Graphene Field-Effect Transistor Biosensors 9811612110, 9789811612114

Citation preview

Shiyu Wang Zakir Hossain Yan Zhao Tao Han

Graphene Field-Effect Transistor Biosensors

Graphene Field-Effect Transistor Biosensors

Shiyu Wang · Zakir Hossain · Yan Zhao · Tao Han

Graphene Field-Effect Transistor Biosensors

Shiyu Wang Division of Electronics and Informatics Graduate School of Science and Engineering Gunma University Kiryu, Gunma, Japan Jihua Laboratory Foshan, Guangdong, China

Zakir Hossain GIAR Gunma University Kiryu, Gunma, Japan Tao Han First Affiliated Hospital of China Medical University Shenyang, China

Yan Zhao Jihua Laboratory Foshan, Guangdong, China

ISBN 978-981-16-1211-4 ISBN 978-981-16-1212-1 (eBook) https://doi.org/10.1007/978-981-16-1212-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Coronavirus disease 2019 (COVID-19) is an emerging human infectious disease associated with severe respiratory distress. Since a series of unexplained pneumonia cases were reported in Wuhan City, Hubei Province, China in December 2019, the number of infections has increased rapidly with human-to-human transmission. As of October 13, 2020, more than 37745,000 COVID-19 cases have been confirmed worldwide, and a conservative estimate has caused 1079963 deaths. As there is currently no specific drug for COVID-19, large-scale and rapid early diagnosis will often play a vital role in controlling the epidemic for this rapidly spreading epidemic. Currently, the main detection methods include Nucleic Acid Detection (NAT), which is a technique used to detect specific nucleic acid sequences, so it is usually used to detect and identify specific viruses or bacteria. NAT needs to collect blood, tissue, urine, etc., and detect whether it contains pathogens. The difference between NAT and other tests is that it detects the genetic material (RNA or DNA) instead of antigens or antibodies. The detection of genetic material can diagnose diseases early because the detection of antigens and/or antibodies takes a while to begin to appear in the blood or body fluids. Since the amount of genetic material collected is usually small, many NATs include steps that can amplify genetic material, that is, copy a large amount of genetic material. This NAT is called a Nucleic Acid Amplification Test (NAAT). However, because the amplification step is necessary for the NATs, it always takes several hours then gets the result. Thus the nucleic acid test is usually unable to achieve large-scale rapid testing, which makes epidemic control a very difficult problem. Therefore, there is an urgent need to develop a new type of sensor that can perform large-scale rapid detection while having a certain sensitivity and specificity, especially for large-scale epidemic diseases like COVID-19. Graphene is a type of carbon. As the first two-dimensional material discovered in the world, it is almost completely transparent. It is not only the thinnest but also the strongest. It has excellent electrical and thermal conductivity. Initially, Profs. Geim and Novoselov extracted graphene from graphite. They used tape to mechanically peel off the graphite to obtain graphene. Although many people believed that this twodimensional crystal material could not exist stably at that time. Now, using the twodimensional material graphene, physicists can study the strange properties of matter in the two-dimensional world. Graphene makes a series of new experimental ideas v

vi

Preface

and designs possible, which brings many surprising new experimental phenomena and exciting new application areas. Currently, graphene has already many novel practical applications. Especially, graphene-based field-effect transistors biosensors, due to their excellent electrical properties and two-dimensional size, are expected to become ultra-sensitive biological and chemical-sensing platforms. At present, various biological technologies such as avidin-biotin technology have been diffusely applied in different types of ELISA (enzyme-linked immunosorbent assay) kits, polymer-based detection, and labeled immunosensors for detecting distinct biomarkers related to different kinds of diseases (such as cancer and influenza). Here we demonstrated these Graphene Field-Effect Transistors (GFET) based on different biological technologies and illustrated its ultrasensitive detection capability for the specific target biomolecule (such as virus, microbes, cells) in the sub-pico molar (pM) range. Although the invention and development of the graphene FET biosensor have only gone through 10 years, this novel rapid detection platform has shown great potential for ultra-sensitive rapid detection, which is difficult to achieve by traditional detection methods. Especially for large-scale epidemic detection and point-of-care testing, this new type of sensor platform is expected to exert its unique advantages. Generally speaking, this monograph is divided into 11 chapters. As the upper part, Chaps. 1–4 mainly state the basic knowledge of graphene FET biosensors, including graphene-related applications, graphene preparation, mathematical principles about the graphene FET, fundamental of graphene FET biosensing, etc. Chapters 5–11, as the lower part, mainly state the application of graphene FET biosensor combined with various biotechnologies. In the lower part, not only previously existed technologies are introduced but also inserted some of my personal ideas and conceives for the first time. In addition, it discusses some related problems existing in graphene FET biosensor and then introduces some improve methods about these problems. Finally, it states the future application prospects. Chapter 1 extensively introduces recent research related to the application of graphene and its potential applications in various fields. Chapter 2 introduces the electrical properties of graphene and provides the relevant electrical theoretical basis for graphene FET sensors. Based on the above theoretical, I proposed two important inferences in the field of graphene FET sensing. At the end of this chapter, the low-temperature characteristics of graphene are also briefly introduced. Chapter 3 systematically introduces the preparation method for graphene. At the same time, some of my personal experiences with graphene preparation are also inserted in this chapter. Chapter 4 introduces the fundamental of the biosensor based on graphene FET and briefly demonstrates its current application in biological detection. Chapter 5 describes the graphene FET biosensor based on the avidin-biotin interaction and systematically explains its preparation method and the expected detection effect. Chapter 6 introduces the graphene FET biosensor based on antigen-antibody interaction, focusing on its application in biomarker detection. Chapter 7 discusses the graphene FET biosensor based on base complementary pairing technology and discusses the possibility of applying this technology to large-scale population detection of COVID-19. Chapter 8 introduces the graphene FET biosensor based on

Preface

vii

aptamer technology and compares its difference with the biosensor based on antigen– antibody technology. Chapter 9 introduces the graphene biosensor based on ConA technology, and based on the technical characteristics of ConA, two different detection methods are proposed: adsorption detection method and dissociation detection method. Chapter 10 summarizes the current problems in the field of graphene FET biosensing and briefly states some of the improved methods for these problems. Chapter 11 summarizes and outlooks the research on graphene FET biosensors based on various biotechnologies. Moreover, some aspects have been proposed, which need to be improved and noted in the future. Currently, the most direct problem facing clinical detection is that most of the existing testing methods cannot achieve rapid testing, including PCR and ELISA, especially in the context of the current COVID-19 pandemic. Based on the above considerations, the GFET biosensor as a new type of biosensor that can achieve rapid quantitative detection of specific biological molecules with ultra-high sensitivity and specificity compared with traditional detection methods. To quickly provide a potentially reliable basis for clinical diagnosis, it is expected to achieve rapid screening for large-scale epidemics. Therefore, the GFET biosensor, as an emerging sensing platform that has only been invented for a decade, I hope that readers can grasp the basic knowledge and future directions of graphene FET biosensors in general through this monograph. At the same time, enjoying the fun of research. Kiryu, Japan Kiryu, Japan Foshan, China Shenyang, China

Shiyu Wang Zakir Hossain Yan Zhao Tao Han

Acknowledgments

Special thanks to my parents and Miss Yuan Gu for their encouragement and supports. Shiyu Wang

ix

Contents

1

Fundamental of Graphene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Graphene Conductive Ink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Graphene Supercapacitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Microbial Fuel Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Graphene Flexible Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Graphene Nanogenerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Thermal Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.1 Drug Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.2 Cell Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.3 DNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.4 Tumor Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.5 Biological Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.6 Graphene Biosafety Research . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 4 5 6 6 7 9 9 11 11 12 13 14 15

2

Graphene Electrical Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Room Temperature Electrical Characteristics . . . . . . . . . . . . . . . . . 2.2 Low-Temperature Electrical Characteristics . . . . . . . . . . . . . . . . . . 2.2.1 Magic Angle Graphene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Moiré Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 22 27 27 27 28

3

Graphene Manufacture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Mechanical Exfoliation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Chemical Vapor Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Pretreatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 CVD Graphene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Transferring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Epitaxial Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Epitaxial Graphene Preparation . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Graphene Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Other Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29 29 30 31 31 33 37 37 37 38 xi

xii

4

5

Contents

3.4.1 Directly Synthesis on SiO2 . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Reduced Graphene Oxide (R-GO) . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38 39 40

Graphene Field-Effect Transistor Biosensor . . . . . . . . . . . . . . . . . . . . . 4.1 Electrical Double Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Stern Model (1924) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 BDM Model (1963) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Debye Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Graphene Field-Effect Transistor . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Graphene Field-Effect Transistor Biosensors . . . . . . . . . . . . . . . . . 4.5 Mechanism of the Graphene Field-Effect Transistor Biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Biological Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 47 48 49 50 51 52 60 61 64

Graphene FET Biosensor Based on the Avidin–Biotin Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Biotinylated Biomolecules Detection . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Device Fabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Graphene Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Quantitative Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Specificity of the Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Exogenous Biotin Interferences . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Comparative Sensitivity and Practical Applicability . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 70 72 72 77 78 80 80 81 82

Graphene FET Biosensor Based on the Antigen–Antibody Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Tumor Marker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Other Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 88 89 91

7

Graphene FET Biosensor Based on the Base Pair . . . . . . . . . . . . . . . . 7.1 COVID-19 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93 95 96

8

Graphene FET Biosensor Based on the Aptamer Technology . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97 99

9

Graphene FET Biosensor Based on the Concanavalin A . . . . . . . . . . 9.1 Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Dissociation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

101 103 103 105

6

Contents

10 Challenges and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Standardization of Transfer and Modification . . . . . . . . . . . . . . . . . 10.2 Signal Interference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

107 108 109 110 112

11 Conclusions and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 11.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 11.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Chapter 1

Fundamental of Graphene

Abstract Graphene is the first two-dimensional material discovered in the world. It has many excellent properties that traditional bulk-materials cannot match, such as ultra-high mechanical strength, high thermal conductivity, electrical conductivity, ultra-high specific surface area, stable chemical properties, etc. This makes graphene have great application potential in many fields such as composite materials, conductive inks, nano power generation, anti-corrosion coatings, and biological applications. Although it has been less than 20 years since Andre Geim and Konstantin Novoselov discovered graphene in 2004, a series of remarkable results have been achieved in the related theories and practical applications of graphene around this new two-dimensional material. This also indicates that scientists have paid great attention to two-dimensional materials including graphene. This chapter introduces the discovery history of graphene and the main application directions of graphene in recent years. Keywords Graphene · Supercapacitor · Conductive ink · Microbial fuel cells · Flexible sensing · Nanogenerator · Thermal applications · Biomedical applications Graphene is the first two-dimensional material discovered in the world. It is an allotrope of carbon and consists of single atomic layers arranged in a two-dimensional honeycomb lattice [1, 2]. The name is the suffix of “graphite”, the suffix is -ene, reflecting that the graphite allotrope of carbon is composed of stacked graphene layers [3, 4]. Each carbon atom is connected to the three neighboring atoms through a σ bond and contributes an electron to the conduction band in the entire graphene film. This is the same type of bonding seen in carbon nanotubes and polycyclic aromatic hydrocarbons, and (partially) in fullerenes and glassy carbon [5, 6]. These conduction bands make graphene a semimetal with unusual electronic properties that are best described by theories for massless relativistic particles [1]. Charge carriers in graphene show linear, rather than quadratic, dependence of energy on momentum, and field-effect transistor s with graphene can be made that show bipolar conduction. Charge transport is ballistic over long distances; the material exhibits large quantum oscillations and large and non-linear diamagnetism [7]. Graphene conducts heat and electricity © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_1

1

2

1 Fundamental of Graphene

Fig. 1.1 The main application directions of graphene since its discovery

very efficiently along its plane. The material strongly absorbs light of all visible wavelengths [8, 9], which accounts for the black color of graphite; yet a single graphene sheet is nearly transparent because of its extreme thinness. The material is also about 100 times stronger than would be the strongest steel of the same thickness [10, 11]. For decades, scientists have been working on graphene and graphene-related applications. In fact, pencils and other graphite-related applications may have been unknowingly produced in small quantities for hundreds of years. It was first observed in an electron microscope in 1962, but it was only studied under the support of a metal surface [3]. But graphene was rediscovered, separated, and identified by Andre Geim and Konstantin Novoselov of the University of Manchester in 2004, [12] due to their research they obtained the 2010 Nobel Prize in Physics. During the PhD research, my major work is to focus on the research of the biological applications based on the graphene FET. Developing a kind of ultrasensitive rapid detection method for specific biomolecules, besides, we should also mention that graphene composite materials have received more attention in recent years, such as graphene composite rubber and graphene composite coatings. The global market for graphene was $9 million in 2012 [13], with most of the demand from research and development in semiconductor, electronics, electric batteries, and composites. In 2019, it was predicted to reach over $150 million by 2021 [14]. Figure 1.1 lists the main application directions of graphene since its discovery, and brief introduction about these main application directions is also followed below this section.

