soybean (TNOS), 186
188 Test body, 17 Test methods, 896

Advanced Sensor Technology

944

Index

Test speed, 388 Testing, 896
897 Testosterone, 274 Tetracycline (TC), 31 tetracycline-imprinted fluorescent nanoparticles, 401 Tetradecyl ammonium-based o-nitrate, 673 3,30 ,5,50 -tetramethylbenzidine (TMB), 129
130, 270
271 Tetrathiafulvalene (TTF), 240 Therapeutic drug monitoring (TDM), 384 Thermal microdevices, 252
254 Thermal pollution, 521 Thermal sensors, 828
829 Thermal stress, 896
897 Thermistors sensors, 319
320 Thionine (TH), 271 Thiram, 657 Three-dimensional microporous CS
Au nanoparticle hybrid (3DOM CS
AuNPs), 274 Three-dimensional MM-FC (3D MM-FC), 422
423 Three-way junctions (TWJs), 94, 133
134 3D microporous carbon (MPC), 243 3D self-rolled biosensor array (3D-SR-BA), 486
488 Thymine, 173
174 Thymine-1-acetic acid-cysteamine, 38 Thyroxine (T4), 274 Tilted fiber Bragg grating (TFBG), 250
251, 283 Time-off light mass spectrometer (TOFMs), 158
159 Tin dioxide (SnO2), 745 Titanium dioxide (TiO2), 745 TiO2-based thin film biosensor, 783
784 Titanium dioxide nanotube arrays (TiO2NTAs), 773
774 Tobacco smoke, 297
298 Total antioxidant potential assay, 596 Total internal reflectance fluorescence (TIRF), 284 Total internal reflections (TIRs), 156
157, 244
246 Total nitrogen (TN), 886 Total organic carbon (TOC), 522 Total oxidant scavenging capacity (TOSC), 594 Total phenolic assay, 596 Total phenolic compounds/content (TPC), 771

Total phenolics conventional methods for determination of, 596
597 novel sensing methods of, 597
635 Total phosphorus (TP), 886 Total polyphenol (TP), 593
594 Total radical trapping antioxidant parameter (TRAP), 594 Total volatile base nitrogen (TVB-N), 781 Toxic chemicals, determination of, 796
798 Toxic pollutant, 544 Toxicity of heavy metals, 567 Toyyib cosmetics, 704
705 tp53 gene, 300
301, 304 Trametes versicolor (TvL), 628
629 Transducers, 24, 262
263, 538
539 components, 538f in hormone detection, 264
285 signals of, 21 Transduction element, 23 Transendothelial electrical resistance (TEER), 488
490 Transforming growth factor-beta (TGF-β), 481 Transmission SPR, 787 Tri-phenyl phosphine (TPP), 207
208 Trihydroxybenzoate derivatives, 605
606 Triiodothyronine (T3), 280
281 2,4,6-trinitrotoluene (TNT), 678
680 Triple-helix molecular switch (THMS), 577
578 Triple-negative breast cancers, 302 Triple-phase boundary theory (TPB theory), 426 Triplex forming oligonucleotide (TFO), 201
202 Trolox equivalent antioxidant capacity assay (TEAC), 594 Tumor progression, 301
302 suppressor genes, 300
301 Tumor necrosis factor-alpha (TNF-α), 308
309, 481 Tumorous cells, 301
302 Two-dimension (2D) 2D nanomaterials/nanosheets, 186 2D-PAGE, 366 Type 1 diabetes mellitus (T1DM), 337
338, 341t, 343 differential diagnosis of T2DM vs., 343
344, 344t

Advanced Sensor Technology

Index

Type 2 diabetes mellitus (T2DM), 337
343 differential diagnosis of T1DM vs., 343
344, 344t ominous octet, 340
343 role of insulin in energy metabolism, 340 Tyramine, 776
777 Tyrosinase (Tyr), 102, 595
596, 624
628

U Ultra-high temperature (UHT), 775
776 Ultra-short-baseline systems (USBL systems), 842 Ultrasonic-based moisture sensors, 675
676 Ultraviolet-visible spectroscopy, 392 Uncooled sensors, 828
829 Upconversion nanoparticles (UCNPs), 67, 136, 199
200, 790
791 Urbanization, 565, 567 Urea, 777
778, 853 Uric acid, 633
634 Urinary tract infection (UTI), 890 US Environmental protection Agency (EPA), 275
276

V Vanadium oxide (V oxide), 599
600 Vanadium pentoxide nanoparticlemodified HMIHPF6 (V2O5/NP-modified HMIHPF6), 774
775 Vascular endothelial growth factor (VEGF), 309
310 Vectorworks software, 894 Vertically aligned-CNTs (VACNT), 773
774 Vibrio V. cholera, 91 V. parahaemolyticus, 102, 789 Viral ribonucleoprotein (vRNP), 54
55 Virgin olive oil (VOO), 602
603, 779 Virtual prototypes, 895
896 Virtual protyping, 895 Virtual/liquid membrane, 420 Virus detection, 53
54. See also Bacteria detection antibody-based biosensors for, 57
60 biosensors, 73
74 current methods in, 56
57 molecularly imprinted polymer-based biosensors for, 69
73 nucleic acid-based biosensors for, 60
65

945

particles, 855
856 peptide-based biosensors for, 65
68 structure and infection mechanism of common viruses, 54
56 Visual cameras, 826
828 Vitamin D, 372 Volatile organic compounds (VOCs), 130, 319 Voltammetric biosensors, 104
105 Voltammetric E-tongue, 722, 779 Voltammetric electrochemical sensors, 29
30 Voltammetric experiments, 623 Voltammetric techniques, 534
535

W Wastewater pollutants, 517 analytical methods for detection of, 527
528 atomic spectroscopy, 536
537 chromatography, 535
536 electrochemical methods, 529
535 Water, 565 monitoring metal and carbon-based sensors for, 38
39 polymer-based sensors for, 39 quality, 566 uses of nanomaterials for water quality monitoring, 554
555 Water pollutants, 886 chemical sensors in water pollutant detection, 537
541 electrochemical sensors in water pollutant detection, 541
546, 542t indicators of, 522
528 biological indicators of water pollution, 527
528 chemical indicators of water quality, 522
525 physical indicators of water pollution, 526
527 optical biosensors for water pollution detection, 546
555 sources of, 517
518 types of, 518
522 emerging water pollution, 521
522 inorganic pollutants, 519 macroscopic pollutants, 520 microbial pathogens, 519
520 organic pollutants, 519 thermal pollution, 521

Advanced Sensor Technology

946

Index

Wearable biosensing, 352
353 Wearable glucose monitors, 352 Wearable microneedle sensors, 407 Wearable sensors, 41 for hormone detection, 284
285 Wearable textile sensors, 41 West Nile Virus, 53
54 Whispering gallery microgravity (WGM), 105
106 Whispering gallery mode (WGM), 111
112 Whole-cell bacterial biosensor, 685 Whole-cell biosensors for bacteria detection, 82
85 Wine, 785
786 quality monitoring of, 784
786 Wireless sensing, 888 Wireless sensor networks (WSNs), 555, 888 Wireless systems and applications, 888
889 Working electrodes (WE), 569
570

World Health Organization (WHO), 4, 174, 567, 699
700

X X-ray fluorescence (XRF), 566 Xanthine oxidase (XOD), 771 catalysis, 782 Xenoestrogens, 276

Y Yersinia enterocolitica, 789, 792

Z Zearalenone (ZEN), 795 Zika virus, 55 Zinc oxide (ZnO), 541, 745 nanocrystal, 857
858 ZnONPs, 773
774

Advanced Sensor Technology