Green Chemical Analysis and Sample Preparations. Procedures, Instrumentation, Data Metrics, and Sustainability 9783030965334, 9783030965341

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Mahmoud H. El-Maghrabey V. Sivasankar Rania N. El-Shaheny   Editors

Green Chemical Analysis and Sample Preparations Procedures, Instrumentation, Data Metrics, and Sustainability

Green Chemical Analysis and Sample Preparations

Mahmoud H. El-Maghrabey V. Sivasankar  •  Rania N. El-Shaheny Editors

Green Chemical Analysis and Sample Preparations Procedures, Instrumentation, Data Metrics, and Sustainability

Editors Mahmoud H. El-Maghrabey Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy Mansoura University Mansoura, Egypt Rania N. El-Shaheny Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy Mansoura University Mansoura, Egypt

V. Sivasankar Post Graduate and Research Department of Chemistry Pachaiyappa’s College (Affiliated to University of Madras) Chennai, Tamil Nadu, India

ISBN 978-3-030-96533-4    ISBN 978-3-030-96534-1 (eBook) https://doi.org/10.1007/978-3-030-96534-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Sustainable development means the practice of development that satisfies the current needs without affecting the ability of future generations to satisfy their needs. One of the major factors affecting the state of sustainability is the chemical activities at both the industrial and the laboratory scale. The report of the Organization for Economic Cooperation and Development in 2001 stated that the production of chemical goods increases by 3% annually while the global population increases at a rate of 0.77% per year. In turn, this highlights the role of chemists for protecting the environment and supporting sustainability. For these reasons, the chemical community adopts the approach of “green chemistry,” and the 12 principles of green chemistry have been stated in 1998. Despite analytical chemistry involves smaller quantities of chemicals and reagents than synthetic chemical activities, the importance and extensive use of analytical methods made its impact very relevant. Thus, the environmental concerns and the ecotoxicity alarm demand analytical chemists to work for introducing the sustainable development concept to analytical chemistry laboratories to minimalize their adverse effects on both the environment and humans. Green Chemical Analysis and Sample Preparations depicts a wide range of the most recent trends for greening analytical activities, beginning with an introduction to green analytical chemistry followed by a discussion of green analytical chemistry metrics and life-cycle assessment approach to analytical method development. Chapters discuss profoundly two main topics: the first topic is the most recent techniques for greening sample pretreatment steps, and the second one is the modern trends for tailoring analytical techniques and instrumentation to implement the green analytical chemistry concept. The role of different kinds of green solvents, such as ionic liquids, supercritical fluids, deep eutectic solvents, bio-based solvents, and surfactants, as well as nanomaterials and green sorption materials in greening sample extraction step is a focus of this book. Furthermore, different approaches for greening chromatography as a key analytical technique are discussed. The applications of nanomaterials in analytical procedures are deeply reviewed. Miniaturization of spectrometers is also discussed as a recently evolved approach for efficient green on-site analysis. v

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This book appeals to a wide readership of academic and industrial researchers in different fields. Besides, it can be used in the classroom for undergraduate and postgraduate students focusing on development of new analytical procedures for organic and inorganic compounds determination in different kinds of samples characterized by complex matrices composition. We believe that it is an important addition for researchers interested in chemical analysis while protecting the environment. Eventually, we would like to mention that this book, “Green Chemical Analysis and Sample Preparations”, comes in recognition of The 27th session of the Conference of the Parties (COP 27) to the UNFCCC that will take place in Sharm El-Sheikh, Egypt in November 2022. We hope that the efforts done in preparing this book will help to get new ideas and solutions for greening the activities in analytical chemistry laboratories and protecting the environment and human beings. Mahmoud H. El-Maghrabey and Rania N. El-Shaheny want to dedicate their efforts and contributions in this book to their beloved son Yousef El-Maghrabey. Mansoura, Egypt  Mahmoud H. El-Maghrabey Chennai, Tamil Nadu, India  V. Sivasankar   Rania N. El-Shaheny

Contents

1 Introduction to Green Analytical Chemistry����������������������������������������    1 Alisha Rani, Harminder Singh, Gurpreet Kaur, and Jandeep Singh 2 Green Analytical Chemistry Metrics and Life-Cycle Assessment Approach to Analytical Method Development������������������   29 Maha Mohamed Abdelrahman 3 Green Sorption Materials Used in Analytical Procedures ������������������  101 David López-Iglesias, Alfonso Sierra-Padilla, José María Palacios-­­Santander, Laura Cubillana-Aguilera, and Juan José García-Guzmán 4 Application of Nanomaterials for Greener Sample Extraction ����������  171 Himshweta, Rajni Sharma, Neelam Verma, Minni Singh, and Mohsen Asadnia 5 Supercritical Fluid Extraction as a Green Approach for Essential Oil Extraction��������������������������������������������������������������������  223 Mohamed A. El Hamd, Mahmoud H. El-Maghrabey, Rania N. El-Shaheny, Ahmed E. Allam, and Fathalla Belal 6 Green Hydrotropic Technology as a Convenient Tool for the Handling of Poor Water-Soluble Candidates Proceeding Their Economic Analytical Measurements������������������������  265 Mohamed A. El Hamd, Mahmoud H. El-Maghrabey, Saud Almawash, and Rania N. El-Shaheny 7 Ionic Liquids as Greener Solvents for Sample Pretreatment of Environmental, Pharmaceutical, and Biological Samples ��������������  311 Gopal Jeya, Ravikumar Dhanalakshmi, Ponmudi Priya, and Vajiravelu Sivamurugan

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8 Functionally Modified Ionic Liquids as Green Solvents for Extraction and Removal of Toxic Metal Ions from Contaminated Water����������������������������������������������������������������������  343 Parveen Saini, Gurpreet Kaur, Jandeep Singh, and Harminder Singh 9 Deep Eutectic Solvents, Bio-Based Solvents, and Surfactant for Green Sample Pretreatment and Determination����������������������������  353 J. Lakshmipraba and Rupesh N. Prabhu 10 Green Chromatography Techniques������������������������������������������������������  379 Surbhi Goyal, Rajni Sharma, Jagdish Singh, and Mohsen Asadnia 11 Superheated Water Chromatography as a Greener Separation Approach ������������������������������������������������������������������������������  433 Lateefa A. Al-Khateeb 12 Applications of Nanomaterials for Greener Food Analysis������������������  471 Diksha Garg, Damnita Singh, Rajni Sharma, Neelam Verma, Ranjeeta Bhari, and Mohsen Asadnia 13 Miniature Infrared Spectral Sensing Solutions for Ubiquitous Analytical Chemistry ������������������������������������������������������������������������������  513 Bassem Mortada, Yasser M. Sabry, Diaa Khalil, and Tarik Bourouina Index������������������������������������������������������������������������������������������������������������������  537

Contributors

Maha  Mohamed  Abdelrahman  Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni‐Suef University, Beni‐Suef, Egypt Lateefa  A.  Al-Khateeb  Department of Chemistry, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia Ahmed E. Allam  Department of Pharmacognosy, Faculty of Pharmacy, Al-Azhar University, Assiut, Egypt Saud Almawash  Department of Pharmaceutical Sciences, College of Pharmacy, Shaqra University, Shaqra, Kingdom of Saudi Arabia Mohsen  Asadnia  School of Engineering, Macquarie University, Sydney, NSW, Australia Fathalla  Belal  Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt Ranjeeta Bhari  Carbohydrate and Protein Biotechnology Laboratory, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Tarik Bourouina  Université Paris-Est, ESYCOM (EA 2552), UPEMLV, ESIEE-­ Paris, CNAM, Noisy-le-Grand, France Laura  Cubillana-Aguilera  Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Puerto Real, Cádiz, Spain Ravikumar  Dhanalakshmi  PG and Research Department of Chemistry, Pachaiyappa’s College, Chennai, Tamil Nadu, India Mahmoud  H.  El-Maghrabey  Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt

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Department of Analytical Chemistry for Pharmaceuticals, Course of Pharmaceutical Sciences, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan Rania  N.  El-Shaheny  Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt Diksha  Garg  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Juan José García-Guzmán  Instituto de Investigación e Innovación Biomédica de Cádiz (INIBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain Surbhi  Goyal  Department of Biotechnology, Punjabi University, Patiala, Punjab, India Bioprocess Technology Lab, Department of Biotechnology, Mata Gujri College, Fatehgarh Sahib, Punjab, India Mohamed  A.  El Hamd  Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, South Valley University, Qena, Egypt Department of Pharmaceutical Sciences, College of Pharmacy, Shaqra University, Al Dawadmi, Shaqra, Kingdom of Saudi Arabia Himshweta  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Gopal Jeya  PG and Research Department of Chemistry, Pachaiyappa’s College, Chennai, Tamil Nadu, India Gurpreet  Kaur  Department of Chemistry, Gujranwala Guru Nanak Khalsa College, Civil Lines, Ludhiana, Punjab, India Diaa Khalil  Electronics and Communication Engineering Department, Faculty of Engineering, Ain-Shams University, Cairo, Egypt J. Lakshmipraba  Post Graduate and Research Department of Chemistry, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India David López-Iglesias  Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Puerto Real, Cádiz, Spain Bassem Mortada  Si-Ware Systems, Cairo, Egypt José  María  Palacios-Santander  Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Puerto Real, Cádiz, Spain

Contributors

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Rupesh N. Prabhu  Post Graduate and Research Department of Chemistry, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India Ponmudi Priya  PG and Research Department of Zoology, Pachaiyappa’s College, Chennai, Tamil Nadu, India Alisha  Rani  Research Fellow, Department of Chemistry, School of Chemical Engineering and Physical Sciences, National Taiwan University, Taipei City, Taiwan Yasser  M.  Sabry  Electronics and Communication Engineering Department, Faculty of Engineering, Ain-Shams University, Cairo, Egypt Parveen  Saini  School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, India Department of Chemistry, Shanti Devi Arya Mahila College Dinanagar, Dinanagar, Punjab, India Rajni Sharma  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India School of Engineering, Macquarie University, Sydney, NSW, Australia Alfonso  Sierra-Padilla  Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Puerto Real, Cádiz, Spain Damnita Singh  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Harminder Singh  Department of Chemistry, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab, India Jagdish Singh  Bioprocess Technology Lab, Department of Biotechnology, Mata Gujri College, Fatehgarh Sahib, Punjab, India Jandeep  Singh  Department of Chemistry, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab, India Minni  Singh  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Functional Food and Nanotechnology Group, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Vajiravelu  Sivamurugan  PG and Research Department Pachaiyappa’s College, Chennai, Tamil Nadu, India

of

Chemistry,

Neelam Verma  Biosensor Laboratory Technology, Department of Biotechnology, Punjabi University, Patiala, Punjab, India Chemistry and Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India

Chapter 1

Introduction to Green Analytical Chemistry Alisha Rani, Harminder Singh, Gurpreet Kaur, and Jandeep Singh Abstract  There has been an exponential rise in chemical research since past three decades, which has witnessed an equal growth in the use of toxic and detrimental solvents, reagents, and reactants, leading to long-term environmental damage. Thereafter, with invent and advancement in green chemistry perspective, there has been a slight shift toward utilization of green chemistry principles in research, devolvement, and implementation. But the past decade has witnessed an immense rise in the use of green analytical chemistry owing to recent development in materials and methods that support the green concept. Keywords  Green analytical chemistry · Green approaches · Twelve Principles of Green analytical chemistry · Eco-friendly techniques · Green chemistry · Sample preparation · GAPI · NEMI List of Abbreviations μTAS Miniaturized total analysis system ACS American chemical society ASE Accelerated solvent extraction CCF Cooled coated fiber CFME Continuous-flow microextraction CO2 Carbon dioxide DHS Dynamic headspace DOE Department of energy

A. Rani Research Fellow, Department of Chemistry, School of Chemical Engineering and Physical Sciences, National Taiwan University, Taipei City, Taiwan H. Singh · J. Singh Department of Chemistry, School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab, India G. Kaur (*) Department of Chemistry, Gujranwala Guru Nanak Khalsa College, Ludhiana, Punjab, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. H. El-Maghrabey et al. (eds.), Green Chemical Analysis and Sample Preparations, https://doi.org/10.1007/978-3-030-96534-1_1

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EPA FIA FTIR FT-NIR GAC GAPI HF-LPME HPHTSE HPLC HPSE HSSE HTWE HWE ITEX MAE MASE NEMI PBT PFE PHSE PHWE PLE PME PSE PSI PT SBSE SDME SFC SFE SFE-SFC SHS SHWE SIA SPDE SPE SPE-GC SPE-HPLC SPMD SPME SSE SWE TRI UAE VLCE

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Environmental Protection Agency Flow injection analysis Fourier transform infrared Fourier transform-near infrared Green analytical chemistry Green Analytical Procedure Index Hollow fiber liquid phase microextraction High-pressure, high-temperature solvent extraction High-performance liquid chromatography High-pressure solvent extraction Headspace sorptive extraction High-temperature water extraction Hot water extraction In-tube extraction Microwave-assisted extraction Membrane-assisted solvent extraction National Environmental Methods Index Persistent, bioaccumulative, and toxic Pressurized fluid extraction Pressurized hot solvent extraction Pressurized hot water extraction Pressurized liquid extraction Polymeric membrane extraction Pressurized solvent extraction Pound-force per square inch Purge and trap Stir bar sorptive extraction Single drop microextraction Supercritical fluid chromatography Supercritical fluid extraction Supercritical fluid extraction-supercritical fluid chromatography Static headspace Superheated water extraction Sequential injection analysis Solid-phase dynamic extraction Solid-phase extraction Solid-phase extraction-gas chromatography Solid-phase extraction-liquid chromatography Semipermeable membrane devices Solid phase microextraction Subcritical solvent extraction Subcritical water extraction Toxic release inventory Ultrasound-assisted extraction Vesicular liquid coacervate extraction

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1  Introduction Over the last few years, the concern of scientists and the public toward environment protection, human health, and safety has dramatically increased. Owing to this, more attention has been paid to the development of new eco-friendly analytical techniques and modifications of old methods such that they consume fewer hazardous chemicals and reduce the generation of waste. Green analytical chemistry (GAC) provides the aforementioned goals. Nowadays, the role of Green analytical chemistry (GAC) is widely popular which indicates the efforts put up sustainable techniquesand getting rod of the unwanted products. Many scientists are discovering and unwinding the branches of GAC day by day. GAC highlights an ethical compromise between the society and environment as well as many economic opportunities that arise with it. At the earlier stage when the green analytical chemistry was not introduced, the recorded instances showed that surroundings were facing many issues because of industries and laboratory toxic wastes, which were spreading dramatically in the air, water, and soil; owing to this, societies were facing a plethora of problems related to their health. Moreover, the operators who handle the toxic chemicals were facing many serious health issues because of accidentally inhaling of toxic chemicals during their work. Therefore, there arose a need for green analytical chemistry. New sustainable methodologies were introduced in order to preserve reagents and solvents; moreover, it also contributes towards the swapping the most toxic chemicals with lesser ones. These methodologies are more efficient and becoming more environmentally friendly. In addition, to make it better, the 12 principles were laid down to minimize the life risk of mankind and optimize the reaction conditions and outcome. This chapter highlights the origin of green analytical chemistry and its 12 principles, as well as its close link to green chemistry. Moreover, the bond between sample preparation technique and sustainable development, along with the facts, which highlight the escalating interest in eco-friendly green analytical chemistry, will also be discussed.

2  The Origin of the Notion of Green Analytical Chemistry Nowadays, the branches of sciences are spreading with time, chemistry is one of them. Indeed, chemistry has also many disciplines like green chemistry, analytical chemistry, environmental chemistry, biochemistry, and so on. Since from few past years, in the field of chemistry, a plethora of inventions is introduced by scientists in our society, most of the inventions are invented with the consideration of environmental safety as well as human health. The most valuable discipline of chemistry, which is motivated toward surrounding safety and mankind’s health, is “green analytical chemistry.” Before knowing the definition of GAC, we have to first understand the terms analytical chemistry and green chemistry. The core of green

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chemistry relies on the designing and processing of desired products taking into consideration of less consumption of hazardous chemicals along with a minimal generation of undesired toxic waste (Anastas 1999). However, the evolution of novel methods, instruments, and strategies to examine the separation, quantification, and identification of the chemical composition is known as analytical chemistry. The identification of analyte from the chemical composition is considered as qualitative analysis. However, the amount of the desired component in the sample is measured by quantitative analysis (Kellner et al. 2004). The term green analytical chemistry was designed via coupling of green chemistry and analytical chemistry as shown in Fig. 1.1 (Tobiszewski 2017). It has been stated that “The techniques and methodologies of analytical chemistry are used differently, and they are used in such a way that they reduce or eliminate hazardous chemicals, which put lives and environment at high risk of danger.” Indeed, these new methodologies give a promising performance with more efficiency and minimal loss of energy (Ferguson and Raynie 2018). Before the introduction of GAC, the major issues such as operator safety and the quality of the environment by using analytical methodologies were neglected. Moreover, sometime during analysis, it was found that the given samples were more toxic than the analytes being determined. By considering all these issues, a report was presented to the National Academy of Sciences entitled “Opportunities in Chemistry” by Professor George Pimentel in 1985. He pursued enormous research in the discipline of chemistry. He pursued enormous research in the discipline of chemistry, especially its proposal to the Environmental Protection Agency, which includes four recommended points: (i) need to raise the amount of funding for pioneering research particularly on environmental problems, (ii) improvement in reaction scheme by considering environment health, (iii) need to detect the unwanted product before it reaches to its level of toxicity, and (iv) support from EPA to

Fig. 1.1  The combination of green chemistry (GC) and analytical chemistry (AC) leads to the origin of green analytical chemistry (GAC)

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analytical chemistry is a prominent way. The report proposed by Pimentel directed a safe way for the analytical chemistry to grow in a fast manner along with the improvement in the identification and quantification of toxic chemicals in the environment (National Research Council 1985). The conclusion of Pimentel’s report was following the idea of Professor Malissa. After 2 years, in 1987, Professor Hanns Malissa expounded his concept at the Euroanalysis VI conference regarding modification in paradigms in analytical chemistry along with ecological paradigm, which was implemented just before the beginning of the twenty-first century (Green et al. 1995). In addition, he successfully introduced six paradigms in the evolution of Analytical Chemistry—from Archeochemistry to Alchemy, Iatrochemistry, Chemiology, and Chemiurgy and finally to Ecological Chemistry. Ecological Paradigm containing all the stages of the analytical process is presented in Fig. 1.2 (Wasylka and Namieśnik 2019). The main purpose of his work was to establish chemical knowledge within the frame of environmental equilibrium. This new framework gave rise to sustainable chemistry, in which all problems related to synthesis, analytical methodologies, must be examined in order to save collateral damage. The need for sustainable chemistry for the development of the analytical community is very high. Therefore, considering the analytical process, the devotion paid to the problem solving and obtaining data is extended along with the concern of

Fig. 1.2  Presented all the stages of the analytical process by taking into account of the Ecological Paradigm (Reproduced with permission from Wasylka and Namieśnik (2019) © Springer)

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nature and the number of reagents used, the waste emitted, the necessity of energy, and the operator’s safety. The evolution of ecological mentality summarized within the analytical laboratories is shown in Fig. 1.3 (Armenta and de la Guardia 2011). In the 1990s, a paradoxical situation was generated owing to the large amount of hazardous chemical waste residue generated by the use of analytical methodologies, which was used to investigate diverse kinds of samples, together with environmental samples (Armenta et  al. 2008). Due to these paradoxical situations, scientists were starting to make analytical methods more eco-friendly by minimizing the risks for operators with the help of mechanized procedures and closed systems (Ružička and Hansen 1975). Moreover, in 1994, some initiatives were proposed like the development of environmentally friendly analytical methods (De la Guardia and Ruzicka 1995) or clean methods (De la Guardia et al. 1995). The GAC methods (or clean analytical methods) term was first coined in 1995 and published in the Royal Society of Chemistry journal (De la Guardia and Ruzicka 1995). Moreover, in the same year, a report entitled “Waste minimization in Analytical Methods” at the DOE Pollution Prevention Conference XI was executed by David W. Green et al. The main objective of that report was to give the new place to waste minimization in the series of characteristics, which is used to choose the suitable analytical method. In addition, the existing analytical methods were modified for the characterization of waste at a low cost (Green et al. 1995).

Fig. 1.3  The overall output of evolution of ecological mentality within the analytical laboratories (Armenta and de la Guardia 2011)

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Before the beginning of the twenty-first century, many scientists were putting their efforts to diminish the impact of toxic compounds on their surroundings. In 1998, after so many efforts, Paul and Warner had outlined the twelve principles of green analytical chemistry, which is following these points including reduction of waste generation, overall cost, energy consumption, and analysis time, along with the maximum use of eco-friendly solvents and reagents to prevent the hazardous tragedies (Anastas and Warner 1998). After this GAC started to gain popularity. From data analysis, it was recorded that from 1995 to 2000 the number of research papers on GAC increased slowly but the end of the twentieth century led to the exponential growth of publications. The aforementioned data was gathered via the “Web of Science Core Collection” (De la Guardia and Garrigues 2020).

3  Milestone of Green Analytical Chemistry Since the middle of the 1970s, with the help of new pioneering work, the recorded progress rate from sample preparation, measurements, to data handling was very high. The highlights of paramount innovation along with their year of the invention are projected in Table 1.1 (Susdorf et al. 2019), which remarks that how the progress of analytical chemistry fits in the framework of GAC. Moreover, by considering the published research papers from 1995 to 2000, only 27 papers were published regarding GAC, but in these papers, the term “green” was not used directly; instead of this, the claim in those papers was related to sustainable methods. However, in 2001, it was the first time when a research paper included the term “Green Analytical Chemistry”, and it was Namiesnik who published this paper (Namieśnik 2001). From the literature survey, it was calculated that up to 2019, less than 60 papers have been printed that contained the whole term “green analytical chemistry” in their headings (De la Guardia and Garrigues 2020). With the passage of time, the publications related to GAC are increasing. Moreover, the number of citations of papers is also dramatically increasing. Although from 1995 to 2000, the number of citations of papers was completely negligible; however, after 2000, it gradually increased till 2004. In 2008, the number of times GAC papers cited reached up to 2693 (De la Guardia and Garrigues 2020). The contribution in the development of GAC is not only made by research papers, some informatic books are also considered. The first book on GAC entitled Green Analytical Chemistry was published in the Royal Society of Chemistry in 2010, introduced by Koel and Kaljurand (2010). This contribution was followed in 2011 with two new books published in Elsevier and the Royal Society of Chemistry. However, it is vital to note that up to 2020, only nine books are available related to green analytical chemistry as shown in Table 1.2. Although from 1996 to 2007, books and journals were more inclined toward only green chemistry (De la Guardia and Garrigues 2020).

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Table 1.1  Paramount inventions in the discipline of green analytical chemistry Year Inventions 2018 Developed index for green analytical procedures. Report on ultrasound-assisted solvent extraction of porous membrane packed solid. 2013 Supramolecular solvent-based HP-LPME introduced to laboratory practice. Parallel artificial liquid membrane extraction. 2012 The six principles of green extraction of natural products introduced. 2011 Carbon nanotubes proposed as extracting agent, instead of a supported liquid membrane for a microextraction that hybridizes HF-LPME and SPME. 2007 Patent of fiber-packed needle for analyzing aldehydes and ketones. 2006 Introduction of an in-needle SPME device for the analysis of VOCs using a copolymer of methacrylic acid and ethylene glycol dimethacrylate. Miniaturization and automation of CCF. 2004 Application of hollow fiber membrane-protected solid-phase microextraction of triazine herbicides in bovine milk and sewage sludge samples. 2003 Thin-film extraction. First report on microextraction in packed syringe. 2000 Solid-phase dynamic extraction. 1999 Origin of green chemistry Origin of integrated approach in analytical chemistry Origin of green analytical chemistry First publication focused on SBSE application 1996 Pressurized solvent extraction Liquid phase microextraction Single drop microextraction 1995 The origin of environmental friendly analytical chemistry Cold fiber HS-SPME device (CCF) 1994 Idea of clean analytical chemistry 1993 Head-space SPME Molecularly imprinted solid-phase extraction 1990 First publication on SPME Micro total analysis system Sequential injection analysis 1987 The origin of ecological chemistry The origin of sustainable development 1985 Microwave-assisted extraction Supercritical fluid extraction 1978 Cloud point extraction 1976 Solid phase extraction 1975 Merits of microwave ovens for sample digestion 1974 Flow injection analysis Purge and trap technique

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Table 1.2  List of most cited books related to green analytical chemistry Year Authors/Editors 2020 S. Garrigues and M. de la Guardia 2019 J. Płotka-Wasylka and J. Namieśnik

2019 M. Koel and M. Kaljurand 2017 E. Ibañez and A. Cifuentes 2014 Inamuddin and A. Mohammad

2012 M. de la Guardia and S. Garrigues 2011 M. de la Guardia and S. Garrigues 2011 M. de la Guardia and S. Armenta 2010 M. Koel and M. Kaljurand

Title Challenges in green analytical chemistry second edition Green analytical chemistry: past, present and perspectives

Publisher Royal Society of Chemistry

Ref. De la Guardia and Garrigues (2020)

Springer Nature Singapore Pte Ltd. Green analytical chemistry, Royal Society second edition of Chemistry Elsevier Green extraction techniques: principles, advances and applications Springer Green chromatographic techniques: separation and purification of organic and inorganic analytes Handbook of green John Wiley & analytical chemistry Sons Challenges in green Royal Society analytical chemistry of Chemistry Green analytical chemistry: Elsevier theory & practice Green analytical chemistry Royal Society of Chemistry

Wasylka and Namieśnik (2019)

Koel and Kaljurand (2019) Ibanez and Cifuentes (2017) Inamuddin and Mohammad (2014) Guardia and Garrigues (2012) De la Guardia and Garrigues (2011) Armenta and de la Guardia (2011) Koel and Kaljurand (2010)

4  S  urrounding of Green Analytical Chemistry via Green Chemistry The twelve principles of green chemistry were framed by Anastas and Warner, and they are the basis of the theoretical development of green analytical chemistry (Anastas and Warner 1998). These principles provide alternative research on sustainable chemistry and are as follows: 1. Chemical waste should be prevented to eliminate the requirement of decontamination procedures. 2. Safe chemicals and products should be designed to minimize the toxic effects. 3. Design less toxic chemical synthesis as much as possible. 4. Renewable feedstock must be used for fossil fuel by replacing depleting feedstock. 5. Catalysts should be used to lessen the amount of reagents. 6. The number of reagents should be minimized by avoiding chemical derivatizations.

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7. Utilize atom economy at its maximum level in order to get excellent synthesis yield with minimal waste generation. 8. Prefer eco-friendly chemicals along with safer reaction conditions to reduce smog formation or ozone layer depletion. 9. At room temperature, the efficiency of work should be maximized. 10. Degradable chemicals and products should be synthesized. 11. Analyze in real-time to prevent pollution, thus involving in-field analysis and real-time monitoring of processes. 12. The probability of accidents such as explosions and toxins released into the environment should be minimized. Among all the principles, the 11th one was inclined toward the need for real-time analysis to avoid pollution; however, many of these analyses could be directly interpreted to the necessities of GAC methods. It was observed that all green principles were devoted to synthetic and process chemistry; however, some of them did not apply to analytical chemistry. Hence, in 2013, Gauszka, Migaszewski, and Namiesnik have recast the principles of green chemistry and established new principles by considering the specific point that was to avoid derivatization, which is a common point between green chemistry and the principles of GAC. Moreover, they arranged those principles into so-called SIGNIFICANCE mnemonic in an excellent manner (Gałuszka et al. 2013). These reinterpret of GAC principles are: S Select direct methods of analytical chemistry. I Integrate analytical processes and operations. G Generate minimal residue. N Never put energy in vain. I Implement automation and miniaturization of methods. F Favor reagents gained from renewable sources. I Increase the operator’s protection. C Carry out in situ measurements. A Avoid derivatizations. N Note that the number and size of the sample must be minimum. C Choose multiparameter methods. E Eliminate hazardous reagents. In 2001, Namiesnik has highlighted the four major key priorities of green analysis by considering the green principles. Hence, it would be a notable step in green analytical methods by following those four routes (Namieśnik 2001). The major points to be adapted in green analytical methods are as follows: 1. For the analytical procedure, the consumption of reagents and organic solvents should be avoided. 2. Waste generated from analytical laboratories in every form either vapors, gas, or solid should be minimized. 3. Exclusion of reagents exhibiting high toxicity from analytical procedures. 4. Energy and cost consumption for analytical methodologies should be minimized.

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Fig. 1.4  Summarizes the top 6 priorities of green analytical chemistry along with the basic strategies for greener analytical chemistry

By considering all the aforementioned principles and priorities, six new basic strategies are formed for green analytical methods. Those strategies are as follows: (i) The in-field direct examination of untreated samples, (ii) reduction in consumption of energy, reagent, and cost for sample treatments, (iii) methods should be miniaturized and automated, (iv) alternative of organic solvents and reagents should be searched, (v) miniaturized and automated methods using on-line decontamination of wastes leads to a huge decrease in the emission of toxicity, (vi) the assessment of energy consumption along with automation to evaluate the drop of labor and energy consumption of analytical procedures (De la Guardia and Garrigues 2020; Armenta and de la Guardia 2011). Figure 1.4 summarizes the top six priorities of GAC along with the basic strategies for greener Analytical Chemistry.

5  Major Tools for Green Analytical Chemistry The frequent consumption of reagents and solvents in analytical methods leads to the generation of waste. Therefore, some principles introduced by Anasta can be taken into consideration in the field of analytical methods; the objective of those principles is to provide the alternative of toxic reagents along with preserving energy and waste generation. Moreover, along with these principles, enormous specific strategies are also there for greening the analytical methods. Three major tools are available to make analytical methods more eco-friendly; those tools are

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chemometrics, automation, and miniaturization. Moreover, tools for the evaluation of green parameters are also vital to perform analytical processes in a green manner. Each tool has its specialty. By using these tools, a tremendous decrease in reagent consumption and waste generation can be achieved (Guardia and Garrigues 2012).

5.1  Chemometrics Professor “Svante Wold” was the first who coined the term “chemometrics” and in 1972, he introduced his pioneering work by using the term “chemometrics.” Chemometrics is a predominant tool that provides the advanced statistical and computational treatment of chemical data (De La Guardia 1999). Chemometrics provides a piece of significant information by performing a small number of experiments and also prevents errors by taking into account environmental safety. By using chemometric approaches along with methodology, the mapping of experiments, signal preprocessing, exploration of data, optimization, calibration, pattern identification, and interpretation of complex data all these processes are executed in a simple way without using trial-and-error approaches (De la Guardia and Garrigues 2020). At the beginning of the twenty-first century, the revolution in chemometrics came as a result of the ultrafast personal and clustering computers providing not only fast processing for data evaluation; moreover, it also assigns easily accessible vast storage space (De la Guardia and Garrigues 2020). The huge demand for improvement in experimental design, the process of optimization, and communication technology in chemical data analysis was increased; owing to this, a huge variety of chemometrics methods have been developed taking into account the GAC principles. The original objective of these methods is to minimize a plethora of steps in chemical analysis, reduce consumption of reagents, labor work, and energy (De La Guardia 1999). The core goals of chemometrics can be accomplished by optimization, automation, and robotization. The utilization of chemometrics methods is not only limited to analytical chemistry, but it is also used in other disciplines such as biological informatics, agricultural chemistry, spectroscopy, and food chemistry (De la Guardia and Garrigues 2020). Moreover, it also can be considered as an excellent tool because its demand for the analysis of sample properties via external calibration and specific procedures is almost negligible. Chemometrics steps forwards toward making analytical methods greener by gathering accurate information directly from the signal in remote sensing methods without using a plethora of reagents, this assigns a very simple measurement process of analysis in a very short period of time (De la Guardia and Garrigues 2011). In addition, when chemometrics is coupled with spectroscopic techniques, as a result a powerful tool originated that can exhibit promising detection ability against abnormal cells such as cancer cells. At present owing to the wide application of chemometrics, the researcher develops a great concern of attention in the evolution of green analytical chemistry by using chemometric techniques. As a result, high progress in the number of publications regarding applications of chemometrics methods in reputed journals such as

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Analytical Chemistry, Analytical Methods, Talanta, Food Chemistry, and Journal of Agricultural and Food Chemistry is observed (De la Guardia and Garrigues 2020).