1.1 Graphene Conductive Ink Series of applications in the emerging field of printed electronics require a new set of functional materials for development including flexible and large-area displays [15], radio frequency identification tags, portable energy harvesting and storage [16,

1.1 Graphene Conductive Ink

3

17], biomedical and environmental sensor arrays [18, 19], and logic circuits [20]. To realize these novel technologies, functional materials must be integrated with appropriate patterning technologies, such as spraying, inkjet, gravure, and flexographic printing [21, 22]. Since electrical conductors are the core components of electronic devices as conductive materials, researchers have invested a lot of effort in developing and manufacturing conductive materials. In the field of printable inks [23], common conductive inks can be divided into three categories: precious metals, conductive polymers, and carbon nanomaterials [24]. We focus on the development of conductive inks based on carbon nanomaterials. Because different types of conductive inks have unique properties suitable for specific applications, for example, among noble metals, silver is the most common printed conductor due to its high conductivity and oxidation resistance, and inks based on silver nanoparticles or silver precursors have used for printing [25]. These inks have the highest conductivity among printed materials. However, because silver is relatively expensive [24], conductive polymers have also been developed for printed electronics applications. These materials provide moderate conductivity at low cost but are limited in terms of chemical and thermal stability. Carbon nanomaterials, including carbon nanotubes and graphene, provide a low-cost alternative with excellent environmental stability and ideal electrical conductivity as well as unique properties suitable for various applications [24]. Carbon nanomaterials provide many opportunities for printing and flexible electronics. The electrical properties produced by the sp2 bonding structure of fullerenes, carbon nanotubes, and graphene are particularly promising and have been developed in many applications from thin-film transistors (TFT) and electrochemical sensors to supercapacitors and photovoltaic cells [26, 27]. Figure 1.2 shows the image of the printable graphene conductive ink. Fig. 1.2 The image of the printable graphene conductive ink

4

1 Fundamental of Graphene

1.2 Graphene Supercapacitor Electric Double Layer Capacitor (EDLC), sometimes also called supercapacitors, is a novel type of energy storage device. Graphene is used in the electrode material so that graphene supercapacitors can store charge in an electric double layer formed by physically adsorbing electrolyte ions to graphene electrodes. With excellent characteristics such as high power density and excellent cycle stability, they are hoping to be used in applications including uninterruptible power supplies, high-power electronic equipment, and electric and hybrid electric vehicles. However, for most practical applications, due to the limited energy density of graphene supercapacitors, their energy density is usually about 4–5 Wh/kg, which is an order of magnitude lower than the energy density of batteries. However, at present, because graphene has an ultra-high theoretical surface area (2630 m2 g−1 ) and electrical conductivity, it has become the focus of research and development of supercapacitor electrode materials. A variety of graphene-based materials with different chemical structures and morphologies have been developed, such as chemically modified graphene, microwave expanded graphite oxide (MEGO), and curved graphene, which are used as electrode materials for supercapacitors. Recently, it has been reported that graphene-derived materials have an extremely high surface area of up to ~3100 m2 g−1 , which is prepared by microwave irradiation of graphite oxide (GO) and then chemical activation with potassium hydroxide (KOH) [28]. This activated microwave-expanded graphite oxide (a-MEGO) has a large part of micropores and mesopores, which can provide a large and accessible surface area to hold the charge, so it can improve specific capacitance in interface and ionic liquid electrolytes, thereby obtaining a relatively high weight energy density. Figure 1.3 shows the schematic diagram of the graphene supercapacitor structure.

Fig. 1.3 The schematic diagram of the graphene supercapacitor structure

1.3 Microbial Fuel Cells

5

1.3 Microbial Fuel Cells Microbial fuel cell (MFC) is a promising technology, currently, the technology is mainly used for sewage treatment and power generation. It can use microorganisms to convert chemical energy (in organic waste) into electrical energy, and the process combines bioremediation with power generation. In MFC, organic compounds are oxidized by bacteria along with the production of bacteria at the anode, and oxygen is reduced at the cathode [29–32]. At present, it can be divided into a single chamber and dual chamber. MFC is a relatively wide-ranging type of bioelectrochemical system research and application, including various applications, such as power generation, wastewater treatment, implantable medical equipment, energy recovery, and biosensors. Figure 1.4 shows an image of a dual-chamber microbial fuel cell. However, the output of MFC is limited by low charge transfer efficiency and internal resistance provided by the electrodes. At present, the power conversion efficiency of MFC is still not high. The losses in the MFC system are evident from the voltage equation, V = E t − ηact − ηohmic − ηconc , where ηact , ηohmic and ηconc are voltage losses due to the reaction kinetics, ohmic polarization, and mass transport, respectively [33–36]. Reaction kinetics are predominantly dependent on the electrode reaction rate. The properties of anode material such as accessible area for bacterial colonization, biocompatibility, and interfacial electron transfer resistance play a vital role in influencing output power of MFC [37, 38]. Hence, the performance of MFC largely depends on the anode material used [39, 40]. So far, carbon fiber materials including carbon cloth, carbon paper, and graphite particles are the most studied anode materials for MFC. Among them, carbon cloth is the most widely used [41, 42], but they have inherent shortcomings, such as low electrical conductivity, biological phase poor tolerance, small surface area, and many other deficiencies. Among carbon-based materials, graphene as an anode material has attracted wide attention from researchers in recent years [43–46]. It has many advantages such as excellent biocompatibility, chemical stability, excellent electrical conductivity, high mechanical strength, and good elasticity. In addition, graphene has a large specific surface area of 2630 m2 g−1 , and the carrier mobility is greater than 200000 cm2 V−1 s−1 [47–49]. Using graphene to modify anodes is one of Fig. 1.4 The image of the double chamber microbial fuel cell

6

1 Fundamental of Graphene

the most effective ways to improve anode performance [50]. In addition, graphene provides a larger surface area for bacterial colonization, promotes the direct electron transfer process, and improves the efficiency of electron transfer [51]. Zhang et al. first explored graphene as an anode material for MFC. They report that the power density of graphene-modified stainless steel is 2668 mW m−3 , which is 18 times higher than SS (stainless steel) bare mesh [52]. In Xiao et al., it is reported that the highest power density of the anode modified with wrinkled graphene is 3.6 W/m3 . In addition, it was observed that the graphene modification increased the power density and energy conversion by 2.7 times and 3 times, respectively [53].

1.4 Graphene Flexible Sensing Because of the unique 2D properties of the graphene, lots of flexible sensing devices have been developed based on graphene [54–56]. Graphene strain sensing is the most significant flexible sensing application that has almost ready for commercial manufacture [56]. In my opinion, the large-scale preparation of multilayer graphene strain sensors through graphene ink is the main direction for the development of graphene strain sensing in the future. The multilayer graphene is made onto the surface of the flexible substrate by the print of the graphene ink. Then the strain force makes the displacement between the interlayer of the graphene, which changes the conductivity of the multilayer graphene. Because the printing of the multilayer graphene by the graphene ink is not difficult processing (sometimes only need to heat curing after screen printing), thus the graphene strain sensing device is easy to be manufactured. At the same time, because graphene is thin enough that it can be developed as the ultrathin strain sensing device, which can break through the thickness level that traditional materials can not arrive. Thus as one of the most promising graphene applications, the graphene flexible sensing is expected to be applied as the ultrathin strain sensing platform. Figure 1.5 shows the image of the graphene flexible sensing platform based on the graphene tape.

1.5 Graphene Nanogenerator The triboelectric nanogenerator is an energy conversion and collection device that can convert external mechanical energy into electrical energy through triboelectric effect and electrostatic induction. In 2012, this new type of nanogenerator was demonstrated for the first time in the research team of Professor Wang Zhonglin of Georgia Institute of Technology [57]. In simple terms, for this power generation unit, in the internal circuit, a potential is generated due to the charge transfer between two organic/inorganic thin films showing opposite tribopolarity due to triboelectricity. In an external circuit, electrons are driven to flow between two electrodes attached to

1.5 Graphene Nanogenerator

7

Fig. 1.5 The image of the graphene flexible sensing platform based on the graphene tape

the back of the film to balance the potential. In the past 10 years, triboelectric nanogenerators have made new developments. Combined with nanomaterials, focusing on the triboelectric effect of the solid–liquid interface has become a research focus in recent years. Nature provides a huge amount of water energy in diverse forms, but very limited part has been collected. Recently, great achievements have been made in carbon nanomaterial-based energy harvesters, as they collect many rich forms of water energy, such as flows, waves, raindrops, moisture, and evaporation, based on radically different principles from traditional electromagnetic generators. Recently, some studies have reported that dropping or waving potential can be induced in graphene when electrolyte droplets or waves move across the graphene surface [58, 59]. Such electric potential is generated due to the moving electrical double layer (EDL) boundary at the interface between graphene and electrolyte, where ions adsorption/desorption occurs and induces charge carriers to flow in graphene. These studies indicated that there is charge transferring between the interface of the graphene and water, which induce the electric potential. The electric potential generated from the liquid flow on graphene has triggered great interest and shown its prospect in harvesting mechanical water energy. Figure 1.6 shows the schematic image of the graphene water–solid triboelectric nanogenerator.

1.6 Thermal Applications Due to the excellent thermal conductivity of graphene and its potential in thermal management applications, heat transfer applications and thermal management applications related to graphene have become a booming research field. Under ideal conditions, the thermal conductivity of graphene at room temperature can reach the range of 3000–5000 W/mK. Compared with the thermal conductivity of pyrolytic graphite

8

1 Fundamental of Graphene

Fig. 1.6 The schematic image of the graphene water–solid triboelectric nanogenerator

at room temperature of about 2000 Wm−1 K−1 , its thermal conductivity is A further improvement. However, some current studies suggest that the thermal conductivity of graphene in practical applications may be slightly lower than this ideal value. For free-suspended graphene samples, the in-plane thermal conductivity of graphene at room temperature is about 2000–4000 Wm−1 K−1 [60, 61]. But this number is still the highest among all known materials. Graphene is considered to be an excellent thermal conductor. According to the size of the sample, some studies have found that graphene has unlimited thermal conductivity, which runs counter to the micron-level thermal conductivity (Fourier’s law). In computer simulations and experiments, the researchers found that the larger the graphene segment, the more heat it transfers. In theory, graphene can absorb an unlimited amount of heat. The thermal conductivity increases logarithmically, and the researchers believe that this may be due to the stable bonding pattern and the two-dimensional material. Since graphene has stronger tear resistance than steel, and is lighter in weight and flexible, its related thermal conductivity applications may have great potential in the future. Since graphene is the most thermally conductive found so far, and graphene has high mechanical strength, light weight, and thinness, this means that it is a good material for manufacturing attached or coated heat dissipation solutions, such as heat sinks or heat dissipation films. This can be useful in both microelectronics (such as making LED lighting more efficient and durable) and large applications (such as thermally conductive foils for mobile devices). In addition, in the field of electric vehicles, due to its super thermal conductivity, fire resistance, light weight, and other characteristics, it can be used as a thermal conductive material to coat the surface of the battery in the thermal management of the battery. Temperature is an important factor affecting semiconductor components. An increase in the operating temperature of the component will increase the power of the component. For portable devices such as mobile phones, the increase in power will lead to excessive battery loss. Therefore, the application of graphene thermal film is beneficially extend the use time of portable devices. Huawei’s latest smartphones, for example, have adopted graphene-based thermal films [62]. Believe that there are more and more different kinds of the graphene-based thermal films that will

1.6 Thermal Applications

9

Fig. 1.7 The image of the graphene thermal tape used on the surface of the semiconductor chip

be applied in the future. Figure 1.7 shows the image of the graphene thermal tape used on the surface of the semiconductor chip.

1.7 Biomedical Applications As a new type of two-dimensional nanomaterial composed of carbon atoms, graphene’s unique and excellent electrical, optical and mechanical properties, as well as the broad application prospects resulting from it, have become a research hotspot that has attracted much attention. At present, the research on graphene and its derivatives is mainly concentrated in its physical research field. The chemistry and material science research of graphene have also developed rapidly, while the research work of graphene in the field of biomedicine has just begun, but some of its recent highlight research has hinted that graphene may have great potential in the biomedical field. This section briefly describes the latest developments in graphene in the field of biomedicine, including targeted drug delivery, cell imaging, DNA sequencing, tumor therapy, biological detection, and graphene biosafety research as shown in Fig. 1.8.

1.7.1 Drug Delivery Drug delivery is one of the prominent biomedical applications of graphene nanomaterials in the current scenario. The rapid growth in the graphene-based drug delivery systems showed the potential of this nanomaterial in the future healthcare industry. The unique monoatomic planar structure and associated properties such as large surface area, chemical and mechanical stabilities, superb electrical conductivity,

10

1 Fundamental of Graphene

Fig. 1.8 The main biomedical applications based on the graphene

and good biocompatibility enabled the utility of these nanomaterials in medical applications. In 2008, Dai Hongjie’s research group [63] first reported the use of polyethylene glycol (PEG) modified graphene oxide as a poorly soluble aromatic-containing anticancer drug carrier. They first oxidized graphite to obtain nanoscale graphene oxide (NGO) with a size of less than 50 nm and then grafted biocompatible PEG onto NGO. This graphene material has good biocompatibility and stability under physiological conditions including serum. Then, the anticancer drug SN38 (camptothecin derivative) is adsorbed on the surface of the PEG-based NGO through physical effects such as π–π stacking to form a graphene–drug complex. Graphene has a single atomic layer thickness, and its two base surfaces can absorb drugs, so it has an ultra-high drug loading rate unmatched by other nanomaterials. Studies have found that the NGO-SN38 complex has good water solubility, indicating that it can be used as a drug carrier to solubilize poorly soluble drugs, and the SN38 in the complex still maintains a high degree of activity. In vitro experiments have found that NGO-SN38 can effectively kill colon tumor cells. More importantly, NGO-PEG as a drug carrier has no obvious cytotoxicity and has good biological safety.

1.7 Biomedical Applications

11

1.7.2 Cell Imaging Graphene quantum dots (GQD) have shown great potential in bioimaging applications due to their excellent biocompatibility, feasibility of surface functionalization, physiological stability, low cytotoxicity, and adjustable fluorescence characteristics. In addition, some of the fluorescent dyes connected with the graphene are also always used for the cell image. In this section, cell imaging based on the graphene will be briefly introduced. Gao et al. proposed a GQD coated with polyethyleneimine (PEI) for in vitro tumor cell imaging [64]. Depending on the molecular weight (MW) of the PEI, the prepared GQD can show red, yellow, and blue emissions, making it possible to perform multicolor imaging of U87 tumor cells in vitro. Besides, they claimed that the reason for the multicolor emission is that the PEI coating not only determines the core structure of the GQD but also changes the energy gap, leading to the multicolor emission GQD. Peng et al. [65] used PEG to connect fluorescent dyes with NGO to perform intracellular imaging. Among them, the PEG molecule acts as a bridge, which can prevent NGO from causing fluorescence quenching of the dye, effectively improves the biocompatibility and stability of NGO, and enhances the absorption of materials by cells. The research results show that the fluorescein-PEG-NGO (Fluo-G) structure exhibits excellent pH-regulated fluorescence characteristics. More importantly, the complex can be efficiently absorbed by cells and used as a fluorescent probe in cell imaging Fluo-G emits green fluorescence when excited with blue light. At the same time, at pH 4.6–8.0, the fluorescence density of Fluo-G increased with the increase in pH value. Studies have found that, compared with active absorption, Fluo-G may be more dependent on being absorbed by cells directly through the cell membrane. In view of the high biocompatibility and carrying capacity of NGO, it is easy to synthesize, and it is expected to be used for cell imaging.

1.7.3 DNA Sequencing DNA is the blueprint of life because it encodes all genetic information. In many genetic diseases, DNA sequencing is used as the gold standard for diagnosis. Researchers have been conducting research to achieve genome sequencing at a low cost while maintaining high precision and high throughput [66]. In principle, graphene is an ideal pore material for DNA sequencing. Its singleatom thickness of 0.35 nm is similar to DNA base spacing, and it can create graphene nanopores with a diameter of only 1.0 nm (about the size of a DNA molecule). Although most of the current research is still in the theoretical stage, it is one of the most promising and revolutionary DNA sequencing technologies. There are two main promising methods of the DNA sequencing based on the graphene nanomaterial, containing the charge tunneling through graphene nanopores

12

1 Fundamental of Graphene

and in-plane charge transport within graphene nanoribbon containing a nanopore [67]. For the charge tunneling, because different bases have different electronic energy level structures, thus when each base passes through the nanopore gap, different degrees of tunneling current will be generated. The idea is to measure the conductance through two graphene-based electrodes and to monitor the current change when DNA bases go through the nanopore. When different DNA bases fall within the voltage range of the electrodes, a special and different current is noticed. For the graphene nanoribbon in-plane detection where DNA bases modulate the ionic current passing through graphene nanoribbon differently. This approach has an advantage over the previous one since the current in the nanoribbons is larger. As a potential rapid sequencing method, although more in-depth research is needed, the rapid DNA sequencing based on graphene nanomaterials is worthy to be expected.

1.7.4 Tumor Therapy Although tumor therapies based on the carbon nanomaterials have been intensively studied in recent years, graphene has been rarely mentioned in this field. The research of the tumor therapies based on the graphene may be still in the primary stage that still needs to continuously research. Currently, Liu Zhuang’s research team has reported for the first time the in vivo behavior of nano-graphene sheets (NGS) coated with polyethylene glycol (PEG) by fluorescent labeling [68]. Their results showed that based on in vivo fluorescence imaging, NGS tumor uptake was abnormally high in several xenograft tumor mouse models. In contrast to PEGylated carbon nanotubes, PEGylated NGS exhibits several interesting behaviors in vivo, including relatively low retention in the reticuloendothelial system and efficient passive tumor targeting. Then, they used the strong light absorption of NGS in the near-infrared (NIR) region for in vivo photothermal therapy. After intravenous administration of NGS and low-power NIR laser irradiation on the tumor, they achieved ultra-efficient tumor ablation. In addition, through histology, blood chemistry, and whole blood plate analysis, no obvious side effects of PEGylated NGS on injected mice were observed in its pilot toxicity study. This research has opened the way for graphene nanosheets to be used for tumor ablation through photothermal therapy. Although more effort is needed to further understand the in vivo behavior and long-term toxicology of this new nanomaterial, their work is the first success in using carbon nanomaterials for effective in vivo photothermal therapy through intravenous administration, which shows the promise of graphene in tumor treatment.

1.7 Biomedical Applications

13

1.7.5 Biological Detection With its excellent physical and optical properties, graphene has become a novel material for biosensors. At the same time, it is also biocompatible, very suitable for clinical diagnosis or medical applications, and is relatively easy to manufacture, with stable chemical properties. At present, the biological detection application of graphene has attracted more and more attention. GFETs and graphene-enhanced surface plasmon polaritons (SPPs) as the two of the most promising applications in biological detection are widely researched in the world. GFET is an improvement of the conventional silicon field-effect transistor (FET). In the conventional FET, silicon material acts as a thin conductive channel, and its conductivity can be adjusted by the gate voltage. GFET is performed in a similar way, where the silicon in conventional FET is replaced by the graphene. Based on the remarkable carrier mobility (15000 cm2 V−1 s−1 at room temperature) and atomicscale thickness, graphene creates a thinner, more sensitive channel area. Compared with the bulk semiconductor materials, such as silicon, because most of the FET channel materials are three-dimensional materials, which induces any charge carrier changes at the interface of the channel do not always penetrate deeper into the device. This phenomenon will greatly limit the response sensitivity of the FET. Especially for applications that require high sensitivity, such as some special gas or biosensing applications, but because the channel of GFET is graphene that is only one carbon atom thick, the entire channel is directly exposed to any molecules in the nearby environment. In addition, stable chemical properties and easy large-scale preparation are also reasons why graphene is more suitable as a channel material than other nanomaterials. Therefore, through the functionalization of graphene surfaces, GFETs have become attractive devices for attaching specific biomolecules. Because graphene has an extremely thin thickness (two-dimensional material), even the smallest concentration of attached molecules will change the channel conductivity, which makes the GFET biosensor very suitable as a potential clinical diagnose platform for rapid detection [69]. Especially in recent years, graphene FET biosensors have shown the ultrasensitive rapid detection capabilities for COVID-19 [70]. Moreover, some studies show that graphene FET biosensors combined with CRISPR technology to develop CRISPR chips that can quickly identify DNA sequences [71]. These outstanding studies have made people gradually pay attention to the related applications of graphene FET biosensors and hope that these novel technologies will be widely used as soon as possible. Figure 1.9 shows the main detection targets for the GFET biosensor. In addition, graphene film materials can also be used in combination with surface plasmon polarons (SPP) on metal films to enhance the performance of biosensors. SPP-based sensors confine light waves to metal surfaces to make small-volume chemical and biological sensors. The sensing volume is given by a tightly enclosed surface wave, which improves the sensitivity of optical detection. The main metals used in SPP biosensors are gold (Au) and silver (Ag) because they have good surface wave propagation characteristics. However, gold has poor adsorption performance,while

14

1 Fundamental of Graphene

Fig. 1.9 The main detection targets for the GFET biosensor

silver corrodes quickly. Note that the layer of graphene on the gold surface will produce excellent adsorption [69].

1.7.6 Graphene Biosafety Research For the biomedical applications of graphene, the safety of graphene is always a topic that cannot be ignored. In the last section of this chapter, the safety of graphene will be briefly discussed. It is almost inevitable that any new material invention or discovery will usually be accompanied by safety warnings from researchers in the field. The same is true for graphene, especially once the concept of nano is mentioned [72]. Currently, the toxicity of graphene is still under study. The literature published by Lalwani et al. systematically elaborated on the detailed study of graphene toxicity. The various mechanisms of graphene toxicity are demonstrated [73]. They claimed that the toxicity of graphene depends on a variety of complex factors, such as size, purity, shape, production process, functional groups, and dispersion state [74]. Researchers at Stony Brook University have shown that graphene nanomaterials such as nanoribbons, nanosheets, and nanoonions are non-toxic at concentrations up to 50 μg/ml. These graphene nanomaterials will not change the differentiation of human bone marrow stem cells into osteoblasts (bone) or adipocytes (fat), which indicates that low-dose graphene nanomaterials are safe for biomedical applications. This result enhances the confidence in the low-dose application of graphene nanomaterials in vitro and in vivo [75]. Researchers at Brown University found that multilayer 10 μm graphene sheets can penetrate cell membranes in solution. It was observed that they initially entered through sharp jagged points, but their specific physiological effects are still unclear [76].

1.7 Biomedical Applications

15

Although significant results have been achieved regarding graphene biomedical applications, the toxicity of graphene is still unclear, so I think that this kind of potential risk should be vigilant before practical applications.

References 1. Geim, A.K., Novoselov, K.S.: The Rise of Graphene. arXiv:cond-mat/0702595 (2007) 2. Peres, N.M.R., Ribeiro, R.M.: Focus on graphene. New J. Phys. 11(9), 095002 (2009). https:// doi.org/10.1088/1367-2630/11/9/095002 3. Boehm, H.P., Clauss, A., Fischer, G.O., Hofmann, U.: Das Adsorptionsverhalten sehr dünner Kohlenstoff-Folien. Zeitschrift für anorganische und allgemeine Chemie 316(3–4), 119–127 (1962). https://doi.org/10.1002/zaac.19623160303 4. Boehm, H.P., Setton, R., Stumpp, E.: nomenclature and terminology of graphite intercalation compounds. report by a subgroup of the international committee for characterization and terminology of carbon and graphite on suggestions for rules for the nomenclature and terminology of graphite intercalation compounds. Synthetic Metals 11 (6), 363–371 (1985). https://doi.org/ 10.1016/0379-6779(85)90068-2 5. Zdetsis, A.D., Economou, E.N.: A Pedestrian Approach to the Aromaticity of Graphene and Nanographene: Significance of Huckel’s (4n + 2)π Electron Rule. J. Phys. Chem. C 119(29), 16991–17003 (2015). https://doi.org/10.1021/acs.jpcc.5b04311 6. Harris, P.J.F.: Transmission Electron microscopy of carbon: a brief history. C—J. Carbon Res. 4(1), 4 (2018). https://doi.org/10.3390/c4010004 7. Li, Z., Chen, L., Meng, S., Guo, L., Huang, J., Liu, Y., Wang, W., Chen, X.: Field and temperature dependence of intrinsic diamagnetism in graphene: theory and experiment. Phys. Rev. B 91(9), 094429 (2015). https://doi.org/10.1103/PhysRevB.91.094429 8. Nair, R.R., Blake, P., Grigorenko, A.N., Novoselov, K.S., Booth, T.J., Stauber, T., Peres, N.M.R., Geim, A.K.: Fine structure constant defines visual transparency of graphene. Science 320(5881), 1308 (2008). https://doi.org/10.1126/science.1156965 9. Zhu, S.-E., Yuan, S., Janssen, G.C.A.M.: Optical transmittance of multilayer graphene. EPL 108(1), 17007 (2014). https://doi.org/10.1209/0295-5075/108/17007 10. Lee, C., Wei, X., Kysar, J.W., Hone, J.: Measurement of the elastic properties and intrinsic strength of monolayer graphene. Science 321(5887), 385–388 (2008). https://doi.org/10.1126/ science.1157996 11. Cao, K., Feng, S., Han, Y., Gao, L., Hue Ly, T., Xu, Z., Lu, Y.: Elastic straining of free-standing monolayer graphene. Nature Commun. 11(1), 284 (2020). https://doi.org/10.1038/s41467-01914130-0 12. Novoselov, K.S., Geim, A.K., Morozov, S.V., Jiang, D., Zhang, Y., Dubonos, S.V., Grigorieva, I.V., Firsov, A.A.: Electric field effect in atomically thin carbon films. Science 306(5696), 666–669 (2004). https://doi.org/10.1126/science.1102896 13. Global Demand for Graphene after Commercial Production to be Enormous, says Report. https://www.azonano.com/news.aspx?newsID=29510. Accessed Sep 28, 2020 14. Reports, V.: Global Graphene Market Size is Expected to Reach $151.4 Million and Register a CAGR of 47.7% by 2021 | Market Trends, Growth & Forecast—Valuates Report. https://www. prnewswire.com/news-releases/global-graphene-market-size-is-expected-to-reach-151-4-mil lion-and-register-a-cagr-of-47-7-by-2021–market-trends-growth–forecast—valuates-report300964539.html. Accessed Sep 28, 2020 15. Wood, V., Panzer, M.J., Chen, J., Bradley, M.S., Halpert, J.E., Bawendi, M.G., Bulovi´c, V.: Inkjet-printed quantum dot-polymer composites for full-color ac-driven displays. Adv. Mater. 21(21), 2151–2155 (2009). https://doi.org/10.1002/adma.200803256