5.2  Miniaturization By using Miniaturization, downsizing the parameters, instruments, procedures, and scale of analytical methods by considering the twelve principles of GAC is quite challenging. From the literature, it was analyzed that researchers were putting efforts to decrease the size of the pretreatment and measurement steps, which is linked to the evolution of microextraction technologies and total analysis in order to move from gram and milliliter scales to micro- and nanoscales (Guardia and Garrigues 2012). Today, miniaturization highlighted its importance and its major place in the discipline of analytical applications. The major and first requirement to make a couple of analytical procedures is miniaturization and can be regarded as the primary essential stage for the development of hyphenated, (semi-)automatic systems (De la Guardia and Garrigues 2011). The decline in the graphs of the amount of reagents consumed, energy, and waste generation can be obtained by using miniaturized processes and instruments, which provides dramatic inclination in the safety of operators and also considers proper waste treatment (Gałuszka et al. 2013). The role of miniaturization in sample preparation is very vital. In sample preparation, many vital steps occurred in analytical procedures but the slowest and most tedious part occurred in the sample digestion and analyte extraction step. These steps not only include the consumption of a large number of reagents but also employ the personal risk to the operator’s health and environment. In order to obtain accurate data, the whole process from sample handling to sample preparation becomes a hugely time-consuming process. To terminate these drawbacks, a plethora of pioneering work is established in recent years (Armenta and de la Guardia 2011). The best instance of miniaturization of processes and operations is explained by miniaturized total analysis systems (μTAS); it was first highlighted in 1990, in which sample treatment and measurements are situated acutely handy to each other (Manz et al. 1990). The progress of these miniaturized systems spread extensively with their application in many disciplines such as clinical/bioanalytical and environmental laboratories (Ríos et al. 2006). The major merits of μTAS are the reduction in reagent amount and sample amount, which is complete with the agreement of GAC principles (Gałuszka et al. 2013). There are several merits by on-line coupling of the miniaturized sample preparation and the determined procedure, such as (i) rate of analysis increase along with proficiency, (ii) reduce the amount of solvent consumed as a result of operation cost decrease. These advantages help us to make the analytical methods more ecofriendly. Procedures can be miniaturized by decreasing the dimensions of the system or by introducing new techniques. A miniaturized pressurized liquid extraction (PLE) device has been invented recently. Lab on a chip is another output of miniaturization. It is a tool that enhances the

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efficiency of the analysis process under its condensed size as well as it provides fast response and enhanced analysis throughput together with multifunctionality, and it reduces the amount of sample need, reagents, manufacturing costs, and waste generation (Armenta and de la Guardia 2011). Moreover, miniaturization is also used in chromatographic techniques, especially in liquid chromatography. Miniaturized liquid chromatography can be obtained by considering small size particles in the stationary phase; as a result, separation becomes fast, and the amount of consumption of the mobile phase decreases. There are mainly three advantages of miniaturized liquid chromatography. They are as follows: (i) in a very short time, better resolving power can be achieved, (ii) less amount of sample is required for examination, and (iii) the costs and unwanted side effects of solvent consumed can be minimized. At present, a plethora of miniaturized chromatographic systems are introduced (e.g., compact and portable chromatographs or microchromatographs) along with their numerous applications (De la Guardia and Garrigues 2020). Moreover, with the help of miniaturization, spectrometric detection techniques became more convenient and eco-friendlier. Its ability is very impressive and it can record low levels of background signals coupled with very sensitive photon detection techniques in order to detect very low limits. Along with miniaturized spectroscopic techniques, miniaturized electrodes are also made for electrochemical detection, these systems help in the reduction of sample used along with minimizing the resistance effects, which is linked to voltammetric measurements in low-ionic-strength water samples (Armenta and de la Guardia 2011). So, by considering all these applications of miniaturization, it is evident that miniaturized processes and instruments are in accordance with the green conditions of the environment.

5.3  Automation Automation has many definitions. The International Union of Pure and Applied Chemistry provides a proper description of automation: automation is the coupling of mechanical as well as instrumental devices which helps to replace, refine, extend, or supplement human effort and facilities in the performance of a given process, moreover, here at least one major operation should be controlled without human intervention, by a feedback mechanism (Stockwell 1990). Due to the increase of legislative control in many areas along with the growing threat for the quality of the environment, the demand for the production of cost-effective analysis was increased; in order to fulfill these demands, the research in automation became increased dramatically. In the mid-1970s, there was a revolution came in analytical methods from automation (Guardia and Garrigues 2012). The main goals of automation are to minimize human interference throughout the chemical analysis and save the valuable time of operators for the more demanding tasks (Stockwell 1990).

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Rapid sampling and cleaning of the measurement devices

Automation

Fast analytical control of the baseline/ background Simple and fast standardization Boost repeatability Minimized reagent, solvent and waste generation Increase analytical sensitivity by online coupling of detection with separation/preconcentration techniques

Fig. 1.5  Summarizes the merits of automation in measurement

In analytical methods, the role of automation is vital, it minimized the number of steps involved in the whole process along by considering operator safety and environmental risks (De la Guardia and Garrigues 2020). Moreover, automated methods also provide an alternative way for the reduction of waste produced during the analytical process. In order to achieve its goal, it permits the mixing sample and reagents on-line; hence, it is capable of avoiding the preparation of a volume of treated sample higher than those essential for analytical measurements (Armenta and de la Guardia 2011). The benefits of automation in measurement are shown in Fig. 1.5. Last but not the least, another merit of automation is to save and minimize the consumption of solvents and detergents by avoiding the washing of the glassware employed throughout the process. Although automation is not only limited to simple instrumentations, it spread its routes also in management techniques; owing to this, industrial, clinical, and process chemists obtained a valuable opportunity to increase their knowledge by sharing their experience of automation in daily use with each other. In the progress of automation, the key milestones correspond with the use of segmented flow, flow injection analysis (FIA) developments, advances regarding monosegmented flow, and those for sequential injection analysis (SIA) and multicommutation. By employing automated analytical techniques such as solid-phase extraction and supercritical fluid extraction, the consumption of energy and labor per analysis can be reduced. However, these automated solid phase extractions are united with flow injection analysis FIA in order to replace toxic reagents and reduction in the demand for solvents, and sample size. There are three major analytical techniques FIA, SIA, and multicommutation, which are specially designed for automation (Guardia and Garrigues 2012). Moreover, automated flow analysis tools provide a perfectly unique way of solution handling along with automation of measurements without any physical contact of operator with toxic chemicals (Armenta and de la Guardia 2011).

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5.4  Green Analytical Evaluation Tools From the literature survey, identification of the trends in green analytical chemistry is a challenging task. Due to lack of biased parameters, it is unfeasible to quantitatively compare the greenness of analytical methods. Since analytical methods contain several steps, as well as the procedures are usually complex, it is very difficult to record the overall impact of analytical procedures on our ecosystem. Therefore, many efforts have also been made by researchers in order to develop evaluation in tools that provides a facility for the proper evaluation of analytical procedures (Wasylka and Namieśnik 2019). To assess the analytical methods, “greenness” criteria were introduced by the ACS Green Chemistry Institute, the major goals of these criteria are to minimize the consumption of harmful solvent, waste generation, and promote the use of safe chemicals. The above-mentioned norms have been applied to the National Environmental Methods Index (NEMI), which has a collection of vast information such as methods, summaries, metadata, and, in addition, it has a bunch of information of more than 800 methods (Keith et al. 2007). NEMI is a tool that is used to evaluate the analytical procedure in a fast manner on its greenness parameters. Four major key terms define the greenness criteria, those are “persistent, bioaccumulative, and toxic reagents (PBT),” hazardous, corrosive, and waste, it concerns the use of PBT as well-defined by the Environmental Protection Agency (EPA) and involved in the toxic release inventory (TRI). To make analytical methods according to “greenness” criteria, there are some important points: • The chemicals used in the analytical process should not be listed as persistent, bioaccumulative. • The range of pH via analysis should be lower than 2 or higher than 12. • The total quantity of waste generated should not be more than 50 g. The outputs of the assessment are printed in the form of the profile’s symbol, which is separated into 4 parts: individual part has different criteria for the assessment of the greenness of the method as shown in Fig.  1.6a (Keith et  al. 2007). Although NEMI pictograms are easy to understand, it is a very time-consuming tool, because of the need to identify each chemical from a voluminous list of harmful substances. Moreover, it does not execute any information related to quantitative

Fig. 1.6  Green evaluation tools (a) NEMI pictogram: (b) Given by Rayne et  al.: (c) GAPI by Wasylka and Namieśnik (2019) (Reproduced with permission from Wasylka and Namieśnik (2019) © Springer)

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and semi-quantitative. Hence, in 2008, Guardia et  al. introduce a unique three-­ colored scale that is used to examine the greenness of each part of the procedure (Armenta et al. 2008). In addition, Raynie and Driver proposed another tool that is following 5 points as shown in Fig. 1.6b. The uniqueness of this tool is that evaluation of the greenness of two procedures or more than two procedures can be compared by using visual representation. In this, the pictogram is divided into five parts, and based on their category’s environmental impact, they are painted as red, yellow, and green (Raynie and Driver 2009). Apart from the aforementioned tools, Namiesnik et al. introduced a new approach. The specialty of this approach is defined by its ability to assess the enormous parameters of the procedure, for example, the number of reagents used and toxic residue emission. However, penalty points are also available for those parameters that are not able to fulfill the mean of GAC principles. From these calculations, it is very easy to understand the greenness of the method; if the final score is higher, it means the given method is more toward greener (Gałuszka et al. 2012). The modern tool is the Green Analytical Procedure Index (GAPI). In this, each step of the analytical procedure is evaluated in a frame of a pictogram, which is divided into five pentagrams, as shown in Fig. 1.6c. A three-colored scale is designed for the analysis of the environmental impact of sample collection and preparation, reagents used, instrumentation along general methods (Płotka-Wasylka 2018). GAPI can be considered as an excellent tool because of its simplicity and ability; moreover, by using this, the semiquantitative information can be obtained (Wasylka and Namieśnik 2019).

6  A  nalytical Techniques for Sample Preparation in the Framework of Sustainable Development In modern chemical analysis, some certain target compounds are required to analyze at trace levels; a sample preparation step is performed in order to fill these requirements. Nowadays, mostly chromatographic techniques are used in the determination of trace organic compounds, it includes extraction of analytes. The oldest technique is liquid-liquid extraction (LLE); however, it is very time-consuming and laborious, automation is difficult, and large amounts of organic solvents are required that are quite toxic, expensive, and volatile. However, still LLE is used in analytical practices despite its demerits. The position of the analytical instrument with respect to the sample is a very crucial topic, four possible combinations are available for it. 1. off-line – For analysis, here the collected sample is transported to the laboratory. 2. at-line – in this mode, the sample is gathered manually and examined at the sampling site. 3. on-line – it provides automatic collection of samples in a sequence manner. 4. in-line – The investigated medium analytical sensor is hired in it. Among all four modes, the off-line mode adapts least advantages because a large amount of energy is required for storage and transport, it also includes a sample

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preparation stage; however, in the alternative approaches, there is no requirement of any sample preparation step. Although the direct analysis does not adapt a sample preparation step in all four modes, most analytical techniques are indirect; hence, they involve a key step that is the sample preparation step (Guardia and Garrigues 2012). Sample preparation and extraction step are the most polluted part of analytical practices. Many pioneering works were done to introduce the green sample preparation tools by considering these parameters: (i) deduction in the amount of sample used, (ii) the use of organic solvent should be minimized, (iii) multiclass compound extraction, (iv) tendency for automation. From the literature survey, it was analyzed that sample treatment is a valuable step to make the analytical method greener. Time spent on the sample preparation is a very vital point in the analysis, it can be minimized by using the automation extraction techniques; moreover, it is more reproducible than the manual ones. The different techniques are classified in accordance with the physical state of the samples, solids, or liquids. It should be highlighted that nowadays, PLE and SPME techniques are growing dramatically because of their wide applications, PLE is highly efficient, productive, and reproducible. Moreover, SPME is simple to perform as well as it is virtually a solvent-less technique (Tobiszewski et al. 2009).

6.1  Green Extraction Techniques for Solid Samples The introductory step of the analytical procedure is the drawing out of analytes from solid samples. A suitable extraction method can be chosen by analyzing the basic properties of the matrix and the analyte; these techniques help us to extract our desired analytes as well as their concentration level in the sample. Here modern, more usable, and eco-friendly techniques will be highlighted. Here five major analytical techniques will be discussed for the extraction of analytes from solid samples. Those are Supercritical fluid extraction (SFE), Pressurized solvent extraction (PSE), superheated water extraction (SHWE), Ultrasound-assisted extraction (UAE), and Microwave-assisted extraction (MAE). The first one is Microwave-­ assisted extraction (MAE), which has been employed to pull out the analytes from a different kind of matrix into the solvent solution; in this, microwave energy is utilized to generate heat for the solvent, which is connected with a sample. Heating is a major part to operate MAE, because of the need of microwave energy by polar molecules. Several factors describe the proficiency of MAE. Major ones are the properties of sample and solvent, the components being extracted, and dielectric constant. The huge value of the dielectric constant leads to an increase in the packet of energy that is consumed by the molecules; as a result, the system reaches toward the desired heat for extraction in a fast manner (Armenta and de la Guardia 2011). Moreover, as compared to traditional extraction methods, MAE requires less organic solvent and a minimum time for extraction. Pastor et  al. presented their work and in that it was mentioned that MAE has the capability to decline the withdrawal time by a factor of 20 along with a reduction in solvent consumption by a

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factor 10 as compared with the Soxhlet extraction. Moreover, by comparing with UAE, MAE reduces solvent consumption and time by factors 15 and 3 (Pastor et al. 1997). Although MAE is merely suitable for thermally stable compounds because of the high demand for high temperatures throughout the extraction process, MAE has been introduced with the help of water and some organic solvent to achieve GAC extraction of atrazine, simazine, and prometryne from synthetic soil samples (Xiong et al. 1999). With the help of a green solvent that is water, by using only 30 ml of it, Triazines could be efficiently extracted in a cheap, safe, and eco-friendly manner. For the extraction of analytes from the soil, MAE coupled with the micellar system can be used; by using this method, phenol from the soil can be extracted easily. This method provides a feasible and eco-friendly alternative by substituting organics with surfactants (Armenta et al. 2008). The five key merits of MAE are as follows: (i) minimum extraction time, (ii) reduced volume of sample required, (iii) high sample throughput, (iv) reduced cost, (v) possibility of automation. Apart from MAE, supercritical fluid extraction (SFE) is also utilized for the extraction of desired organic analytes from the solid sample. The specialty of SFE is that it provides an alternative path to diminish the unwanted effect of nonpolar solvents, which is used in the extraction of nonpolar molecules. Mostly, in SFE, carbon dioxide is used as an extraction solvent; it provides more selective extraction along with fast kinetic reaction than the use of other solvents. By altering pressure and temperature along with the small addition of solvents as modifiers can lead to the modification of the solvation power of the fluid. To enhance the efficiency of extraction of polar compounds and even ionic compounds, CO2 is combined with one or more modifiers. There are several benefits to using carbon dioxide as a solvent in the extraction process: it is inexpensive, nonflammable, and nontoxic. Moreover, after extraction, the complete supercritical CO2 can be easily unemployed via reducing the pressure (Armenta and de la Guardia 2011). The major merits of SFE are as follows: (i) high concentration of analytes obtained; (ii) provide quantitative values; (iii) fast, simple, and selective. The success of widely used SFE can be attributed to its ability to get easily and automated, which made it a more eco-friendly analytical technique (Armenta et al. 2008). Moreover, the residue of pesticides from plants and fruits can be easily obtained by using SFE (Lehotay 1997). In addition, SFE has the ability to execute in both static and dynamic modes. In the static mode, at fixed temperature and pressure, the solvent and sample are mixed and kept for a specific time. However, in the dynamic mode, the fluid flows continuously through the sample. The collected analytes can be deposited inside an off-line device; this stage is employed by depressurizing the supercritical fluid and absorbing our desired analytes into a solvent. In 1962, the SFE counterpart that is chromatographic was invented (Armenta and de la Guardia 2011) and appeared in the mid-1980s as an efficient instrument to reduce the complications associated with solid sample extraction (Luque de Castro and Jiménez-Carmona 2000). Besides the merits of SFE, extraction conditions such as lack of consistency and the robustness of SFE are the major challenges in it as compared to other techniques. The third attractive and alternative method for the extraction of organic molecules is pressurized solvent extraction (PSE), which can be called accelerated solvent

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extraction (ASE), pressurized liquid extraction (PLE), pressurized fluid extraction (PFE), high-pressure solvent extraction (HPSE), high-pressure, high-­temperature solvent extraction (HPHTSE), pressurized hot solvent extraction (PHSE), and subcritical solvent extraction (SSE); it was first introduced in 1995 (Tobiszewski et al. 2009). These techniques help us to extract the analyte from solid and semisolid sample matrices through rising the temperature (50–200 °C) and pressure (500–3000 psi) in a small interval of time (5–20 min) (Richter et al. 1996). By escalating the temperature, the solubility of target analytes also rises, and also the interaction between analyte and matrix became weak; owing to this, the diffusion of analyte takes place at the surface of the matrix and extraction becomes easy. High pressure is applied in order to maintain the solvent into a liquid state at a high temperature. The pressure, temperature, and the properties of the matrix are the three major factors on which the efficiency of PSE is completely dependent. PSE has the capability to perform in static mode as well as in dynamic modes. In case water is used as a solvent in the extraction process, then the whole process is known as superheated water extraction (SHWE). SHWE provides the green solvent for the extraction process in order to diminish the consumption of organic solvents, so it prefers to utilize the water in the condensed phase between 100 °C and the critical point and it can be called as subcritical water extraction (SWE), hot water extraction (HWE), pressurized hot water extraction (PHWE). Although SHWE also has a major disadvantage, especially for trace analysis, SHWE provides the required analyte into diluted aqueous solution form, so formerly the analysis, a concentration step is highly essential. Moreover, in the PSE technique, there is also a demerit that is if the sample contains a large amount of water in it then the hydrophobic organic solvent will not be able to make interaction with analytes as a result efficiency of analyte extraction became decrease (Armenta and de la Guardia 2011). The last technique is ultrasound-assisted extraction, which is called as sonication. In this ultrasonic vibration is used for the close interaction among the sample and the reagent solution in order to extract the desired analyte. Besides extraction ability, sonication is also used for sample dissolution, it is a very less time-consuming technique rather than the traditional extraction or dissolution techniques. Moreover, it prefers to perform in static mode. Although it is not merely limited to static mode, it can also be executed in dynamic mode. Ultrasound-assisted extraction faces a major disadvantage, in this technique; it is very hard to achieve on-line combination as well as its automation is very challenging excluding those cases in which high-power probes are used (Armenta and de la Guardia 2011).

6.2  G  reen Extraction Techniques for the Liquid Sample and Volatile Analytes There are a plethora of green analytical techniques available for the extraction of the liquid sample and liquid analyte from a solid sample such as Solid-phase extraction (SPE), Solid-phase microextraction (SPME), In-tube SPME, Solid-phase dynamic

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extraction (SPDE), Stir bar sorptive extraction (SBSE), Single drop microextraction (SDME), Liquid phase microextraction (LPME), Continuous-flow microextraction, Membrane-based techniques, Surfactant-based analyte separation, and Direct thermal desorption. However, some of them are used most frequently because of their high approaches toward sustainable development. Here some major techniques will be discussed. Talking about SPE, it was introduced in the early 1980s in order to reduce the need for solvents in laboratories. It is included in the list of highly popular sample preparation techniques used for liquid samples and organic analytes (Hennion 1999). The basic principle of SPE is the sorption of analytes on a solid phase from the original sample. The detailed evolution of SPE in the analysis of pollutants from water since the last 50  years is described in an excellent review (Liška 2000). SPE has many advantages such as reduction in solvent demand, easy automation, high precision, and throughput; as a result, the accuracy, precision, and laboratory throughput become improved. In 1990, a new interesting SPE technique is Solid-phase microextraction (SPME), which was announced by Arthur and Pawliszyn (1990). This technique is completely within the agreement of principles of green analytical chemistry, and it is very simple to perform with high efficiency. Moreover, there is no need of solvent while performing this technique; as a result, it minimizes the waste generation derived from the sample preparation step. In addition, in a single stage sampling preparation, extraction, concentration, and sample introduction can be achieved. SPME contains a coated fiber that is employed in contact with the sample matrix. The main merits of SPME are to minimize the time consumed during sample preparation along with maintaining low-cost solvent disposal; in addition, it also helps to enhance detection limits because of the convenient dimensions of the SPME system. Moreover, SPME has wide applications in the discipline of bioanalysis, food, and environmental monitoring, and it is mostly used to examine the quality of air and water and detect toxins in food (Billiard et al. 2020). However, SPME faced challenges with the liquid chromatography system; thus, in-tube SPME was developed in which internally coated capillaries are used. It can be easily automated, and by using a standard autosampler, it can continuously perform extraction, desorption, and injection. Another greener technique is SPDE, which is a recent variant of dynamic in-tube SPME employed for the investigation of pesticides in water samples, and it was introduced by Lipinski (2001). The next method, which is based on sorptive extraction, is SBSE, which was highlighted in 1999 (Baltussen et al. 1999). Its principle is the same as SPE or SPME: the only difference is extraction phase volume, which is 50–20 times more than SPME.  Moreover, in 1996, SDME was developed; the uniqueness of this technique can be attributed to its applicability for headspace extraction and its selectivity, easy automation, which represent it as a green analytical technique (Liu and Dasgupta 1996). Besides SPME, there is also an LPME that is employed for the extraction of analytes from a liquid sample. LPME has many advantages over SPME: the major one is its capability to analyze a plethora variety of compounds by simply modifying its mode from the two-phase to the three-phase along with manipulation in the composition of the different phases by considering green principles (Armenta and de la

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Guardia 2011). The next microextraction technique is CFME originated from conventional SDME and, in 2000, it was initially introduced by Liu and Lee (2000). It is a unique technique, in which solvent makes proper and continuous interaction with the flowing sample solution and provides a very high preconcentration factor by using the small volume of aqueous sample. However, some modification was required in it. So Xia et al. came up with a new approach and developed a recycling-­flow system; this new system helps us in reduction of the amount of sample consumed by putting sample waste from the chamber into sample vial (Xia et al. 2004). Another green analytical technique for sample preparation is the membrane-based technique. This technique is convenient for the extraction of membrane from the liquid as well as air samples. It is based on solvent-free techniques for preconcentration and isolation of analytes. There are two types of membranes: porous and nonporous. Extraction methods are defined by the type of membrane used during extraction. Some techniques follow membrane-based techniques: microdialysis, membrane-assisted solvent extraction (MASE), polymeric membrane extraction (PME), membrane extraction with sorbent interface, and semipermeable membrane devices (SPMD) (Armenta and de la Guardia 2011). In recent years, surfactant-based analytes extraction has become one of the most vital techniques and it has given rise to a new novel technique, that is, vesicular liquid coacervate extraction (VLCE). In this technique, vesicles are formed by the precipitation of an immiscible phase via a charge neutralization chemical reaction in the existence of a water-miscible cosurfactant. The uniqueness of this technique is that in the same procedural scheme, it permits both electrostatic forces with polar analytes and hydrophobic interactions, considering nonpolar analytes to be encountered simultaneously. Owing to this, the extraction and preconcentration of both metal ions and meta-chelates can be accomplished within a solo experimental process (Giokas et al. 2004). Another powerful technique that overcomes the demerits of surfactant micellar solution is microemulsion, which helps in the removal of pollutants from the given sample of soil and groundwater because of its high solubilization capacity and its high extraction power (Zhao et  al. 2005). Moreover, with the help of aphrons, a range of organic molecules can be extracted by considering green parameters. Colloidal liquid aphrons is employed as predispersed solvent, it is a micron-sized solvent droplet fenced via a thin aqueous film, which is stabilized by a mixture of ionic surfactant and nonionic. This technique was introduced by Sebba, which provides support for enzyme immobilization in multiphase biocatalytic processes, which provide an alternative environmentally friendly way of using organic solvents (Armenta and de la Guardia 2011). Moreover, volatile or semivolatile analytes can be extracted by using gas extraction techniques such as static headspace (SHS), dynamic headspace (DHS), in-tube extraction (ITEX), purge, and trap (PT), and headspace sorptive extraction (HSSE). These techniques are solvent-free. Based on today’s regularly used systems such as headspace and purge-and-trap, the development of direct thermal desorption technique has been taken place especially in the field of gas chromatography and mass spectrometry. The headspace and purge-and-­ trap techniques provide a very simple way of sample preparation; the vapor phase is introduced without using any organic solvent just to make this technique a green

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Table 1.3  Some sample preparation techniques are examined by five green parameters Green parameters

High/yes (Y)

Medium

Low/no (N)

Organic solvent

Soxhlet extraction, LL,

QuEChERS,

PSE, MAE (N), SFE (N), M.

consumption

UAE, Wet and dry ashing SPE, LPME

QuEChERS, SPME, SDME

Waste generation

Soxhlet extraction,

LL, M.

Wet and dry ashing, UAE,

QuEChERS, SPE

QuEChERS

PSE, MAE, SFE, SPME

Soxhlet extraction, LL,

SFE,

UAE, PSE, MAE, M.

Wet and dry ashing,

QuEChERS,

QuEChERS

LPME

SDME, SPE,

Time consumption

SPME Energy consumption

Soxhlet extraction, LL,

QuEChERS, M. QuEChERS,

Wet and dry ashing,

SPE, SPME

UAE, PSE, MAE, SFE Automation (yes/no)

(PSE, MAE, QuEChERS,

(Soxhlet extraction, LL, Wet

M. QuEChERS, SPE,

and dry ashing, UAE, SFE) N

SPME, SDME, LPME) Y

Colors indicate the level of compliance to the green analytical chemistry principles, Red: High/Yes (Y) for automation, Orange: Medium, Green: Low/No for automation (Billiard et al. 2020)

technique. In this, both dynamic and static systems can be utilized for the extraction. With the help of sorption techniques, such as solid-phase microextraction (SPME) and headspace sorptive extraction (HSSE), the headspace can also be sampled (Armenta and de la Guardia 2011). In short, the outcome of some aforementioned green techniques along with five green parameters is shown in Table 1.3.

6.3  Sample Analysis via Chromatographic Techniques Chromatography is a major technique in the discipline of green analytical chemistry, especially in sample analysis. Many organic samples are originated from different types of matrix; owing to this, the extraction of analytes became very challenging. However, with the help of gas and liquid chromatographic methods, these challenges can be overcome. From sample collection to preparation of sample and extraction of analytes till final destination, all the steps can be performed in a green manner with the help of chromatographic methods. The type of chromatographic method used decides the green efficiency of chromatographic for separations. In gas chromatography, a nonrenewable resource is not preferred as a carrier gas such as helium. However, use of low thermal mass technology for the extraction is more environmentally friendly because this method saves a lot of energy. Another method is liquid chromatography, it has two major goals: first one is the reduction in the amount of reagents consumption and the second motive is minimizing the

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generation of organic waste during sample analysis. In reversed-phase liquid chromatography, low volatile and less toxic hydrophilic solvents such as tetrahydrofuran, acetone, isopropanol, methanol, ethanol, and acetonitrile are employed rather than the hydrophobic solvents. The main purpose of using alcohol is that it can be easily reused for many purposes after sample analysis such as for fuel and it will lead toward the reduction of organic waste. Moreover, the ionic liquid can also be employed as a green solvent because of its low vapor pressure ability, it minimizes the waste produced in the surrounding. In addition, it can be cast as an additive in liquid chromatography to enhance the peak shape by pairing ion mechanisms (Earle and Seddon 2000). However, multidimensional separation techniques have the ability to make the analysis process greener in both gas chromatography and liquid chromatography. Moreover, by combining the merits of liquid chromatography and gas chromatography, a new technique known as supercritical fluid chromatography (SFC) is developed. This method can be considered a solvent-free method as it excludes the addition of alcohols as modifiers and this method uses carbon dioxide as the mobile phase in analytes separation. Sometimes organic modifiers such as methanol are offered instead of CO2 to enhance polarity and density of mobile phase and as a result solubility increases. SFC has the ability to substitute HPLC in some pharmaceutical applications: enantiomeric extraction of antiulcer drugs (Toribio et  al. 2005). Moreover, it has applications in the area of pharmaceutics, chiral separations, natural products, food science, forensic, lipidomic analysis, cosmetic analysis, bioanalysis, and plasticizers in medical devices and polymers (West 2018). Besides the green methods of chromatography, its instrumentation also plays a vital role to make the whole process eco-friendly. A plethora of green chromatography instruments is available such as Microbore Liquid Chromatography, Capillary Liquid Chromatography, Nano Liquid Chromatography, and Ultra Performance Liquid Chromatography.

7  Conclusion The demand for Green Analytical chemistry is spreading day by day. The approach of GAC is to make new green strategies in order to save the environment as well as diminish the risk to the operator. Maximum analytical methods can be considered as green methods if they follow some green criteria such as elimination of toxic reagents, lessening of the consumption of reagents or preferred green solvents, minimum generation of organic waste, and increase in operator safety. New green sample preparation techniques were developed by using chemometrics, miniaturization, or automatization tools, although except these tools, there are many ways, which help us to make analytical processes environmentally friendly. Moreover, the GAC principles will provide essential guidelines in order to make analytical laboratories green, but its implementation requires a change in mindset, especially in industries where different analytical methods are used frequently. However, owing to the

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diversity of analytical methods and their demand, it is very challenging to design green principles that would be implemented in all aspects of all possible analytical methods. By highlighting the importance of the green mentality in scientific publications as well as in academics, the popularity of green analytical chemistry has increased.

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Green DW et  al. (1995) Waste Minimization in Analytical Methods. Paper presented at DOE Pollution Prevention Conference XI, Knoxville, Tennessee, 16–18 May 1995. Guardia M and Garrigues S (eds) (2012) Handbook of Green Analytical Chemistry Handbook of Green Analytical Chemistry. John Wiley & Sons, Ltd, Chichester. Hennion MC (1999) Solid-phase extraction: method development, sorbents, and coupling with liquid chromatography. J Chromatogr A 856:3–54. https://doi.org/10.1016/s0021-­9673(99)00832-­8. Ibanez E and Cifuentes A (2017) Green Extraction Techniques: Principles, Advances and Applications, vol 76. Elsevier, Oxford. Inamuddin and Mohammad A (eds) (2014) Green Chromatographic Techniques: Separation and Purification of Organic and Inorganic Analytes. Springer, Dordrecht. https://doi. org/10.1007/978-­94-­007-­7735-­4. Keith LH et al. (2007) Green analytical methodologies. Chem Rev 107:2695–2708. https://doi. org/10.1021/cr068359e. Kellner R et al. (2004) Analytical chemistry: A Modern Approach to Analytical Science, 2nd edn. Weinheim, Germany. https://doi.org/10.1002/cphc.200500062. Koel M and Kaljurand M (2010), Green Analytical Chemistry, RSC Publishing, Cambridge. M.  Koel and M.  Kaljurand (2019) Green Analytical Chemistry, 2nd edn. Royal Society of Chemistry, Cambridge. Lehotay SJ (1997) Supercritical fluid extraction of pesticides in foods. J Chromatogr A 785:289–312. https://doi.org/10.1016/S0021-­9673(97)00461-­5. Lipinski J (2001) Automated solid phase dynamic extraction  – Extraction of organics using a wall coated syringe needle. Fresenius J Anal Chem 369:57–62. https://doi.org/10.1007/ s002160000618. Liška I (2000) Fifty years of solid-phase extraction in water analysis - Historical development and overview. J Chromatogr A 885:3–16. https://doi.org/10.1016/S0021-­9673(99)01144-­9. Liu H and Dasgupta PK (1996) Analytical chemistry in a drop. solvent extraction in a microdrop. Anal Chem 68:1817–1821. https://doi.org/10.1021/ac960145h. Liu W and Lee HK (2000) Continuous-flow microextraction exceeding 1000-fold concentration of dilute analytes. Anal Chem 72:4462–4467. https://doi.org/10.1021/ac000240x. Luque de Castro MD and Jiménez-Carmona MM (2000) Where is supercritical fluid extraction going? TrAC  - Trends Anal Chem 19:223–228. https://doi.org/10.1016/ S0165-­9936(99)00228-­9. Manz A et al. (1990) Miniaturized Total Chemical Analysis Systems: A Novel Concept for Chemical Sensing. Sensors and Actuators B 1:244–248. https://doi.org/10.1016/0925-­4005(90)80209-­I. Namieśnik J (2001) Green analytical chemistry - Some remarks. J Sep Sci 24:151–153. https://doi. org/10.1002/1615-­9314(20010201)24:23.0.CO;2-­4. National Research Council (1985) Opportunities in Chemistry. Washington, DC: The National Academies Press. https://doi.org/10.17226/606. Pastor A et  al. (1997) Efficiency of the microwave-assisted extraction of hydrocarbons and pesticides from sediments. Anal Chim Acta 344:241–249. https://doi.org/10.1016/ S0003-­2670(97)00012-­3. Płotka-Wasylka J (2018) A new tool for the evaluation of the analytical procedure: Green Analytical Procedure Index. Talanta 181:204–209. https://doi.org/10.1016/j.talanta.2018.01.013. Raynie D and Driver J (2009) Green assessment of chemical methods. In: 13th annual green chemistry and engineering conference, College Park, Maryland, 23–25 June 2009. Richter BE et al. (1996) Accelerated solvent extraction: A technique for sample preparation. Anal Chem 68:1033–1039. https://doi.org/10.1021/ac9508199. Ríos A et al. (2006) Challenges of analytical microsystems. TrAC - Trends Anal Chem 25:467–479. https://doi.org/10.1016/j.trac.2005.11.012. Ružička J and Hansen EH (1975) Flow injection analyses. Part I. A new concept of fast continuous flow analysis. Anal Chim Acta 78:145–157. https://doi.org/10.1016/S0003-­2670(01)84761-­9. Stockwell PB (1990) The Role of Flow Injection Analysis Within the Framework of an Automated Laboratory. J Automat Chem 12:95–103. https://doi.org/10.1155/S1463924690000116.