16

1 Fundamental of Graphene

16. Gaikwad, A.M., Whiting, G.L., Steingart, D.A., Arias, A.C.: Highly flexible, printed alkaline batteries based on mesh-embedded electrodes. Adv. Mater. 23(29), 3251–3255 (2011). https:// doi.org/10.1002/adma.201100894 17. Hoth, C.N., Choulis, S.A., Schilinsky, P., Brabec, C.J.: High photovoltaic performance of inkjet printed polymer: fullerene blends. Adv. Mater. 19(22), 3973–3978 (2007). https://doi.org/10. 1002/adma.200700911 18. Jang, J., Ha, J., Cho, J.: Fabrication of water-dispersible Polyaniline-Poly(4-Styrenesulfonate) nanoparticles for inkjet-printed chemical-sensor applications. Adv. Mater. 19(13), 1772–1775 (2007). https://doi.org/10.1002/adma.200602127 19. Sokolov, A.N., Roberts, M.E., Bao, Z.: Fabrication of low-cost electronic biosensors. Mater. Today 12(9), 12–20 (2009). https://doi.org/10.1016/S1369-7021(09)70247-0 20. Ng, T.N., Schwartz, D.E., Lavery, L.L., Whiting, G.L., Russo, B., Krusor, B., Veres, J., Bröms, P., Herlogsson, L., Alam, N., Hagel, O., Nilsson, J., Karlsson, C.: Scalable printed electronics: an organic decoder addressing ferroelectric non-volatile memory. Sci Rep. 2(1), 585 (2012). https://doi.org/10.1038/srep00585 21. Arias, A.C., MacKenzie, J.D., McCulloch, I., Rivnay, J., Salleo, A.: Materials and applications for large area electronics: solution-based approaches. Chem. Rev. 110(1), 3–24 (2010). https:// doi.org/10.1021/cr900150b 22. Lupo, D., Clemens, W., Breitung, S., Hecker, K.: OE-a roadmap for organic and printed electronics. in applications of organic and printed electronics: a technology-enabled revolution. In: Cantatore, E. (Ed.) Integrated Circuits and Systems; Springer US: Boston, MA, 2013; pp 1–26. https://doi.org/10.1007/978-1-4614-3160-2_1 23. Cummins, G., Desmulliez, M.P.Y.: Inkjet printing of conductive materials: a review. Circuit World 38(4), 193–213 (2012). https://doi.org/10.1108/03056121211280413 24. Kamyshny, A., Magdassi, S.: Conductive Nanomaterials for printed electronics. Small 10(17), 3515–3535 (2014). https://doi.org/10.1002/smll.201303000 25. Walker, S.B., Lewis, J.A.: Reactive silver inks for patterning high-conductivity features at mild temperatures. J. Am. Chem. Soc. 134(3), 1419–1421 (2012). https://doi.org/10.1021/ja2 09267c 26. Rouhi, N., Jain, D., Burke, P.J.: High-performance semiconducting nanotube inks: progress and prospects. ACS Nano 5(11), 8471–8487 (2011). https://doi.org/10.1021/nn201828y 27. Weiss, N.O., Zhou, H., Liao, L., Liu, Y., Jiang, S., Huang, Y., Duan, X.: Graphene: an emerging electronic material (Adv. Mater. 43/2012). Adv. Mater. 24(43), 5776–5776 2012. https://doi. org/10.1002/adma.201290269 28. Zhu, Y., Murali, S., Stoller, M.D., Ganesh, K.J., Cai, W., Ferreira, P.J., Pirkle, A., Wallace, R.M., Cychosz, K.A., Thommes, M., Su, D., Stach, E.A., Ruoff, R.S.: Carbon-Based Supercapacitors produced by activation of graphene. Science 332(6037), 1537–1541 (2011). https://doi.org/10. 1126/science.1200770 29. Wu, X.-T., Li, J.-C., Pan, Q.-R., Li, N., Liu, Z.-Q.: Gallic acid-assisted synthesis of Pd uniformly anchored on porous N-RGO as efficient electrocatalyst for microbial fuel cells. Dalton Trans. 47(5), 1442–1450 (2018). https://doi.org/10.1039/C7DT04063F 30. Venkata Mohan, S., Velvizhi, G., Annie Modestra, J., Srikanth, S.: Microbial fuel cell: critical factors regulating bio-catalyzed electrochemical process and recent advancements. Renew. Sustain. Energy Rev. 40, 779–797 (2014). https://doi.org/10.1016/j.rser.2014.07.109 31. Venkata Mohan, S., Veer Raghavulu, S., Sarma, P.N.: Biochemical evaluation of bioelectricity production process from anaerobic wastewater treatment in a single chambered microbial fuel cell (MFC) Employing glass wool membrane. Biosens. Bioelectron. 23(9), 1326–1332 (2008). https://doi.org/10.1016/j.bios.2007.11.016 32. Logan, B.E., Hamelers, B., Rozendal, R., Schröder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., Rabaey, K.: Microbial fuel cells: methodology and technology. Environ. Sci. Technol. 40(17), 5181–5192 (2006). https://doi.org/10.1021/es0605016 33. Zhang, P.-Y., Liu, Z.-L.: Experimental study of the microbial fuel cell internal resistance. J. Power Sources 195(24), 8013–8018 (2010). https://doi.org/10.1016/j.jpowsour.2010.06.062

References

17

34. Butti, S.K., Velvizhi, G., Sulonen, M.L.K., Haavisto, J.M., Oguz Koroglu, E., Yusuf Cetinkaya, A., Singh, S., Arya, D., Annie Modestra, J., Vamsi Krishna, K., Verma, A., Ozkaya, B., Lakaniemi, A.-M., Puhakka, J.A., Venkata Mohan, S.: Microbial electrochemical technologies with the perspective of harnessing bioenergy: maneuvering towards upscaling. Renew. Sustain. Energy Rev. 53, 462–476 (2016). https://doi.org/10.1016/j.rser.2015.08.058 35. Vamshi Krishna, K., Venkata Mohan, S.: Purification and characterization of NDH-2 protein and elucidating its role in extracellular electron transport and Bioelectrogenic sctivity. Front. Microbiol., 10 (2019). https://doi.org/10.3389/fmicb.2019.00880 36. Yellappa, M., Sravan, J.S., Sarkar, O., Reddy, Y.V.R., Mohan, S.V.: Modified conductive polyaniline-carbon nanotube composite electrodes for bioelectricity generation and waste remediation. Biores. Technol. 284, 148–154 (2019). https://doi.org/10.1016/j.biortech.2019. 03.085 37. Hou, J., Liu, Z., Zhang, P.: A New method for fabrication of Graphene/Polyaniline Nanocomplex modified microbial fuel cell anodes. J. Power Sources 224, 139–144 (2013). https://doi. org/10.1016/j.jpowsour.2012.09.091 38. Section, S.: Microbial fuel cells. Environ. Sci. Technol. 40(17), 5162 (2006). https://doi.org/ 10.1021/es062758+ 39. Tsai, H.-Y., Hsu, W.-H., Liao, Y.-J.: Effect of electrode coating with graphene suspension on power generation of microbial fuel cells. Coatings 8(7), 243 (2018). https://doi.org/10.3390/ coatings8070243 40. Azuma, M., Ojima, Y.: Catalyst development of microbial fuel cells for renewable-energy production. Current Topics Bioch. Eng. (2018). https://doi.org/10.5772/intechopen.81442 41. Mohamed, H.O., Obaid, M., Yasin, A.S., Kim, J.H., Barakat, N.A.M.: Electrodepositing technique for improving the performance of crystalline and amorphous carbonaceous anodes for MFCs. RSC Adv. 6(113), 111657–111665 (2016). https://doi.org/10.1039/C6RA22867D 42. Mohanakrishna, G., Krishna Mohan, S., Venkata Mohan, S.: Carbon Based nanotubes and nanopowder as impregnated electrode structures for enhanced power generation: evaluation with real field wastewater. Appl. Energy 95, 31–37 (2012). https://doi.org/10.1016/j.apenergy. 2012.01.058 43. Xie, X., Hu, L., Pasta, M., Wells, G.F., Kong, D., Criddle, C.S., Cui, Y.: Three-dimensional carbon nanotube − textile anode for high-performance microbial fuel cells. Nano Lett. 11(1), 291–296 (2011). https://doi.org/10.1021/nl103905t 44. Hu, Y., Lian, H., Zu, L., Jiang, Y., Hu, Z., Li, Y., Shen, S., Cui, X., Liu, Y.: Durable electromechanical actuator based on graphene oxide with in situ reduced graphene oxide electrodes. J. Mater. Sci. 51(3), 1376–1381 (2016). https://doi.org/10.1007/s10853-015-9456-4 45. Wang, W., Yuan, Y., Yang, J., Meng, L., Tang, H., Zeng, Y., Ye, Z., Lu, J.: Hierarchical CoreShell Co3 O4 /Graphene hybrid fibers: potential electrodes for supercapacitors. J. Mater. Sci. 53(8), 6116–6123 (2018). https://doi.org/10.1007/s10853-017-1971-z 46. Basu, S., Lee, M.C., Wang, Y.-H.: Graphene-based electrodes for enhanced organic thin film transistors based on pentacene. Phys. Chem. Chem. Phys. 16(31), 16701–16710 (2014). https:// doi.org/10.1039/C3CP55440F 47. Wan, L., Wang, B., Wang, S., Wang, X., Guo, Z., Dong, B., Zhao, L., Li, J., Zhang, Q., Luo, T.: Well-dispersed PEDOT: PSS/graphene Nanocomposites synthesized by in situ polymerization as counter electrodes for dye-sensitized solar cells. J. Mater. Sci. 50(5), 2148–2157 (2015). https://doi.org/10.1007/s10853-014-8777-z 48. Meng, X., Yu, C., Song, X., Liu, Y., Liang, S., Liu, Z., Hao, C., Qiu, J.: Graphene Nanoribbons: Nitrogen-Doped Graphene Nanoribbons with surface enriched active sites and enhanced performance for dye-sensitized solar cells (Adv. Energy Mater. 11/2015). Adv. Energy Mater. 5(11) (2015). https://doi.org/10.1002/aenm.201570060 49. Garino, N., Sacco, A., Castellino, M., Muñoz-Tabares, J.A., Chiodoni, A., Agostino, V., Margaria, V., Gerosa, M., Massaglia, G., Quaglio, M.: Microwave-assisted synthesis of reduced graphene Oxide/SnO2 Nanocomposite for oxygen reduction reaction in microbial fuel cells. ACS Appl. Mater. Interfaces. 8(7), 4633–4643 (2016). https://doi.org/10.1021/acsami.5b11198

18

1 Fundamental of Graphene

50. Gautam, R.K., Bhattacharjee, H., Mohan, S.V., Verma, A.: Nitrogen doped graphene supported α-MnO2 nanorods for efficient ORR in a microbial fuel cell. RSC Adv. 6(111), 110091–110101 (2016). https://doi.org/10.1039/C6RA23392A 51. Han, T.H., Parveen, N., Ansari, S.A., Shim, J.H., Nguyen, A.T.N., Cho, M.H.: Electrochemically synthesized sulfur-doped graphene as a superior metal-free cathodic catalyst for oxygen reduction reaction in microbial fuel cells. RSC Adv. 6(105), 103446–103454 (2016). https:// doi.org/10.1039/C6RA14114E 52. Zhang, Y., Mo, G., Li, X., Zhang, W., Zhang, J., Ye, J., Huang, X., Yu, C.: A graphene modified anode to improve the performance of microbial fuel cells. J. Power Sources 196(13), 5402–5407 (2011). https://doi.org/10.1016/j.jpowsour.2011.02.067 53. Xiao, L., Damien, J., Luo, J., Jang, H.D., Huang, J., He, Z.: Crumpled graphene particles for microbial fuel cell electrodes. J. Power Sources 208, 187–192 (2012). https://doi.org/10.1016/ j.jpowsour.2012.02.036 54. Singh, E., Meyyappan, M., Nalwa, H.S.: Flexible Graphene-Based Wearable Gas and Chemical Sensors. ACS Appl. Mater. Interfaces. 9(40), 34544–34586 (2017). https://doi.org/10.1021/acs ami.7b07063 55. Chung, M.G., Kim, D.-H., Seo, D.K., Kim, T., Im, H.U., Lee, H.M., Yoo, J.-B., Hong, S.-H., Kang, T.J., Kim, Y.H.: Flexible hydrogen sensors using graphene with palladium nanoparticle decoration. Sens. Actuators B: Chem. 169, 387–392 (2012). https://doi.org/10.1016/j.snb.2012. 05.031 56. Wang, Y., Yang, R., Shi, Z., Zhang, L., Shi, D., Wang, E., Zhang, G.: Super-elastic graphene ripples for flexible strain sensors. ACS Nano 5(5), 3645–3650 (2011). https://doi.org/10.1021/ nn103523t 57. Fan, F.-R., Tian, Z.-Q., Lin Wang, Z.: Flexible triboelectric generator. Nano Energy 1(2), 328–334 (2012). https://doi.org/10.1016/j.nanoen.2012.01.004 58. Zhong, H., Xia, J., Wang, F., Chen, H., Wu, H., Lin, S.: Graphene-piezoelectric material heterostructure for harvesting energy from water flow. Adv. Func. Mater. 27(5), 1604226 (2017). https://doi.org/10.1002/adfm.201604226 59. Kwak, S.S., Lin, S., Lee, J.H., Ryu, H., Kim, T.Y., Zhong, H., Chen, H., Kim, S.-W.: Triboelectrification-Induced Large electric power generation from a single moving droplet on graphene/polytetrafluoroethylene. ACS Nano 10(8), 7297–7302 (2016). https://doi.org/10. 1021/acsnano.6b03032 60. Cai, W., Moore, A.L., Zhu, Y., Li, X., Chen, S., Shi, L., Ruoff, R.S.: Thermal transport in suspended and supported monolayer graphene grown by chemical vapor deposition. Nano Lett. 10(5), 1645–1651 (2010). https://doi.org/10.1021/nl9041966 61. Faugeras, C., Faugeras, B., Orlita, M., Potemski, M., Nair, R.R., Geim, A.K.: Thermal conductivity of Graphene in Corbino membrane geometry. ACS Nano 4(4), 1889–1892 (2010). https:// doi.org/10.1021/nn9016229 62. Huawei continues the use of graphene cooling films in its new P 40 series | GrapheneInfo. https://www.graphene-info.com/huawei-continues-use-graphene-cooling-films-its-newp40-series. Accessed Sep 28, 2020 63. Liu, Z., Robinson, J.T., Sun, X., Dai, H.: PEGylated nanographene oxide for delivery of waterinsoluble cancer drugs. J. Am. Chem. Soc. 130(33), 10876–10877 (2008). https://doi.org/10. 1021/ja803688x 64. Hai, X., Mao, Q.-X., Wang, W.-J., Wang, X.-F., Chen, X.-W., Wang, J.-H.: An acid-free microwave approach to prepare highly luminescent boron-doped graphene quantum dots for cell imaging. J. Mater. Chem. B 3(47), 9109–9114 (2015). https://doi.org/10.1039/C5TB01 954K 65. Peng, C., Hu, W., Zhou, Y., Fan, C., Huang, Q.: Intracellular imaging with a graphene-based fluorescent probe. Small 6(15), 1686–1692 (2010). https://doi.org/10.1002/smll.201000560 66. Graphene nanopore DNA sequencing. https://mappingignorance.org/2017/01/23/graphenenanopore-dna-sequencing/. Accessed Oct 11, 2020 67. Wasfi, A., Awwad, F., Ayesh, A.I.: Graphene-based nanopore approaches for DNA sequencing: a literature review. Biosens. Bioelectron. 119, 191–203 (2018). https://doi.org/10.1016/j.bios. 2018.07.072

References

19

68. Yang, K., Zhang, S., Zhang, G., Sun, X., Lee, S.-T., Liu, Z.: Graphene in mice: ultrahigh in vivo tumor uptake and efficient photothermal therapy. Nano Lett. 10(9), 3318–3323 (2010). https:// doi.org/10.1021/nl100996u 69. Graphene Biosensors. https://www.graphenea.com/blogs/graphene-news/51855425-gra phene-biosensors. Accessed Oct 11, 2020 70. Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., Kim, S.-J., Lee, J.-O., Kim, B.T., Park, E.C., Kim, S.I.: Rapid Detection of COVID-19 Causative Virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4), 5135–5142 (2020). https://doi.org/10.1021/acs nano.0c02823 71. Hajian, R., Balderston, S., Tran, T., deBoer, T., Etienne, J., Sandhu, M., Wauford, N.A., Chung, J.-Y., Nokes, J., Athaiya, M., Paredes, J., Peytavi, R., Goldsmith, B., Murthy, N., Conboy, I.M., Aran, K.: Detection of unamplified target genes via CRISPR–Cas9 immobilized on a graphene field-effect transistor. Nature Biomed. Eng. 3(6), 427–437 (2019). https://doi.org/10.1038/s41 551-019-0371-x 72. Is graphene safe?. https://doi.org/10.1016/S1369-7021(12)70101-3. Accessed Oct 11, 2020 73. Lalwani, G., D’Agati, M., Khan, A.M., Sitharaman, B.: Toxicology of graphene-based nanomaterials. Adv. Drug Deliv. Rev. 105(Pt B), 109–144 (2016). https://doi.org/10.1016/j.addr. 2016.04.028 74. Li, Y., Yuan, H., Bussche, A. von dem, Creighton, M., Hurt, R.H., Kane, A. B., Gao, H.: Graphene Microsheets enter cells through spontaneous membrane penetration at edge asperities and corner sites. PNAS 110(30), 12295–12300 (2013). https://doi.org/10.1073/pnas.122227 6110 75. Talukdar, Y., Rashkow, J.T., Lalwani, G., Kanakia, S., Sitharaman, B.: The effects of graphene nanostructures on mesenchymal stem cells. Biomaterials 35(18), 4863–4877 (2014). https:// doi.org/10.1016/j.biomaterials.2014.02.054 76. Joshi, S., Siddiqui, R., Sharma, P., Kumar, R., Verma, G., Saini, A.: Green synthesis of peptide functionalized reduced Graphene Oxide (RGO) nano bioconjugate with enhanced antibacterial activity. Scientific Reports 10(1), 9441 (2020). https://doi.org/10.1038/s41598-020-66230-3

Chapter 2

Graphene Electrical Characteristics

Abstract As one of the important characteristics of graphene, the electrical properties of graphene have been extensively studied and applied whether in the field of low-temperature physics or the field of room temperature physics. In the field of physics at room temperature, relying on graphene’s ultra-high carrier mobility, ultra-high specific surface area (small size effect), and chemical stability, series of graphene-based sensing applications such as magnetic, optical, biological, flexible sensors have been reported. Starting from the classical theory, this chapter algebraically describes the electrical conductivity of graphene at room temperature and the variables related to the electrical conductivity of graphene. These conclusions provide a theoretical basis for the sensing application of graphene at room temperature. Besides, at the end of this chapter, a brief introduction is given to the hot spot (magic-angle graphene) of graphene’s low-temperature physics in recent years. Keywords Graphene · Room-temperature · Electrical characteristics · Magic-angle · Moiré pattern Graphene electrical characteristics are the electrical properties of graphene, which arise from the unique behavior of electrons in such a thin layer, that have led to the breakthrough applicated cases for graphene in sensors and other fields. At the same time, the superconductivity for this remarkable material has been more and more focused in recent years. These excellent electrical applications are both inseparable from the corresponding mathematical models. In this chapter, the electrical characteristics of graphene in room temperature will be illustrated with the mathematical formula at first. Some of the derivation processes are based on the work of Wojtaszek [1], then introducing some enlightening conclusions about the graphene sensing applications at room temperature from them. Finally, the superconductivity of the magic angle graphene at low temperatures is briefly demonstrated.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_2

21

22

2 Graphene Electrical Characteristics

2.1 Room Temperature Electrical Characteristics Generally speaking, the two types of charge carriers: electrons and holes that usually describe the graphene electrical characteristics at the room temperature particularly the work point is nearing the Dirac point. Based on the classical description for the net electrostatic force, Fel = q E is used to describe the charge carrier drift, because of the relationship between the electric field E and the drift velocity Vq can also be depicted as Vq = μq E that the net electrostatic force Fel satisfies the relation: Fq = q Vq /μq . At the same time, the moving charges around the graphene also affect with the Lorentz force: FL = q(Vq × B). The net electrostatic force Fel and the Lorentz force FL regulate the moving charges in the graphene at the room temperature. Based on these two principle relations, the traditional graphene electrical transfer characters can be analyzed using an ambipolar conductor model as shown in Fig. 2.1. When a voltage drop is applied in x direction, electrons and holes begin to move in opposite directions along the same axis. Since the magnetic field is perpendicular to its movement, the Lorentz force deflects the movement of the carriers to the same side

Fig. 2.1 The schematic diagram of the traditional graphene electrical transfer characters using an ambipolar conductor model

2.1 Room Temperature Electrical Characteristics

23

of the graphene. Therefore, both holes and electrons are gathered based on the Lorentz force (eV hxBz, eVexBz). When the density of carriers and their mobility are equal, there is no potential drop in y direction (negative electrons compensate for positive holes). However, when the density of different carriers or their mobility differs, it will have an asymmetric distribution of the charge density across the graphene. This establishes an electric field E y that prevents further charge migration, and thereby a stable electric potential (Hall potential) is formed. For any charge q in a magnetic field (since graphene is a two-dimensional plane, we just only consider magnetic field with Z component B = (0, 0, B) in our experiment.), which moves in x and y direction as presented in Fig. 2.1, thus the charge q motion equation can be obtained as follows: q Vq = Fnet,q = Fel + FL μq Here Fel = q E represents the electric force in this system. For the case of holes, for which q = e, note that e represents the elementary charge (e ∼ = 1.602 × 10−19 C), we obtain the following equations: eVhx = e(E x + Vhy B) μh eVhy = e(E y − Vhx B) μh (For holes, their velocity and mobility are marked as “h”). For the case of electrons, here q = −e, note that their moving direction is opposite to the direction of the holes, thus we get: eVex = e(E x − Vey B) μe eVey = e(E y − Vex B) μe (For electrons, their velocity and mobility are marked as “e”). Reintegrate the velocity equations in the form of electrons and holes into a matrix form:       E 1 μe B Vex = −μe x · Vey Ey −μe B 1       1 −μh B Ex Vhx = μh · Vhy Ey μh B 1

24

2 Graphene Electrical Characteristics

After conversion, we can get the velocities expression as follows: 

   −μe 1 −μe B Ex = 2 Ey 1 + (μe B) μe B 1      μh Vhx Ex 1 μh B = · Vhy Ey −μh B 1 1 + (μh B)2 Vex Vey



Note that there is usually no current in the y direction during the actual experiment, so:Jy = Jhy + Jey = epVhy + enVey = 0, where as p represents the concentration of holes and n represents the concentration of electrons. Thus we can get: pVhy = nVey . However, the current flowing in the direction x is not zero, it consists of two components Jx = Jhx + Jey = epVhx + epVex . Combine this expression with the definition of conductivity J = σ E, We can get the corresponding hole and electron conductivity:   epμh 1 −μh B . 1 + (μh B)2 μh B 1   enμe ∧ 1 μe B σe = · −μe B 1 1 + (μe B)2 ∧

σh =

The total conductivity of a dual current-carrying system is the sum of hole conduc∧ ∧ ∧ tivity and electron conductivity (for uncoupled carriers) σ = σe + σh . In the experiment, because we usually measure resistivity instead of conductivity, we use the ∧ ∧ tensor relationship to convert the equation: ρ = σ −1. For graphene, since electrons and holes have equal mobility, we mark it as: μe = μh = μ. After reversing the total tensor of conductivity, we can get: 1 + (μB)2   P= eμ ( p + n)2 + (μB)2 ( p − n)2 ∧



p + n −Bμ( p − n) Bμ( p − n) p+n



For expressing the vertical ρx x and horizontal resistivity ρx y clearly, we get:   eμ ( p + n)2 + (μB)2 ( p − n)2  ( p − n) · 1 + (μB)2 ·B =−  e ( p + n)2 + (μB)2 ( p − n)2

ρx x = ρx y

 ( p + n) · 1 + (μB)2

From the last formula, we can derive the Hall coefficient R H , based on ρx y = R H · B. In the two type model, R H is related as follows:  (n − p) · 1 + (μB)2

 RH =  e ( p + n)2 + (μB)2 ( p − n)2

2.1 Room Temperature Electrical Characteristics

25

But when we consider the conductive properties of graphene at room temperature, we should replace this description with a simpler description. With the help of the external gate voltage Vg , we can change the Fermi level of the system, from one carrier type electron or hole transport to another carrier type (Drude system). In this case, the resistivity equation should be simplified by adding an appropriate form of carrier concentration. This leads us to the Drude model of graphene resistivity:  1. when Vg VDirac , p = 0, n = α Vg − VDirac

ρx x = Note that 2.

1 ne

1 B , ρx y = = RH B eμn en

= RH

 when Vg VDirac , n = 0, p = α VDirac − Vg

ρx x =

1 B , ρx y = = RH B eμp ep

1 Note that pe = RH According to the actual electrical experiment results, the ρx x , ρx y that can be calculated. Applying the priviously formulas that have been mentioned, we can get the mobility and the carrier concentration of the graphene, as follows:

1 enρx x B n= eρx y μ=

Based on the two of the above formulas, we can obtain some simple conclusions as follows if ignoring the affected from temperature: When Vg is far from the VDirac 1. 2.

ρx x is only regulated by the Vg , it is not affected by other factors. ρx y is both regulated by the Vg and B.

These are two very important conclusions, which lay a simple mathematical model for graphene-based electrical sensors. The relationship implied by these conclusions is shown in Fig. 2.2.

26

2 Graphene Electrical Characteristics

Fig. 2.2 a The schematic diagram of the functional relationship implied by the conclusion 1. b The schematic diagram of the functional relationship implied by the conclusion 2

Conclusion 1 implies that any variations from the Vg that can be monitored by the current of the graphene. The Vg variations can be generated from chemical, biological, and other complex effects. So theoretically speaking, based on the graphene ρx x monitoring that has the enormous potential to develop a novel sensing platform. Conclusion 2 implies that if Vg keeps constant, the graphene can be seen as a novel 2D Hall device, it will also own the same broad application prospects with the traditional Hall device in the future.

2.2 Low-Temperature Electrical Characteristics

27

2.2 Low-Temperature Electrical Characteristics 2.2.1 Magic Angle Graphene Yuan et al. is the first group to reveal that the heterostructure composed of doublelayer graphene has tunable low-temperature superconductivity. They used doublelayer graphene and twisted one of the graphene layers to a certain angle relative to the other [2, 3]. Experiments show that when this angle is close to the so-called “magic” angle (1.1°, 0.5°, 0.24°) due to strong interlayer coupling, the electronic band structure of the graphene becomes flat near zero Fermi energy. These flat band structures induce the graphene to exhibit an insulating state when half-filled. They also pointed out that the relative state in the half-filled state is similar to that of Mott-type insulators, where the electrons are located in the superlattice, which may be the possible cause of the graphene in the insulating state. But the funny thing is when the graphene is at the “magic” angle, after electrostatic doping these graphenes that are originally in the relevant insulating state, a tunable zero resistance state is observed, that is, it enters the superconducting state of graphene. The critical temperature of the superconducting state is 1.7 K. In addition, they also claimed that the temperature-carrier density phase diagram of the twisted double-layer graphene is similar to the copper oxide (or cuprate) and also includes the dome-shaped region corresponding to superconductivity. Twisted double-layer graphene is the first known two-dimensional insulator– superconductor switchable material that can be precisely tuned at low temperatures. It opens up a whole new field of research, and it can be used as a new platform for studying superconductivity theory and properties. In addition, the conversion of superconductivity and insulation makes it possible to produce low-power semiconductor devices. In short, there are still many projects worthy of research and exploration for the dramatic properties of twisted double-layer graphene and other twisted two-dimensional materials in low-temperature environments.

2.2.2 Moiré Pattern In fact, our group found through STM that moiré patterns can also be observed on epitaxial graphene, as shown in Fig. 2.3. This result shows that the graphene epitaxially grown on SiC has a certain angle between each other, which makes epitaxial graphene as a potential research platform for the study of the magic-angle graphene.

28

2 Graphene Electrical Characteristics

Fig. 2.3 Two-dimensional STM image of the surface of the graphene. The hexagonal moiré presents on the surface of the graphene, and the moiré wavelength is about 1.6 nm

References 1. Wojtaszek, M.: Graphene: A Two Type Charge Carrier System, 81 2. Cao, Y., Fatemi, V., Fang, S., Watanabe, K., Taniguchi, T., Kaxiras, E., Jarillo-Herrero, P.: Unconventional superconductivity in magic-angle graphene superlattices. Nature 556(7699), 43–50 (2018). https://doi.org/10.1038/nature26160 3. Cao, Y., Fatemi, V., Demir, A., Fang, S., Tomarken, S.L., Luo, J.Y., Sanchez-Yamagishi, J.D., Watanabe, K., Taniguchi, T., Kaxiras, E., Ashoori, R.C., Jarillo-Herrero, P.: Correlated insulator behaviour at half-filling in magic-angle graphene superlattices. Nature 556(7699), 80–84 (2018). https://doi.org/10.1038/nature26154

Chapter 3

Graphene Manufacture

Abstract Almost all application research based on graphene is inseparable from the preparation of graphene. The preparation process of graphene is very important for almost all graphene application research. Currently, preparation methods of graphene can be roughly divided into five categories, namely, mechanical exfoliation, chemical vapor deposition, epitaxial growth, reduced graphene oxide, and direct synthesis. In fact, the best choice of graphene preparation methods may be also different for facing different types of applications. For graphene field-effect transistor biosensors, graphene prepared by chemical vapor deposition is mainly used to make conductive channels. Besides, mechanical exfoliation methods are also used in early experiments. Recently, reduced graphene oxide methods and epitaxial growth methods are also reported to use as conductive channels. Since the preparation quality is directly related to the conductive characteristics of the conductive channel, thus it affects the performance of the sensor. Therefore, it is necessary to sufficiently introduce the preparation method and the details about the preparation process. Keywords Mechanical exfoliation · Chemical vapor deposition · Epitaxial growth · Reduced graphene oxide · Direct synthesis It is necessary to introduce the preparation method of graphene before formally discussing the graphene field-effect transistor. Because the premise of any form of electrical application requires the preparation of high-quality graphene films as a sensor platform. In this chapter, the common methods of preparing graphene will be introduced including mechanical exfoliation, chemical vapor deposition, epitaxial growth, and other methods. Figure 3.1 shows the major methods for the graphene preparation at present.