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Susdorf AK et  al. (2019) Green analytical chemistry: Social dimension and teaching. TrAC  Trends Anal Chem 111:185–196. https://doi.org/10.1016/j.trac.2018.10.022. Tobiszewski M (2017) Introduction to Green Analytical Chemistry. In: Beach E S and Kundu S (eds) Tools for Green Chemistry, vol 10. Wiley-VCH, Weinheim, p  103–116. https://doi. org/10.1002/9783527628698.hgc117. Tobiszewski M et  al. (2009) Green analytical chemistry in sample preparation for determination of trace organic pollutants. Trends in Analytical Chemistry 28(8):943-951. https://doi. org/10.1016/j.trac.2009.06.001. Toribio L et al. (2005) Comparative study of the enantioselective separation of several antiulcer drugs by high-performance liquid chromatography and supercritical fluid chromatography. Journal of Chromatography A, 1091:118–123. https://doi.org/10.1016/j.chroma.2005.07.018. Wasylka JP and Namieśnik J (eds) (2019) Green Analytical Chemistry; Past, Present and Perspectives. Springer Nature Singapore Pte Ltd, Singapore. https://doi.org/ 10.1007/978-­981-­13-­9105-­7. West C (2018) Current trends in supercritical fluid chromatography. Anal Bioanal Chem 410:6441–6457. https://doi.org/10.1007/s00216-­018-­1267-­4. Xia L et al. (2004) Single-drop microextraction combined with low-temperature electrothermal vaporization ICPMS for the determination of trace Be, Co, Pd, and Cd in biological samples. Anal Chem 76:2910–2915. https://doi.org/10.1021/ac035437e. Xiong G et al. (1999) Comparison of microwave-assisted extraction of triazines from soils using water and organic solvents as the extractants. Talanta 48:333–339. https://doi.org/10.1016/ S0039-­9140(98)00250-­1. Zhao B et  al. (2005) A novel solubilization of phenanthrene using Winsor I microemulsion-­ based sodium castor oil sulfate. J Hazard Mater 119:205–211. https://doi.org/10.1016/j. jhazmat.2004.12.009.

Chapter 2

Green Analytical Chemistry Metrics and Life-Cycle Assessment Approach to Analytical Method Development Maha Mohamed Abdelrahman

Abstract  The environmental consequences of chemical and analytical research, particularly the use and production of toxic reagents and solvents, have sparked widespread concern. Accordingly, the assessment of analytical techniques’ greenness is becoming increasingly relevant in order to evaluate their environmental impact and reveal their validity in establishing sustainable strategies. This chapter addresses the state of knowledge of various approaches adopted for evaluation of the greenness profile. Green metric approaches, such as the National Environmental Method Index (NEMI), Assessment of Green Profile established by Raynie & Driver, analytical Eco-Scale, HPLC-Environmental Assessment Tool (HPLC-EAT), Analytical Method Volume Intensity (AMVI), Green Analytical Procedure Index (GAPI), Analytical Method Greenness Score (AMGS) Calculator, Analytical Greenness Metric (AGREE), and other tools, have been investigated. All the above metrics are discussed and compared in terms of their criteria, applicability, benefits, and drawbacks. Concerns have been raised about the potential of understanding how to use such assessment tools to minimize the hazardous environmental effect of harmful chemicals and regulate irresponsible activities, besides the necessity of implementing such metric tools in method development rather than postanalysis evaluation. The application of Life-Cycle Assessment for sustainable development and different solvent selection guides for alternative green solvents/reagents were discussed as well. Keywords  Greenness Assessment Tools · NEMI · AGP · Analytical Eco-Scale · HPLC-EAT · AMVI · GAPI · AMGS · AGREE - Life-Cycle Assessment · Solvent Selection Guide M. M. Abdelrahman (*) Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt, Alshaheed Shehata Ahmad Hegazy St, e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. H. El-Maghrabey et al. (eds.), Green Chemical Analysis and Sample Preparations, https://doi.org/10.1007/978-3-030-96534-1_2

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Abbreviations ACS AGP AGREE AMGS AMVI CE CED CZE DAD EAT

American Chemical society Assessment of green profile Analytical GREEnness Analytical Method Greenness Score Analytical method volume intensity Capillary electrophoresis Cumulative energy demand Capillary zone electrophoresis Diode array detection Environmental assessment tool ELISA Enzyme-linked immunosorbent assay EHS Environmental, Health, and Safety FID Flame ionization Detector FTIR Fourier transform infrared GAC Green analytical chemistry GAPI Green analytical procedure index GCI-PR Green chemistry institute–pharmaceutical roundtable GC-MS Gas chromatography–mass spectroscopy GHS Globally harmonized system H NMR Proton Nuclear magnetic resonance HPTLC High-performance thin layer chromatography LCI Life-Cycle Inventory LCIA Life-Cycle Impact Assessment LC-ICP-MS liquid chromatography-inductively coupled plasma mass spectrometry LC-MS Liquid chromatography–mass spectrophotometry LCA Life-cycle assessment NEMI National environmental method index NFPA National Fire Protection Association NP Normal phase PBT Persistent, bioaccumulative and toxic PDA Photodiode array PROMETHEE Preference ranking organization method for enrichment evaluation QqQ-MS Triple-quadrupole mass spectrometer QToF Quadrupole Time of Flight RCRA Resource conservation and Recovery Act RP-HPLC Reversed-phase High-performance liquid chromatography SFC Supercritical fluid chromatography SSG Solvent Selection Guide TLC Thin liquid chromatography TOPSIS Technique for Order of Preference by Similarity to Ideal Solution TRI Toxic release inventory UFLC Ultra-Fast Liquid Chromatography

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UPLC Ultra-performance liquid chromatography UV Ultraviolet UV–Vis Ultraviolet–visible μECD Micro electron capture detector

1  Introduction Green chemistry is described as the application of chemical processes and procedures that reduce or eliminate the usage or creation of precursors, products, intermediates, solvents, and chemicals that pose a health or environmental risk (Keith et al. 2007). A research released in 1994 reported that among the major chemical firms, the pharmaceuticals contributed the greatest waste per unit of product (Sheldon 1994). It has long been recognized that analytical chemistry and environmental conservation are inextricably linked. In addition, all analytical endeavors must take into account a number of issues linked to the maintenance of our ecosystem. As a result, we must realize that it is our professional obligation to protect both the operator’s safety and the environment’s long-term viability (De la Guardia and Armenta 2011). Thereby, when observing the analytical procedure, it is critical to consider not only the practices, tests, and data to be gained but also the identity and quantity of the chemicals utilized, waste emissions and consumption across the process, energy requirements, and hazards to users and the environment (de la Guardia and Garrigues 2012). Green Chemistry is supposed to be a sequence of reductions. These reductions contribute to the objective of achieving triple bottom-line benefits in the form of economic, environmental, and social expansions (Cue et al. 2009). Reducing waste and energy consumption are the two sides of the same coin; both result in cost savings. Increasing process efficiency by decreasing resources utilization improves sustainability in terms of material consumption along with end-of-life disposal. Similarly, increasing usage of renewable resources will make the manufacturing industry more environmentally friendly (Verhe et al. 2004). Diminished hazardous incidents and hazardous material management give significant social benefits, not just to operators, but also to nearby communities and, indirectly, to users of chemical-related items. Green Analytical Chemistry (GAC) is a paradigm that motivates analytical chemists to consider environmental, health, and safety concerns while performing their tasks (Armenta et al. 2008). It was initially presented in 2000 as a means of reducing the adverse effects of analytical methods on individuals and the ecosystem (Armenta et al. 2008). Figure 2.1 highlights the growing number of scientific publications associated with the topic of green chemistry since 2000 to the present. The Egyptian Knowledge Bank website was exploited to retrieve data from the Scopus and Web of Science databases on August 2021.

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Documents by year

Scopus

8k 7k

Documents

6k 5k 4k 3k 2k 1k 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Year

Copyright © 2021 Elsevier B.V. All rights reserved. Scopus® is a registered trademark of Elsevier B.V. 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 600 400 200 0

Number of publications

Web of Science

00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15

16

20

17

20

18

20

19

20

20

20

21

20

20

Year

Fig. 2.1  The Chart analysis about number of publications for the topic “Green Chemistry” based on databases of both Scopus and Web of Science accessed from The Egyptian Knowledge Bank, data retrieved on august 2021

It has become critical to strike a balance between attaining high-quality outputs and reducing the environmental risks of analytical procedures. Hence, The GAC concepts and standards are fundamental to maintain this balance (Płotka-Wasylka et al. 2019; Gałuszka et al. 2013). Green analytical chemistry (GAC) aims to make analytical methods more environmentally friendly and human-safe. The amount and toxicity of chemicals, generated waste, energy needs, the number of processes required, miniaturization, and automation are just a few of the many aspects considered when assessing an analytical process’s greenness. Therefore, using greenness evaluation criteria necessitates the employment of ultimate tools (Tobiszewski 2016).

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Fig. 2.2 The 12 principles of Green Analytical Chemistry as represented in the word “SIGNIFICANCE” as described by Gałuszka et al.

However, the absence of well-established techniques for assessing greenness is one of the most persistent difficulties facing GAC (Tobiszewski 2016). The metrics used to determine whether or not an analytical method is green should be standardized and verified. As a result, such a tool should be evaluated, confirmed, and employed as the key parameter in the development of a green analytical technique (Płotka-Wasylka et al. 2018). The term “SIGNIFICANCE” symbolizes the 12 concepts of GAC (Gałuszka et al. 2013), as illustrated in Fig. 2.2.

2  Green Analytical Metrics Green Analytical Metric is a design platform for measuring an analytical technique’s greenness and aiding in the avoidance of potential environmental consequences connected with its development and implementation. It frequently takes

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into account the amount and type of reagent employed, energy intake, possible hazards, and waste generation in order to analyze and help the decision-making process of such a procedure (Płotka-Wasylka et al. 2019). Green chemistry already has several defined metrics systems, such as E Factor (Sheldon 1994, 2007, 2008), Atom economy (Wang et al. 2011), reaction mass efficiency (Dicks and Hent 2015), and Process Mass Intensity (Constable et al. 2001; Kjell et al. 2013), which are the utmost frequently utilized approaches for assessing the environmental impact of chemical synthesis (Andraos 2005). These green chemistry metric systems are commonly based on the mass of the product; however, in analytical chemistry, where there is no obvious product with a definite mass, this is not a practical way. The metrics in this case can be calculated “per analytical finding” and can relate to components and energy intake (Turner 2013). Since 2002, several methods have been used to support current attempts to standardize greenness metrics for tracking analytical procedures’ environmental effect. The greenness assessment metric tools that will be covered in this chapter are as follows: • • • • • • • • •

National Environmental Methods Index (NEMI) Analytical Eco-Scale Assessment of Green Profile (AGP) by Raynie and Driver HPLC-EAT (Environmental Assessment Tool) Analytical Method Volume Intensity (AMVI) Green Analytical Procedure Index (GAPI) Analytical Method Greenness Score (AMGS) Calculator Analytical Greenness Metric Approach (AGREE) Other metric tools

A thorough review of the history, criteria, comprehensiveness, and reliability of the above-mentioned greenness assessment metric tools, recognized for evaluation of analytical methods, is provided in this chapter. In addition, a comparison between these metric tools regarding their assessment parameters, consistency, and applicability is included. A comparison of their application in the literature is presented as well. Exhibition of life-cycle assessment to green chemistry objectives in addition to different selection guides for solvents as an alternative to conventional solvents/ reagents usually employed in pharmaceutical chemistry processes and the current green renewable resources have been revealed.

2.1  National Environmental Methods Index (NEMI) 2.1.1  Background The National Environment Methods Index (NEMI), first launched in 2002, is a greenness assessment database for environmental and analytical methods evaluation (Keith et al. 2007). This metric tool helps chemists to understand and compare methods during all stages of the analytical process.

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Until 1995, the published analytical and environmental methods focused on essential analytical and validation parameters without taking into consideration the particular data that permit users to assess whether information attained from certain method is comparable with information attained from other methods. There were no exact criteria for comparing significant components of analytical methods with each other or with a user’s project-specific needs. To manage this demand, a multiagency Methods and Data Comparability Board settled NEMI, a design tool to compare analytical methods and the information produced from those methods. NEMI is one of the most extensive approaches for determining the environmental friendliness of analytical procedures. It is an open internet accessible database that could be reached at http://www.nemi.gov. 2.1.2  Criteria For NEMI metric tool, in order to judge the greenness profile for certain analytical method, acceptance criteria were exploited and used to assess the method of interest. Criteria for acceptance transform analytical procedure data (covering chemicals involved, pH, and waste created) to a pictogram. The assessment of the analytical methods is performed on the basis of four key terms that depends on properties of reagents generated and wastes consumed by this method: PBT (persistent, bioaccumulative, and toxic), Corrosive, Hazardous, and Waste. These four criteria were derived from the 12 Principles of Green Chemistry as well as the agreement of the most main significant features from a legislative aspect. From the regulatory basis, the Environmental Protection Agency (EPA) Toxic Release Inventory (TRI) chemicals list (Gerde and Logsdon 2001), the PBT chemicals identified on the TRI list (Gerde and Logsdon 2001) and the Resource Conservation and Recovery Act (RCRA)‘s D, F, P, and U hazardous waste lists and the characteristics of hazardous wastes, such as the definition of corrosive (Elzanfaly et  al. 2015), were denoted during establishment of such acceptance criteria. The method is considered as “green” if one or more of the following criteria is attempted: 1. PBT = chemicals and/or reagents exhausted in the process is not designated as a PBT, as listed by the EPA’s TRI (Gerde and Logsdon 2001). 2. Hazardous = chemicals and/or reagents consumed in the process is not recorded in the Toxic Release Inventory directory (Gerde and Logsdon 2001) or any of the RCRA’s D, F, P or U harmful waste directories (Elzanfaly et al. 2015). 3. Corrosive = the pH during the analysis is not more than 12 or less than 2. 4. Waste = the amount of waste produced is less than 50 g. A greenness profile symbol was drawn as a four-quadrant pictogram to provide a summary of the four assessment criteria to help in evaluation of the method’s environmental impact. As the approval benchmark, this pictogram has four quadrants indicating PBT, Hazardous, Corrosive, and Waste. If one or more of the acceptance requirements were met and recognized as “green” as specified in the profile criteria, the quadrant(s) corresponding to that criteria are colored green. But if one or more of the acceptance requirements are not fulfilled and the technique is

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Fig. 2.3  The diagram depicts the evaluation score obtained using the NEMI metric technique, which includes four quadrants (PBT, hazardous, corrosive, and pH), each of which is colored green if the related attribute is fulfilled

identified as “less green,” as stated in the preceding evaluation criteria, the quadrant(s) relating to that approval requirement are not colored (set plain). A representative diagram of a NEMI pictogram is shown in Fig. 2.3. It was noticed that the most common reason of a method to be assigned as “less green” was the amount of generated waste, which is most frequently greater than 50 g, which disagrees with the requirements of waste greenness criterion by NEMI assessment tool. 2.1.3  Reliability The NEMI assessment tool is distinguished by its simple and easy-to-read pictogram representing the four assessment criteria. Accordingly, anyone can understand the results by looking at the assessment profile pictogram from which an overall decision about method’s effect on environment can be easily concluded. The main drawbacks of NEMI tool are that a tedious searches for every reagent and/or chemicals being in use; if present in lists of hazardous, persistent, or toxic chemicals are employed. It takes time since each chemical must be verified against one or more reference databases (Tobiszewski et al. 2015; Waste 2019). Moreover, NEMI tool didn’t deliberate energy as a criterion for assessment as recognized as one of the major concepts of the 12 principles of GAC. Besides the evaluation findings are just qualitative; no data regarding the amount of waste or hazards is given, where each threat is just presented as being above or below certain limit.

2.2  Analytical Eco-Scale 2.2.1  Background Eco-Scale was first introduced as an assessment procedure for evaluation of green organic reactions recognized by Van-Aken et al. (Aken et al. 2006). This approach is based on, if a reaction consumed low-cost reagents/chemicals, carried out at room

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temperature, generated 100% yield, and nonhazardous to worker or the environment, is estimated as an “Ideal Reaction” with a penalty points of 100. Accordingly, any deviation for each parameter from the ideal value is accompanied by decrease of the penalty points and lowering of the assigned score. The higher the score, the more greener the investigated method. This model may be practiced to verify the greenness of research methodologies. 2.2.2  Criteria of Analytical Eco-Scale The analytical Eco-Scale is a semiquantitative tool to assess the greenness of an analytical method, it assesses the different parameters and steps of the analytical process comprising amount of reagents, hazards, energy, and waste each parameter assigned a penalty score (Aken et al. 2006). Since the identity and amount of hazardous materials affects the method greenness, by multiplying the sub-total penalty points by the quantity of threats, the overall penalty points are computed. The score of the analytical Eco-Scale is measured by subtracting the total penalty points from the ideal score (100) as the following equation:

Analytical Eco scale = 100 − total penalty points

A scale is provided based on the findings of the Eco-Scale computation: if the score is more than 75, the method is expressed as “excellent green” procedure, if the score is more than 50, then “acceptable green” procedure, and if the score is less than 50, then “inadequate green” procedure (Gałuszka et al. 2012). Penalty points are calculated for each of the four main parameters of the analytical method that deviate from an ideal green analysis: (a) amount of reagents, (b) energy consumption, (c) chemical hazards, and (d) waste generated. For the parameter of reagents, penalty points are allotted based on hazard categories (physical, environmental, and health) that each reagent creates. Varying amounts of reagent will assign different penalty points, it is based on the Globally Harmonized System of Classification and Labeling of Chemicals (GHS). In GHS classification, each chemical reagent is categorized by one or more of nine items, representing their hazardous characteristics. Two specific words are used in GHS classification and are as follows: “danger” (for more severe hazards), this type of hazards are assigned 2, and “warning” (for less severe hazards), this type is assigned 1 penalty point (GHS 2007). For the parameter of energy, penalty points for energy consumption will be appointed according to the type of instrument employed (Dunn et al. 2010). The least energy-consuming procedures and instruments that exhausts less than 0.1 kWh for each sample, like titrimetric, spectrofluorimetric, immunoassay, needle evaporator, solvent evaporation using hot plate (for less than 10 min), sonication, UV-visible spectrophotometric, and UPLC methods, are scored “0” penalty point (Dunn et al. 2010). Whereas instruments of medium energy-consumption that exhausted less than or equal (1.5 kWh per sample), for example, GC, inductively coupled plasma mass spectrometry, atomic absorption spectrometry, liquid chromatography, are

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scored “1” penalty point. The highest energy-consuming instruments that exhausted more than 1.5 kWh per sample, for example, LC-MS, NMR, X-ray diffraction, GC-MS, solvent evaporation with hot plate (for more than 2.5 hour), are scored 2 penalty points (Gałuszka et al. 2012). Concerning the amount of hazardous chemicals, the sum of penalty points assigned to hazardous chemical is multiplied by the quantity of chemical. The hazard score is multiplied by one for quantities less than 10 mL (g) of toxic chemical, and by two for quantities between 10 and 100 mL (g). The hazards value is increased by three for reagent amounts greater than 100  mL (g). Furthermore, analytical methods with emitted vapors to the air are also penalized by 3. Regarding the waste generation, if the analytical process produces an amount of waste less than 1 mL or 1 g of waste, penalty point score of one is assigned; a penalty point score = 3 is allotted if the amount of generated waste is a 1–10 mL or g; however, greater amounts of generated waste scored penalty points equal to 5. In case the waste is not treated at all, the method is punished by 3 penalty points (Tobiszewski 2016). A summary of penalty points assigned for each parameter in analytical eco-scale method is given in Table 2.1. Table 2.1  The penalty points for calculation of analytical Eco-Scale metric tool (Reproduced with permission from Ref. Gałuszka et al. (2012) © Elsevier) Reagents

Reagent amount

Hazardous reagents (environmental, health, physical)

Instruments Energy cosumed

Occupational hazards Waste generated

100 mL (g) None Less severe hazard More severe hazard

Subtotal penalty points 1 2 3 0 1

Total penalty points Amount penalty point × hazard penalty point

2

≤0.1 KWh per sample ≤1.5 KWh per sample > 1.5 KWh per sample Occupational method hermetization Emission of vapors and gases to air None 10 mL (g) Recycling Degradation Passivation No treatment

0 1 2 0 3 0 1 3 5 0 1 2 3

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2.2.3  Reliability In the case of Analytical Eco-Scale, marked by its simplicity for application and providing a semiquantitative tool for environmental assessment, the penalty points scoring is easy to comprehend and read. Also, it is useful for comparing different analytical methods. Unfortunately, Eco-Scale approach has some negative points, as it provides a score that does not reflect any information about identity of the hazardous components. In addition, no data about reason of non-ecofriendly impact on the environment including identity of the solvents and chemicals being used and nature of the waste generated.

2.3  Assessment of Green Profile (AGP) by Raynie and Driver 2.3.1  Background Raynie and Driver (2009) created the Assessment of Green Profile (AGP) technique in 2009 as an improvement on the previously existing NEMI method. This measuring tool is intended to provide each aspect a three level scores, these scores were calculated using data widely available in chemical databases, whereas the evaluation is depicted by a pentagram segmented into five risk attributes: health, safety, the environment, waste, and energy. Each attribute can be shaded in one of three colors: green, yellow, or red. The amount utilized/produced throughout the method is manipulated to calculate the environmental risks and waste, meanwhile energy is measured based on the evaluated method. 2.3.2  Criteria Raynie and Driver (2009) created a methodology for evaluating chemical processes in terms of green chemistry aspects. The assessment divided hazardous risks into 5 attributes centered on reactivity, production of waste, toxicity, corrosivity, bioaccumulation, energy usage, safety, and other variables: safety, the environment, waste, health, and energy. Using freely accessible chemical data, chemical procedures are given a 1–3 score for each potential attribute. As illustrated in Fig. 2.4, a pentagonshaped pictograph divided into five triangles representing the five characteristics is displayed as a graphical description of the scores. A score of 1 was associated with the color green, while scores of 2 and 3 were denoted by the colors yellow and red, respectively. The three color score from red to yellow to green symbolizes poor, medium, and high conformity to the principles of eco-friendly chemical process, respectively. The National Fire Prevention Association (NFPA) offers data sheets of material safety for all health hazardous chemicals and reagents, which were used to construct the score of health risk. The highest of these rankings was picked and interpreted

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Fig. 2.4  Assessment of green profile developed by Raynie and Driver metric method considering safety, health, energy, waste, and environmental hazard parameters

into the score of health hazard (Conover 2019). Likewise, the method’s safety hazard score was estimated by looking up documented NFPA flammability and instability values, while the highest of these was chosen. The environmental hazard score was calculated using two criteria: initially, the existence of any of the utilized chemicals/reagents on the EPA’s TRI ranking of PBT chemicals (Sarkis 2017), the EPA-­mandated lists of pollutants under the Clean Water Act or the EPA-mandated lists of pollutants under the Clean Air Act (Goffman and Bloomer 2019), and at last, the amount of environmentally toxic chemical used. Table 2.2 abridges the three levels of AGP score rating of green, yellow, and red shades, regarding the five attributes comprising safety, health, environmental, energy, and waste, which are as follows: –– Regarding health threats: A health hazard’s score for slightly toxic, slight irritant compounds (NFPA health hazard with score of 0 or 1) is green colored. A yellow color for moderately toxic compounds, which may cause temporary incapacitation (NFPA = 2 or 3), while compounds causing significant damage on short term exposure with probable animal carcinogen (NFPA = 4) are assigned a red color. –– In terms of safety concern, components with the maximum NFPA flammability, instability score of 0 or 1 with no specific dangers is assigned a green shade. If the NFPA flammability or instability score is 2 or 3, or if a particular danger is present, a yellow shade is issued. Compounds with the worst NFPA flammability or instability score of 4 were assigned a red shade. –– Regarding environmental risks: If less than 50 g of environmental hazards was used, a green shade is allotted. If more than 50 g but less than 250 g is consumed, a yellow shade is given. And, if more than 250  g was handled, a red color is designed. –– Considering energy attribute: For wet analytical methods, such as titration with very little solvent evaporation, were appointed a green tint. For method such as GC and HPLC with moderate solvent evaporation, a yellow tint is chosen. For analytical techniques like GC-MS with high volume of solvent evaporated, a red tint was selected.

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Table 2.2  Assessment of green profile recognized by Raynie and Driver for greenness evaluation considering safety, health, energy, waste, and environmental hazard parameters Category Safety hazards

Green Highest NFPA flammability, instability score = 0 or 1 with no special hazards

Health threats

Slightly toxic, slight irritant, NFPA health hazard with score = 0 or 1

Environmental 250 g was consumed Analytical method like GC-MS with high volume of solvent evaporation >250 g of total waste

–– Judging waste dangers: Analytical processes with total waste of ≤50 g for one sample are awarded a green color, while analytical methods generating a total waste of ≤250 g per one sample an yellow color. And for methods consuming a total waste per one sample of >250 g, a red color is provided.

2.3.3  Reliability The Assessment of Green Profile (AGP) method is a semiquantitative tool that evaluates each possibility at three-level scale and measures more information about energy and environmental hazards. The pictorial demonstration of the assessment tool permits researcher to make their judgments concerning competing eco-friendly criterion. As a result, while comparing approaches, this assessment tool comes in handy. However, there is no particular information on the source of the hazards.

2.4  HPLC-EAT (Environmental Assessment Tool) 2.4.1  Background HPLC-EAT (Environmental Assessment Tool) was designed by Gaber et al. in 2011 (Gaber et al. 2011) as a greenness assessment profiling tool for high-performance liquid chromatography techniques. This metric design takes into account the

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environmental, health, and safety concerns for all solvents utilized in the chromatographic technique, assigning an overall score, which could be employed for comparing the greenness of various analytical methods. A HPLC-EAT software was created to modify the assessment practice and is freely available at the website http://www.biotek.lu.se/hplc-­eat/. 2.4.2  Criteria HPLC-EAT (HPLC-Environmental Assessment Tool) was developed for the assessment of environmental impact of HPLC, preparative HPLC, and flash chromatographic methods. The assessment tool involves the safety, environmental, and health issues associated with all solvents utilized throughout any chromatographic process generating a total score judging the overall green performance of the analytical procedure, whereas the minor the score, the greener is the evaluated process. The score calculation is conducted using the following equation: HPLC − EAT = S1 m1 + H1 m1 + E1 m1 + S2 m2 + H 2 m2 + E2 m2 + … Sn mn + H n mn + En mn where S, H, and E refer to safety, health, and environmental parameters, respectively, derived from Koller et al. method (Koller et al. 2000), “n” refers to number of solvents, and “m” is the mass of the solvent(s) consumed during the chromatographic method. The EHS (environmental, health, and safety) method originated by Koller et  al. afforded a meaningful values for different chemical and solvents. It based on the physical, environmental, safety, and chemical characteristics of different materials gathered from various databases (Koller et al. 2000). The database of HPLC-EAT software was constructed using the most frequently handled organic solvents throughout HPLC assay. Besides, HPLC-EAT can be applied for calculating the score of mobile phase composed of even three organic solvents in both isocratic and gradient modes. In case of, the mobile phase containing either pure water, water included modifiers, or buffer solution, the estimated EHS values for this solvent considered zero. Additionally, HPLC-EAT incorporates the amount and type of the waste produced beyond the green profiling of HPLC method. HPLC-EAT measures the mass of the solvents generated in the final waste, which could be further applied in Eco-Solvent software (Capello et al. 2007). Yasser and coworkers (Gaber et  al. 2011) combined HPLC-EAT with Eco-Solvent software for waste disposal either through distillation or incineration options. The life-cycle assessment by means of Eco-Solvent tool (Capello et al. 2007) can appraise the most appropriate approach for treatment of the generated waste via calculating the cumulative energy demand (CED) for each of the distillation and incineration strategies. The lower the CED value, the more preferable is the strategy.

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2.4.3  Reliability HPLC-EAT presents an automated tool to assess the eco-friendly HPLC methods via summing of the safety, environmental, and health impacts for the masses of all solvent used. The tool also helps to decide the suitable waste disposal approach. The major shortcoming of this tool is that it comprises only the ecological impact of solvents without considering other aspects of GAC such as energy, instrumentation, and sample preparation criteria. Also some solvents utilized in HPLC methods are still missing in the software database.

2.5  Analytical Method Volume Intensity (AMVI) 2.5.1  Background The Analytical Method Volume Intensity (AMVI) is a greenness metric tool created by Hartman et al. in 2011 for evaluating HPLC methods. AMVI is a method for calculating the total volume of solvent consumed and the waste generated by a given analytical process. The lower the AMVI score, the more environmentally friendly the analytical method (Hartman et al. 2011). 2.5.2  Criteria In an attempt to assess the ecological performance of various analytical procedures, the Analytical Method Volume Intensity (AMVI) estimates the entire volume of solvent used and waste created by an analytical methodology. There are two types of waste flows to address for each HPLC analytical method: waste from sample processing and waste from analytical instrument operating. Furthermore, in order to compare various analytical techniques properly, the volume of solvent generated by each method should be standardized. As a result, the AMVI is recommended to be normalized to a single HPLC analysis, with net solvent consumption equaling the total of all solvents and chemicals used in sample processing and the solvents and chemicals requested by the HPLC assay. This approach takes into account the number of samples examined and also the truth that certain analytical processes only require one chromatographic run for each sample, while others average the findings of several replicate injections (Hartman et al. 2011). Afterward, the entire solvent consumption of the process may be calculated from the next equation (eq. 2.1). Total consumption of solvent = ( ∑ solvent of samplepreparation + HPLC solvent ) × replicates (2.1)

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The sample preparation phrase refers to the waste solvent generated during the production of any and all experimental sample or preparation stages, all quality tests, blanks, spikes, and handlers necessary for a single HPLC assay. The HPLC analysis term is the total of all waste solvents and reagents discharged for each references, spikes, blanks, surrogates, and material injections. When normalizing the AMVI into a more relevant measure, the number of peaks of interest should also be taken into account. As a result, calculating the AMVI on a “components of concern” base instead of a “per sample” one seems appropriate. It is also crucial to distinguish between coeluted elements and a constituent of concern. In Equation (eq. 2.2), the analytical approach volume intensity brings it all together into a consistent metric. The AMVI is just the method’s total solvent consumption (as indicated in eq. 2.1) subdivided by the number of interest peaks (De Soete et al. 2017) as follows: AMVI =

Total consumption of solvent No.chromatogaphic interest peaks

(2.2)

–– By applying AMVI, HPLC can be made more environmentally friendly: –– Based on the fact that rapid chromatography is the first focus of an ecologically efficient separation. A number of advances have permitted rapid HPLC analysis in recent years, most of which are also helpful in reducing the amount of solvent waste accompanying instrumental analysis during an analytical process, which may be accomplished by: 1. Decease solvent consumption: Because the amount of solvent consumed by an HPLC instrument is correlated to the flow rate and the time of the analysis, reducing either of these variables has proven a great way to start when trying to make separations more environmentally friendly. 2. Adopt column with small particle size: The most notably improvements are the adoption of effective column technologies based on uniform smaller particles, have enabled faster chromatography. Such technologies can frequently result in significant reductions in HPLC analysis time, leading to significant reduction in the AMVI’s instrumental waste production. 3. Use short column length: Columns loaded with smaller molecules have a higher efficiency, allowing shorter columns to replace longer columns filled with larger molecules. As a consequence, HPLC analysis time and hence instrumental contaminant emissions were reduced even more dramatically. By lowering column length and molecule size at the same time, one can reduce analysis time while keeping accuracy. 4. Lessen column diameter: Because flow rate is proportional to the square of the HPLC column diameter, switching to a half-­diameter column results in a fourfold reduction in flow rate and waste production at the same linear velocity.