3.1 Mechanical Exfoliation Method Mechanical exfoliation is a very old but very effective method of obtaining graphene, and this method can even trace back to the era when graphene was discovered by Professor Andre Geim and Professor Konstantin Novoselov in 2004 [1]. They used © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_3

29

30

3 Graphene Manufacture

Fig. 3.1 The major methods for the graphene preparation at present

scotch tape to mechanically exfoliate the graphene from the graphite crystal and then transferred the graphene onto a silicon substrate. Although most people at that time still believed that thin crystalline materials like graphene could not exist stably. Finally, they confirm the single-layer graphene by the AFM and integer quantum Hall effect. Until now, much research on two-dimensional materials still uses this method, such as black phosphorus, transition metal dichalcogenide [2–7]. It is known that graphite is composed of graphene stacked on each other [8]. Because the Van der Waals interaction forces between graphene are weak, it is easy to peel off. When graphene is produced by this mechanical exfoliation method, highquality single-crystal samples of several to several tens of µm are obtained, which is very useful for elucidating basic physical properties and verifying the operation principle of the device using graphene. However, in order to use graphene for device applications, large areas of wafer size (several inches), high-quality graphene is indispensable and currently impossible to achieve by mechanical exfoliation. Therefore, there is a need to establish a method capable of using a large-area, high-quality graphene manufacturing rather than mechanical exfoliation.

3.2 Chemical Vapor Deposition Chemical vapor deposition (CVD) has been widely used in the industrial field as one of the important thin film growth methods [9–14]. Since the advent of graphene, much research has been conducted worldwide due to the prospect of high-quality and large-area synthesis of large-area graphene [15, 16]. The principle is that the carbon source gas is supplied onto a high-temperature metal catalyst, and the decomposed growth precursor is polymerized to form graphene, but there are many parameters that affect the growth, so the quality of the exfoliated sample has not reached. However,

3.2 Chemical Vapor Deposition

31

from the application point of view, it is important to develop low-cost synthesis methods. Nowadays, CVD graphene has become one of the most important graphene manufacturing methods. In this section, the CVD graphene manufacture method will be reviewed at first. Then CVD graphene transfer methods will be simply illustrated. The graphene chemical vapor deposition method is the method that can directly synthesize the graphene on to the substrate. Since the binding energy of a hydrocarbon gas such as methanol is relatively large, it is very difficult to grow graphene by utilizing the principle of thermal decomposition. For the graphene biosensor, catalytic CVD methods are mainly used. In this section, the commonly graphene chemical vapor deposition method will be demonstrated. In common, graphene film was synthesized on iridium [17], nickel [18] and copper [19] film. For the poly layer, graphene synthesizes, the nickel or the copper–nickel alloy is commonly used [20]. The copper film is commonly used to synthesize the monolayer graphene [19]. That is because the copper and the nickel film have different solubility to the carbon atom at high temperatures, thus induce the different number of layers in copper and nickel film during the synthesizing process. The monolayer graphene is commonly synthesized on copper foils using chemical vapor deposition (There are some studies that also show that the Pt film can also synthesize the monolayer graphene in recent years [21]). We mainly introduce the monolayer graphene synthesis on copper foil.

3.2.1 Pretreatment Before chemical vapor deposition, it is usually necessary to heat the copper foil at 300° for 30 min in the atmosphere to form an oxide layer on the surface of the copper foil. Because some studies have shown that the formation of an oxide layer on the surface of copper foil helps to significantly decrease the graphene nucleation density [22]. Therefore, this step is usually used as a pretreatment process of copper foil before CVD synthesis, in order to synthesize graphene with higher quality. After the pretreatment, it can be clearly observed that the color of the copper foil becomes darker, which commonly indicates that the formation of an oxide layer on the surface of the copper foil.

3.2.2 CVD Graphene The chemical vapor deposition (CVD) method is a vacuum deposition method that has been widely used to deposit high-performance and high-quality solid materials. This process is usually used in semiconductor manufacturing processes to deposit thin films [23]. Generally, the wafer (substrate) is first exposed to the atmosphere of one or more volatile precursors, and then the precursors will react and/or decompose

32

3 Graphene Manufacture

on the surface of the substrate to produce the desired deposits. Note that volatile byproducts are usually produced in the chemical vapor deposition process, but these by-products will be discharged from the reaction chamber. At present, as an important deposition method, CVD is widely used in semiconductor processing technology and microfabrication technology to deposit various materials, including single crystal, polycrystalline, epitaxial, and amorphous. These materials include silicon, various high-k dielectrics, and others. In recent years, the formation of two-dimensional thin films including molybdenum disulfide and graphene by chemical vapor deposition has gradually become an important method for preparing two-dimensional materials. In terms of the CVD synthesis of graphene, there are many different kinds of methods. As far as the types of carbon sources are concerned, methane gas has been widely used to produce graphene as the most commonly used carbon source. (In addition, ethylene and pitch can also be used as carbon sources to synthesize graphene in the gas phase.) However, it is far from enough to introduce only a carbon source into the reaction chamber. During the preparation process, hydrogen gas is also required to promote carbon deposition on the substrate. Because in the process of graphene crystal growth, the role of methane is to provide a carbon source, and the role of hydrogen is to provide H atoms to corrode amorphous C [24], but too many hydrogen atoms will also corrode graphene, leading to the destruction of the integrity of the graphene lattice [25]. Therefore, it is necessary to adjust the ratio of carbon source to hydrogen to achieve the best synthesis effect. For substrate, metal film and alloy film are usually used to deposit graphene, different kinds of metal film, and the percentage of the alloy film also determine the quality and the thickness of the synthesized graphene. In addition, temperature, pressure, reaction chamber materials, and other complex factors also play an important role in graphene synthesis. In most CVD synthetic graphene applications, low-pressure CVD (LPCVD) synthesis technology with a pressure ranging from 1 to 1500 Pa is usually used [16, 26–28]. However, some reports also use atmospheric pressure CVD (APCVD) synthesis technology [29]. Low pressures are currently the more commonly used methods because they help produce a more uniform deposition thickness on the substrate and avoid other unnecessary reactions. The synthesis temperature is usually in the range of 800– 1050 °C, [16, 26–29], but note that although high-temperature environments can speed up the reaction rate. However, attention should also be paid to the energy consumption caused by high temperature, the tolerance of the materials in the reaction chamber, and the potential hazards. In addition, it is usually required that hydrogen and inert protective gas (such as argon) flow into the system together. Excessive hydrogen concentration may increase the risk of explosion. In the application of CVD synthetic graphene, a quartz tube is usually used as a reaction chamber [16, 26–29]. Because quartz has a high melting point and is chemically inert. Finally, we talk about some details about CVD graphene using methane sources. In Common, first the copper film is H2 annealing at the 1040 °C under 3% hydrogen and 97% argon flow at 500 sccm for 40 min. Then the carbon atoms are adsorbed at 1040 °C under 3% hydrogen and 97% argon flow at 1500 sccm and 5% methane and 95% argon flow at 15 sccm for 55 min. Finally, the temperature is slowly decreased to 700 °C under 3% hydrogen and 97% argon flow at 500 sccm for 40 min to

3.2 Chemical Vapor Deposition

33

Fig. 3.2 The schematic diagram of the CVD graphene synthesis process

synthesize graphene. In addition, during the initial heat process (50 min) and the cooling process after synthesis (55 min), argon gas is required to protect against oxidation (argon flow at 500 sccm). This process diagram is shown in Fig. 3.2. At present, there are researchers using ethylene as a C source for graphene growth. Compared with methane gas, ethylene is cheaper and has almost the same synthesis quality [30, 31]. Generally speaking, growing graphene on copper foil using methane as a C source has become a mature method for the continuous preparation of graphene films. Figure 3.3 shows graphene grown on A4 paper size copper foil.

3.2.3 Transferring Graphene synthesized by chemical vapor deposition has many uses, but for most applications, it must be transferred from the metal film to other different substrates [32–35]. In this section, we will demonstrate two of the most popular methods that are transferring graphene from metal films to substrates based on electrochemical transfer and etching methods. The first method is the electrochemical exfoliation method, it can quickly exfoliate the CVD graphene from the metal film. The second method is the etching transfer method. This method uses etchant to etch the metal film, then transferring graphene to the substrate. Although it will take a long time, the further higher quality of the continuous graphene can obtain compared with the electrochemical exfoliation method. Thus the etching method is usually preferable. For electrochemical transfer, a flexible polymer is coated on the graphene/metal foil so that it can be used as the cathode in the water electrolysis cell. Generally, NaOH is used as an electrolyte in an aqueous solution. Generate hydrogen bubbles and squeeze them into the graphene–metal interface to mechanically delaminate the graphene/polymer from the metal. The electrochemical bubble transfer of graphene

34

3 Graphene Manufacture

Fig. 3.3 The graphene grown on A4 paper size copper foil through the CVD method

has become a technology with high industrial potential due to its scalability, time and cost-effectiveness, and environmental protection. However, graphene is often damaged due to turbulence and direct H2 O and H + infiltration through the supporting polymer to form bubbles [36]. If the experiment requires high-quality continuous graphene, it is recommended to use the etching transfer method. Generally speaking, the etching method requires spin-coating an auxiliary transfer layer on the graphene film. The more commonly used ones include poly(methyl methacrylate) (PMMA). In addition, some research also reported that rosin can also be used as an auxiliary transfer layer. Taking PMMA as an example, the etching method is more complicated, but it can almost continuously cover the surface of graphene and prepare high-quality graphene films. First, a layer of PMMA is spin-coated onto graphene to serve as a support. An etchant is then used to remove the metal film, and the PMMA/graphene stack is transferred to another substrate. Finally, a solvent is used to remove PMMA to complete the graphene transfer. At present, FeCl3 or ammonium persulfate is usually used as an etchant to etch copper foil [37–40]. However, the etching time is long and the transfer process requires a superb transfer method. Thus transferring graphene using the PMMA method is not an easy procedure. Figure 3.4 shows the etching process of graphene copper foil based on ammonium persulfate, which shows the relationship between etching and time.

3.2 Chemical Vapor Deposition

35

Fig. 3.4 The etching process of graphene copper foil based on ammonium persulfate, a 0 h, b 1 h, c 2 h, d over 3 h

36

3 Graphene Manufacture

Finally, we talk about some experiences about the PMMA transfer method. First of all, in general, the vibration of the device should be avoided as much as possible during the transfer process, which may cause damage and wrinkles in the graphene film. Second, during the graphene transfer process, the transfer should start along the edge of the graphene first, and the substrate should first contact the edge of the graphene, and then pull it out diagonally and should try to avoid repeated transferring. Although the CVD method has been considered as the most potential synthesis process for the larger areas of high-quality graphene, the high temperatures (around 1000 °C) also induce a series of problems such as the copper’s significant evaporation and thermal load. In sharp contrast, the synthesis of graphene by plasmaenhanced CVD (PECVD) can improve this problem by substantially shortening the processing time and the prospect of lowering the substrate temperature. PECVD method allows that the graphene can be synthesized at a lower temperature. It is an important current research direction that is, hoping to, widely used to synthesize the high-quality graphene substrate. Figure 3.5 shows the image of the PECVD equipment and the heat pretreatment (annealing) equipment.

Fig. 3.5 a The image of the PECVD equipment. b The image of the heat pretreatment (anneal) equipment

3.3 Epitaxial Growth

37

3.3 Epitaxial Growth Although the traditional graphene FET biosensor is made from chemical vapor deposition (CVD) graphene, this method has to bring the surface contamination during the transfer step [41]. During the transfer step, for easier transferring the CVD graphene, CVD graphene is covered an organic protection layer like PMMA. Although this method made it easier to be transferred, this organic protection layer is too difficult to remove, even by the H2 anneal. This contamination is a kind of craft defect in actually. In order to avoid this contamination from the transfer step, using the SiC substrate to manufacture the graphene FET directly, it can make sure the surface clean during the whole manufacturing step [42]. In this section, we briefly describe how to prepare epitaxially grown graphene and how to characterize it.

3.3.1 Epitaxial Graphene Preparation The SiC graphene was prepared by directly heating the 6H-SiC(0001) sample at 1350 °C for six cycles of 60 s under ultrahigh vacuum (UHV) condition (maximum pressure 5.0 × 10−9 Torr). Thus the prepared SiC graphene on the Si-face of SiC substrate usually contains one to three layers of graphene. The typical sample size (cut from a 6H-SiC(0001) wafer) was about 10 mm × 10 mm. For making sure the quality of the epitaxial graphene, the sample after preparation is examined by scanning X-ray photoelectron spectroscopy (XPS).

3.3.2 Graphene Characterization Graphene was directly manufactured onto the surface of the SiC substrate. Figure 3.6 shows the wide-range XPS spectra of the SiC graphene substrate. This XPS spectrum shows only the peaks corresponding to C (C 1 s) and Si (Si 2 s and Si 2p). There are no other peaks that exist in these XPS spectra. This result indicates that there are no contaminants on the surface of the SiC graphene substrate. The high-resolution C 1 s peak of the SiC graphene substrate is shown in Fig. 3.7. It can be deconvoluted into two parts as 283.6 and 284.7 eV. The 283.6 eV part represents the C atoms of the SiC. The 284.7 eV part represents the C atoms of the graphene [43]. This result confirms that the graphene exists on the surface of the SiC substrate and there are no surface contaminants from other kinds of elements. Compared with the original SiC substrate, after graphene is formed, the surface becomes dark, as shown in Fig. 3.8.