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5. Modify the sample preparation technique: By altering the sample preparation procedure, it is possible to reduce waste solvent creation even further, hence improving the method’s environmental efficiency. A small change to the sample preparation procedure can have a major influence on how long the method lasts in the environment. It is important to adjust both instrument and sample preparation parameters while aiming to enhance the AMVI of an analytical technique. 6. Shorten the method’s run time: It is advisable to expand from traditional HPLC to Ultrahigh-Pressure Liquid Chromatography to eliminate instrument-related waste solvent production and shorten the method’s run time even more. As demonstrated, in order to accurately assess a method’s environmental efficiency and direct potential growth, one must consider the entire analytical system: waste solvent generated during sample preparation is sometimes as important as waste solvent generated from instrument (Bernardoni et al. 2019). 2.5.3  Reliability The AMVI is a simple metric for determining total solvent consumption in a highperformance liquid chromatography (HPLC) technique. AMVI raises knowledge of green chromatography and, more significantly, integrates it into the technique development process for bench analytical scientists. It is a useful tool for comparing alternative analytical techniques in terms of environmental impact and long-­term sustainability. Nevertheless, the method considered only amount of consumed solvents regardless of their toxicity. Also this metric tool ignored other components of GAC such energy, equipment, and sample preparation requirements and their impact on the environment.

2.6  Green Analytical Procedure Index (GAPI) 2.6.1  Background Potka-Wasylka recently established the Green Analytical Procedure Index (GAPI) as a semiquantitative approach to evaluate the greenness of analytical methodologies in 2018 (Płotka-Wasylka 2018). Initially, it was applied to judge wine samples containing biogenic amines as well as wastewater containing polycyclic aromatic hydrocarbon. Using a three-stage color scale, the designed metric tool creates a pictogram to estimate the health and ecological effect of an analytical technique. The strategy of this metric tool is based on a five pentagram divided into 15 aspects covering the whole stages of analytical procedure comprising method type, reagents, waste generated, instrumentation, and quantification. The colors used to appraise the impact of each aspect are green-low, yellow-medium, and red-high

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environmental impact, where the green color points out an eco-friendly procedure, while the red indicates a non-eco-­friendly process. 2.6.2  Criteria GAPI was originated by Płotka-Wasylka in 2018, as an ecological assessment tool for evaluation of different stages of the analytical methodology, early from the sample collection followed by sample treatment, instrumentation up to method quantification. The following are the major five elements of the GAPI pictogram, as well as the 15 detailed aspects: A. Sample preparation aspect incorporates the following: (1) sample collection, (2) preservation, (3) transport, (4) storage, (5) method type, (6) scale of extraction, (7) solvents/ reagents utilized, and (8) further treatments. B. Reagents and solvents aspect comprise the following: (9) amount, (10) health threats, and (11) safety threats. C. Instrument aspect covers the following: (12) energy, (13) hazards in workplace, (14) wastes, and (15) treatment of waste. D. Other aspects of quantification are as follows: a circle in the middle denotes that the technique can quantify the analytes. A five pentagram symbol represents a classification of the greenness of each stage of an analytical process, using a color scale, with three shades of assessment for each stage, as shown in Fig. 2.5. Each of the 15 elements represents a distinct component of the investigated analytical method, with the field becoming green if the given conditions are met, while yellow for moderate environmental impact and red for non-eco-friendly aspect. Table  2.3 displays the essential requirements revealed by GAPI parameters declared previously (Płotka-Wasylka 2018).

Fig. 2.5  Representative diagram of GAPI metrics tool including the 15 attributes

On-line or at-line Chemical or physical Required Under normal condition Simple method Microextraction Green solvents and reagents utilized Simple treatment 10–100 mL (g) Moderately toxic, NFPA = 2 or 3

In-line None None None No preparation of sample

Nanoextraction Method without solvent

None

10 ml (g) No treatment

1–10 mL (g) Passivation or degradation

No circle in in the center of graph indicates that the procedure is qualitative only

>1.5 KWh per sample Emission of vapors

>100 mL (g) Serious injury on short-term exposure, NFPA = 4 Highest NFPA flammability or instability score = 4

Macroextraction Nongreen solvents and reagents utilized Advanced treatment

Off-line Physicochemical ― Under special condition Required extraction

Red

≤1.5 KWh per sample ―

Highest NFPA flammability or instability score = 2 or 3, or a special hazard is used

Yellow

Green

C. Instrument (12) Energy ≤0.1 KWh per sample Hermetic sealing of analytical process (13) Hazards in workplace (14) Waste 150 min), GC-MS, LC-MS, X-ray diffraction, nuclear magnetic resonance, and soxhlet extraction, are given the lowest score of 0.0 (Wang et al. 2010). –– The tenth standard of GAC indicated the use of reagents from renewable source concerning environmental and health impact. The assigned value is 1 if no reagents are handled or if all reagents are acquired from bio-based sources. If some of the chemicals were derived from bio-based sources but others were not, the proposed score would be 0.5. The tenth principle, on the other hand, assigned a value of 0 if none of the reagents were obtained from bio-based sources, as revealed in Table 2.4. –– The eleventh standard of GAC stated that toxic reagents should be removed or substituted by greener alternatives. The type and amount of chemicals consumed in an analytical process is a significant matter. In case no hazardous chemicals are used, the score is set to 1. If not, the amount of the reagent is converted into score using the following equation:

Score = −0.156 × ln ( amount of reagent or solvent in g or mL ) + 0.5898



(2.8)

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–– The twelfth GAC standard comprised that operator safety should be intensified taking into account societal concern and environmental hazards. The possible threats of hazardous materials either toxic to aquatic life, highly oxidizable, persistent, explosive, corrosive, bioaccumulative, or highly flammable. The score is 1 if no possible risks are listed. The assigned score is 0.8, 0.6, 0.4, and 0.2 if 1, 2, 3, or 4 threats are present, respectively. When 5 or more threats are identified, the score is reset to zero, as given in Table 2.4. 2.8.3  Reliability The major advantage of the AGREE tool is that the pictogram reveals strong and weak zones in the method, from which the effect of each of the 12 standards across their weight in each zone can be deduced. In addition, its overall score is relevant and informative in accordance with the GAC principles. AGREE, unlike other evaluation tools, considers the size of the sample, the output of the analytical method, the usage of bio-based solvents, and the risks of chemicals. The method’s only flaw is that the rationale for assigning certain weight to each zone is unclear.

2.9  Other Not Commonly Applied Tools Various multivariate multianalyte techniques are used to evaluate analytical procedures based on various data analysis criteria, including greenness. These techniques are distinguished by the fact that they are best suited for comparing variable methods and parameters at the same time. 2.9.1  Multicriteria Decision Analysis (MCDA) Method The application of multicriteria decision analysis (MCDA) allows for the simultaneous evaluation of various analytical processes based on several performance criteria. If these criteria are referring to GAC, then MCDA may be considered a GAC metric technique that has been successfully used to this goal (Tobiszewski 2016). This application allows you to compare and rank up to 12 different analytical procedures depending on their environmental friendliness. The typical application strategy for MCDA technique is divided into two phases (Tobiszewski and Orłowski 2015). The first phase is the analysis’ aim, that includes the analytical performance and greenness issues that define the goal. The second phase is to identify reasonable alternatives for achieving the aim, which may be different analytical approach as an assessment alternative in chemical analysis. Each parameter is given a weight, since the parameters are not always of equal significance. After that, the appropriate MCDA algorithm is used, and the ranking of various analytical techniques is completed. The simultaneous examination of many

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analytical approaches and the incorporation of several parameters into a single, convenient score are the main advantages of MCDA (Cinelli et al. 2014). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is one of the MCDA methods (Behzadian et al. 2012). TOPSIS was employed to test a variety of analytical methods for determining pharmaceuticals in samples of wastewater (Al-Hazmi et al. 2016). Nineteen analytical techniques were assessed using eight criteria, the majority of which focused on analytical efficacy and greenness. It is worth noting that TOPSIS exhibited optimal performance only in the multianalyte analysis; however, it worked well but not best in the other analyses. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is the second MCDA technique used as a green analytical chemistry measure (Behzadian et al. 2010). It is an outranking approach, similar to TOPSIS, that results in a comprehensive ranking of possibilities. Using nine entry criteria, it was performed to evaluate 25 techniques for detecting water-containing chloroorganic pesticides (Tobiszewski and Orłowski 2015). The conclusions of this investigation’s rating agreed with outputs of Eco-Scale evaluation but had no relation with the NEMI conclusions. Mostly, Analytical techniques that received good marks from PROMETHEE also received excellent marks from the Eco-­Scale evaluation procedure. 2.9.2  Multivariate Statistical Methods Multivariate statistics is a well-known technique in chemistry for the simultaneous determination of multianalytes in different matrices. It is built on classifying analytical procedures according to their similarities. Green analytical chemistry metrics can utilize chemometrics if the variables used to define the analytical procedures are pertaining to their greenness or environmental impact (Tobiszewski and Orłowski 2015). Cluster analysis and principal component analysis are two chemometric techniques that may be selected to organize analytical processes in environmental assessments (Tobiszewski et al. 2013). Furthermore, one of the most powerful chemometric approaches, self-organizing maps, gives more interesting conclusions by allowing the detection of outlier objects that are typically the highest or lowest green. Self-organizing maps make it simple to differentiate between positive and negative correlations within variables (Astel et al. 2007). It was observed that the results of the evaluation with self-organizing maps coincide with the results of the NEMI and Eco-Scale assessments (Tobiszewski et al. 2014). 2.9.3  Greenness Index with Spider Diagram The Greenness Index (Shen et al. 2016) was created as an assessment tool to give a comprehensive review of chemicals used in various sectors and their influence on EHS (environment, health, and safety). This tool’s analysis is based on information

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contained in reagent Safety Data Sheets as well as parameters for possible outcomes when the reagent is employed in a specific application. Safety Data Sheets provide information on a reagent’s different characteristics and how they absolutely affect EHS. Also, these data consider the frequent possibilities of how the reagent may alter during usage in an application. As a result, the Greenness Index was developed to provide a more valid assessment of a reagent’s EHS impact. The Greenness Index allows for a more comprehensive, multiparametric evaluation of reagents, whereas five clusters of factors (health impact, general properties, odor, fire safety, and stability) were merged based on generally recognized sustainability pillars such as the 12 principle of Green Chemistry. Each factor or cluster of factors was measured using a specific algorithm to provide a quantifiable score. The scale goes from −5 to +5, with −5 being the least green and + 5 being the most green. The scores are graphically shown in the form of a hierarchy of spider diagrams to indicate the relative greenness of the reagent, with higher contained regions signifying greener reagents (Shen et al. 2016). This Greenness Index technique has been used to a range of reagents, including Potassium Amyl Xanthate (Shen et al. 2016) analysis and the selection of the correct green reagent during proton nuclear magnetic resonance determination of lamotrigine (Abou-Taleb et  al. 2021). The findings of such an analysis may be included into a decision-making process, which helps the selection of greener reagents and gives important insights on how to enhance and create sustainable processes. 2.9.4  Hasse Diagram as a Green Metric Tool The Hasse Diagram Technique is a partial order theory application built on a data matrix (Bruggemann and Voigt 2008). It has been applied in analytical chemistry to depict partial order relationships between analytical processes represented by a set of variables, including the method’s greenness. It was conducted to investigate the similarities and differences between published analytical techniques for determining benzo[a]pyrene in sediments, keeping in mind the greenness and analytical performance of the approaches under consideration (Bigus et al. 2016). The Hasse diagram technique is applied for the partial ordering of multivariate sets, whereas 41 methods were defined by greenness variables, and only 26 of these have both metrological and greenness data to describe them. The results of the Hasse diagram technique were compared to those of the NEMI and Eco-Scale evaluations. It was revealed that the comparison with the NEMI evaluation findings shows no dependence as NEMI metric reflect greenness as a pictogram, while Hasse Diagram represents greenness as a score. Furthermore, the Hass Diagram method outperforms NEMI labeling in terms of resolving power. Furthermore, comparing the Hass Diagram method findings with the Eco-­Scale evaluation score is more useful, because the results are almost identical (Bigus et al. 2016).

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3  Overview of the Described Metrics Tools Herein, various greenness evaluation techniques have been presented in terms of their criteria and practices. Table  2.5 provides a summary of different aspects involved during the application of each evaluation tool, as well as their advantages and limitations, in order to construct a comparison between the investigated assessment approaches. NEMI appears to be the most simple greenness tool; nevertheless, when compared to other tools, the information it exhibits is markedly limited in extent and just qualitative. In addition to incorporating energy usage, the AGP developed by Raynie & Driver gives more information and a better comprehension of each of the five evaluation attributes based on three color levels than the NEMI tool. The analytical Eco-scale provides a valid greenness evaluation with respect to accuracy and consistency. It has the virtue of using numbers to allude to greenness, enabling to compare analytical approaches, but it has the main flaw of not providing more detailed justifications of nongreen-related processes in an analytical procedure. Besides, the HPLC-EAT and AMVI tools are primarily concerned with the solvents consumed and generated during chromatographic techniques, without taking into account other concerns such as energy consumption, sample handling, and instrumentation. Instead, the GAPI tool considers all phases involved in the performance of an analytical procedure, from sample collection to final result. The most effective assessment approaches for evaluating the greenness of analytical procedures are GAPI and AGREE, which provide integrated information about the whole applicable methodology. AGREE provides the advantages of simplicity, automation, and numerical value computation over GAPI. AGREE is the most successful approach for sticking to GAC’s 12 principles. To conclude, it is worth that applying more than one greenness evaluation tool is strongly suggested when more trustworthy and reliable results are necessary, especially when Eco-­ Scale, GAPI, and AGREE are utilized. If NEMI is employed, it could be used in association with other acceptable tool as it is not always the best evaluation approach.

4  L  iterature Outline of the Investigated Greenness Assessment Approaches A thorough literature review of the previously discussed greenness assessment approaches is listed in Table 2.6. It seems that the described evaluation tools were operated for assessment of various analytical methods including HPLC, UPLC, LC-MS, GC, CZE, TLC, spectrophotometry, and spectrofluorimetry, for different analytes in various matrices. As presented in Table  2.6, majority of publications utilized Eco-Scale approach followed by GAPI and NEMI. This might be due to their simplicity and comprehensiveness. Besides, some articles adopted more than one assessment approach for more reliable evaluation.

Table 2.5  Comparison between the investigated greenness assessment metric approaches comprising NEMI, AGP developed by Raynie & Driver, analytical Eco-Scale, HPLCEAT, AMVI, GAPI, AMGS, and AGREE tools Greenness Assessment Metric Tools Item NEMI Eco-Scale Reagents Assessment criteria PBT (persistent, Hazards bioaccumulative, Energy and toxic) Waste Hazardous Corrosive Waste Penalty points Comprehensiveness Each criterion score represents assigned either green or blank color hazardous effect of method is depends on the method matched to subtracted from 100 (ideal score) the standard Output Qualitative only Semiquantitative

AGP Health Safety Environment Waste Energy

HPLC-EAT Environmental health and sSafety hazards for all solvents used in HPLC

GAPI Sample preparation Reagents and solvents Instrumentation Quantification Calculation 3 shaded scale Accessible Presented at 3 (green- yellowsoftware calculates of total level scale solvents used red) assigned for EHS impact of (green-­yellow-­ each parameter for total solvents red) based on components based on certain relative standard benchmarks of interest criteria Semiquantitative Quantitative Quantitative Semiquantitative

Visualization

Four-quadrant pictogram

Numerical score

Five-segments pentagram

Numerical score

Merits

Simple and easy And pictogram is legible and accessible

The penalty points scoring is easy to comprehend and calculate

Automated tool Pentagram is understandable and easy to apply More accurate than NEMI

Demerits

Tedious Doesn’t include energy criterion

Penalty point score No information doesn’t reflect any about reason of information about hazards hazards

Energy, instrumentation, and sample preparation criteria are not included

AMVI Solvent consumed Waste generated

Numerical score Consider number of components of interest

AMGS Instrument energy Solvent energy Solvent safety

Calculator’s equation with aid of reference values includes SHE/SSG and CED values Quantitative & qualitative Five pentagram Numerical with 15 segments score + color coding Assessment of the Suitable for overall analytical running methods from start method-to-­ method to end comparison

Considered Complexity of only amount manipulation of solvents consumed regardless of their toxicity

Require searching in different databases

AGREE Based on the 12 principles of GAC, each is interpreted into a scale range of 0–1 Accessible software with 12 attributes with final score as a fraction of unity Quantitative & qualitative Pictogram with 12 sections The overall score is relevant and informative in accordance with the GAC principles On what basis the weight of each section is given

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Table 2.6  Literature review of the described greenness assessment metric approaches including NEMI, AGP developed by Raynie & Driver, analytical Eco-Scale, HPLC-EAT, AMVI, GAPI, AMGS, and AGREE tools Assessment metric approach NEMI

Criteria Analyte Sulfadiazine and trimethoprim in bovine meat and chicken muscles Lamotrigine Indipamide, Perindopril, and Amlodipine in tablets 5-fluorouracil and cisplatin in drug-eluting films Dapagliflozin and saxagliptin in tablet Brompheniramine, phenylephrine, and dextromethorphan in syrup Guaifenesin and Bromohexine HCl in human plasma Brivaracetam, piracetam, and carbamazepine in capsules and human plasma Methacholine chloride complexed with 4-sulfocalix[4]arene with application to human plasma Tamsulosin and tadalafil mixture and alfuzosin and solifenacin mixture Trimebutine and its degradation product Cangliflozin (CGF) and Metformin in tablets Phenols in water and wastewater Tamoxifen or its analog clomiphene using erythrosine B dye Artesunate and amodiaquine drugs and their impurities Salbutamol, terbutaline, and thymol Tinidazole and ibuprofen in vivo study

Technique RP-HPLC, Micellar LC and UPLC-MS/ MS (1H NMR)

Reference Mohamed and Fouad (2020)

RP-HPLC

Abou-Taleb et al. (2021) Saleh et al. (2020)

RP-HPLC

Youssef et al. (2021)

UV-spectrophotometry Abdulwahab et al. and HPLC-UV (2021) TLC and RP-HPLC Mohamed et al. (2020) UPLC-MS/MS El-Naem and Saleh (2021) RP-HPLC Mansour et al. (2021) UV-spectrophotometry ElDin et al. (2021)

HPLC-DAD

Abdel-Moety et al. (2021)

Spectrofluorimetry

El-Shaheny and Belal (2020) UV-spectrophotometry Lotfy et al. (2020a) Spectrophotometry Spectrofluorimetry

Lavilla et al. (2012) Tolba and Salim (2021b)

RP-HPLC

Yabré et al. (2020)

Spectrophotometry

Al Majidi et al. (2019) Abdelwahab et al. (2021)

TLC and HPLC

(continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Axitinib, doxorubicin, and tramadol in spiked human plasma Moxifloxacin/dexamethasone and moxifloxacin/prednisolone combinations in eye drops Deoxynivalenol (DON) and T-2 toxin (T2) in maize and oats Hydrogen sulfide in water samples

Technique Spectrofluorimetry HPLC

Reference Tolba and Salim (2021a, b) Ibrahim et al. (2019c)

UPLC-MS-MS

Tahoun et al. (2021)

Microplate fluorescence assay Fluorescence probe

El-Maghrabey et al. (2019) El-Shaheny (2019)

Stability indicating determination of spiramycin and josamycin using eosin Y Donepezil hydrochloride in tablets Chemometricsassisted spectrophotometry Ponatinib and curcumin in tablet Synchronous and spiked human plasma spectrofluorimetry Mycotoxins in Astragali Radix UFLC-MS/MS Determining ethanol in alcoholMicrovolume free cosmetics fluorospectrometry Daclatasvir and sofosbuvir in Liquid-liquid urine samples microextraction combined with HPLC-DAD n-alkanes, polycyclic aromatic GC-MS hydrocarbons, and sterols in peat GC-FID Assay of Lacosamide, its degradation products, and coadministered drug Zonisamide in tablets and in human urine HPLC-QToF-MS/MS Ethanolic extract of Citrus sinensis L. fruit peels nanoparticles antiaging cream Gluten determination ELISA immunoassay Tropicamide in rat plasma with TLC-densitometry and pharmacokinetic study UPLC-DAD

Korany et al. (2018b) El Sharkasy et al. (2022) Wang et al. (2020) Cabaleiro et al. (2012) Jouyban et al. (2021)

Argiriadis et al. (2020) Korany et al. (2018a, b)

Amer et al. (2021)

Lores et al. (2017) Abdelrahman et al. (2020a) (continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Hydrochlorothiazide and telmisartan Stability indicating analysis of sofosbuvir and ledipasvir Hyoscine N-butyl bromide

Dimenhydrinate, cinnarizine, and their toxic impurities Metformin and fenugreek extract in real rat plasma samples Tamsulosin HCl and tadalafi in their formulation Metoclopramide, ergotamine, caffeine, and paracetamol in antimigraine formulation Stability indicating assay of citicoline and piracetam Analysis of Biogenic Amines in Wine Assay of dantrolene sodium Quinfamide and Mebendazole Tenofovir alafenamide in dosage forms Ofenopril Calcium and Hydrochlorothiazide in Presence of Hydrochlorothiazide Major Impuritie Stability indicating assay of Sofosbuvir Stability study of Canagliflozin Clidinium bromide/ chlordiazepoxide hydrochloride, phenobarbitone/pipenzolate bromide, mebeverine hydrochloride/sulpiride, and chlorphenoxamine hydrochloride/ caffeine/8-­chlorotheophylline mixtures Furosemide, spironolactone, and canrenone

Technique Micellar HPLC and spectrophotometry HPTLC Comparative study of greenness assessment tools RP-HPLC HPLC

Reference Ibrahim et al. (2019a) El-Yazbi et al. (2020) Gamal et al. (2021)

Edrees et al. (2021)

HPTLC

Abdelwahab et al. (2021) Tantawy et al. (2020)

HPTLC

Farid et al. (2020)

HPLC and HPTLC

Abdelrahman et al. (2020b) Woźniakiewicz et al. (2018) Abdalah et al. (2021) Naguib et al. (2020b) Said et al. (2021)

CE-MS and GC-MS Spectrofluorimetry HPLC and HPTLC UPLC and HPTLC Capillary zone electrophoresis

Fayed et al. (2018)

LC–MS-MS

Nebsen and Elzanfaly (2016) Emam (2018) Elzanfaly et al. (2015)

HPTLC HPLC

HPLC and HPTLC

Naguib et al. (2018) (continued)

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63

Table 2.6 (continued) Assessment metric approach Eco-Scale

Criteria Analyte Sulfadiazine and trimethoprim in bovine meat and chicken muscles Lamotrigine Indipamide, Perindopril, and Amlodipine in tablets 5-fluorouracil and cisplatin in drug-eluting films Dapagliflozin and saxagliptin in tablet Brompheniramine, phenylephrine, and dextromethorphan in syrup Guaifenesin and Bromohexine HCl in human plasma Brivaracetam, piracetam, and carbamazepine in capsules and human plasma Methacholine chloride complexed with 4-sulfocalix[4]arene with application to human plasma Tamsulosin and tadalafil mixture and alfuzosin and solifenacin mixture Trimebutine and its degradation product Cangliflozin (CGF) and Metformin in tablets Tamoxifen or its analog clomiphene using erythrosine B dye Artesunate and amodiaquine drugs along with their impurities Salbutamol, terbutaline, and thymol Tinidazole and ibuprofen in vivo study Axitinib, doxorubicin, and tramadol in spiked human plasma Deoxynivalenol (DON) and T-2 toxin (T2) in maize and oats Hydrogen sulfide in water samples

Technique RP-HPLC, Micellar LC and UPLC-MS/ MS 1 H NMR

Reference Mohamed and Fouad (2020)

RP-HPLC

Abou-Taleb et al. (2021) Saleh et al. (2020)

RP-HPLC

Youssef et al. (2021)

UV-spectrophotometry Abdulwahab et al. and HPLC-UV (2021) TLC and RP-HPLC Mohamed et al. (2020) UPLC-MS/MS El-Naem and Saleh (2021) RP-HPLC Mansour et al. (2021) UV-spectrophotometric methods

ElDin et al. (2021)

HPLC-DAD

Abdel-Moety et al. (2021)

Spectrofluorimetry

El-Shaheny and Belal (2020) UV-spectrophotometry Lotfy et al. (2020a) Spectrofluorimetry

Tolba and Salim (2021a, b)

RP-HPLC

Yabré et al. (2020)

Spectrophotometry

UPLC-MS-MS

Al Majidi et al. (2019) Abdelwahab et al. (2021) Tolba and Salim (2021a, b) Tahoun et al. (2021)

Microplate fluorescence assay

El-Maghrabey et al. (2019)

TLC and HPLC Spectrofluorimetry

(continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Technique Stability indicating determination Fluorescence probe of spiramycin and josamycin using eosin Y Donepezil hydrochloride in tablets Chemometricsassisted spectrophotometry Ponatinib and curcumin in tablet Synchronous and spiked human plasma spectrofluorimetry Mycotoxins in Astragali Radix UFLC-MS/MS Daclatasvir and sofosbuvir in Liquid-liquid micro urine samples extraction combined with HPLC-DAD GC-FID Assay of Lacosamide, its degradation products, and coadministered drug Zonisamide in tablets and in human urine HPLC-QToF-MS/MS Ethanolic extract of Citrus sinensis L. fruit peels nanoparticles antiaging cream Gluten determination ELISA immunoassay TLC-densitometry and Tropicamide in rat plasma with pharmacokinetic study UPLC-DAD Micellar HPLC and Hydrochlorothiazide and telmisartan spectrophotometry Hyoscine N-butyl bromide Comparative study of greenness assessment tools Dimenhydrinate, cinnarizine, and RP-HPLC their toxic impurities Metformin and fenugreek extract HPLC in real rat plasma samples Tamsulosin HCl and tadalafi in HPTLC-densitometry their formulation Analysis of Biogenic Amines in CE-MS and GC-MS Wine

Reference El-Shaheny (2019)

Korany et al. (2018a, b) El Sharkasy et al. (2022) Wang et al. (2020) Jouyban et al. (2021)

Korany et al. (2018a, b)

Amer et al. (2021)

Lores et al. (2017) Abdelrahman et al. (2020a) Ibrahim et al. (2019a) Gamal et al. (2021)

Edrees et al. (2021) Abdelwahab et al. (2021) Tantawy et al. (2020) Woźniakiewicz et al. (2018) (continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Assay of dantrolene sodium Tenofovir alafenamide in dosage forms Stability indicating determination of flibanserin Endocrine-disrupting compounds

Technique Spectrofluorimetry UPLC and HPTLC

Reference Abdalah et al. (2021) Said et al. (2021)

RP-HPTLC and NP-HPTLC HPLC-PDA

Foudah et al. (2021)

Polybrominated diphenyl ethers in GC-μECD dusts Tricyclic antidepressant drugs in GC-MS the human urine and plasma Steroids in water samples HPLC-DAD Stability study of timolol and latanoprost in dosage forms Amlodipine, Telmisartan, Hydrochlorothiazide, and Chlorthalidone in their pharmaceutical dosage form Diazepam, clonazepam, and bromazepam Tetracyclines in water samples

El-Deen and Shimizu (2020) Adeyi et al. (2020)

Mohebbi et al. (2018) El-Deen and Shimizu (2019) RP-HPLC Ibrahim et al. (2019b) UV-spectrophotometry Attala and Elsonbaty (2021)

Micellar HPLC-UV

Elmansi and Belal (2019) Sereshti et al. (2021)

Dispersive liquidliquid microextraction HPLC-UV El-Yazbi et al. Sofosbuvir, ledipasvir, daclatasvir, RP-HPLC (2021b) velpatasvir in dosage forms and biological fluids Impurity profiling of ebastine and UPLC-PDA Abd El-Rahman phenylephrine et al. (2020) Micellar HPLC Rashed et al. (2020) Clorsulon, albendazole, triclabendazole, and ivermectin in their dosage forms V, Co, Ni, Cu, Zn, Se, Mo, Cd, Mass spectrometry Sajid et al. (2021) and Pb in seawater samples Mometasone furoate and salicylic Capillary zone El-Yazbi et al. acid in topical preparation electrophoresis (2021a) HPLC-fluorescence Mabrouk et al. Metoprolol and amlodipine in (2019) dosage form and spiked human plasma Sofosbuvir and ledipasvir HPLC-UV and El-Shorbagy et al. HPLC-Fluoresence (2019)

(continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Polar benzotriazoles in aqueous samples Masitinib in bulk and dosage forms Nitrogen in cereals

Stability indicating study of ribavirin, sofosbuvir, and ledipasvir Atorvastatin, rosuvastatin, and simvastatin in their binary mixtures with ezetimibe Coumarins in plant samples Linagliptin and saxagliptin with metformin Liquid-liquid microextraction determination of biogenic amines in meat Pesticides in soil Determination of erdafitinib in human plasma Plastic additives (Phthalates and bisphenol A) in microplastic-­laden beach sand Monoiodoacetic acid and diiodoacetic acid in drinking water

Endocrine-disrupting compounds and their derivatives in packaged vegetables Nizatidine nitrosatability in simulated gastric juice In silico study of famotidine and famotidone gastric instability

Technique LC-MS/MS

Reference Kraševec and Prosen (2021) UV-spectrophotometry Mabrouk et al. (2021) Microwave assisted-­ Rastogi et al. (2021) ultraviolet ion chromatography RP-HPLC-UV El-Shorbagy et al. (2020) FTIR spectrometry

Nasr et al. (2020)

HPLC-fluoresence

Hroboňová et al. (2021) El-Yazbi (2021)

Capillary electrophoresis GC-MS

GC-MS LC-MS-MS HPLC-DAD

Liquid chromatography-­ inductively coupled plasma mass spectrometry (LC-ICP-MS) LC-MS/MS

HPLC-MS HPLC-UV

Wojnowski et al. (2019) Orazbayeva et al. (2020) Elawady et al. (2020) Trujillo-Rodríguez et al. (2021) Liu et al. (2020a)

Szczepańska et al. (2020) El-Shaheny et al. (2019) El-Shaheny et al. (2020) (continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Lesinurad and febuxostat with application to human plasma Postmortem diagnosis of Glycated haemoglobin as a biomarker Dextromethorphan hydrobromide and its degradation products Carbamate pesticides (desmedipham, phenmedipham, and chlorpropham) chlorobenzenes in environmental samples Monosodium glutamate Norfloxacin and tinidazole in bulk and in tablet Agri-food by-products metabolites in sugarcane solid residues Sudan I, butylated-hydroxytoluene and its major metabolites in water samples Methyl red in wastewater samples Removal of acidic drugs from wastewater Determination of trace lead in red pepper samples

Technique Synchronous spectrofluorimetry UPLC-QqQ-MS/MS

Nowak et al. (2020)

UHPLC

Boussès et al. (2015)

Liquid chromatography with amperometric detection GC-MS

Diuzheva et al. (2019)

Colorimetry Micellar HPLC UPLC-UV and GC-MS UPLC-MS/MS

UV–Vis spectrophotometry High-temperature liquid chromatography Slotted quartz tube-­flame atomic absorption spectrophotometry GC-MS

Endocrine-disrupting phenolic compounds (alkyl phenols and bisphenol A) Etformin hydrochloride, alogliptin Ion pair RP-HPLC benzoate, and repaglinide in tablets GC-MS Chlorthiamid, ethyl parathion, penconazole, and fludioxonil pesticides in well, tap, and lake water samples

Reference Magdy et al. (2021)

Campillo et al. (2020) Ali et al. (2021) Kamal and El-Malla (2019) Assirati et al. (2020) Zamzam et al. (2020) Atsever et al. (2021) Al-Khateeb et al. (2021) Zaman et al. (2020)

Karayaka et al. (2019) Mahrouse and Lamie (2019) Bulgurcuoğlu et al. (2021)

(continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Quetiapine and paroxetine in rat plasma Methocarbamol in three of its combined tablets Raspberry ketone in dietary supplement 4-tert-octylphenol, chlorpyrifosethyl, and penconazole Sildenafil, tadalafil, vardenafil, and avanafil in human plasma and urine Zofenopril calcium and hydrochlorothiazide Dimenhydrinate, cinnarizine, and their toxic impurities Carbamazepine and oxcarbazepine, their potential impurities and formulation excipients Triclosan in environmental water samples Tedizolid Phosphate Tramadol assay with ibuprofen or chlorzoxazone Enantioseparation of racemic amino alcohols Racemic amino acids Impurity profiling of niacin Cadmium in wastewater samples Racemic amino alcohols Enantiomers of esmolol Ipratropium bromide, glycopyrronium bromide, tiotropium bromide, bambuterol hydrochloride, formoterol fumarate, and indacaterol maleate Naproxen, diclofenac, and paracetamol in human biological samples

Technique TLC-densitometry and RP-HPLC HPLC

GC–MS

Reference Abdelwahab et al. (2020a) Mohamed and Belal (2019) Abdelaal et al. (2021) Akkaya et al. (2019)

LC-MS/MS

Er et al. (2019)

Spectrofluorimetry

UV-spectrophotometry Lotfy et al. (2020b) TLC-densitometry HPTLC-densitometry

Abdelwahab et al. (2020b) Abdelwahab and Abdelrahman (2021)

Ion-selective electrode Safwat et al. (2021) Ion-selective electrode Moaaz et al. (2021) Spectrofluorimetry Abdel Moneim and Hamdy (2021) Micellar RP-HPLC Alwera et al. (2020a) RP-HPLC HPTLC Atomic spectrophotometry HPLC HPLC HPLC

HPLC

Alwera et al. (2020a) Naguib et al. (2020a) Tekin et al. (2020) Alwera et al. (2021) Alwera et al. (2020b, c) Zayed et al. (2021)

Bagheri Zomoorodi et al. (2021) (continued)

69

2  Green Analytical Chemistry Metrics and Life-Cycle Assessment Approach… Table 2.6 (continued) Assessment metric approach

AGP

HPLC-EAT

Criteria Analyte Cefepime and cefazolin in bulk powder and dosage form Capecitabine and Lapatinib in Rat Plasma Telmisartan, hydrochlorothiazide, and amlodipine besylate Indipamide, Perindopril, and Amlodipine in tablets Dapagliflozin and saxagliptin in tablet Guaifenesin and Bromohexine HCl in human plasma n-alkanes, polycyclic aromatic hydrocarbons, and sterols in peat Analysis of Biogenic Amines in Wine: Dispersive liquid-liquid microextraction based on floating organic droplet solidification (DLLME-SFOD) was introduced for the enrichment of five endocrine-disrupting compounds. Quinfamide and Mebendazole Nizatidine nitrosatability in simulated gastric juice In silico study of famotidine and famotidone gastric instability Fingerprinting of Eugenia uniflora L. leaves Sudan I, butylated-hydroxytoluene and its major metabolites in water samples Fingerprinting of Lippia sidoides Cham., Verbenaceae leaves Nucleosides and nucleotides

Veterinary quaternary mixture of sulphadimidine sodium, sulphaquinoxaline sodium, diaveridine, and vitamin K3

Technique Spectrofluorimetry UPLC-MS/MS RP-HPLC RP-HPLC

Reference Abdel-Aziz et al. (2021) Alrobaian et al. (2021) Mohamed and Lamie (2016) Saleh et al. (2020)

UV-spectrophotometry Abdulwahab et al. and HPLC-UV (2021) UPLC-MS/MS El-Naem and Saleh (2021) GC-MS Argiriadis et al. (2020) CE-MS and GC-MS Woźniakiewicz et al. (2018) HPLC-PDA El-Deen and Shimizu (2020)

HPLC and HPTLC HPLC-MS HPLC-UV HPLC-PAD

Naguib et al. (2020b) El-Shaheny et al. (2019) El-Shaheny et al. (2020) Souza et al. (2021)

UPLC-MS/MS

Zamzam et al. (2020)

HPLC-UV

Funari et al. (2018)

Beilke et al. (2016) Enhanced-fluidity liquid chromatography (EFLC) HPLC-UV Michael et al. (2021)

(continued)

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Table 2.6 (continued) Assessment Criteria metric approach Analyte AMVI Tinidazole and ibuprofen in vivo study Tropicamide in rat plasma with pharmacokinetic study Metformin and fenugreek extract in real rat plasma samples GAPI Sulfadiazine and trimethoprim in bovine meat and chicken muscles Indipamide, Perindopril, and Amlodipine in tablets 5-fluorouracil and cisplatin in drug-eluting films Dapagliflozin and saxagliptin in tablet Brompheniramine, phenylephrine, and dextromethorphan in syrup Guaifenesin and Bromohexine HCl in human plasma Brivaracetam, piracetam, and carbamazepine in capsules and human plasma Tamsulosin and tadalafil mixture and alfuzosin and solifenacin mixture Cangliflozin and Metformin in tablets Tamoxifen and its analog clomiphene using erythrosine B dye Axitinib, doxorubicin, and tramadol in spiked human plasma Stability indicating determination of spiramycin and josamycin using eosin Y Ponatinib and curcumin in tablet and spiked human plasma Mycotoxins in Astragali Radix Hyoscine N-butyl bromide

Assay of dantrolene sodium Endocrine-disrupting compounds in water Daclatasvir and sofosbuvir in urine samples n-alkanes, polycyclic aromatic hydrocarbons, and sterols in peat

Technique TLC and HPLC

Reference Abdelwahab et al. (2021) TLC-densitometry and Abdelrahman et al. UHPLC-DAD (2020a) HPLC Abdelwahab et al. (2021) RP-HPLC, MLC and Mohamed and Fouad UPLC-MS/MS (2020) RP-HPLC Saleh et al. (2020) RP-HPLC

Youssef et al. (2021)

UV-spectrophotometry Abdulwahab et al. and HPLC-UV (2021) TLC and RP-HPLC Mohamed et al. (2020) UPLC-MS/MS El-Naem and Saleh (2021) RP-HPLC Mansour et al. (2021) HPLC-DAD

Abdel-Moety et al. (2021)

UV-spectrophotometry Lotfy et al. (2020a) Spectrofluorimetry Spectrofluorimetry Fluorescence probe

Synchronous spectrofluorimetry UFLC-MS/MS Comparative study of greenness assessment tools Spectrofluorimetry HPLC-PDA HPLC-DAD GC-MS

Tolba and Salim (2021a, b) Tolba and Salim (2021a, b) El-Shaheny (2019)

El Sharkasy et al. (2022) Wang et al. (2020) Gamal et al. (2021)

Abdalah et al. (2021) El-Deen and Shimizu (2020) Jouyban et al. (2021) Argiriadis et al. (2020) (continued)

2  Green Analytical Chemistry Metrics and Life-Cycle Assessment Approach…

71

Table 2.6 (continued) Assessment metric approach

Criteria Analyte Ethanolic extract of Citrus sinensis L. fruit peels nanoparticles antiaging cream Dimenhydrinate, cinnarizine, and their toxic impurities Polybrominated diphenyl ethers in dusts Steroids in water samples Stability study of timolol and latanoprost in dosage forms Diazepam, clonazepam, and bromazepam Tetracyclines in water samples

Technique HPLC-QToF-MS/MS

Reference Amer et al. (2021)

RP-HPLC

Edrees et al. (2021)

GC-μECD

Adeyi et al. (2020)

HPLC-DAD

El-Deen and Shimizu (2019) Ibrahim et al. (2019b) Elmansi and Belal (2019) Sereshti et al. (2021)

RP-HPLC Micellar HPLC-UV

Dispersive liquidliquid micro extraction HPLC-UV El-Yazbi et al. Sofosbuvir, ledipasvir, daclatasvir, RP-HPLC (2021b) velpatasvir in dosage forms and biological fluids Micellar HPLC Rashed et al. (2020) Clorsulon, albendazole, triclabendazole, and ivermectin in their dosage forms V, Co, Ni, Cu, Zn, Se, Mo, Cd, Mass spectrometry Sajid et al. (2021) and Pb in seawater samples Mometasone furoate and salicylic Capillary zone El-Yazbi et al. acid in topical preparation electrophoresis (2021a) HPLC-fluorescence Mabrouk et al. Metoprolol and amlodipine in (2019) dosage form and spiked human plasma Sofosbuvir and ledipasvir HPLC-UV and El-Shorbagy et al. HPLC-Fluorescence (2019) Polar benzotriazoles in aqueous LC-MS/MS Kraševec and Prosen samples (2021) Masitinib in bulk and dosage UV-spectrophotometry Mabrouk et al. forms (2021) Rastogi et al. (2021) Nitrogen in cereals Microwave-assisted ultraviolet ion chromatography RP-HPLC-UV El-Shorbagy et al. Stability indicating study of (2020) ribavirin, sofosbuvir, and ledipasvir (continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Atorvastatin, rosuvastatin, and simvastatin in their binary mixtures with ezetimibe Coumarins in plant samples Linagliptin and saxagliptin with metformin Liquid-liquid microextraction determination of biogenic amines in meat Pesticides in soil Determination of erdafitinib in human plasma Plastic additives (Phthalates and bisphenol A) in microplastic-­laden beach sand Monoiodoacetic acid and diiodoacetic acid in drinking water

Endocrine-disrupting compounds and their derivatives in packaged vegetables Nizatidine nitrosatability in simulated gastric juice In silico study of famotidine and famotidone gastric instability Lesinurad and febuxostat with application to human plasma Postmortem diagnosis of glycated haemoglobin as a biomarker Carbamate pesticides (desmedipham, phenmedipham, and chlorpropham)

Technique FTIR spectrometry

Reference Nasr et al. (2020)

HPLC-fluorescence

Hroboňová et al. (2021) El-Yazbi (2021)

Capillary electrophoresis GC-MS

GC-MS LC-MS-MS HPLC-DAD

Liquid chromatography-­ inductively coupled plasma mass spectrometry LC-MS/MS

HPLC-MS HPLC-UV Synchronous spectrofluorimetry UPLC-QqQ-MS/MS Liquid chromatography with amperometric detection

Wojnowski et al. (2019) Orazbayeva et al. (2020) Elawady et al. (2020) Trujillo-Rodríguez et al. (2021) Liu et al. (2020a)

Szczepańska et al. (2020) El-Shaheny et al. (2019) El-Shaheny et al. (2020) Magdy et al. (2021) Nowak et al. (2020) Diuzheva et al. (2019)

(continued)

2  Green Analytical Chemistry Metrics and Life-Cycle Assessment Approach… Table 2.6 (continued) Assessment Criteria metric approach Analyte Chlorobenzenes in environmental samples Carbamazepine and oxcarbazepine, their potential impurities, and formulation excipients Triclosan in environmental water samples Tedizolid Phosphate Tramadol assay with ibuprofen or chlorzoxazone Enantioseparation of racemic amino alcohols Acemic amino acids Racemic amino alcohols Enantiomers of esmolol Ipratropium bromide, glycopyrronium bromide, tiotropium bromide, bambuterol hydrochloride, formoterol fumarate, and indacaterol maleate Naproxen, diclofenac, and paracetamol in human biological samples NSAID drugs (Ibuprofen, ketoprofen, fenoprofen, naproxen, and diclofenac) Risperidone and its related impurities Favipiravir in pharmaceutical formulation Dalfampridine Perfluoroalkyl substances in wastewater Phytocannabinoids in oil-based formulations

Technique GC-MS HPTLC-densitometry

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Reference Campillo et al. (2020) Abdelwahab and Abdelrahman (2021)

Ion-selective electrode Safwat et al. (2021) Ion-selective electrode Moaaz et al. (2021) Spectrofluorimetry Abdel Moneim and Hamdy (2021) Micellar RP-HPLC Alwera et al. (2020b, c) RP-HPLC Alwera et al. (2020b, c) HPLC Alwera et al. (2021) HPLC Alwera et al. (2020b, c) HPLC Zayed et al. (2021)

HPLC

Bagheri Zomoorodi et al. (2021)

HPLC

Dogan and Tobiszewski (2020)

HPLC with Corona Charged Aerosol Detector Spectrofluorimetry and RP-HPLC Ion selective electrode LC-MS/MS

Maljurić et al. (2020)

Fayed et al. (2021) Liu et al. (2020b)

LC–MS/MS

Merone et al. (2021)

Mikhail et al. (2021)

(continued)

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Table 2.6 (continued) Assessment metric approach

AMGS

AGREE

Criteria Analyte Stability indicating determination of alcaftadine

Technique UPLC-UV/MS TLC-densitometry and UV-spectrophotometry Microsaponification-based method GC-FID for determination of sterol and squalene in cyanobacterial biomass Pharmaceutical residues in water LC-MS/MS samples Vanadium Spectrophotometry Ranitidine hydrochloride and Metronidazole Vanafil, sildenafil, apomorphine, trazodone, yohimbine, tramadol, and dapoxetine in pharmaceutical dosage forms and human plasma Alfuzosin enantiomers and solifenacin Separation itraconazole stereoisomers Lamotrigine 5-fluorouracil and cisplatin in drug-eluting films Dapagliflozin and saxagliptin in tablet Hyoscine N-butyl bromide

Stability indicating determination of flibanserin V, Co, Ni, Cu, Zn, Se, Mo, Cd, and Pb in seawater samples Agri-food by-products metabolites in sugarcane solid residues Removal of acidic drugs from wastewater

Reference Abdel Razeq et al. (2021) Fagundes et al. (2021)

Dogan et al. (2020)

Milcheva et al. (2021) TLC-densitometry and Mohamed and spectrophotometry El-Maraghy (2020) HPLC Ibrahim et al. (2020)

Chiral HPLC

Wadie et al. (2021)

SFC and NP-LC

Agrawal et al. (2020) Abou-Taleb et al. (2021) Youssef et al. (2021)

(1H NMR) RP-HPLC UV-spectrophotometry and HPLC-UV Comparative study of greenness assessment tools RP-HPTLC and NP-HPTLC Mass spectrometry

Abdulwahab et al. (2021) Gamal et al. (2021)

Foudah et al. (2021) Sajid et al. (2021)

UHPLC-UV and Assirati et al. (2020) GC-MS High-temperature Al-Khateeb et al. liquid chromatography (2021) (continued)

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Table 2.6 (continued) Assessment metric approach

Criteria Analyte Favipiravir in pharmaceutical formulation Chiral pharmaceutical residue in wastewater samples Polycyclic aromatic hydrocarbons in different matrices Carbazochrome

Bozantinib in pharmaceutical dosage forms

Technique Spectrofluorimetry and RP-HPLC LC-MS/MS GC-MS Paper-based device based on fluorescence quenching and a smartphone-based all-in-one device with UV torch RP-HPTLC and NP-HPTLC

Reference Mikhail et al. (2021) Dogan et al. (2020) Kamal El-Deen and Shimizu (2021) El-Shaheny et al. (2021)

Alam et al. (2021)

5  Application of Life-Cycle Assessment The phrase “life-cycle assessment” refers to the investigation of a process’s or product’s entire lifecycle, as well as the evaluation of environmental consequences in view of the different areas of ecological effect that go over energy or mass fluxes (Parr and Schmidt 2018). The Life-Cycle Assessment (LCA), which analyzes the environmental friendliness of processes or products, is now a required component of professional statement. LCA can be used alone or in association with other environmental, risk, economic, or social evaluation methodologies. When compared to alternative techniques of monitoring the environmental effect of items or processes, the LCA method offers several advantages. The LCA approach has acquired international acceptance as a valuable method for strategy development, project planning, and policy-making due to its broad application and validity. Because of its comprehensiveness, by shifting to subsequent stages of the process, the LCA method assures the absence of a difficulty (Kralisch et al. 2015). When system limits are chosen on a life-cycle perspective, the growth of environmental effects beyond these boundaries is prevented. In comparison studies utilizing the LCA technique, the assessment of items or processes means that totally various expanses of different components, which are required for achieving the exact function, may be competed. Figure 2.7 reveals the cornerstone steps of LCA studies, which consist of the following stages: 1. Goal and Scope Definition 2. Life-Cycle Inventory Analysis (LCI) 3. Life-Cycle Impact Assessment (LCIA) 4. Life-Cycle Interpretation

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Fig. 2.7  The main stages for development of life-cycle assessment approach

5.1  Goal and Scope Definition The goal of the research, how the results will be utilized, and the target audience to whom the results will be conveyed are all identified in the goal and scope definition stage of LCA. A clear characterization of the decision context is essential to ensure that the research delivers objective information that allows the study investigator and target audience to make qualified decisions based on their standards and objectives. The scope specification also includes the description of some guidelines governing the study’s methodological approach (Kralisch et al. 2015). As a comparable performance parameter, the examined synthetic process or product is likewise defined in units of a specific function. This functional unit is responsible for all inputs and outputs.

5.2  Life-Cycle Inventory Analysis During the Life-Cycle Inventory (LCI), all energy and mass fluxes inside the scope of this research are recorded. The emphasis is on organizing the whole life-cycle into discrete unit activities, as well as data gathering and processing. The gathering of all energy and material inputs and outputs is referred to as data collecting (products, wastes, emissions). The Ecoinvent Centre offers the LCI database “Ecoinvent” (Simons 2016), which contains the most reliable, accessible, and updated available data of Life-Cycle Inventory (LCI) anywhere in the world. Based on industry data, this database contains important LCI data for biofuels and biomaterials, shipping, energy supply, basic and valuable minerals, construction and packaging elements, and disposal management. In the instance of an operation that involves over than single commercially viable outputs and recyclable input resources, the energy and material flows must be allocated (Kralisch et  al. 2015). In general, different allocation techniques are presented and are widely debated (Azapagic and Clift 1999).

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5.3  Life-Cycle Impact Assessment The Life-Cycle Impact Assessment (LCIA) is carried out using the LCI data. Several environmental effect classifications are assigned to energy and mass streams based on the specified characterization model. In this manner, The LCIA delivers assertions on the environmental integrity of the operation as well as the individual production step or product under investigation. There are different approaches to allocate environmental impacts to the inventory; however all of them follow the same protocol described in the EN ISO 14040 and 14044 standards (Kralisch et al. 2015), which includes the following: (a) the choice of a characterization model, (b) classification, (c) characterization and optional, and (d) normalization. The effect categories are determined by the objective and scope of the investigation. A category indicator is a numerical representation of a definite effect category. The category indicator and characterization model are based on the environmental route, which is recognized for the effect category. These impact categories are designed to categorize the potential environmental consequences of the LCI’s fundamental fluxes. During characterization, the potential effect of an examined object is assessed in perspective of a typical entity. There are several LCIA approaches that use various types of characterization models and hence fall into distinct effect categories. Although the inclusion of several LCIA approaches in commonly used LCA software program helps the LCA operator to concentrate on the inputs for the LCI, the LCIA methodology and impact categories investigated must be carefully taken into account of the study’s goal and scope. Input-related and output-related effects are the two sorts of effect categories; meanwhile input-related indicator evaluation is a challenging process (Hauschild et al. 2013). Since the effects of the outputs are increasingly multilayered, the output-related categories are considerably more difficult to identify. An output’s primary, secondary, tertiary, and other impacts can all be used to illustrate its categorization. A midpoint indication is one that is near to the inventory of a certain effect category. When an indication describes a tertiary impact, it is referred to be an endpoint indicator. Because of the chain of events between cause and effect, the indication of the impacts becomes more complicated. Impacts at the tertiary and higher levels are complex to describe, and the number of impact categories continues to rise as a result of the fact that the emission of a single substance frequently has numerous consequences, such as evaluating different regions of protection. Nonetheless, the endpoint category would display the current impacts that the protected areas are subjected to instead of the environmental modifications that cause prospective consequences. As a result, one of the major goals of existing LCA method’s progress is the use of endpoint categories, which poses a potential risk (Buchgeister 2012). The characterization model is supported by scientific data and works as the framework for connecting LCI and LCIA findings. The observed effects, however, should not be taken as verified predictions. Conceptualizations do not account for temporal and spatial aspects as Environmental mechanisms are typically complex. Impact

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evaluation is always a compromise between scientific precision and practicality. As a result, the LCIA results are inherently uncertain and should be regarded as statements on the potential effects. The LCIA approach is a set of characterization models for various impact categories. There are several LCIA techniques available, such as CML 2002 (Guinée and Lindeijer 2002) and IMPACT 2002 (Jolliet et al. 2003), which are both midpoint-directed approaches, or Eco-Indicator 99 (Dreyer et  al. 2003), which is an endpoint-directed indicator. ReCiPe 2008 (Goedkoop et al. 2009) is a combination of the well-known CML 2002 and Eco-Indicator 99 methods, and is one of the most modern LCIA methodologies accessible today, with both mid- and endpoint assumptions.

5.4  Life-Cycle Interpretation According to EN ISO 14044 (Kralisch et al. 2015), the Life-Cycle Interpretation may be split into the following elements: (a) identifying major concerns, (b) evaluating them, and (c) drawing conclusions. The identification of important outcomes is mostly accomplished by arranging the LCI and LCIA data. The database’s integrity, the reliability of the data quality indicator, and the analysis of the results’ sensitivity to variations in input are all part of the evaluation. After checking for these criteria, judgments may be formed about the subsequent recommendations as well as the LCA study’s limitations. Besides that, uncertain definitions, the choice of inappropriate environmental impacts, the omission of key upstream and downstream operations, and erroneous interpretations all have the potential to affect the overall LCA, undermining the strong validity and reliability of life-cycle analyses when compared to ordinary green metrics. It is each evaluator’s role to carefully eliminate these sources of uncertainty without losing sight of practicability and to record the quality of the data utilized. Expert knowledge-based sensitivity analysis is one of the strategies for reducing data collection effort. The identification of the most significant process modules is aided by modifying standard synthesis or process parameters. Those process modules that have made just a modest contribution can be reviewed in further depth (Kressirer et al. 2013). The LCA technique is not specifically developed for evaluating chemical processes or for use in decision-making during process design. As a result, the operator must choose the best-fitting strategy among a large number of LCA ways without losing sight of the LCA approach’s holistic and complete evaluation concept. The most advantageous application of LCA is in the early development of novel chemicals or processes. Crucial weak areas can be discovered and avoided at this time. Nonetheless, the complexity of an LCA study, as well as the time and effort necessary to do it, are usually reasons why other, simpler techniques of evaluation are favored at this point particularly during evaluation of novel, unproven advancements with limited accessible data.

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Furthermore, in instance of utilizing Life-Cycle Costing as a suitable cost assessment technique, the life-cycle-based evaluation may be expanded to include the economic component of sustainability (Rebitzer and Hunkeler 2003) The outcomes of one another may be shown in two-dimensional graphs to visualize the success of specific activities in terms of both the environment and the economy (Sell et  al. 2014). Moreover, when coupled with the results of a Societal LCA (Benoît et al. 2010), all elements of sustainability may be addressed in a methodologically comprehensive manner. The appropriate identification and control of risks to the environment, and safety, and human health is another essential challenge, particularly in product development and chemical processes. Finally, because LCA is typically a continuous process, problems with data collecting may emerge, or new information may need changes to previously specified parameters. As a result, it is frequently helpful and required to return to earlier phases and alter these settings.

6  Selection Guides for Solvents and Reagents In the context of green chemistry, solvents have received much interest (Pollet et al. 2014; Pena-Pereira et al. 2015). This is due to the significant amount of solvent that is generally employed in a process or formulation (Abou-Shehada et  al. 2016). Replacement approaches frequently seek structurally similar substances in an attempt to avoid unwanted solvents. Solvent selection guides have been created in an attempt to categorize solvents according to their environmental, health, and safety (EHS) profiles, providing more information than the indistinct results of regulatory evaluations. It is feasible to construct a green chemical process by substituting greener, primarily bio-based organic solvents for traditional organic solvents using solvent selection models. A green solvent is one that has had both an environmental, health, and safety (EHS) evaluation as well as a cumulative energy demand assessment (CED). The energy required to manufacture a solvent, as well as the alternatives available to recover part of that energy, must be considered in order to determine the overall solvent formation CED (Byrne et al. 2016). Incineration and solvent recycling are two methods for recovering energy. However, distillation uses less energy than generating equal amount of new solvent to purify old solvent. Incineration produces energy right away, yet it also demands the creation of more solvent to replace it. The EHS tool and CED evaluation are publicly accessible as easy-to-use spreadsheets for the most commonly used solvents/reagents, and they can be used in a procedure with any solvent or mix of solvents (Byrne et  al. 2016). This rating spreadsheet is based on risk and hazard classifications, and also regulatory exposure restrictions (Ab Rani et al. 2014). The Global Harmonized System (GHS) revised the EHS list and combined three criteria from each of the three EHS categories to construct a numerical ranking system. Greener solvents, such as alcohols and esters, received lower scores. When

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energy demand is combined with solvent EHS ratings, a more comprehensive solvent’s representation effect emerges. For instance, alcoholic solvents compromise both low EHS and CED values. Similarly, researchers at Rowan University developed a freely available spreadsheet that allows users to compare the many solvent alternatives for a given procedure (Slater and Savelski 2007). For each solvent, an index was created based on 12 environmental variables, comprising occupational health concerns. However, safety concerns are not considered as solvent selection criteria: a value of 0 for the most environmentally friendly solvents and 10 for the least environmentally friendly solvents. Processes may be compared to determine which have the lowest solvent effect by accounting for the amount of solvent utilized. This technique allows for producing a 60-solvent-table for solvent’s choice (Byrne et  al. 2016). The drawbacks of this guide are that it accounts for carcinogenic solvents but does not take into consideration reprotoxic solvents.

6.1  Solvent Selection Guides for Medicinal Laboratories The overall idea of developing green solvent rating is practically different within the chemical industry, meanwhile the recognition that, in a basic medicinal production process the most essential element is the solvent, hence the pharmaceutical era has been eager to build their unique organizational hierarchies of solvent greenness (Constable et al. 2007). Consequently, chemical solvents account for the majority of waste, energy use, and gas discharges (Jimenez-Gonzalez et al. 2004). This prioritizes the reduction of solvent usage and the adoption of greener substitutes, which is frequently an acceptable mark in green chemistry activities (Dunn et al. 2004). Although solvent-free chemistry has long piqued the curiosity of green chemists (Tanaka and Toda 2000; James et al. 2012), it is not frequently appropriate to the pharmaceuticals manufacture. Pfizer, GlaxoSmithKline, and Sanofi pharmaceutical firms collaborated to create three major manuals for medicinal chemists. The uniform color coding is achieved using a “traffic light” scheme, with each solvent having a comment relevant to the constraints set by each firm. Pfizer was the first to provide medicinal chemists with a color scheme, structured-guide for solvent choice (Alfonsi et al. 2008). The tool consists of a basic document that categorizes solvents as preferred, useable, or unacceptable. In creating this solvent selection guide, Pfizer emphasized user-friendliness. GlaxoSmithKline had also started generating solvent choice recommendations for industrial chemistry around the period the Pfizer’s platform was released by pharmaceutical chemists (Jimenez-Gonzalez et  al. 2004). Following that, GlaxoSmithKline implemented a simplified solvent choosing strategy for drug design units, which was derived from a revised and expanded solvent appraisal (Henderson et al. 2011). The approach is more complex than the Pfizer guide, with a thorough breakdown of ratings for several EHS areas. Both solvent-selecting

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guidelines are interpreted differently depending on how these tools are perceived, with the Pfizer guide favoring safety and health. GlaxoSmithKline’s medicinal chemistry solvent selection guidance comprises more environmental issues. An update of GlaxoSmithKline solvent selection guide is now available (Alder et al. 2016). Sanofi has just provided an analogous solvent selection guidance (Prat et  al. 2013). The Sanofi’s tool was inspired by an early version of the firm’s current solvent-selecting guideline, which divided solvents into two categories: preferred and replacement. Following that, a standard sheet was designated as a new guide for every solvent that included the important selected features. A table for selection of every solvent class is provided as well as a general rating, anticipated limitations, and relevant hazard alerts for each solvent. With extra indications, the typical traffic light color coding is employed. The International Council for Harmonization’s residual solvent limitations for medicines are utilized. Sanofi’s solvent-­selecting guideline includes many more solvents than GlaxoSmithKline and Pfizer’s. Because of the usage of legal categories, the Sanofi solvent selection guide is economically practical. The overall score and description of other issues make the tool useful for laboratory working researchers who overrides the legal limitations of solvent usage.

6.2  S  olvents Selection Guides for Pharmaceutical Manufacture More detail about each solvent is required for developing bigger scale reactions, which is not available in solvent selection guidelines for medicinal chemistry, because the method is focused toward commercial scale manufacture. GlaxoSmithKline was the first firm to issue a solvent selection guidance for product design (Curzons et  al. 1999). Thirty-five highlighted solvents have a numerical score range of 1–10 for nongreen to green ones, respectively, depending on different main classes including health, safety, waste, and environmental effect. A new reactivity/stability score and legislative elements were added to GlaxoSmithKline’s solvent selection guidance (Henderson et  al. 2011). Health, reactivity, waste, flammability, life-cycle assessment, and environmental effect are the most recent additions to the GlaxoSmithKline list. Each solvent score has been updated further over time. In 2005, The American Chemical Society Green Chemistry Institute’s (ACSGCI) Pharmaceutical Roundtable, in partnership with 14 organizations, created a solvent-selecting guideline ((ACS) 2021), built on the GlaxoSmithKline’s guideline for selecting solvents and the undisclosed AstraZeneca analogue. A smart phone software was also adapted for solvent choice (Ekins et al. 2013). Solvent-selecting guideline established by The ACS GCI includes one health and one safety element, as well as three environmental factors. The color coding appears to be identical, with the three lowest red-colored scores (eight, nine, and ten) and the three highest

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green-colored scores (one, two, and three). The alternatives that are still available are highlighted in yellow. Scientists from Sanofi, GlaxoSmithKline, Pfizer, the University of York, and Charnwood consultants have created the CHEM21 joint research initiative (Prat et  al. 2015). The Global Harmonized System (GHS) of chemical classification, labeling, and packing has a considerable impact on the technique used to assign solvent greenness. Rather than an average or sum of inappropriate attributes, the final rating of every solvent in the CHEM21 guidance is based on the lowest greener attribute. The lowest score on the scale is ten; however, unlike previous tools, a score of seven is now highlighted in red, and a statement summarizing the hazardous concerns associated with each solvent is provided (Prat et al. 2014). Finally, while solvent selection guidelines from any generator have become a key component in the fight to enhance the greenness of chemical manufacturing practices, little measures have been done to highlight the renewability of solvents or simply to incorporate bio-based solvents into these tools (Moity et al. 2012). It is proposed that solvent-selecting guidelines be updated to include information on which solvents may be made from biomass, as well as the potential of switching to biomass as a feedstock. Accordingly, management may steer bench scientists toward greener solvent employment using the most basics of solvent selection tools merely by raising knowledge of solvent problems.

7  Conclusion The chemical and analytical research disciplines triggered major concerns about the environmental implications of their activities, notably the usage and production of hazardous reagents/solvents. The greenness evaluation of methodologies is becoming more important in order to evaluate pharmaceuticals with the least amount of ecologically hazardous chemicals. The evaluation of greenness is necessary to measure their validity in developing sustainable approaches. Various attempts have been taken to establish green procedures; nevertheless, it is only feasible with adequate assessment approaches. In recent decades, various greenness evaluation approaches have been conducted to examine the greenness of analytical procedures. The majority of researches claiming greener analytical procedures were not properly assessed by adequate tools. Several approaches for assessing the greenness profile, as well as their importance in designing and evaluating analytical procedures with a low environmental impact, have been covered, such as NEMI, Raynie & Driver model, analytical Eco-Scale, HPLC-EAT, AMVI, GAPI, AMGS, AGREE, and others. Each assessment tool described has advantages, disadvantages, and certain criteria for evaluating analytical practices. Development stages of life-cycle assessment and solvent selection guides for green solvents and chemicals have been considered as well.

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8  Future Perspectives The analytical waste created by the pharmaceutical business and analytical procedures has a significant impact on the environmental ecosystem in many countries due to insufficient legal oversight. This discussion emphasizes primarily on the integration of greenness assessment approaches in analytical procedures for evaluating pharmaceutical processes, and it may be extended to other areas as well (Meyers 2012). Moreover, the necessity of understanding how to apply assessment techniques to reduce the creation of poisonous environmental and dangerous analytical hazardous waste, which serves as a cornerstone for better future, has risen to the fore. Furthermore, other emerging strategies to reduce the environmental impact of these analytical techniques, such as using minimal sample treatment, miniaturization of procedures, reducing waste generated, replacing hazardous chemicals/ reagents with others derived from renewable resources, and providing greener alternatives, have been grown. Therefore, the appropriate green assessment tools should be integrated into the methodological approach instead of postanalysis of the established analytical procedure to create a greater contribution on sustainability and environment. To promote the attractiveness of Green Analytical Chemistry and encourage its future adoption, it is necessary to emphasize the importance of green mindset strategy across scholarly researches in addition to analytical chemistry education (Armenta et al. 2008). This strategy will not only help to change community’s perceptions of analytical chemistry and chemistry in general, but it will also help to integrate activities in the field of green chemistry. In general, it is highly recommended to include an assessment of the greenness of analytical procedures in method validity requirements. Furthermore, before undertaking realistic experiments in laboratories, a strategy for the greenness of analytical procedures should be guaranteed in order to reduce chemical threats emitted into the environment. Besides, additional legislative regulations are requested to control the harmful impact of chemical activities on the ecosystem.

References (ACS), A. C. S. (2021). The ACS GCI pharmaceutical roundtable solvent selection guide. Retrieved 30 august 2021, from https://www.acsgcipr.org/. Ab Rani, M.  A., N.  Borduas, V.  Colquhoun, R.  Hanley, H.  Johnson, S.  Larger, P.  D. Lickiss, V.  Llopis-Mestre, S.  Luu and M.  Mogstad (2014). The potential of methylsiloxanes as solvents for synthetic chemistry applications. Green Chemistry 16(3): 1282-1296. doi: https://doi. org/10.1039/C3GC42036A Abd El-Rahman, M. K., E. Fawaz, E. M. Abdel-Moety and A. M. A. Al-Alamein (2020). UPLCPDA: A greener miniaturized tool for the analysis and purity assessment of ebastine and phenylephrine hydrochloride. Microchemical Journal 159: 105400. https://doi.org/10.1016/j. microc.2020.105400

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Tobiszewski, M., S.  Tsakovski, V.  Simeonov and J.  Namieśnik (2014). Multivariate statistical comparison of analytical procedures for benzene and phenol determination with respect to their environmental impact. Talanta 130: 449-455. https://doi.org/10.1016/j.talanta.2014.07.039 Tolba, M. M. and M. M. Salim (2021a). Inclusive study for segregation of two commonly used anticancer drugs with tramadol: Applying a green fluorimetric strategy to pharmaceutical dosage forms and human plasma. Microchemical Journal 162: 105859. https://doi.org/10.1016/j. microc.2020.105859 Tolba, M. M. and M. M. Salim (2021b). Insights for applying erythrosine B as a green fluorescence probe for estimation of anticancer tamoxifen and its analog; clomiphene in nanogram concentration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 263: 120156. https://doi.org/10.1016/j.saa.2021.120156 Trujillo-Rodríguez, M. J., R. M. Gomila, G. Martorell and M. Miró (2021). Microscale extraction versus conventional approaches for handling gastrointestinal extracts in oral bioaccessibility assays of endocrine disrupting compounds from microplastic contaminated beach sand. Environmental Pollution 272: 115992. https://doi.org/10.1016/j.envpol.2020.115992 Turner, C. (2013). Sustainable analytical chemistry—more than just being green. Pure and Applied Chemistry 85(12): 2217-2229. https://doi.org/10.1351/pac-­con-­13-­02-­05 Verhe, R., P. De Caro, Z. Mouloungui and C. V. Stevens (2004). Industrial products from lipids and proteins. Renewable bioresources: scope and modification for non-food applications: 208-250. ISBNs: 0-470-85446-4 (HB); 0-470-85447-2 (PB) Wadie, M., E. M. Abdel-Moety, M. R. Rezk and M. A. Tantawy (2021). Eco-friendly chiral HPLC method for determination of alfuzosin enantiomers and solifenacin in their newly pharmaceutical combination: Method optimization via central composite design. Microchemical Journal 165: 106095. https://doi.org/10.1016/j.microc.2021.106095 Wang, P., Q. Zhang, Y. Wang, T. Wang, X. Li, L. Ding and G. Jiang (2010). Evaluation of Soxhlet extraction, accelerated solvent extraction and microwave-assisted extraction for the determination of polychlorinated biphenyls and polybrominated diphenyl ethers in soil and fish samples. Analytica Chimica Acta 663(1): 43-48. https://doi.org/10.1016/j.aca.2010.01.035 Wang, W., J. Lü, L. Zhang and Z. Li (2011). Real atom economy and its application for evaluation the green degree of a process. Frontiers of chemical Science and Engineering 5(3): 349-354. https://doi.org/10.1007/s11705-­010-­1030-­7 Wang, Y., C. Zhang, L. Zhang, M. Zhao, Z. OuYang and M. Yang (2020). A comprehensive strategy for screening and exploring multi-class mycotoxins contamination status in Astragali Radix. Microchemical Journal 158: 105294. https://doi.org/10.1016/j.microc.2020.105294 Waste, D.  H. (2019). Listed Characteristic and Mixed Radiological Wastes, United States Environmental Protection Agency. https://www.epa.gov/hw/ defining-­hazardous-­waste-­listed-­characteristic-­and-­mixed-­radiological-­wastes. Wojnowski, W., J.  Namieśnik and J.  Płotka-Wasylka (2019). Dispersive liquid-liquid microextraction combined with gas chromatography–mass spectrometry for in situ determination of biogenic amines in meat: Estimation of meat’s freshness. Microchemical Journal 145: 130-138. https://doi.org/10.1016/j.microc.2018.10.034 Woźniakiewicz, M., A. Woźniakiewicz, P. M. Nowak, E. Kłodzińska, J. Namieśnik and J. PłotkaWasylka (2018). CE-MS and GC-MS as “Green” and Complementary Methods for the Analysis of Biogenic Amines in Wine. Food Analytical Methods 11(9): 2614-2627. https://doi. org/10.1007/s12161-­018-­1219-­9 Yabré, M., L. Ferey, T. I. Somé, G. Sivadier and K. Gaudin (2020). Development of a green HPLC method for the analysis of artesunate and amodiaquine impurities using Quality by Design. Journal of Pharmaceutical and Biomedical Analysis 190: 113507. https://doi.org/10.1016/j. jpba.2020.113507 Youssef, S.  H., F.  Afinjuomo, Y.  Song and S.  Garg (2021). Development of a novel chromatographic method for concurrent determination of 5-fluorouracil and cisplatin: Validation, greenness evaluation, and application on drug-eluting film. Microchemical Journal 168: 106510. https://doi.org/10.1016/j.microc.2021.106510

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Chapter 3

Green Sorption Materials Used in Analytical Procedures David López-Iglesias , Alfonso Sierra-Padilla , José María Palacios-­Santander , Laura Cubillana-Aguilera and Juan José García-Guzmán

,

Abstract  The development of green analytical procedures for the monitoring of several sample features is demanded by the general society and is an interesting topic investigated by the scientific community. The use of green sorbents for sampling diverse compounds before analysis in different foodstuffs, gas samples, water, and/or pharmaceutical tablets is considered as an environmentally friendly procedure. Reliable results after analysis can be obtained in a short span of time, using simple and online experimental set-ups. In this sense, several materials are briefly covered in this chapter, reporting an extensive literature. Finally, future perspectives are discussed. Keywords  Green sorbents · Sample preparation · Extraction procedures · Clays · Sol-gel materials · Ionic liquids · Molecular imprinted polymers · Carbon allotropes · Biopolymers

D. López-Iglesias · A. Sierra-Padilla · J. M. Palacios-Santander · L. Cubillana-Aguilera Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Puerto Real, Cádiz, Spain e-mail: [email protected]; [email protected]; [email protected]; [email protected] J. J. García-Guzmán (*) Instituto de Investigación e Innovación Biomédica de Cádiz (INIBICA), Hospital Universitario Puerta del Mar, Universidad de Cádiz, Cádiz, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. H. El-Maghrabey et al. (eds.), Green Chemical Analysis and Sample Preparations, https://doi.org/10.1007/978-3-030-96534-1_3

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Abbreviations AAS APGC-MS/MS

Atomic absorption spectroscopy Atmospheric pressure chemical ionization-tandem mass spectrometry ATP Attapulgite BPA Bisphenol A CE Capillary electrophoresis CNPrTEOS 3-Cyanopropyltriethoxysilane CNT Carbon nanotubes COFs Covalent organic frameworks CPE Cloud point extraction CTAB Cetyl trimethyl ammonium bromide DAD Diode array detection DES Deep eutectic solvent DI-SPME Direct-immersion solid-phase microextraction d-micro-SPE Dispersive micro-solid-phase extraction DMSPE Dispersive micro solid-phase extraction d-MSP-μ-E Dispersive magnetic solid-phase microextraction DPE Dispersed particle extraction DPT-MSPE Double layer pipette tip magnetic dispersive solid-phase extraction d-SPE Dispersive solid-phase extraction dSPE Dispersive solid-phase extraction dSPME Dispersive solid-phase microextraction D-μ-SPE Dispersive micro-solid-phase extraction EA-DM-μ-SPE Effervescent salt-assisted dispersive magnetic micro solid-­ phase extraction ESI Electrospray ionization FAAS Flame atomic absorption spectrometry FI Flow injection FIA Flow injection analysis FID Flame ion detector GC Gas chromatography GC-MS Gas chromatography-mass spectrometry GC-MS/MS Gas chromatography coupled with tandem mass spectrometry GFAAS Graphite furnace atomic absorption spectrometry GO Graphene oxide HPLC High performance liquid chromatography IC Ion chromatography ICP Inductively coupled plasma ICP-OES Inductively coupled plasma atomic emission spectrometry IL Ionic liquid

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IT-SPME LC LC-MS/MS

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In-tube solid-phase microextraction Liquid chromatography Liquid chromatography coupled with tandem mass spectrometry M-BG-dSPE Magnetic bucky gel-based dispersive solid-phase extraction method MCC Microcrystalline cellulose MCNPs Magnetic cellulose nanoparticles MDSPE Magnetic dispersive solid-phase extraction MDSPME Magnetic dispersive solid-phase microextraction ME Magnetic microextraction MEPS Microextraction by packed sorbent MET-ILM Magnetic effervescent tablet containing ionic liquid microextraction MGO Magnetic graphene oxide MIL Metal organic frameworks with zeotype crystal structure MINPs Molecular imprinted nanoparticles MINs Molecularly imprinted nanospheres MIP Molecular imprinted polymer MIPFMR Molecularly imprinted phloroglucinoleformaldehydeemelamine resin MISG Molecularly imprinted silica gel MISPE Magnetic imprinted solid-phase extraction MISs Molecularly imprinted silicas MM/ZIF-8/IL Modified magnetic multiwalled carbon nanotube/zeolitic imidazolate framework-8 and magnetic ionic liquid MMA Methylmethacrylate MMHDSPE Magnetic mixed hemimicelles dispersive solid-phase extraction MMIP Magnetic molecularly imprinted polymer MMT Montmorillonite MMWCNT Magnetic multiwalled carbon nanotubes MNPs Magnetic nanoparticles MOF Metal organic framework MPEF Multiphase electrical field-assisted extraction MS Mass spectrometry MS/MS Tandem mass spectrometry MSPD Matrix solid-phase dispersion MSPE Magnetic solid-phase extraction MSPME Magnetic solid-phase microextraction MWCNT Multiwalled carbon nanotubes MWCNT-MMIP Modified multiwalled carbon nanotube-based magnetic molecularly imprinted polymer M-μ-SPE Magnetic micro-solid-phase extraction NPs Nanoparticles

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NSAIDs Nonsteroidal anti-inflammatory drugs OCPs Organochlorine pesticides OES Optical-emission spectrometry OPPs Organophosphorus pesticides PAHs Polycyclic aromatic hydrocarbons PANI Polyaniline PCBs Polychlorinated biphenyls PDMS Polydimethylsiloxane Pectin/Fe3O4/GO Pectin-coated magnetic graphene oxide PPy Polypyrrole PT-SPE Pipette tip solid-phase extraction Q-TOF Quadrupole time-of-flight Q-TOF/MS Quadrupole time-of-flight tandem mass spectrometry RAM-dSPE Restricted access matrix dispersive solid-phase extraction RAM-SPE Restricted-access matrix solid-phase extraction RDSE Rotating-disk sorptive extraction r-DSPE Reversed-dispersive solid-phase extraction RP-HPLC Reversed phase-high performance liquid chromatography RP-MSPE Reversed phase magnetic solid-phase extraction SBSDME Stir-bar sorptive dispersive microextraction SBSE Stir-bar sorptive extraction SB-μ-SPE Stir-bar supported micro solid-phase extraction SCSE Stir cake sorptive extraction SERS Surface-enhanced Raman spectroscopy S-FAAs Micro sampling flame atomic adsorption spectroscopy Sil Grafted silica SMIPs Surface molecularly imprinted polymers SOT Sotalol SPE Solid-phase extraction SPLE Selective pressurized liquid extraction SPME Solid-phase microextraction TEOS Tetraethoxysilane TFME Thin-film microextraction TF-SPME Thin-film–solid-phase microextraction UA-d-SPE Ultrasonic-assisted dispersive solid-phase extraction UA-DSPME Ultrasonic-assisted dispersive solid-phase microextraction UA-MR-IL-DLLME Ultrasound-assisted magnetic retrieval-linked ionic liquid dispersive liquid-liquid microextraction UASEME Ultrasound-assisted surfactant-enhanced emulsification microextraction UFLC Ultra-fast liquid chromatography UHPLC Ultra-high-performance liquid chromatography USA-SLTPE Ultrasound-assisted solid-liquid trap phase extraction UV Ultraviolet UVM-7 A type of mesoporous silica

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VA-D-μ-SPE VA-MSPE VFMSPD VOCs μ-SPE

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Vortex-assisted dispersive micro-solid-phase extraction Vortex-assisted magnetic solid-phase extraction Vortex-forced matrix solid-phase dispersion Volatile organic compounds Micro-solid-phase extraction

1  Introduction Nowadays, the analysis of complex mixtures is considered as a challenging task. The analytical monitoring of several sample features using fast, online, automatized, and robust procedures to obtain reliable results is currently demanded by scientific society. In this way, the research community invests great efforts in the investigation of several methodologies, characterized by using eco-friendly reagents, simple set-up, low time of analysis, robustness, and low organic solvent consumption, among others (Bartolucci et al. 2020; García-Guzmán et al. 2020). The recovery of the analyte from the raw chemical system, namely, extraction, is a common procedure before analysis. Liquid-liquid extraction (LLE) emerged for the isolation of a great variety of compounds and their analytical quantitation using several detection systems, such as liquid-chromatography and gas-chromatography (Kim et al. 2015; Larriba et al. 2016). Although LLE offers great results as sampling method, it usually involves high hazardous solvent consumption and complex experimental set-up. The decrease of organic solvents can be achieved using alternative LLE methods, such as salting out liquid-liquid extraction (SALLE) (Tang and Weng 2013; Tejada-Casado et al. 2018). Several papers report the use of this method coupled with spectrophotometric and chromatographic techniques for the analytical monitoring of foodstuffs and drinks (Sereshti et al. 2014; Magiera and Kwietniowska 2016; Moreno-González and García-Campaña 2017). Other environmentally friendly procedures have been proposed in the last few years. The use of green sorbents is also included for clean-up and preconcentration purposes (Hashemi et al. 2018). The adsorptive performance of diverse compounds for the pollutants removal was discussed in some pieces of research found in the literature. Toxic dyes and pollutants have been successfully removed from wastewaters, with removal percentages close to 100% (Kyzas and Kostoglou 2014; Aichour and Zaghouane-Boudiaf 2019). In these reports, the maximum amount of removal, adsorption capacity, and kinetics are also discussed for further understanding of the adsorption performance. Solid-phase extraction (SPE) presents suitable features for extraction purposes. This separation technique consists of the transport of the selected analytes from a mobile phase to a solid phase, where they are retained. Afterward, target analytes are recovered by elution or thermal desorption of the solid phase (Poole 2003). Four general steps can be involved in SPE: (1) conditioning, (2) loading, (3) washing, and

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Fig. 3.1  Schematic representation of SPE in cartridge format

(4) elution (Andrade-Eiroa et  al. 2016). A schematic representation is shown in Fig. 3.1. The solid-phase extraction is considered as a promising analytical technique, since some advantages over LLE were found. The number of metabolites recovered in urine under SPE is remarkably higher than those recovered using LLE. In other works, higher accuracy for the quantification of phenolic compound was reported (Pinto et al. 2010). Additionally, less time consumption and lower limit of detection of several drugs were also found using SPE format (Kumari et al. 2016). Low consumption of organic solvents and reusability are also highlighted. For all these reasons, SPE is considered a green alternative to conventional sampling methods (Trenholm et al. 2009). Other SPE formats were investigated to minimize some problems in common SPE. Solid-phase microextraction (SPME) was also developed for analysis in biomedical and environmental applications, reducing the organic solvent consumption and analysis time. Furthermore, this procedure enables the automatization and miniaturization of analysis (Kataoka et  al. 2016). Dispersive solid-phase extraction (dSPE) is reported as another alternative to SPE. In this method, the sorbent is dispersed with the sample solution. After performing the extraction, the target compound was analyzed. Figure 3.2 shows the schematic representation of dSPE. Magnetic solid-phase extraction (MSPE) has also been exploited for the isolation of diverse compounds contained in complex matrices. The magnetic sorbents can be dispersed in the sample solution and, then, extracted easily using a magnet (Amiri et al. 2019; Chisvert et al. 2019).

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Fig. 3.2  Schematic representation of dispersive solid phase extraction (dSPE)

The trace analysis of different phenolic acids, metals, and other compounds in foodstuffs and wastewaters using different SPE formats coupled with mass spectrometry, flame ionization, spectrophotometry, and inductively-coupled plasma (ICP) detection systems is critically revised in the literature (Faraji et  al. 2019; Billiard et  al. 2020). The quantitation of several electroactive compounds can be also made using adsorptive stripping voltammetry after performing the extraction procedure. Wray and coworkers reported the separation of curcumin from other similar redox moieties by nickel complexation. Afterward, the adsorption of this analyte onto screen-printed surface and its electrochemical determination were also achieved (Wray et al. 2012). The analysis of mercury by stripping voltammetry was carried out after extraction with different biological matrices (Razmi et al. 2016; Tootoonchi and Davarani 2016). Other metals, such as cadmium and lead, were analyzed as well (Paukpol et al. 2020). In this chapter, the analytical monitoring of diverse real matrices (wastewaters, foodstuffs, gases, pharmaceutical tablets, and biological fluids) using green sorbents is discussed. In this sense, several compounds, including zeolites, clays, oxides, ionic liquids, and carbon allotropes, are proposed as green sorbents for clean-up and preconcentration purposes based on their adsorption ability. The quantitation of the analytes using diverse techniques is also reported. Finally, a critical revision about the future trends of the employment of green routes for analysis is summarized.

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2  Employment of Adsorbent Materials for Analysis 2.1  Mineral Clay Composites Mineral clays are formed by aluminum silicates with different metals. Tetrahedral silicates and octahedral hydroxide sheets are arranged in different blocks (Nagendrappa 2002). The different arrangements lead to different mineral clay materials (see Table 3.1) (Ghadiri et al. 2015). As an illustrative example, the structure of montmorillonite, a 2:1 phyllosilicate-­ clay, is shown in Fig. 3.3. Structural differences between mineral clays and zeolites can be found. Mineral clays are composed of tetrahedral and octahedral blocks arranged in two-­dimensional layers. On the other hand, zeolites were constituted by tridimensional frameworks, occupied by exchangeable cations and water molecules (Bish 2013). Their main feature is the ion-exchange capacity, mainly consisting of the replacement of the counterions located in the interlayer of the clay material by other cations or anions. For this reason, they can be employed in green processes, such as bioremediation and catalysis of several pollutants (Lazaratou et al. 2020). They may be also employed as adsorbents for removal or preconcentration purposes (Han et al. 2019). Montmorillonite modified clays for trace determination of different metals and organic compounds have been developed (Lee et al. 2016; Yadav et al. 2020). Other clay-based composites can be employed as sorbents, such as attapulgite and sepiolite (Wang et al. 2016; Mateos et al. 2018). Table 3.2 exposes some relevant sorbents for analysis. Table 3.1  Different types of clay composites Tetrahedral:octahedral sheets ratio 1:1 (phyllosilicates) 2:1 (phyllosilicates) 2:1 (inverted ribbons)

Clay composites Kaolinite Montmorillonite Sepiolite, attapulgite

Fig. 3.3  Schematic structure of the montmorillonite clay

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As it is reported in Table 3.2, clay composites can be used as sorbent prior analysis. The kaolinite powder was used for extraction and analysis of calcitriol, obtaining a suitable recovery value and high accuracy (Wang et al. 2021b). Other hybrid clay composites are reported in Table 3.2. The modification of clay particles with organic frameworks improves the sensing performance of Rhodamine B and chromium in comparison with pristine clay (Ulusoy 2017; Amran et  al. 2020). Additionally, other examples containing biochar-clay composites for the preconcentration of some drugs in water samples (Aftafa et al. 2014; Chai et al. 2018; Jia et al. 2020), as well as in other drinks and foodstuffs (Uygun et al. 2020) are shown. In the work published by C.  Jia and coworkers, an adsorption mechanism of Table 3.2  Most relevant cases in the employment of clay composites as sorbents for analysis Clay-based composite Method Analyte Kaolinite (Kaol) Kaol powder SPE Calcitriol Attapulgite (ATP) ATP@COFs

DSPE

PolyethyleneimineSPE ATP Montmorillonite (MMT) PolyacrylamideSPE bentonite-­graphene Bentonite-MCCs –

Technique Sample

Reference

HPLC-­ DAD

Wang et al. (2021b)

Capsules

Pyrethroids

HPLC-­ Water DAD 2,4,6-trichlorophenol HPLC-UV Water

Rhodamine B

HPLC FIA-AAS

Jia et al. (2020) Chai et al. (2018)

Cosmetic

Ulusoy (2017) Water Amran et al. (2020) Wastewaters Almasoud et al. (2020)

CTAB-MMT

SPE

Cr (III), Cr (IV) Rhodamine B

Chitosan-­ polyhydroxyethyl­ methacrylate-­MMT IL-MMT

SPME

Zn (II)

UHPLC-­ ESI-­MS/ MS UV-Vis

SPME

Estrone, 17β-estradiol

LC-MS/ MS

DSPE

Polycylic aromatic hydrocarbons

RP-HPLC Wastewaters Mateos et al. (2019)

SPE

Pb (II)

FAAS

Sepiolite (Sep) Graphene/sepiolite C/sepiolite/2-[(5bromo-2-­pyridyl) azo-5)-(diethyl-­ amino)phenol]

Water, milk, Uygun et al. vitamin (2020) Water

Water

Aftafa et al. (2014)

Esmailzadeh et al. (2020)

AAS atomic absorption spectrometry, ATP attapulgite, COFs covalent organic frameworks, CTAB cetyl trimethyl ammonium bromide, DAD diode array detector, DSPE dispersive solid phase extraction, ESI electrospray ionization, FAAS flame atomic absorption spectrometry, FIA flow injection analysis, HPLC high-performance liquid chromatography, IL ionic liquid, MMT montmorillonite, MS mass spectrometry, RP-HPLC reversed-phase–high-performance liquid chromatography, SPME solid-phase microextraction, UHPLC ultrahigh-performance liquid chromatography, UV ultraviolet-visible

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attapulgite-modified composite involving π-π stacking and hydrogen bond interaction between the analyte and the composite material is proposed (Jia et al. 2020). It is also noteworthy to mention the use of cetyl trimethyl ammonium bromide (CTAB) in analytical sensing. The adsorption role of this surfactant in conjunction with clay layers is also reported by R.  Hanouati and coworkers, who provided a detailed mechanism of the adsorption performance of clay-modified, involving hydrophilic, hydrophobic, and hydrogen bonding interactions between the target compound and the surface of the montmorillonite layers (Haounati et al. 2021). Therefore, clay materials modified with carbon allotropes, metal-organic frameworks, and surfactants are promoted as suitable sorbents, reaching low limits of detection for the determination of several compounds in real matrices at trace concentration levels.

2.2  Sol-Gel-Based Composites Sol-gel route is a low-cost procedure mostly employed to extract ceramic materials based on their oxide networks with different shapes, such as glass, coatings, powders, and monoliths, under mild conditions (Parashar et al. 2020). The overall process involves the hydrolysis, condensation, gelation, and drying steps of a metallic and nonmetallic precursors (Owens et al. 2016). The hydrolysis and condensation steps of a general silicon alkoxide can be schematized in Fig. 3.4 (Malucelli 2016). This process can be considered as a green approach, based on their simple experimental set-up and low-energy requirements. Furthermore, several bioactive compounds can be embedded within the porous oxide network for clean-up and preconcentration purposes. S. Merkle and coworkers overviewed the application of sol-gel fiber coatings for the isolation and analysis of several food and environmental matrices, such as fruits, drinks, and air and water samples (Merkle et al. 2015). In another review article, an exhaustive analysis of sol-gel process for SPME

Fig. 3.4  Hydrolysis and condensation steps for a general silicon alkoxide

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extraction is performed, including different operation modes and the advantages and drawbacks on the use of sol-gel based composites (Amiri 2016). Silica and nonsilica hybrids synthesized by sol-gel procedures can be employed as suitable sorbents. In the following subsections, some aspects regarding their adsorptive performance and their application for analysis are discussed. 2.2.1  Silica Sorbents Silica-based compounds have been exploited as sorbents for analysis due to their high porosity, chemical inertness, and high surface. Several silica hybrid materials were synthesized using sol-gel routes, proving their efficiency in the removal of mercury (Da Silva et al. 2019). The application of silica hybrid composites in SPME is addressed by many authors. For example, P. Hashemi and coworkers developed a fiber coated with an amino functionalized-nanosilica to the clean-up and determination of triazines by means of high-performance liquid chromatography (HPLC). H. Bagheri and coworkers investigated different silica fiber coatings in the SPME of distinct compounds, including polycyclic aromatic hydrocarbons, herbicides, estrogens, and triazine before their analysis using HPLC. No significant differences in terms of extraction efficiency were found with the majority of the silica precursors, indicating similar sorbent-analyte interactions (Bagheri et al. 2012). Other review papers remark the importance of these sorbents for analysis (Casado et al. 2017, 2020). Thus, the use of silica hybrids as sorbents before analysis of a great diversity of inorganic and organic compounds is a prolific research field, currently demanded by the general society. Several silica hybrids are listed in Table 3.3. Table 3.3 exposed several analytical results using different silica-based materials: for sample, analysis in different ambits, such as food and environmental sectors. The hybrid composite constituted by two silicon precursors (MTMOS-MPTMS) was employed for SPE extraction of some anti-inflammatory drugs by M.  Abd Rahim and coworkers, reporting adequate analytical results (Abd Rahim et  al. 2016). Based on the analytical eco-scale published by K. Van Ken and coworkers (Van Aken et al. 2006), the SPE method reported in the last work can be considered as a green methodology. Graphene-based silica offered appropriate analyte recovery via MSPE using low extraction time. Additionally, no further centrifugation and filtration were required. Concerning analytical performance, low detection limit for acrylamide analysis in a complex foodstuff was found (Rashidi Nodeh et al. 2018). Another graphene-silica modified material was employed in the solid-phase microextraction of several polycyclic aromatic hydrocarbons in honey samples and their simultaneous quantitation by HPLC analysis (Sun et  al. 2021). In this work, the extraction repeatability is remarkable for all analytes tested, indicating high accuracy in the adsorptive performance of the material. The use of conducting polymer-silica hybrids as sorbents is promoted for a wide variety of analytes. A core-shell polyaniline silica nanocomposite was successfully applied in the quantitation of some benzophenones, displaying high adsorption

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Table 3.3  Most relevant cases in the employment of silica hybrids as sorbents for analysis Silica hybrid Sol-gel film/SiO2 NPs

Method μ-SPE

Methacrylate-­ poly(propylene glycol)coated silica Agarose-silica

Stir Chlorinated bar-μ-SPE hydrocarbons

GeO2-Al2O3-SiO2 Methyltrimethoxysilane-­ mercaptopropyltri­ methoxysilane Fe3O4@G-TEOS-­ methyltrimethoxysilane GO-mesoporous silica

SPE

Analyte Halobenzenes

Ractopamine

Stir Chlorinated bar-μ-SPE hydrocarbons SPE Non-steroidal anti-­ inflammatory drugs RP-MSPE Acrylamide SPME

Core-shell silica DSPE microspheres@PANI MGO/SiO2@PANI-PPy MSPE Silica aerogel/basalt fibers SPME

Polycyclic aromatic hydrocarbons Benzophenonetype UV filters Cr (III), Pb (II) Estrogens

Technique Sample Reference GC-MS Water Mohammadiazar et al. (2019) GC-MS Milk Alhooshani (2019) HPLC-UV Pork Peng et al. muscle (2021) GC-MS Water Tanimu et al. (2021) HPLC-UV River Abd Rahim et al. water (2016)

GC-MS HPLC-­ DAD CE-MS/ MS ICP-MS HPLC

Potato chips Honey

Rashidi Nodeh et al. (2018) Sun et al. (2021)

Water

Wang et al. (2021a) Suo et al. (2019) Bu et al. (2017)

– Water

AAS atomic absorption spectroscopy, CE capillary electrophoresis, DAD diode array detector, DSPE dispersive solid-phase extraction, GC gas chromatography, GO graphene oxide, HPLC high-performance liquid chromatography, ICP inductively coupled plasma inductive, LC liquid chromatography, MGO magnetic graphene oxide, MS mass spectrometry, MSPE magnetic solid-­ phase extraction, NPs nanoparticles, PANI polianiline, PDMS polydimethylsiloxane, PPy polypyrrole, RP-MSPE reversed phase magnetic solid-phase extraction, SPE solid-phase extraction, SPME solid-phase microextraction, TEOS tetraethoxysilane, UV ultraviolet, UVM-7 a type of mesoporous silica

ability, reproducibility, and reusability (Wang et al. 2021a). The conducting role of PANI is revised in other research works, reporting better recoveries for three triterpenic acids in comparison with commercial SPE cartridges under the same experimental conditions (Sowa et al. 2014). Therefore, silica materials are promoted for analysis based on their high adsorption ability, reusability, high mechanical stability, and analytical performance. 2.2.2  Nonsilica Sorbents Although silica is commonly employed as sorbent due to its high mechanical strength, surface characteristics, and catalytic inertness, their adsorptive performance may be limited at extreme pH values. For this reason, other nonsilica hybrids can be considered as suitable sorbents.

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Table 3.4  Most relevant cases in the employment of nonsilica hybrids as sorbents for analysis Nonsilica hybrid Ti-CNPrTEOS

Method Analyte SPE Aromatic amines

SPE Stainless steel meshes coated with poly(ethylene glycol) and carbon nanotubes Cu-MOF SPE

Organophoshporus pesticides

Nonsteroidal anti-inflammatory drugs

Technique Sample Reference GC-FID Water Miskam et al. (2014) GC-FID Fruit Amiri et al. juices (2020b)

HPLC-UV Water

Amiri and Ghaemi (2021)

CNPrTEOS 3-cyanopropyltriethoxysilane, CNT carbon nanotubes, FID flame ionization detector, GC gas chromatography, MOF metal organic framework, SPE solid-phase extraction

The application of some nonsilica materials in the solid-phase extraction of several analytes prior analysis is reported in Table 3.4. Table 3.4 gives interesting information on the use of nonsilica sorbents used for analysis. The Ti-CNPrTEOS material was successfully prepared by sol-gel route and applied in the monitoring of several aromatic amines. In this regard, the recoveries of most of them are higher for the developed material, in comparison with those obtained with commercial system (Miskam et al. 2014). The PEG-CNT material shows suitable results for fruit juices analysis. In this work, the recoveries of organophosporous compounds in fruit juices were greater than those obtained with a commercial cartridge, C18, which indicates that carbon nanotubes play a remarkable role in the extraction performance (Amiri et  al. 2020b). The monitoring of commercial drugs was performed using Cu-metal organic frameworks in another research work, obtaining better extraction recoveries in comparison with the unmodified material. This improvement can be mainly ascribed to the higher porosity of the organic framework (Amiri and Ghaemi 2021). Therefore, the modification of bare nonsilica materials led to improvements in the extraction recoveries of different compounds in real matrices, mainly water samples. Furthermore, the use of these materials can be promoted in some cases instead of silica composites, thanks to their higher mechanical stability at extreme pH values.