38

3 Graphene Manufacture

Fig. 3.6 The wide-range XPS spectra of the SiC graphene substrate

Fig. 3.7 The high-resolution C 1 s peak of the SiC graphene substrate

3.4 Other Methods 3.4.1 Directly Synthesis on SiO2 Some recent new studies have shown that graphene can be directly synthesized on SiO2 substrates by chemical vapor deposition [44, 45]. In fact, low-temperature growth of graphene and transfer-free growth on the substrate have always been the focus of graphene research, because they can better integrate with current semiconductor technology. Riteshkumar et al. reported a simple method to realize the growth of transfer-free graphene on a Si (SiO2 /Si) substrate covered by SiO2 at 250 °C [46]. The key to this method is the catalyst metal, which is not the same as growing graphene by chemical vapor deposition. A 500 nm thick catalyst metal film was first deposited onto a SiO2 /Si substrate coated with amorphous C (50 nm thick), then the sample was annealed under vacuum at 250 °C. Finally, the catalyst metal film was removed by chemical etching and measured. The Raman spectra showed that strong G and 2D peaks, and small D peaks, these results confirm that the transfer-free growth

3.4 Other Methods

39

Fig. 3.8 After graphene is formed, the surface becomes dark compared with the original SiC substrate

of multilayer graphene on SiO2 /Si substrate. Based on the optical microscope and atomic force microscope, the average domain size of graphene is about 5 µm. Therefore, this method will open up a new way for the growth of non-transferred graphene at low temperatures. Xu et al. proved that by using a chemical vapor deposition system assembled with two different temperature regions, it is possible to directly grow high-quality graphene films uniformly and continuously on a SiO2 substrate [47]. The graphene growth process is started by nucleating the carbon precursor in the high-temperature zone through the low-temperature zone. Most of the graphene films synthesized by this method are single-layer graphene, and the multilayer area accounts for only a small proportion of them, and its optical transmittance and electrical conductivity can be comparable to the transferred metal substrate graphene. The method avoids the etching process of the metal substrate and the complicated graphene transfer process, which is beneficial to combine graphene with the current semiconductor process.

3.4.2 Reduced Graphene Oxide (R-GO) Reduced graphene oxide (R-GO) is obtained by treating graphene oxide (GO) with chemical, thermal, and other methods to reduce the oxygen content, while graphene oxide (GO) is produced by graphite oxide, which can lead to increased interlayer spacing and functionalization of the graphite base surface [48].

40

3 Graphene Manufacture

The reduction step, as an important production step for reducing graphene oxide, plays a decisive role in the quality of reduced graphene oxide. Compared with the mechanical exfoliation method, the CVD method, reduced graphene oxide can be expected to realize the mass production of graphene. In fact, in a large number of industrial applications such as friction nanogenerators, energy storage, composite materials, conductive inks, etc., a large amount of graphene is required for these applications. R-GO is an effective method for the mass production of high-quality graphene. Because it can relatively quickly and easily prepare sufficient quantities of graphene to achieve the required quality level [49]. In terms of specific reduction steps, there are many methods to achieve reduction, although they are all based on chemical, thermal, or electrochemical means. Some of these technologies have been able to produce very high-quality R-GO (already very similar to the original graphene in terms of its structure).

References 1. Novoselov, K.S., Geim, A.K., Morozov, S.V., Jiang, D., Zhang, Y., Dubonos, S.V., Grigorieva, I.V., Firsov, A.A.: Electric field effect in atomically thin carbon films. Science 306(5696), 666–669 (2004). https://doi.org/10.1126/science.1102896 2. Li, L., Yu, Y., Ye, G.J., Ge, Q., Ou, X., Wu, H., Feng, D., Chen, X.H., Zhang, Y.: Black phosphorus field-effect transistors. Nat. Nanotechnol. 9(5), 372–377 (2014). https://doi.org/ 10.1038/nnano.2014.35 3. Koenig, S.P., Doganov, R.A., Schmidt, H., Castro Neto, A.H., Özyilmaz, B.: Electric field effect in ultrathin black phosphorus. Appl. Phys. Lett. 104(10), 103106 (2014). https://doi.org/ 10.1063/1.4868132 4. Ling, X., Wang, H., Huang, S., Xia, F., Dresselhaus, M.S.: The renaissance of black phosphorus. PNAS 112(15), 4523–4530 (2015). https://doi.org/10.1073/pnas.1416581112 5. Tsai, M.-L., Su, S.-H., Chang, J.-K., Tsai, D.-S., Chen, C.-H., Wu, C.-I., Li, L.-J., Chen, L.-J., He, J.-H.: Monolayer MoS2 Heterojunction solar cells. ACS Nano 8(8), 8317–8322 (2014). https://doi.org/10.1021/nn502776h 6. Yoon, Y., Ganapathi, K., Salahuddin, S.: How good can monolayer mos2 transistors be? Nano Lett. 11(9), 3768–3773 (2011). https://doi.org/10.1021/nl2018178 7. Li, H., Zhang, Q., Yap, C.C.R., Tay, B.K., Edwin, T.H.T., Olivier, A., Baillargeat, D.: From bulk to monolayer MoS2: evolution of raman scattering. Adv. Func. Mater. 22(7), 1385–1390 (2012). https://doi.org/10.1002/adfm.201102111 8. Boehm, H.P., Clauss, A., Fischer, G.O., Hofmann, U.: Das Adsorptionsverhalten sehr dünner Kohlenstoff-Folien. Zeitschrift für anorganische und allgemeine Chemie 316(3–4), 119–127 (1962). https://doi.org/10.1002/zaac.19623160303 9. Lee, Y.-H., Zhang, X.-Q., Zhang, W., Chang, M.-T., Lin, C.-T., Chang, K.-D., Yu, Y.-C., Wang, J.T.-W., Chang, C.-S., Li, L.-J., Lin, T.-W.: Synthesis of large-area MoS2 atomic layers with chemical vapor deposition. Adv. Mater. 24(17), 2320–2325 (2012). https://doi.org/10.1002/ adma.201104798 10. Kong, J., Cassell, A.M., Dai, H.: Chemical vapor deposition of methane for single-walled carbon nanotubes. Chem. Phys. Lett. 292(4), 567–574 (1998). https://doi.org/10.1016/S00092614(98)00745-3 11. Meyerson, B.S.: Low-temperature silicon epitaxy by ultrahigh vacuum/chemical vapor deposition. Appl. Phys. Lett. 48(12), 797–799 (1986). https://doi.org/10.1063/1.96673

References

41

12. Wu, J.-J., Liu, S.-C.: Low-temperature growth of well-aligned ZnO nanorods by chemical vapor deposition. Adv. Mater. 14(3), 215–218 (2002). https://doi.org/10.1002/1521-4095(200 20205)14:3%3c215:AID-ADMA215%3e3.0.CO;2-J 13. Wang, X., Gong, Y., Shi, G., Chow, W.L., Keyshar, K., Ye, G., Vajtai, R., Lou, J., Liu, Z., Ringe, E., Tay, B.K., Ajayan, P.M.: Chemical vapor deposition growth of crystalline monolayer MoSe2. ACS Nano 8(5), 5125–5131 (2014). https://doi.org/10.1021/nn501175k 14. Yarbrough, W.A., Messier, R.: Current issues and problems in the chemical vapor deposition of diamond. Science 247(4943), 688–696 (1990). https://doi.org/10.1126/science.247.4943.688 15. Vlassiouk, I., Fulvio, P., Meyer, H., Lavrik, N., Dai, S., Datskos, P., Smirnov, S.: Large scale atmospheric pressure chemical vapor deposition of graphene. Carbon 54, 58–67 (2013). https:// doi.org/10.1016/j.carbon.2012.11.003 16. Liu, Z., Tu, Z., Li, Y., Yang, F., Han, S., Yang, W., Zhang, L., Wang, G., Xu, C., Gao, J.: Synthesis of three-dimensional graphene from petroleum asphalt by chemical vapor deposition. Mater. Lett. 122, 285–288 (2014). https://doi.org/10.1016/j.matlet.2014.02.077 17. Pletikosi´c, I., Kralj, M., Pervan, P., Brako, R., Coraux, J., N’Diaye, A.T., Busse, C., Michely, T.: Dirac cones and Minigaps for graphene on Ir(111). Phys. Rev. Lett. 102(5), 056808 (2009). https://doi.org/10.1103/PhysRevLett.102.056808 18. Losurdo, M., Giangregorio, M.M., Capezzuto, P., Bruno, G.: Graphene CVD growth on copper and nickel: role of hydrogen in kinetics and structure. Phys. Chem. Chem. Phys. 13(46), 20836– 20843 (2011). https://doi.org/10.1039/C1CP22347J 19. Liu, W., Li, H., Xu, C., Khatami, Y., Banerjee, K.: Synthesis of high-quality monolayer and bilayer graphene on copper using chemical vapor deposition. Carbon 49(13), 4122–4130 (2011). https://doi.org/10.1016/j.carbon.2011.05.047 20. Cho, J.H., Gorman, J.J., Na, S.R., Cullinan, M.: Growth of monolayer graphene on nanoscale copper-nickel alloy thin films. Carbon 115, 441–448 (2017). https://doi.org/10.1016/j.carbon. 2017.01.023 21. Nam, J., Kim, D.-C., Yun, H., Shin, D.H., Nam, S., Lee, W.K., Hwang, J.Y., Lee, S.W., Weman, H., Kim, K.S.: Chemical vapor deposition of graphene on platinum: growth and substrate interaction. Carbon 111, 733–740 (2017). https://doi.org/10.1016/j.carbon.2016.10.048 22. Hao, Y., Bharathi, M.S., Wang, L., Liu, Y., Chen, H., Nie, S., Wang, X., Chou, H., Tan, C., Fallahazad, B., Ramanarayan, H., Magnuson, C.W., Tutuc, E., Yakobson, B.I., McCarty, K.F., Zhang, Y.-W., Kim, P., Hone, J., Colombo, L., Ruoff, R.S.: The role of surface oxygen in the growth of large single-crystal graphene on copper. Science 342(6159), 720–723 (2013). https:// doi.org/10.1126/science.1243879 23. Ludowise, M.J.: Metalorganic chemical vapor deposition of III–V semiconductors. J. Appl. Phys. 58(8), R31–R55 (1985). https://doi.org/10.1063/1.336296 24. Park, H.J., Meyer, J., Roth, S., Skákalová, V.: Growth and properties of few-layer graphene prepared by chemical vapor deposition. Carbon 48(4), 1088–1094 (2010). https://doi.org/10. 1016/j.carbon.2009.11.030 25. Wei, D., Lu, Y., Han, C., Niu, T., Chen, W., Wee, A.T.S.: Critical crystal growth of graphene on dielectric substrates at low temperature for electronic devices. Angew. Chem. 125(52), 14371–14376 (2013). https://doi.org/10.1002/ange.201306086 26. Murakami, K., Tanaka, S., Hirukawa, A., Hiyama, T., Kuwajima, T., Kano, E., Takeguchi, M., Fujita, J.: Direct synthesis of large area graphene on insulating substrate by gallium vaporassisted chemical vapor deposition. Appl. Phys. Lett. 106(9), 093112 (2015). https://doi.org/ 10.1063/1.4914114 27. Zhang, C., Lin, W., Zhao, Z., Zhuang, P., Zhan, L., Zhou, Y., Cai, W.: CVD synthesis of nitrogen-doped graphene using urea. Sci. China Phys. Mech. Astron. 58(10), 107801 (2015). https://doi.org/10.1007/s11433-015-5717-0 28. Kim, S.-M., Kim, J.-H., Kim, K.-S., Hwangbo, Y., Yoon, J.-H., Lee, E.-K., Ryu, J., Lee, H.-J., Cho, S., Lee, S.-M.: Synthesis of CVD-graphene on rapidly heated copper foils. Nanoscale 6(9), 4728–4734 (2014). https://doi.org/10.1039/C3NR06434D 29. Patel, R.B., Yu, C., Chou, T., Iqbal, Z.: Novel synthesis route to graphene using iron nanoparticles. J. Mater. Res. 29(14), 1522–1527 (2014). https://doi.org/10.1557/jmr.2014.165

42

3 Graphene Manufacture

30. Sagar, R.R., Zhang, X., Xiong, C.: Growth of graphene on copper and nickel foils via chemical vapour deposition using ethylene. Mater. Res. Innov. 18(sup4), S4-706-S4-710 (2014). https:// doi.org/10.1179/1432891714Z.000000000771 31. Cushing, G.W., Johánek, V., Navin, J.K., Harrison, I.: Graphene growth on Pt(111) by ethylene chemical vapor deposition at surface temperatures near 1000 K. J. Phys. Chem. C 119(9), 4759–4768 (2015). https://doi.org/10.1021/jp508177k 32. Suk, J.W., Kitt, A., Magnuson, C.W., Hao, Y., Ahmed, S., An, J., Swan, A.K., Goldberg, B.B., Ruoff, R.S.: Transfer of CVD-grown monolayer graphene onto arbitrary substrates. ACS Nano 5(9), 6916–6924 (2011). https://doi.org/10.1021/nn201207c 33. Ambrosi, A., Pumera, M.: The CVD graphene transfer procedure introduces metallic impurities which alter the graphene electrochemical properties. Nanoscale 6(1), 472–476 (2014). https:// doi.org/10.1039/C3NR05230C 34. Ambrosi, A., Pumera, M.: Electrochemistry at CVD grown multilayer graphene transferred onto flexible substrates. J. Phys. Chem. C 117(5), 2053–2058 (2013). https://doi.org/10.1021/ jp311739n 35. Chen, Y., Gong, X.-L., Gai, J.-G.: Progress and challenges in transfer of large-area graphene films. Adv. Sci. 3(8), 1500343 (2016). https://doi.org/10.1002/advs.201500343 36. Sun, J., Deng, S., Guo, W., Zhan, Z., Deng, J., Xu, C., Fan, X., Xu, K., Guo, W., Huang, Y., Liu, X.: Electrochemical bubbling transfer of graphene using a polymer support with encapsulated air gap as permeation stopping layer. J. Nanomaterials 2016, 51 (2016). https://doi.org/10.1155/ 2016/7024246 37. Sohn, J.S., Patil, U.M., Kang, S., Kang, S., Jun, S.C.: Impact of different nanostructures of a PEDOT Decorated 3D multilayered graphene foam by chemical methods on supercapacitive performance. RSC Adv. 5(130), 107864–107871 (2015). https://doi.org/10.1039/C5RA21 851A 38. Rikhari, R., Saklani, B., Bisht, A., Mehtab, S., Zaidi, M.G.H.: Graphene oxide assisted modification in electrical and electrochemical characteristics of polypyrrole. Sensor Lett. 17(7), 511–515 (2019). https://doi.org/10.1166/sl.2019.4116 39. Lee, W.I., Sohn, I.Y., Kim, B.Y., Son, Y.M., Shin, J.H., Kim, H.M., Suh, Y.J., Lee, N.-E.: A simple and clean transfer method of chemical-vapor deposited graphene on Cu. Sci. Adv. Mater. 7(8), 1540–1545 (2015). https://doi.org/10.1166/sam.2015.2372 40. Nosheen, S., Raza, M.A., Alam, S., Irfan, M., Iftikhar, A., Iftikhar, F., Waseem, B., Abbas, Z., Soomro, B.: Synthesis and characterization of polypyrrole and graphene/polypyrrole/epoxy composites. Arab. J. Sci. Eng. 42(1), 193–199 (2017). https://doi.org/10.1007/s13369-0162218-z 41. Lin, L., Zhang, J., Su, H., Li, J., Sun, L., Wang, Z., Xu, F., Liu, C., Lopatin, S., Zhu, Y., Jia, K., Chen, S., Rui, D., Sun, J., Xue, R., Gao, P., Kang, N., Han, Y., Xu, H.Q., Cao, Y., Novoselov, K.S., Tian, Z., Ren, B., Peng, H., Liu, Z.: Towards super-clean graphene. Nature Commun. 10(1), 1912 (2019). https://doi.org/10.1038/s41467-019-09565-4 42. Taniguchi, Y., Miki, T., Ohno, Y., Nagase, M., Arakawa, Y., Yasuzawa, M.: Observation of the interaction between avidin and Iminobiotin using a Graphene FET on a SiC substrate. Jpn. J. Appl. Phys. 58(SD), SDDD02 (2019). https://doi.org/10.7567/1347-4065/ab0544 43. Tehrani, Z., Burwell, G., Azmi, M.A.M., Castaing, A., Rickman, R., Almarashi, J., Dunstan, P., Beigi, A.M., Doak, S.H., Guy, O.J.: Generic epitaxial graphene biosensors for ultrasensitive detection of cancer risk biomarker. 2D Mater. 1(2), 025004 (2014). https://doi.org/10.1088/ 2053-1583/1/2/025004 44. Chen, J., Wen, Y., Guo, Y., Wu, B., Huang, L., Xue, Y., Geng, D., Wang, D., Yu, G., Liu, Y.: Oxygen-aided synthesis of polycrystalline graphene on silicon dioxide substrates. J. Am. Chem. Soc. 133(44), 17548–17551 (2011). https://doi.org/10.1021/ja2063633 45. Yan, Z., Peng, Z., Sun, Z., Yao, J., Zhu, Y., Liu, Z., Ajayan, P.M., Tour, J.M.: Growth of bilayer graphene on insulating substrates. ACS Nano 5(10), 8187–8192 (2011). https://doi.org/10. 1021/nn202829y