2.3  Ionic Liquids Currently, ionic liquids (ILs) can be considered as one of the most popular approaches to perform extraction processes. Despite their first appearance in 1970 in a piece of research made by Atwood et al., their real born was in 1992, when the first stable compound namely, 1-ethyl-3-methylimidazolium, was achieved (Wilkes 2002). Previously known as molten salts, this old cunning name hides their main feature: a very low melting point (  Th4+  >  La3+ (Liang et al. 2019). Biologically dangerous Th4+ ions and their compounds cause severe damages to the bones and kidneys upon long-term exposures to human. The selective and

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Fig. 9.6  Pretreatment of DESs in biomass

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Non-ionic surfactant

Clould-point extraction (CPE)

Coacervative extraction (CAE)

Micelle-assisted extraction (MAE)

Supramolecular solvent extraction

Ionic surfactants

Alcohols and carboxylic acids surfactant

Magnetic-assisted dispersive solidphase extraction (m-dPSE)

Magnetic-assisted dispersive solidphase extraction (m-dPSE)

Fig. 9.7  Mobile phase or modifiers in liquid chromatography

sensitive preconcentration of thorium in water and rock materials using dispersive liquid-liquid microextraction (DLLME) based on hydrophobic DES. Thorin as chelating agent and cetyltrimethylammonium bromide (CTAB) were used as pairing agents, and DES consisted of 1-hexyl-3-methylimidazolium and salicylic acid as an extraction solvent (Sadeghi and Davami 2019). TX100 was applied as adsorbent for the removal of Ar(III) using cloud point extraction method. New CPE methods were successfully used for the removal of Ar(III) with respect to time, surfactant, temperature, and salt concentration. The method was initiated to be spontaneous and endothermic in nature. Ar(III) ion as solubilized in micellar-rich phase. U(VI) and Th(IV) ions can be recovered using this method (Hamed and Aglan 2019). Enantiomeric study of β--agonists (terbutaline, clorprenaline, tulobuterol, clenbuterol, and salbutamol) produced using various hydrophobic acids (HBD). More sensitivity, recovery, and accuracy were achieved using DLLME (Liu et al. 2019a, b). Microextraction using undecylamine (UA)-DLLME-DES solvent for the determination of pyrethroid insecticides in environmental water samples was reported. The recovery of insecticides within the range of ~80% to ~109% was obtained (Liu et al. 2019a, b). Centrifugation-free dispersive liquid-liquid microextraction method was used for the solidification of hydrophobic deep eutectic solvents of benzyltriphenylphosphonium bromide and undecanol. This HDES was used for the separation of selenium in aqueous samples (Mostafavi et al. 2019).

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HDES of iron oxide-oleic acid DES was used for the analysis for the determination of mefenamic acid (MFA) in the urine sample. The MFA microextraction in urine samples was between 80% and 97% (Dil et al. 2019). Decanoic acid or octanoic acid with various HBAs of tetrabutylammonium chloride, tetraoctylammonium chloride, tetraheptylammonium chloride, and methyltrioctylammonium chloride was used as HDES for the removal of chlorophenol compounds like 4-chlorophenol, 2,4-dichlorophenol, and 2,4,6-trichlorophenol from wastewater with DLLME methods. The recovery from 90% to 93% was reported (An et al. 2020). DESs of tetrabutylammonium chloride (TBACl) and decanoic acid (DA) were prepared at 1:2 ratio and with DESs being applied for the extraction of Cr(IV) ion from the environmental pollution using electrochemical reactors (Ruggeri et al. 2019). Hydrophobic DES was prepared by mixing ethylparaben and methyltrioctylammonium chloride. Hydrophobic DESs act as the effective additives for the sol-gel coating of polydimethylsiloxane (PDMS) fiber. Under optimum condition, the PDMS-DMS fiber can be used for the effective microextraction of volatile organic compounds like toluene, ethylbenzene, and o-xylene by GC-FID. The PDMS-DMS fiber was in the range between 10 and 1000 μg L−1 with the detection ranging from 0.005 to 0.025 μg L−1 (Li et al. 2019). Methyltrioctylammonium chloride (HBA) and butanol at 1:4 ratio showed higher extraction yield. The plant biomass extraction using microporous resin with DES yields 85.65% (Cao et al. 2017). The DES was carried out with the mole ratio of 35:5:40 using choline chloride and levulinic acid; choline chloride and melonic acid; MCO (mixing methyltrioctylammonium chloride, capryl alcohol, and octanoic acid at a molar ratio of 1:2:3). DESs were used for the extraction of various bioactive compounds from Ginkgo biloba leaves with different polarities (Cao et al. 2018). Vortex-assisted LLME-based DESs (decanoic acid as HBD and methyltrioctylammonium bromide as HBA with 2:1 mole ratio) has been synthesized to extract malondialdehyde and formaldehyde after the derivatization with 2,4-­dinitrophenylhdyrazine. This extraction procedure is used for the extraction of low-molecular-weight aldehydes (Safavi et al. 2018). Hydrophobic deep eutectic solvent was prepared by the decanoic acid and with different quaternary ammonium salts. The recovery of volatile fatty acids forms the dilute aqueous solution. Increasing the carbon chain length decreases the water content and salt leaching. The extraction efficiency increases with increase in chain length (van Osch et al. 2015). Hydrophobic DESs were prepared by mixing two solid components at different mole ratios 2:1, 1:1, and 1:2. Thiomal and different compounds like menthol, coumarin, and menthol are considered as natural deep eutectic solvents. They are used for extraction of riboflavin from water. The highest removal of riboflavin was observed by decanoic acid:lidocaine in which 81% was achieved (van Osch et al. 2019).

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Lower alcohols are extracted using the DESs of DL-menthol and dodecanoic acid (2:1) by LLE methods. Butanol shows higher distribution selectively and coefficient when compared to propanol and ethanol (Ge et al. 2019). DESs of TBAB and carboxylic acids were prepared and used for the analysis of polycyclic aromatic hydrocarbons in environmental samples. That produces the ultrarace analysis in the range of nanomole range and good linearity (Yousefi et al. 2018). Trioctylmethylammonium chloride and oleic acid are composed of a mixture of DESs used for the DLLME of the biological samples that contain nitrite from water with HPLC. Micromolar quantitative deduction was done, and the recovery range was 90–115% (Zhang et al. 2019).

2.3  Pretreatment of Bio-Based Solvents Sugar substances such as sugarcane juice and molasses; starch materials such as wheat, corn, and cassava; and lignocellulosic substances such as forest residuals, straws, and other agricultural by-products are mainly used for the production of ethanol. In this method, sulfuric acid is added to the starch materials, and it is converted into low-molecular-weight dextrains and glucose. But in this case, there are some drawbacks like high cost, by-products, etc.. So the acid hydrolysis process is performed by dilute acid for the pretreatment by enzymatic hydrolysis. The raw materials are initially soaked in the dilute acid. Then the raw materials are continuously passed into the steam jet heater into a jet cooker or cooking tube with a plug flow residence time for a few minutes. Further, it is subjected to an enzymatic hydrolysis process. For the production of ethanol, the industry primarily utilises the dry and wet processes, as well as the enzymatic and fermentable processes (Kim et al. 2018). The hydrolyzed lignocellulose can be obtained in many ways. Lignocellulose is obtained by electron beam irradiation, γ-rays, and microwave irradiation, but the methods are commercially unsuitable. The commonly applied methods are chemical hydrolysis and enzymatic hydrolysis. Chemical hydrolysis involves the exposure of materials for a particular period of time at specific temperature. Sulfuric acid, nitric acid, or hydrochloric acids are used for the pretreatment process to remove hemicellulose materials followed by enzymatic hydrolysis process. The concentrated acid yields higher ethanol from sugars when compared to the dilute acid process. But the drawback of this hydrolysis process is that it is toxic and corrosive. Removal of acid after hydrolysis and maintenance of equipment from acids are also challenging. So, the environmental impressions strongly limit the usage of concentrated hydrochloric acid. The dilute acid hydrolysis is used before enzymatic hydrolysis. The pretreatment produces a high reaction rate and significantly improves the enzymatic hydrolysis. Lignocellulosic feedstock produces hemicellulose sugar, and 95 percent of the

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Lignocellulosic biomass feedstocks (Wood, wood wastes, corn strove, straw, agricultural waste etc) (Cellulose, hemicellulose, liganin)

Fast pyrolysis/ Liquification (Sugars, acids, aldehydes, aromatics)

Pre-treatment and hydrolysis (C5 sugar, (xylose) & C6 sugar (glucose and fructose)

Fermentation (ethanol, butanol)

Fig. 9.8  Steps involved in ethanol and butanol production

hemicellulose is obtained this way. The enzymatic process, as well as the addition of 20 to 80 efficient nonionic surfactants as additives, are used to boost the fermentation process and, as a result, ethanol production (Fig. 9.8). 1-Butanol is widely used as an industrial solvent. This was prepared by the petrochemical industry in a bulky way. When they are prepared by the renewable feedstocks, they are called biobutanol (Bankar et al. 2013). 1-Butanol is produced by the fermentation using the ABE process (i.e., acetone-butanol-ethanol) with Clostridia bacteria and various commercial raw materials (Ndaba et al. 2015). 2-Octanol is a colorless oily liquid with low toxicity with eight carbon atoms. The bio-based alcohol is produced by the cracking process of ricinoleic acid which is the major component of castor oil (Mubofu 2016). 1,3-Propanediol is another solvent which is manufactured by the renewable feedstocks such as corn through the fermentation process using plant-derived glucose.

3  D  etermination of Deep Eutectic/Surfactants/ Bio-Based Solvents The following section will briefly focus the notable experimental techniques utilized in the examination of the green solvents.

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3.1  Percentage of Sample Solvents The amount of fiber dissolved in DES is calculated by the formula Fiber dissolved ( % ) =

Weight i − Weight f ×100 Weight i



where Weighti = weight of initial biomass, Weightf = weight of undissolved fiber which recovered after the pretreatment.

3.2  Fourier Transform Infrared Spectroscopy In this analysis the functional group determination and change in the percentage of pretreament material was calculated. The relative changes were calculated as follows: Intensity untreated − Intensity Pretreated Relative changes( % ) = ×100 Intensity untreated where Intensityuntreated = intensity of untreated sample peak, IntensityPretreated = intensity of pretreatment sample peak. The scan in the range from 280 to 4000 cm−1 was used for the IR spectrum, and the sample is prepared by attenuated total reflectance (UA-TR) or diamond pressing method.

3.3  Nuclear Magnetic Resonance Analysis NMR is a frequently used approach for analyzing the structure and composition of materials. It is primarily used to determine the presence and/or contributions of various functional groups as well as the purity of the samples. Information about impurities, water content present, interaction and rearrangement of functional groups, and transport phenomena in the samples can also be obtained (Zaid et al. 2017). Pulsed field gradient (PFG) NMR is the widely used NMR technique. PFG NMR superimposes spin echo and magnetic field gradients. In the techniques, quantitative information pertaining to the diffusion processes are obtained in the case of liquids or liquid-like systems. The technique is noninvasive and does not require labeling of molecules under analysis (D’Agostino et al. 2011; D’Agostino et al. 2015; Hossain and Samanta 2017; Wolf et al. 2012). Diffusion ordered spectroscopy (DOSY) proton NMR technique can also be used to study the diffusion coefficients for the individual resonances. It is used to analyze the oligomeric state of biomolecules,

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polymers, and small molecules (Giernoth and Bankmann 2005; Glanzer and Zangger 2014; Smith et al. 2020).

3.4  Fourier Transform Infrared Spectroscopy It can be used to predict the functional groups present in the sample and give an idea about the structures of various molecules present in the sample. FTIR gives an insight to the shift in the molecular structures when different components are mixed together at various compositions. The technique can be used to study the inter- and intramolecular vibrational molecular dynamics of various samples (AlOmar et al. 2016; Cui et al. 2019; Delgado-Mellado et al. 2018; Ibrahim et al. 2019; Kareem et al. 2021; Mamilla et al. 2019; Mjalli et al. 2017; Pramanik et al. 2010; Singh et al. 2015; Sugumaran et al. 2017).

3.5  Raman Spectroscopy Rotations, vibrations, and other low-frequency modes present in a system can be monitored using Raman spectroscopy. The unique fingerprints give inputs on what is present in a sample. Raman spectroscopy is generally used in conjunction with NMR and FTIR to obtain valuable information related to the purity and composition of the substance. The technique can also be used to probe the presence of hydrogen bonding in small molecules, biomolecules, and supramolecular systems. Measurements of deformations of bond angle in the condensed state can also be determined. (Badawi and Förner 2011; Cheng et al. 2017; Chromá et al. 2021; He et al. 2020; Klein et al. 2020; Pandey and Pandey 2017; Pradeepkumar et al. 2018; Singh et al. 2021; Xia et al. 2018; Zhu et al. 2016).

3.6  Broadband Dielectric Spectroscopy Broadband dielectric spectroscopy (BDS) is a multifaceted technique for inspecting the mobility of dipolar and ionic substances. It is used to measure the movement of electric dipole in an applied oscillating electric field, which in turn is a measure of the cumulative interfacial and molecular contributions. Various factors such as interfacial polarisation, Broadband dielectric spectroscopy (BDS), relaxation process, charge conduction, and dipole relaxation are evaluated in homogeneous media, emulsions, polymers, multiphase liquids, and biological systems. BDS also furnishes affirmation for structure and molecular dynamics in various systems (Lian and Zhao 2018; Mazzer et al. 2018; Schrödle et al. 2006). BDS can be used to study

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dielectric relaxations in various hydrogen-bonded compounds such as water, imidazoles, secondary amides, thiols, furanoates, monohydroxy alcohols, etc. (Adrjanowicz et al. 2015; Arrese-Igor et al. 2017; Böhmer et al. 2014; Chen and Nozaki 2012; Iacob et al. 2008; Sasaki et al. 2017; Shiraga et al. 2015; Sinha et al. 2004; Soccio et al. 2020; Wolf et al. 2011). The information obtained from BDS assists in the correlations of various microscopic mechanisms with the different macroscopic properties such as surface tension, eutectic composition, viscosity, ionic conductivity, etc.

3.7  Thermogravimetric Analysis Thermogravimetric analysis (TGA) is a thermoanalytical technique that measures the changes in mass as a function of time and/or temperature. This technique is very useful in detecting deviations due to phase changes. Changes due to reduction, oxidation, decomposition, sublimation, vaporization, absorption, and desorption can be probed using TGA. Since the technique furnishes a great diversity of fundamental information, it is used as a preliminary step to gain data in crystal- and glass-­forming transitions (Craveiro et al. 2016; Dai et al. 2013; Dietz et al. 2019; Gajardo-Parra et al. 2019; Liu et al. 2017; Maleki et al. 2018; Shahbaz et al. 2016; Teng et al. 2019; Zhao et al. 2011).

3.8  Differential Scanning Calorimetry Differential scanning calorimetry (DSC) is thermoanalytical technique where the heat flow into or out of a sample is monitored as a function of time or temperature. Information of various properties such as thermal stability, purity, specific heat capacity, melting, oxidation behavior, cure process, crystallization, vaporization enthalpy, and glass transition temperature can be obtained from this technique (Ali et al. 2019; Ding et al. 2007; Inoue et al. 2007; Mansueto et al. 2013; Marino et al. 2019; Melo et al. 2013; Russo et al. 2017; Stappert et al. 2014; Verevkin et al. 2012).

3.9  Fluorescence Spectroscopy Fluorescence is characterized by various parameters such as excitation wavelength, emission wavelength, anisotropy, quantum yield, and life time of the excited state (Siraj et al. 2016). The technique is a pivotal tool to understand the structure, solvation, interactions, aggregation, and microenvironmental polarity within the sample (Das and Biswas 2015; Dhingra et al. 2019; Gazi et al. 2011; Kadyan et al. 2016; Kadyan et al. 2019; Pal et al. 2014; Pandey et al. 2014; Pandey et al. 2017; Wu et al. 2014).

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3.10  Neutron Scattering Neutron scattering is based on the difference in the scattered length density of hydrogen and deuterium. This technique gives insight into the complex structure and dynamics of the substances and are particularly used in the study of organic materials. Neutron scattering can be utilized to examine mixing, alignment, dispersion, and assembly of nanoscale materials. Conformation and torsional motion of various small molecules and the presence of hydrogen bonding and ionic clustering were also probed using this technique (Araujo et al. 2017; Eastoe and Gold 2005; Faraone et al. 2018; Hammond et al. 2016; Hammond et al. 2017a; Hammond et al. 2017b; Kaur et al. 2016; Yang et al. 2019).

3.11  Dynamic Light Scattering The size distribution profile of particles in suspension can be determined using dynamic light scattering DLS by analyzing the constructive or destructive interference of the scattered light. Structural reorientation and molecular structure can also be understood by this analytical technique. The technique can also be used to probe the extraction mechanism, aggregation, dispersion, solubility, and solvation behavior at different temperatures. It is also a useful tool to determine the distribution of hydrodynamic radius (Banjare et al. 2018; Liu et al. 2016; Mokhtarpour et al. 2020; Pramanik et al. 2011; Rausch et al. 2014; Teklebrhan et al. 2012; Zhang et al. 2016; Zhang et al. 2017).

3.12  X-Ray Scattering Small- and wide-angle X-ray scattering (SAXS and WAXS) are utilized for the structural determination of materials. The techniques differ in the angle at which the diffraction is analyzed and is used for the analysis of nanoscale structures and dimensions. SAXS is used to view long-range ordering in materials, whereas WAXS is used for studying short-range ordering (Busato et al. 2021; Cao et al. 2020; Ling et al. 2020; Miele et al. 2020; Raghuwanshi et al. 2014; Tan et al. 2019a, b; Tran et al. 2020).

References Abbott AP, Barron JC, Ryder KS, Wilson D (2007) Eutectic-based ionic liquids with metal-­ containing anions and cations. Chem Eur J 13:6495-6501. https://doi.org/10.1002/ chem.200601738

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Safavi A, Ahmadi R, Ramezani AM (2018) Vortex-assisted liquid-liquid microextraction based on hydrophobic deep eutectic solvent for determination of malondialdehyde and formaldehyde by HPLC-UV approach. Microchem J 143:166-174. https://doi.org/10.1016/j.microc.2018.07.036 Saha A, Deb SB, Sarkar A, Saxena MK, Tomar BS (2016) Simultaneous preconcentration of uranium and thorium in aqueous samples using cloud point extraction. RSC Adv 6:20109-20119. https://doi.org/10.1039/C5RA23734C Sasaki K, Panagopoulou A, Kita R, Shinyashiki N, Yagihara S, Kyritsis A, Pissis P (2017) Dynamics of uncrystallized water, ice, and hydrated protein in partially crystallized gelatin– water mixtures studied by broadband dielectric spectroscopy. J Phys Chem B 121:265-272. https://doi.org/10.1021/acs.jpcb.6b04756 Schrödle S, Annat G, MacFarlane DR, Forsyth M, Buchner R, Hefter G (2006) Broadband dielectric response of the ionic liquid N-methyl-N-ethylpyrrolidinium dicyanamide. Chem Commun 2006:1748-1750. https://doi.org/10.1039/B602209J Shahbaz K, Mjalli FS, Vakili-Nezhaad G, AlNashef IM, Asadov A, Farid MM (2016) Thermogravimetric measurement of deep eutectic solvents vapor pressure. J Mol Liq 222:61-66. https://doi.org/10.1016/j.molliq.2016.06.106 Shiraga K, Suzuki T, Kondo N, Tajima T, Nakamura M, Togo H, Hirata A, Ajito K, Ogawa Y (2015) Broadband dielectric spectroscopy of glucose aqueous solution: Analysis of the hydration state and the hydrogen bond network. J Chem Phys, 142:234504. https://doi.org/10.1063/1.4922482 Singh N, Sharma M, Mondal D, Maru DA, Rathod MR, Sequeira RA, Chudasama NA, Prasad K. (2021) Seaweed biomass derived bio solvents for the large-scale production of few layered graphene nanosheets from graphite. Mater Sci Technol, 4:100-106. https://doi.org/10.1016/j. mset.2021.01.005 Singh R, Bhaskar T, Balagurumurthy B (2015) Effect of solvent on the hydrothermal liquefaction of macro algae Ulva fasciata. Process Saf Environ 93:154-160. https://doi.org/10.1016/j. psep.2014.03.002 Sinha G, Glorieux C, Thoen J (2004) Broadband dielectric spectroscopy study of molecular dynamics in the glass-forming liquid crystal isopentyl cyanobiphenyl dispersed with aerosils. Phys Rev E Stat Nonlin Soft Mater Phys, 69:031707. https://doi.org/10.1103/PhysRevE.69.031707 Siraj N, El-Zahab B, Hamdan S, Karam TE, Haber LH, Li M, Fakayode SO, Das S, Valle B, Strongin RM, Patonay G, Sintim HO, Baker GA, Powe A, Lowry M, Karolin JO, Geddes CD, Warner IM (2016) Fluorescence, phosphorescence, and chemiluminescence. Anal Chem 88:170-202. https://doi.org/10.1021/acs.analchem.5b04109 Sirviö JA, Visanko M, Liimatainen H (2016) Acidic deep eutectic solvents as hydrolytic media for cellulose nanocrystal production. Biomacromolecules 17:3025-32. https://doi.org/10.1021/acs. biomac.6b00910 Smith CJ, Wagle DV, Bhawawet N, Gehrke S, Hollóczki O, Pingali SV, O’Neill H, Baker GA (2020) Combined small-angle neutron scattering, diffusion NMR, and Molecular dynamics study of a eutectogel: illuminating the dynamical behavior of glyceline confined in bacterial cellulose gels. J Phys Chem B 124:7647-7658. https://doi.org/10.1021/acs.jpcb.0c04916 Smith EL, Abbott AP, Ryder KS (2014) Deep eutectic solvents (DESs) and their applications. Chem Rev 114:11060-11082. https://doi.org/10.1021/cr300162p Soccio M, Martínez-Tong DE, Guidotti G, Robles-Hernández B, Munari A, Lotti N, Alegria A. (2020) Broadband dielectric spectroscopy study of biobased poly (alkylene 2, 5-furanoate) s’ molecular dynamics. Polymers 12:1355. https://doi.org/10.3390/polym12061355 Stappert K, Ünal D, Mallick B, Mudring AV (2014) New triazolium based ionic liquid crystals. J Mater Chem C 2:7976-7986. https://doi.org/10.1039/C3TC31366B Sugumaran V, Prakash S, Ramu E, Arora AK, Bansal V, Kagdiyal V, Saxena D (2017) Detailed characterization of bio-oil from pyrolysis of non-edible seed-cakes by Fourier Transform Infrared Spectroscopy (FTIR) and gas chromatography mass spectrometry (GC–MS) techniques. J Chromatogr B Analyt Technol Biomed Life Sci 1058:47-56. https://doi.org/10.1016/j. jchromb.2017.05.014

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Tan X, Chen L, Li X, Xie F (2019a) Effect of anti-solvents on the characteristics of regenerated cellulose from 1-ethyl-3-methylimidazolium acetate ionic liquid. Int J Biol Macromol 124:314-320. https://doi.org/10.1016/j.ijbiomac.2018.11.138 Tan YT, Ngoh GC, Chua AS (2019b) Effect of functional groups in acid constituent of deep eutectic solvent for extraction of reactive lignin. Bioresour Technol 281:359-66. https://doi. org/10.1016/j.biortech.2019.02.010 Tarif E, Mondal J, Biswas R (2020) How frictional response during solute solvation controls solute rotation in naturally abundant deep eutectic solvent (NADES)? A case study with amino acid derivative containing DES.  J Mol Liq 303:112451. https://doi.org/10.1016/j. molliq.2020.112451 Teklebrhan RB, Ge L, Bhattacharjee S, Xu Z, Sjöblom J (2012) Probing structure–nanoaggregation relations of polyaromatic surfactants: a molecular dynamics simulation and dynamic light scattering study. J Phys Chem B 116:5907-5918. https://doi.org/10.1021/jp3010184 Teng N, Yang S, Dai J, Wang S, Zhao J, Zhu J, Liu X (2019) Making benzoxazine greener and stronger: renewable resource, microwave irradiation, green solvent, and excellent thermal properties. ACS Sustain Chem Eng 7:8715-8723. https://doi.org/10.1021/acssuschemeng.9b00607 Tommasi E, Cravotto G, Galletti P, Grillo G, Mazzotti M, Sacchetti G, Samorì C, Tabasso S, Tagliavini E (2017) Enhanced and selective lipid extraction from the microalga P. tricornutum by dimethyl carbonate and supercritical CO2 using deep eutectic solvents and microwaves as pretreatment. ACS Sustain Chem Eng 5:8316-8322. https://doi.org/10.1021/ acssuschemeng.7b02074 Tran TTV, Kongparakul S, Karnjanakom S, Reubroycharoen P, Guan G, Chanlek N, Samart C (2020) Selective production of green solvent (isoamyl acetate) from fuel oil using a sulfonic acid-functionalized KIT-6 catalyst. Mol Catal 484:110724. https://doi.org/10.1016/j. mcat.2019.110724 van Osch DJGP, Dietz CHJT, van Spronsen J, Kroon MC, Gallucci F, van Sint Annaland M, Tuinier R (2019) A search for natural hydrophobic deep eutectic solvents based on natural components. ACS Sustain Chem Eng 7:2933-2942. https://doi.org/10.1021/acssuschemeng.8b03520 van Osch, DJGP, Zubeir LF, van den Bruinhorst A, Rocha, MAA, Kroon MC. (2015) Hydrophobic deep eutectic solvents as water-immiscible extractants. Green Chem 17:4518-4521. https://doi. org/10.1039/C5GC01451D Vekariya RL (2017) A review of ionic liquids: Applications towards catalytic organic transformations. J Mol Liq 227:44-60. https://doi.org/10.1016/j.molliq.2016.11.123 Verevkin SP, Emel’yanenko VN, Zaitsau DH, Ralys RV, Schick C (2012) Ionic liquids: differential scanning calorimetry as a new indirect method for determination of vaporization enthalpies. J Phys Chem B 116:4276-4285. https://doi.org/10.1021/jp300834g Wolf L, Hoffmann H, Linders J, Mayer C (2012) PFG-NMR self-diffusion measurements in the single phase channels of a microemulsion system with an anionic–nonionic surfactant mixture. Soft Matter 8:6731-6739. https://doi.org/10.1039/C2SM07301C Wolf M, Gulich R, Lunkenheimer P, Loidl A (2011) Broadband dielectric spectroscopy on human blood. Biochim Biophys Acta Gen Subj 1810:727-740. https://doi.org/10.1016/j. bbagen.2011.05.012 Wu BP, Wen Q, Xu H, Yang Z (2014) Insights into the impact of deep eutectic solvents on horseradish peroxidase: Activity, stability and structure. J Mol Catal B Enzym 101:101-107. https:// doi.org/10.1016/j.molcatb.2014.01.001 Xia Q, Liu Y, Meng J, Cheng W, Chen W, Liu S, Liu Y, Li J, Yu H (2018) Multiple hydrogen bond coordination in three-constituent deep eutectic solvents enhances lignin fractionation from biomass. Green Chem 20:2711-2721. https://doi.org/10.1039/C8GC00900G Yang M, Zhao W, Wang S, Yu C, Singh S, Simmons B, Cheng G (2019) Dimethyl sulfoxide assisted dissolution of cellulose in 1-ethyl-3-methylimidazoium acetate: small angle neutron scattering and rheological studies. Cellulose 26:2243-2253. https://doi.org/10.1007/s10570-­018-­2218-­0 Yousefi SM, Shemirani F, Ghorbanian SA (2018) Hydrophobic deep eutectic solvents in developing microextraction methods based on solidification of floating drop: application to the trace

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Chapter 10

Green Chromatography Techniques Surbhi Goyal, Rajni Sharma, Jagdish Singh, and Mohsen Asadnia

Abstract  It is crucial demand of recent times to make the industrial productions eco-friendly as much as possible which not only promotes sustainable development but also lowers the financial burden by increasing yield with cost-effective approaches. Hence, the interest has leaned toward the use of green downstream processing for industrial products precisely reflecting from increased number of publications on green analytical techniques. As the research on organic compounds production makes use of chromatographic techniques to analyze different components, the extensive efforts have been performed to make chromatography techniques green which can potentially be possible at each step right from preparation of sample to analytical identification. Usually, gas chromatography can be greener with low energy consumption whereas liquid chromatography with lesser solvents usage. This chapter depicts various green techniques used in gas and liquid chromatography, for instance, manipulating the process parameters like analyte preparation, chromatography type, stationary phase, mobile phase, column size, and temperature. In addition to discussing various aspects of green chemistry, there is substantial discussion about assessment of green techniques using various mathematical and statistical approaches. Finally, the advanced research approaches are recommended to explore the potential and overcome the challenges for the commercialization of green chromatographic techniques.

S. Goyal Department of Biotechnology, Punjabi University, Patiala, Punjab, India Bioprocess Technology Lab, Department of Biotechnology, Mata Gujri College, Fatehgarh Sahib, Punjab, India R. Sharma (*) · M. Asadnia School of Engineering, Macquarie University, Sydney, NSW, Australia e-mail: [email protected] J. Singh Bioprocess Technology Lab, Department of Biotechnology, Mata Gujri College, Fatehgarh Sahib, Punjab, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. H. El-Maghrabey et al. (eds.), Green Chemical Analysis and Sample Preparations, https://doi.org/10.1007/978-3-030-96534-1_10

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Keywords  Green chromatography · Eco-friendly chromatography · Green GC · Green LC · Assessment of greenness

Abbreviations ACE AMGS CEC CED CFM CMC DLLME EAT EFC ESI FID GAC GAPI GC GCCC GGC GLC GPE HF HSI HTLC IL LC LCA LEL LPME LTM MAE MASE MEMS MEPS MESI MMLLE MS MSLE NADES NEMI PAHs

Acetyl choline esterase Analytical method greenness score Capillary electrochromatography Cumulative energy demand Consumable free modulator Critical micelle concentration Dispersive liquid-liquid microextraction Environment assessment tool Enhanced fluidity chromatography Electron spray ionization Flame ionized detector Green analytical chemistry Green analytical procedure index Gas chromatography Green countercurrent chromatography Green gas chromatography Green liquid chromatography Gas-phase extraction Hollow fiber Headspace injection High-temperature liquid chromatography Ionic liquid Liquid chromatography Life cycle assessment Lower explosive limit Liquid-phase microextraction Low thermal mass Microwave-assisted extraction Membrane-assisted solvent extraction Microelectromechanical system Microextraction with packed sorbent Membrane extraction using sorbent interface Microporous membrane liquid-liquid extraction Mass spectroscopy Microdialysis sampling liquid extraction Natural deep eutectic solvents National environment method index Polyaromatic hydrocarbons

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PCB Polychlorobiphenyls PDMS Polydimethylsiloxane PLE/PFE Pressurized liquid/fluid extraction PT Purge trap PTFE Polytetrafluoroethylene QuEChERS Quick, easy, cheap, effective, rugged, safe extraction RP Reversed phase S/H/PWE Subcritical/hot/pressurized water extraction SBSE Stir bar sorptive extraction SDME Single drop microextraction SDS Sodium dodecyl sulfate SFC Supercritical fluid chromatography SFE Supercritical fluid extraction SHE Safety health environment SHFC Superheated fluid chromatography SHS Static headspace SHWC Superheated water chromatography SLME Supported liquid microextraction SPE Solid-phase extraction SPME Solid-phase microextraction SPNE Solid-phase nanoextraction TFME Thin-film microextraction TOF Time of flight UAE Ultrasound-assisted extraction UHPLC Ultra-high pressure liquid chromatography VOCs Volatile organic compounds WHSI Whole headspace injection

1  Introduction In today’s era with huge applications of industrial products, it becomes crucial to make the industrial processes as much economical and eco-friendly as possible. Among several stages of industrial fermentation processes, the downstream processing of the products is the most critical stage in terms of resources and cost, which critically needs to be made green and inexpensive (Kaplitz et al. 2021). The complexity of downstream processing, that is, separation and analysis of the product, lies in the fact that it consists of many steps; hence, it costs a lot and generates a huge amount of waste to finally provide a purified product. Chromatography is one of the most commonly used downstream techniques in various fermentations of major pharmaceutical, food, and other industries. It is defined as a chemical technique employed to purify molecules based on the extent of their purity (Al-Khateeb and Dahas 2021). The basic process of chromatography involves preparation of test sample in specific solvents; separation in different matrices such as paper, liquid, or

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Fig. 10.1  Basic chromatography and green analytical chemistry principles. (a) Schematic representation of general principle of chromatography for separation of sample analytes, (b) twelve basic principles of green analytical chemistry (GAC) for green chromatography

gas, based on the type of chromatographic technique (Fig. 10.1a); and final detection in the form of a colored graph known as chromatogram (Dembek and Bocian 2020). Eventually, it is a time-consuming process requiring substantial amounts of energy, capital, toxic organic solvents, and other resources. In an estimate of hazardous impacts of conventional chromatography techniques, it was pointed out that a conventional liquid chromatography (LC) emits

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approximately 2–4 liters of daily waste (Płotka et  al. 2013). Hence, due to the increase in global attention toward the sustainable development and need of economical downstream techniques for commercial processes, green chromatography techniques have come into limelight which conforms with the green analytical chemistry (GAC) principles in compliance with environmental protection (Kaplitz et  al. 2021). The 12 GAC principles (Fig.  10.1b) were applied to articulate the green chromatography techniques with 5 main objectives, namely, substantial reduction of r­ eagents/solvents from analytical protocols; reducing the generation of huge amount of effluents including gases/vapors, liquids, and solids in analytical labs; substituting toxic reagents by lesser toxic or non-toxic solvents; reduction of overall labor, energy, time from analytical procedures; and converting multistep procedure into a single step with ensured purity of analyte (PłotkaWasylka et al. 2021). There has been ample research in green chromatography every passing year due to its enormous applications in medical, food, research, industrial, and environmental analysis (Korany et al. 2017). The economy also extensively promotes “green” procedures which utilize no or lesser number of toxic solvents, which eventually avoid the use of enormous reagents and cut off the expenses in analytical and chemical laboratories. Usually, the chromatography is capable of greening at each operational step, that is, sample preparation, separation of analyte from medium or its final analysis, by manipulating the process parameters like analyte preparation, chromatography type, stationary phase, mobile phase, column size, and temperature (González-Ruiz et al. 2011); for instance, Fig. 10.2a represents the various aspects of green chromatography. Many recent studies (Table 10.1) have been conducted to investigate these aspects which are discussed comprehensively in the chapter such as solvent reduction approaches, that is, Nano LC (Asensio-Ramos et  al. 2017), solvent replacement method (Shen et  al. 2015), green sample preparation (Inamuddin and Mohammad 2014), green gas chromatography (GGC) (Shaaban et  al. 2017), green liquid chromatography (GLC) (Kannaiah et al. 2021), and ionic liquids as stationary phase (Korany et al. 2017). Furthermore, some specialized and extended versions of these green chromatographic approaches are also elucidated including medium pressure LC (Galyan and Reilly 2018), green countercurrent chromatography (Cai et al. 2021); micellar LC (Patyra and Kwiatek 2021), enhanced fluidity chromatography (GonzálezRuiz et  al. 2011; Płotka et  al. 2013), and superheated fluid chromatography (Al-Khateeb and Dahas 2021). Henceforth, this chapter outlines (Fig. 10.2b) the green chromatography concept explaining various approaches along with their advantages, limitations, and recent applications. A section is devoted to green assessment techniques for chromatography. This chapter also focuses on future perspectives to make the process greener and more economical for widespread use of green chromatography techniques from a commercial point of view.

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Fig. 10.2  Green chromatography techniques. (a) Sample analysis stages at which chromatography can be greener or eco-friendly, (b) outline of chapter with green chromatography methods and assessment technique Table 10.1  Recent case studies of green chromatography Green chromatography Reversed-phase HPLC Micellar LC

Chromatography conditions Solvent-free chromatography

Applications Drug determination

Solvent free, use of water, eutectic solvents, and surfactants Micro-column for GC

Estimation of CV drugs

Micro-circulatory GC LTM-GC

Low-pressure GCMS

SWLC

High-temperature LC

2D-SFC

RP-LC with 2D SFC chromatography

Reference Ibrahim et al. (2020) Ramezani and Absalan (2020)

Separation of isomers Hsieh and Kim (2020) Comparison study Fialkov et al. (2020) Pharmaceutical drugs Al-Khateeb and identification Dahas (2021) Oil separation from Kaplitz et al. drugs (2021)

Abbreviations: HPLC high-performance liquid chromatography, LC liquid chromatography, GC gas chromatography, LTM low-temperature mass, GCMS gas chromatography mass spectroscopy, SWLC superheated water liquid chromatography, 2D-SFC two-dimensional supercritical fluid chromatography, RP reversed phase liquid chromatography

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2  Omitting Analyte Pretreatment The pretreatment of sample in conventional chromatography is the very first stage in separation and analysis of any biomolecule which may involve extraction, precipitation, evaporation, dialysis, distillation and/or crystallization (Napolitano-­ Tabares et al. 2021). The core objectives of the preliminary treatment are to evade any type of sample interference in the analytical process, to increase analytical instrument sensitivity, and, finally, to enhance sample concentration for accurate and precise analysis (Aly and Górecki 2019). Additionally, the majority of analytical instruments are known to employ liquid samples, thereby increasing the prerequisite to dissolve sample in specific solvent before separation and analysis. Since most of the reagents used in sample dissolution are toxic, inflammable, volatile, disposed of gas or liquid effluent, and impact the environment negatively (Napolitano-Tabares et al. 2021), it is the most polluting stage in the complete analysis. Therefore, GAC principles endorsed chromatography without analyte treatment in toxic solvents for sustainable environment protection (Patyra et al. 2021). First of all, direct aqueous analyte injection for gas chromatography was performed using on-column injection (Grob 1984). They combined direct injection of the analyte after capturing electron detection in the estimation of water samples in the environment. Direct injection was also used in GC determination of volatile/ polar/non-polar components in water with flame ionization detector as well as mass spectrometric detection (Middleditch et al. 1987). There are some reports on GC of polluted surface water for analysis of highly volatile compounds by direct analyte injection by FID, EC and flame photometric detection (Aeppli et al. 2008; Biziuk 2006; Moldoveanu and David 2015). Later, studies were conducted on the use of solid samples for GC/LC using the direct injection technique (Tobiszewski and Namieśnik 2017). Table  10.2 represents some important case studies along with their applications in the analysis and separation of biologically important products. Direct sample injection fulfils the GAC criteria for green assessment in three ways mainly: the absence of sample treatment allows the online/at-line analysis of chromatography decreasing analysis time relatively; the duration from the test sample to detecting results (total analysis time) is shortened significantly; and finally, the harmful consequences of pretreatment substances, namely, solvents, cartridges, fibers, and sorbents are substantially evaded (Espinosa et al. 2021). Therefore, the real-time monitoring with skipping sample pretreatment follows the 11th GAC principle (Fig. 10.1b) in this green chromatographic technique. Though the direct injection does not come without disadvantage such as the need to have extremely fresh and hygienic matrices to avoid sample accumulation resulting from lack of solvent (Inamuddin and Mohammad 2014). This major problem was advised to be overcome by injecting petrol, spirit, and water into a column with no prior treatment (Aly and Górecki 2019). Omitting the sample pretreatment has been more successful in GC as compared to LC (Turska et al. 1997). This is because in LC, the analyte needs minor pretreatment like filtration, centrifugation, and analyte dilution. Still, there are a few studies on chemicals estimation by direct LC-MS of analyte (Burrows and Parr 2020; Jemal et al. 2000; Kumar et al. 2017).

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Table 10.2  Direct sample injection case studies Sample preparation technique Analyte/sample Water On column injection Plant extract Direct injection of crude sample Inorganic polymers Cross-linked polystyrene sorbent Direct injection of gas sample Large volume Complex samples (water, vegetables, sample injection PAHs)

Chromatography technique Application Gas chromatography Detect halogens in water Countercurrent chromatography Size exclusion Electrolyte’s chromatography separation HPLC Online LC-GC

Biomolecules analysis

Reference Grob (1984) DeAmicis et al. (2011) Davankov et al. (2019) Astanin and Baram (2017) Espinosa et al. (2021)

Abbreviations: HPLC high-performance liquid chromatography, PAH polycyclic aromatic hydrocarbons, LC liquid chromatography, GC gas chromatography

3  Analyte Pretreatment by Green Strategies The most crucial step in chromatographic analysis of a biomolecule is sample pretreatment/preparation. It includes extraction and dissolution in solvents mostly in complex matrices (Aly and Górecki 2020). Various manners in which analyte greening could be done are presented in Fig. 10.3a. All of these are means to make analysis reliable and accurate but at the same time eco-friendly. One or more than one way works in tandem to achieve the status of green chromatography. This sample pretreatment consists of extraction using solid, gas, and liquid sorbents in conjunction with green solvents and the use of safe techniques as well as green media (Namieśnik et al. 2015).

3.1  Extraction of Analytes Using Solid Sorbents It is popularly known as solid-phase extraction (SPE), a routine green analyte preparation in recent years. It involves cleaning and concentrating the analyte with a little amount of solvent (specific for analysis) (Manousi and Samanidou 2021). A small portion of the sample is adsorbed on a solid surface, and an aqueous analyte is injected into SPE analytical column, and then sample is eluted with a small amount of solvent which leads to extraction and concentration of the sample (Moldoveanu and David 2015). It is a green analyte preparation due to the usage of a smaller amount of solvent when compared to conventional techniques. Nowadays, it is being automated with inexpensive apparatus to improve precision and throughput analysis (Moldoveanu and David 2015). However, cons involve the loss of analyte due to poor extraction, uniformity of packing material which is necessary, the need

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Fig. 10.3  Green strategies in analyte preparation or extraction methods. (a) General methods for eco-friendly pretreatment of analyte, (b) solid-phase extraction (Manousi and Samanidou 2021), (c) liquid-phase extraction (Jalili et al. 2020), (d) gas-phase extraction (Aly and Górecki 2020), (e) thin-film microextraction (Olcer et al. 2019), (f) natural deep eutectic solvents extraction (Carasek et  al. 2021), and (g) ultrasound-assisted extraction systems (Liu et  al. 2021b) for sample pretreatment

of specific sorbents and cartridges for polar compounds, competition between sample matrix and sample for retention that affect sorbent capacity, and tedious elution process that affects the efficient extraction of the sample (Demirhan et al. 2017). There are many examples where SPE was used for sample extraction and enrichment, that is, chromatographic separation of antibiotics from urine samples (Fikarová et  al. 2021), pesticides determination using GCMS via SPE extraction (Sun et al. 2021) and estimation of purity of milk by LCMS via SPE (Zhou et al. 2022). Following are some widely used variants of SPE along with their protocol, advantages, disadvantages and some recent case studies (Table 10.3). QuEChERS is defined as quick, easy, cheap, effective, rugged, safe extraction of analyte from the sample (Anastassiades et al. 2003). This extraction technique is known to use a small amount of solvent and is carried out in two steps: continuous shaking for solvent extraction followed by SPE via quick dispersive purification of the analyte. SPME is an efficient and simple extension of SPE, developed in 1989 (Belardi and Pawliszyn 1989). It is an integration of sample procurement, extraction, and enrichment, injecting into a column in a single stage. SPME creates a partition between samples matrix and extraction phase for analytes. There are two variants of SPME: direct SPME and headspace SPME based on the positioning of fibre-coated stationary phase in the analyte (Curyło et al. 2007). If sampling is not terminated for some duration manually, analytes may move into phase created by extraction until an equilibrium is achieved between sample and fiber-coated

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Table 10.3  Recent case studies for green sample preparation strategies GAC Sample pretreatment principle followed strategy Solid-phase extraction QuEChERS 7th

SPME

11, 12th

MEPS

5, 9th

SBSE

10th

SPNE

7th

Liquid-phase extraction SDME 7, 10th

LPME

7th

DLLME

7, 12th

IL-DLLME

10, 11th

Gas-phase extraction Static HSI 10th

Whole HSI

7, 12th

Dynamic HSI

10, 11th

Pros

Cons

Applications

References

Safe, fast, large range of samples Low cost, reliable, short time, non-­ destructive Reusable, efficient High temperature efficient, safe Rapid, simple, lesser solvent needed

Management and accessibility Skills needed, fragile handling

Food, wastewater

Montemurro et al. (2021)

Food samples, volatile organic analytes

Devi et al. (2021); Reyes-Garcés et al. (2021)

Need quality assurance High maintenance, expensive Expense of gold nanoparticles

Body fluids, drugs Vejar-Vivar et al. (2021) M. He et al. PAHs, (2021b) pharmaceuticals, water Environmental Feng et al. samples (2021); Khan et al. (2020)

Micro-level solvents needed, faster analysis Lesser solvents, enriched analyte Reusable, rapid, less solvent Simple, fast, green solvents

Quality assurance, accessibility

Dairy samples, body fluids, phenolics

Chullasat et al. (2020); Mafra et al. (2021)

Skilled analyst, handling, management Management and accessibility Expensive, quality assurance needed

Human samples, foods, drugs

Dugheri et al. (2020)

Milk products, beverages, water samples PAHs, phenolics, heavy metals, pesticides

Salim et al. (2021)

Basic, simple method

Non-reliable, quality assurance needed Management and accessibility Specificity, narrow range of analytes

Beverages, VOCs

Zhu et al. (2021)

VOCs, pollutants, beverages

Liberto et al. (2020)

Food and dairy VOCs

Alderman et al. (2021)

Sensitive, reliable technique Reliability, accuracy, low temperature

Feng et al. (2020); Galuch et al. (2019)

(continued)

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Table 10.3 (continued) GAC Sample pretreatment principle followed strategy Membrane extraction SLME 5, 10th

MMLLE

11th

MESI

10, 12th

MASE

12th

MSLE

6, 11th

TFME

10, 12th

Pros

Cons

Reliable, easy, Management recyclable and accessibility Time Lesser consuming, solvents and non-reusable simple technique Skilled Best personnel solventless required technique, large range samples Management Enriched and analyte, safe accessibility and fast issues method Stable, Need skills portable, faster and equipment specific Cost, Sensitive, accessibility on-site issues analysis

Alternative green solvents Ionic liquids 7, 10th Non-toxic, Cost, high safer synthesis maintenance SFE

10th

Non-corrosive, Reliability, less flammable sensitivity, skills needed Skilled SWE 6, 9th Recyclable, personnel, cost accessibility, higher maintenance NADES 6, 9, 11th Non-volatile, High viscosity, high solubility, polarity, management reusable and safe Assisted extraction MAE 7, 10th Closed system, Cost non-invasive, ineffective, maintenance, simple reliability

Applications

References

Human fluids, pesticides, phenolics Herbicides, pesticides, beverages, water samples Human samples, aromatics, BTEX in water samples

Dolatabadi et al. (2021) Pabby et al. (2020)

Z. Liu et al. (2021d)

Pesticides, amines, Maghsoudi human fluids et al. (2021)

Industrial samples, microbial metabolites Human fluids, water, trace elements

El Zahar et al. (2021)

Plant extract, phenolics, pesticides Organic analytes, hydrocarbons

Sada Khan et al. (2021)

Surfactants, pesticides, water samples Flavonoids, pollutants, organic components

Pinto et al. (2021)

Darvishnejad et al. (2021)

Kaplitz et al. (2021)

Cai et al. (2021); Carasek et al. (2021)

PCBs, water, food, Rizwan et al. herbal samples (2021)

(continued)

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Table 10.3 (continued) Sample pretreatment strategy UAE

GAC principle followed 7, 12th

PLE

4, 12th

Pros Precision, accuracy, reliability, simple High temperature, low viscosity, high solubility

Cons Applications Cost, invasive, Plant extracts, accessibility human fluids, heavy metals High maintenance, skilled personnel

References Li and Ding (2021)

Ahmad et al. Crop, water, environment, food (2021) analytes

Abbreviations: GAC green analytical chemistry, QuEChERS quick easy cheap rugged safe, SPME solid-phase microextraction, MEPS microextraction with packed sorbent, SBSE stir bar sorptive extraction, PAHs polycyclic aromatic hydrocarbons, SPNE solid-phase nanoextraction, LPME liquid-phase microextraction, DLLME dispersive liquid-liquid microextraction, IL-DLLME ionic liquids dispersive liquid-liquid microextraction, HSI headspace injection, VOCs volatile organic compounds, SLME supported liquid microextraction, MMLLE microporous membrane liquid-liquid extraction, MESI membrane extraction using sorbent interface, BTEX benzene toluene ethylbenzene xylene, MASE membrane-assisted solvent extraction, MSLE microdialysis sampling liquid extraction, TFME thin-film microextraction, SFE supercritical fluid extraction, SWE superheated water extraction, NADES natural deep eutectic solvents, MAE microwave-assisted extraction, PCB polychloride biphenyls, UAE ultrasound-assisted extraction, PLE pressurized liquid extraction

stationary phase. Subsequently, after extraction is done, SPME fiber is transferred (desorption of concentrated analyte) for further analysis. MEPS is a miniaturized version of SPE, barring some technical differences because it uses a sorbent for sampling which is coated inside the chromatographic column surface (Abdel-Rehim et al. 2004) (Fig. 10.3b). Solvent extraction is performed to extract the sample from sorbent before injecting it into the chromatography. The sorbent is reusable and recycled after washing with an organic solvent. As its name depicts, it uses a little amount of solvent (20−40 ul), with extraction time as low as 1 min and the lowest energy consumption (Moein et al. 2019). SBSE is also a sorptive technique of analytes extraction as in SPME, but with a larger sorbent volume. The sorbent is usually a magnetic stir bar coated with thick polymer contain (polystyrene, PDMS, etc.) (Vercauteren et  al. 2001). Here, extraction is directly linked to the partition coefficient of analyte between sorbent and sample matrix. The sample desorption is carried out by high temperature; hence, it becomes a green solventless analyte pretreatment method following GAC principles (M. He et al. 2021a). SPNE involves the use of nanoparticles; the rest is similar to SPME. The basic principle is the application of attraction between the target sample and gold nanoparticles used for extraction (Poole 2003). Firstly, the sample solution is dissolved in colloidal gold particles quantitatively, followed by centrifugation (to recover leftover nanoparticles) (Khezeli and Daneshfar 2017). After that, it is injected for further analysis, hence detecting trace amounts of components in complex matrices of analytes.

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3.2  Extraction of Analyte in Liquid Phase The extraction of analyte in this strategy is performed in liquid medium generally. First of all, SDME/liquid-liquid microextraction, characterized by sample extraction inside solvent drop which in turn dropped from micro-syringe (Kailasa et al. 2021). Thus, the sample mixes with solvent (Approx. 2ul) in the droplet which is injected back into the syringe of the chromatographic column. Secondly, LPME is based on enrichment of sample before analysis. Based on analyte enrichment stages, three types of LPME are dynamic LPME-1-2ul solvent mixed with analyte and injected in chromatographic column after concentration (Shen and Lee 2003); secondly in hollow fiber LPME, the analyte is present between a porous capillary (semipermeable membrane) and immobilized solvent coated onto this tube creating two phases for the sample before injecting it in chromatograph (Zhao and Lee 2002); lastly in three-phase HF-LPME, along with two phases mentioned above, the analytes are embedded inside the lumen of capillary creating the third phase for themselves before going into chromatograph (He and Lee 1997). In DLLME, another variation of LLME composed of three phases, namely, dispersive solvent/low density, high-density solvent for extraction, and the target analyte (Rezaee et  al. 2010). Both types of solvents are mixed with analyte using a micro-syringe, and a cloudy emulsion is formed before extraction of the sample. This is followed by centrifugation and separating high-density phase injected in micro-syringe for further analysis (Fig.  10.3c). Also, IL-DLLME constitutes the consumption of ionic liquids (green solvents) rather than other extraction solvents and thus becomes IL-DLLME, an improved version. It can be performed with or without dispersive solvents by using dispersive mechanical methods (ultrasonic waves) instead of solvents (Merib et al. 2018). There is one more variation to this, which is heating the mixture of analyte and ionic liquid to make a homogeneous solution which was cooled down and centrifuged before chromatographic analysis, hence known as temperature-controlled IL-DLLME.

3.3  Gas-Phase Extraction (GPE) This technique as depicted by its name is a solventless method of sample pretreatment and enrichment. It is based on the principle of inert gas consumption for the extraction of the target sample (Aly and Górecki 2020). These techniques are collectively used in the isolation or enrichment of thermolabile and volatile components from complex ample matrices. Here are explained some GPE techniques used widely (Table 10.3). SHS-GPE is the most basic approach to analyze high viscosity liquid samples, foam-forming samples and analytes containing sludge or semisolids in nature (Wenzl and Lankmayr 2002). There are some recent studies on the use of SHS-GPE in the determination of volatile analytes (Zhu et al. 2021) and to asses volatile compounds in food materials (Md Nor et al. 2021). WHSI is a variant of

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SHS-GPE; to increase the sensitivity of the latter technique, WHSI-GPE was invented (Schuberth 1996). The basic principle involves the injection of the whole volume of headspace into the chromatographic column using a microtrap/syringe. DHSI involves a temperature-controlled container for target analyte linked to headspace which vapors are eliminated dynamically with the help of inert gas in the chamber (Kremser et al. 2016). The low concentration of samples in purged gas was adsorbed on a solid adsorbent and further used for GC analysis (Fig. 10.3d). This GPE technique is an advantageous variation of SHS-GPE, as it is useful in case of undesirable analytes in SHS (Fuchsmann et al. 2019).

3.4  Membrane Extraction (ME) It is the extraction of analytes using a membrane of non-porous, solid/liquid nature hence called membrane extraction in which membrane is placed between surrounding phases (liquid/gas) (Tabani et  al. 2019). The following are some extensively used membrane extraction techniques explained in detail (Table 10.3). SLME is a type of both liquid-phase (HF-LPME) and membrane extraction based on its principle (Kumar and Sastre 2000; Parhi 2013) which has the impregnation of a porous PTFE membrane onto the organic solvent hence forming three phases aqueous (donor)/organic (membrane)/organic (acceptor). A concentration gradient forms between the donor (non-ionic solute) and acceptor (ionic solute) phase due to irreversibly steeped solutes in membranes and analyte move between these two phases due to gradient (Pont et al. 2018). The gradient can be maintained by adjusting the pH of both solutes. After the pretreatment, the acceptor solute phase is transferred for further analysis (Aly and Górecki 2020). MMLLE is also a liquid extraction technique similar to SLME based on membranes used (PTFE, non-polar membranes) (Cai et al. 2006). However, the basic principle involves sample entrapment between two phases instead of three, that is, aqueous (donor) and organic (acceptor) phase in the result of which gradient forms across the membrane leading to extraction of analyte from aqueous to organic phase (Ndungu and Mathiasson 2000; Pabby et al. 2020). In MESI, a semipermeable membrane is used for sample extraction from a gas/ liquid phase, and the sample is moved from the analyte into the membrane (Segal et al. 2000). The acceptor phase is gaseous, and the analyte is removed from the membrane by the gas stream and trapped onto the sorbent interface and concentrated there. Thermal desorption is carried out for detaching them from sorbent, and carrier gas takes analyte into the chromatography column (Kaykhaii et al. 2002). However in MASE strategy, the non-polar analytes are transferred across a semipermeable membrane to an organic solvent, first introduced in 2001 (Hauser and Popp 2001). The temperature is kept high for rapid transfer and after enrichment analytes are transferred into the analytical column (Ali et  al. 2019). MSLE is specific for

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polar samples of low molecular weight (Kissinger 1991). A non-porous membrane probe is in contact with the analyte, while electrolytes are injected inside to create a concentration gradient of the sample between the probe and inside surface (Lin et al. 2005). Thus, the sample moves inside the membrane probe/dialysate, collected for chromatography. Last but not the least, TFME is denoted by the use of very thin membrane as the solid phase of SPME here becomes a thin film of extracting solvent shown in Fig. 10.3e, eliminating the use of a coated fiber (Olcer et al. 2019).

3.5  Green Solvents for Analyte Extraction As it becomes unavoidable to use solvent for any kind of chromatography even if it is in smaller amounts, to fulfil our objective of green chromatography, alternative solvents are a good solution (Tobiszewski and Namieśnik 2017). There are many studies that report different kinds of solvent for green chromatography generating lesser waste, easily degradable, and also eco-friendly in nature (Yabré et al. 2018). The following are some examples of such solvents (Table 10.3). Ionic liquids are considered better as compared to conventional organic solvents as ILs are amphiphilic, thermolabile (250  °C) and non-volatile (Kunz and Häckl 2016; Welton 2018). Therefore, these liquids are considered green ones as they do not emit toxic vapors unlike other organic solvents keeping themselves eco-friendly (Clark et al. 2018). SFE is one of the best sample pretreatment techniques in recent times especially for powdered samples (Zhao et al. 2019). Two types of SFE techniques are static SFE and dynamic SFE based on the incubation of sample and extraction solvent mixture (Hofstetter et al. 2018). To collect the samples, the pressure of SF is decreased, and analytes are adsorbed onto a liquid/solid interface/or direct transfer to chromatographic column (Hofstetter et al. 2019). Examples of SFs include NO2, CO2, ethane, pentane, propane, and NH3, among which CO2 is the most popular being non-corrosive and less inflammable; in some cases, ethanol is also reported (Roy et al. 2020). SWE is similar to SFE, just the fluid is water here, mostly hot water (374 °C) or pressurized (218 atm) water used for selective extraction of water-soluble samples (Gbashi et al. 2017). Some organic compounds can also be extracted using SWE by using relatively high temperatures to increase the solubility (Zhang et  al. 2020). DES are the green solvents that are isolated from cellular extracts, namely, choline derivatives, alcohols, aldehydes, amino acids, and sugars, discovered earlier this decade (Dai et al. 2013; Santana-Mayor et al. 2021), also known as Natural DES (NADES) (Fig.  10.3f). These solvents mixtures are resultant of basic chemistry principles, in which one molecule is hydrogen bond donor and the other is hydrogen bond acceptor, and together they decrease melting point, and extraction is feasible (Carasek et al. 2021).

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3.6  Assisted Sample Extraction Above-mentioned techniques are largely dependent on the use of a green analyte extraction medium (solid/liquid/gas/membrane/ionic liquid) for sample preparation which is often a tedious task to achieve due to resource limitations in general. To neutralize these weaknesses, some techniques also use assisted extraction methods explained below with some special kind of extraction maintaining equipment (Table 10.3) to achieve the green status of chromatography (MoredaPiñeiro and Moreda-Piñeiro 2019). First of all, MAE is assisted by microwave energy in which the system is heated to a substantially high temperature generated by water-soluble molecules which absorb microwave energy (Hu et  al. 2021). This enhances the temperature of the extraction solvent mixed with the analyte to achieve higher sample extraction pressure which is increased in a closed system often (Du et al. 2018). UAE is another most common sample pretreatment method as shown in Fig. 10.3g which consumes ultrasonic energy in the form of vibration/ultrasonic cavitation to confirm accurate sample solvent contact and precise sample matrix injection in the column under normal conditions (P. Li et al. 2021a). On the other hand, PLE is an accelerated liquid extraction of analyte performed at a relatively high temperature (usually more than a boiling point) of extracting solvent (Andreu and Picó 2019; Richter et al. 1996). Due to high temperature and pressure solubility, the diffusion of sample into solvent increases with a decline in viscosity and surface tension of solvent (Pereira et al. 2019). As a result, the analyte is easily injected into the sample matrix for chromatographic analysis.

4  Gas Chromatography: Green Techniques Gas chromatography (GC) is one of two widely used types which need green technologies, that is, gas chromatography and liquid chromatography (Napolitano-­ Tabares et al. 2021). Although GC is itself a green technology as there is no use of solvents for extraction of volatile analytes, rather it uses gases like helium, hydrogen, and nitrogen as carrier gases (Biswas and Mitra 2013). The greening of GC can be achieved at various stages of chromatography including sample preparation, separation, and analysis stage (Fig. 10.4a). The green approaches to GC encompass selection of green gases for sample carrying, using short length/diameter columns to decrease analysis time, adopting other green alternatives such as oven temperature programmed GC or low thermal mass (LTM)-GC, direct resistive heating, miniaturization and the most important two-dimensional GC (2DGC) (Nolvachai and Marriott 2021; Sciarrone et  al. 2015). The application of these approaches in GC analysis is summarized in Table 10.4 and explained in the following sections.

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Fig. 10.4  Green gas chromatography and liquid chromatography techniques. (a) Major types of green gas chromatography (GC) techniques for volatile organic samples, (b) green strategies in liquid chromatography (LC)

4.1  Green Carrier Gases The use of green carrier gases is the first and foremost significant step in green GC as this type of chromatography is wholly based on gases (Korany et al. 2017). The most common green gas is helium, as it is inert, highly diffusible, less viscous, inflammable, safe to handle and having high velocity. Despite these benefits, it also represents some limitations such as expensive and non-renewable (González-Ruiz et al. 2011). Nitrogen is another option of an easily available carrier gas, but because of its low linear velocity and hence a longer duration of analysis, it is usually a less suitable gas for green GC (Saito-Shida et al. 2021). The best option was known to be hydrogen, due to the flow rate much higher than the above-mentioned gases,

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Table 10.4  Green strategies of GC and LC Green strategy used GAC principle Gas chromatography Green carrier gases Non-toxic, availability Smaller column Cost, rapid size Temperature Rapid, easy programmed LTM, direct Fast, accurate resistive heating Multidimensional Reliable, fast, GC safe Miniaturized GC Accurate, rapid Liquid chromatography Shorter column size Trace amounts

Analytes

Applications

References

Pesticides

Food

Pesticides

Food, medicinal

Antifungals

Pharmaceutical

Heroin

Drug analysis

Triacylglycerols, fatty acids Animal tissues

Biological, environmental VOC’s

Saito-Shida et al. (2021) Sargazi et al. (2020) Reddy et al. (2021) Fialkov et al. (2020) Waktola et al. (2020) Schanzer et al. (2021)

Proteins

mAb’s analysis

High-temperature LC Green solvents

Accurate, rapid Acidic drugs Non-toxic, cost, solubility

Antibiotics, plant extracts

2D-LC

Inflammable, easy Safe, less effluents Reliable, easy

Dairy, animal waste Antihistamines

Micellar LC Miniaturized Enhanced fluidity LC

Eco-friendly

Mycotoxins, pesticides Biomolecules

Wastewater analysis Pharmaceutical, pesticides Environmental, biological Pharmaceuticals, drugs Food, environmental Pharmaceuticals, drugs

Nguyen et al. (2021) Al-Khateeb et al. (2021b) Musarurwa and Tavengwa (2021) Kaplitz et al. (2021) Nasr et al. (2021) Mejía-Carmona et al. (2020) Molineau et al. (2021)

Abbreviations: LTM low thermal mass, GC gas chromatography, LC liquid chromatography, GAC green analytical chemistry, VOCs volatile organic compounds, mAb monoclonal antibody, 2D two dimensional

higher efficiency, accuracy and resolving power to do the analysis in the shortest possible time (Bernardoni et  al. 2019). Despite the inflammable and explosive nature, hydrogen can safely be used as a carrier gas in GC analysis below a certain concentration known as lower explosive limit (LEL) (Buse et al. 2019). Moreover, there are very rare chances that LEL can exceed beyond a certain limit, as it is highly diffusible as compared to other gases which, in turn, decreases the opportunity to accumulate at one place and create destruction (Wampler 2020). Additionally, its safe use can be ensured by using generators rather than cylinders because the former can be controlled easily. There are some recent studies on efficient and green carrier gases for the chromatographic analysis. For instance, the analysis of pesticide residues in green tea samples with GC/GCMS using helium gas as a carrier gave intense peaks (Saito-Shida et al. 2021). The trueness obtained with He was in the range of 73–95%, and nitrogen was highly sensitive in terms of GC peaks which ensures a green alternative to other gases.

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4.2  Reducing Duration of GC Analysis A typical GC experiment takes 30–40  min including sample preparation, system cooling, equilibration and analysis (Scott 2021). This time-consuming approach can be made greener by somehow decreasing its run time, which leads to saving energy and time and is an eco-friendly approach (Blumberg 2021). Various methods are used to speed it up like small-diameter column, low-pressure GC, oven temperature-­ programmed and LTM-GC approaches which are discussed in further sections. 4.2.1  Reducing Column Size The simplest way to reduce time in GC analysis is to use small size columns. Nevertheless, it somewhat compromises the efficiency and resolution in the shortest possible time, but at the same time, it has a very lesser loading capacity due to its smaller diameter (Mommers and van der Wal 2021). The examples where fast capillary GC has been functional for identifying analytes include tofu wastewater (Rosmalina et al. 2020), pesticides (Sargazi et al. 2020) and environmental samples analysis (Jennings and Poole 2021). 4.2.2  Low-Pressure GC Another important strategy used for decreasing the time duration of GC analysis to   0.132  μm−1, then the cavity gap g = 1/2FSRv