References

43

46. Vishwakarma, R., Zhu, R., Abuelwafa, A.A., Mabuchi, Y., Adhikari, S., Ichimura, S., Soga, T., Umeno, M.: Direct synthesis of large-area graphene on insulating substrates at low temperature using microwave plasma CVD. ACS Omega 4(6), 11263–11270 (2019). https://doi.org/10. 1021/acsomega.9b00988 47. Xu, S.C., Man, B.Y., Jiang, S.Z., Chen, C.S., Yang, C., Liu, M., Gao, X.G., Sun, Z.C., Zhang, C.: Direct synthesis of graphene on SiO2 substrates by chemical vapor deposition. CrystEngComm 15(10), 1840–1844 (2013). https://doi.org/10.1039/C3CE27029G 48. Papageorgiou, D.G., Kinloch, I.A., Young, R.J.: Graphene/Elastomer Nanocomposites. Carbon 95, 460–484 (2015). https://doi.org/10.1016/j.carbon.2015.08.055 49. Reduced Graphene Oxide—What Is It? How Is It Created?. https://www.graphenea.com/pages/ reduced-graphene-oxide. Accessed Sep 29, 2020

Chapter 4

Graphene Field-Effect Transistor Biosensor

Abstract As a new type of sensing platform, graphene field-effect transistor biosensor has unique advantages for sensing applications such as magnetism, pressure, chemistry, and biology. Using graphene as a sensing channel, because any local graphene conductance changes caused by magnetic force, pressure, chemical, and biological, and other complex factors coupling will affect the conductance of the entire graphene channel, thus this new type of sensing platform theoretically has a high sensitivity. For the graphene field-effect transistor biosensor, the surface of the graphene sensing channel directly contacts the test liquid, and then the electric signal (current Ids signal is usually used) is used to quickly and quantitatively detect the concentration of specific molecules in the test liquid. This chapter first introduces the unique electric double layer structure formed at the interface between graphene and the liquid to be measured. After that, the basic structure and sensing principle of graphene field-effect transistors are introduced, and then the relationship between the adsorption of specific molecules and the change of current is proposed. Finally, the main applications of current biosensors based on graphene field-effect transistors are briefly introduced. This chapter first introduces the unique electric double layer structure formed at the interface between graphene and the test liquid. After that, the basic structure and sensing principle of graphene field-effect transistors are demonstrated. Then the relationship between the adsorption of specific molecules and the variation of current Ids (the variation of current Ids and the concentration of specific molecules) is proposed. Finally, the main applications of graphene field-effect transistor biosensors are briefly introduced. Keywods Graphene · Electrical double layer · Field-effect transistor · Biosensor · Graphene FET In the past few decades, fast and reliable sensing technology for biomolecules detection in medical diagnosis, healthcare, and lab on a chip has become a more and more important central issue that had been widely studied, such as many optical methods and other electrochemical sensors, which have been applied to the clinical diagnosis in our daily life. Strictly speaking, the graphene field-effect transistor biosensor is not a completely novel technology. As far as we know, Mohamed M. Atalla and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_4

45

46

4 Graphene Field-Effect Transistor Biosensor

Dawon Kahng invented MOSFET (Metal Oxide Semiconductor Field-Effect Transistor, or MOS transistor) in 1959 and proved it in 1960 [1]. Two years later, Leland C. Clark and Champ Lyons first proposed the concept of biosensors in 1962 [2, 3]. The biosensor MOSFET (BioFET) was developed after 1970 and has been widely used to measure chemical, biological, physical, and environmental parameters [4]. In 1970 Piet Bergveld invented the first BioFET, an ion-sensitive field-effect transistor (ISFET), for electrochemical and biological applications [5, 6]. In addition, other early research on BioFET also includes P.F. Cox’s patented adsorption FET (ADFET) in 1974. I. Lundstrom, M.S. Shivaraman, C.S. Svenson, and L. Lundkvist demonstrated hydrogen-sensitive MOSFETs in 1975 [4]. ISFET is a special MOSFET with a certain distance from the gate [4]. The metal grid is replaced by an ion-sensitive membrane, electrolyte solution, and reference electrode [7]. ISFET has been widely used in different kinds of biomedical applications, such as blood biomarker detection, antibody detection, DNA hybridization detection,glucose measurement, pH sensing, and genetic technology [7]. By the mid-1980s, other kinds of BioFETs had been developed, including the gas sensor FET (GASFET), pressure sensor FET (PRESSFET), chemical fieldeffect transistor (ChemFET), immunologically modified FET (IMFET), and enzymemodified FET (ENFET) [4]. By the early 2000s, BioFETs such as the gene-modified FET (GenFET), DNA field-effect transistor (DNAFET), and cell-potential BioFET (CPFET) have also been reported [7]. Since 2004 K.S. Novoselov and coworkers find the new miracle material, graphene, which brings lots of outstanding property, such as the large surface area, atom-scale thickness, high carrier mobility [8]. This breakthrough opened the 2D materials gate, and let the bioFET technology based on the 2D materials such as graphene is possible. Graphene as the channel of the FET for the sensing application was also first demonstrated by K.S. Novoselov and his group in 2007 [9]. They fabricated sensors made from graphene nanosheet and found that the conductivity of the graphene is sensitive to gas molecule adsorption. This work pioneered the introduction of 2D materials into FETs for the first time and predicted the other potential applications of graphene-based FETs in chemical and biological sensing. Then in 2009, Ohno et al. first demonstrate the bovine serum albumin sensing based on the electrolyte-gated graphene FET sensor [10]. They demonstrate that through interface adsorption, the protein concentration in the liquid pool can be detected by the change of Ids. This is the first time that graphene FET sensor has been used for protein detection. In the past 10 years, many kinds of linkers had been reported for specific biomolecules detection such as nanoparticles, 1-pyrenebutyric acid n-hydroxysuccinimide ester (PBASE), and tetrakis(4-carboxyphenyl)porphyrin (TCPP) [11–13]. These linkers can bind the specific biomolecules to the interface of the graphene such as the antibodies, aptamers, and DNA [14–20]. Based on these properties from these biomolecules, the graphene FET biosensor can execute many forms of specific detection and be given different designated functions. Linkers and biomolecules modified into the surface of the graphene greatly enriched the application scope of the graphene FET biosensor. Nowadays, graphene FET biosensor has become one of the most potentially promising technologies for rapid diagnosis and

4 Graphene Field-Effect Transistor Biosensor

47

MOSFET IS INVENTED IN 1960 Mohamed M. Atalla and Dawon Kahng

BioFET IS INVENTED IN 1970 Piet Bergveld

GRAPHENE IS DISCOVERED IN2004 K.S. Novoselov and coworkers

GRAPHENE FET BIOSENSOR IS INVENTED IN 2009 Ohno et al.

FUNCTIONALIZED GRAPHENE FET BIOSENSOR UNTIL NOW

Fig. 4.1 The development history of graphene FET biosensors

real-time sensing. Relying on rapidity and real-time performance, graphene biosensors can complement traditional detection methods. Especially for the large-scale detection of epidemic diseases such as COVID-19, graphene FET biosensors have become the potentially promising detection method [21]. This development history of graphene FET biosensors is depicted in Fig. 4.1. However, there are also a series of non-ignorable problems, have to face, that hinder its practical application, such as the transfer of the graphene, the merge of the semiconductor craft, the quality of the graphene, and other complex problems. These problems hinder the further practical applications for the graphene FET biosensors.

4.1 Electrical Double Layer A double layer (DL, also called an electrical double layer, EDL) is a structure that appears on the surface of an object when it is exposed to a fluid. The object might be a solid particle, a gas bubble, a liquid droplet, or a porous body. The DL refers to two parallel layers of charge surrounding the object. The first layer, the surface charge (either positive or negative), consists of ions adsorbed onto the object due to chemical interactions. The second layer is composed of ions attracted to the surface charge via the Coulomb force, electrically screening the first layer. This second layer is loosely associated with the object. It is made of free ions that move in the fluid under the influence of electric attraction and thermal motion rather than being firmly anchored. It is thus called the “diffuse layer”. A double layer (DL) sometimes it is also called an electric double layer, (EDL) which is a specific structure that appears on the interface between an object when it is exposed to the fluid. Objects can take many forms, including solid particles, flat surfaces, porous bodies, and other complex shapes. In

48

4 Graphene Field-Effect Transistor Biosensor

simple terms, DL refers to two parallel charge layers formed around an object. The first layer of surface charge (positive or negative) is the composition of ions adsorbed on the object due to chemical interaction. The second layer is composed of ions that are attracted to surface charges by Coulomb force and electrically shields the first layer. The second layer is loosely associated with the object. It is often affected by a series of complex factors such as liquid flow rate and vibration. Therefore, the second layer is not firmly anchored to the interface, and therefore, it is called the “diffusion layer”. In fact, in a broader sense, essentially after any two phases are in contact (solid–liquid, solid–gas, liquid–gas), an electric double layer will be formed at the interface of different phases. Note that even if different substances in the same phase are in contact, the similar electric double layer phenomenon still exists. Here, we mainly discuss the phenomenon of the electric double layer at the solid–liquid interface. The development of the (interfacial) double-layer can be traced back to 1853 [22]. Hermann von Helmholtz was the first to realize that penetrating a charged electrode into an electrolyte solution would attract counter ions to its surface and drive out the same ions in the charge. He also claimed that the double layer with opposite polarity will be formed at the interface between the electrode and the electrolyte. In 1853, he proved that the electric double layer (DL) is essentially a molecular dielectric and stores electric charge electrostatically, then, it has undergone seven major improvements. They are Gouy–Chapman model (1910) [23], Stern model (1924) [24], Grahame model (1947) [25], Bockris/Devanathan/Müller model (BDM) (1963) [26], Trasatti/Buzzanca model (1971) [27], Conway model (1991) [28], Marcus (1992) [29]. For the graphene bio-FET sensor, as relatively simple models, the Stern model (1924) and the Bockris/Devanathan/Müller model (BDM) (1963), can explain the phenomenon of biosensing very well. The Stern model (1924) macroscopically summarizes the overall situation of the graphene electric double layer, and the Bockris/Devanathan/Müller model microscopically explains the actual distribution of ions on the interface.

4.1.1 Stern Model (1924) Because the Gouy–Chapman model cannot describe the highly charged DLs. well. In 1924, Otto Stern proposed to combine the Helmholtz model with the Gouy–Chapman model to form the Stern model. In Stern’s model, some ions adhere to the electrode as suggested by Helmholtz, forming the internal Stern layer, while others form the Gouy–Chapman diffusion layer at external [24]. The Stern layer illustrates the finite size of the ions, so the nearest approach between the ions and the electrode is about the ion radius. However,the Stern model still has its limitations, that is, it treats ions as point charges, assumes that allimportant interactions in the diffusion layer are Coulombic, and assumes that the dielectric constant of the entire double layer is constant, and the fluid viscosity is a constant plane. The schematic diagram of the Stern model is shown in Fig. 4.2.

4.1 Electrical Double Layer

49

Fig. 4.2 The schematic diagram of the stern model

4.1.2 BDM Model (1963) J. O’M. Bockris, M. A. V. Devanathan and Klaus Müller first reported a two-layer BDM model in 1963, which included the role of solvent in the interface [26]. They proposed that the solvent attached molecules (such as water) should have a fixed alignment with the electrode surface. The first layer of solvent molecules shows a strong orientation to the electric field according to the charge. This orientation has a great influence on the dielectric constant of the solvent that changes with the field strength. The inner Helmholtz plane (IHP) passes through the centers of these molecules. Particularly adsorbed, partially solvated ions appear in this layer. The solvated ions of the electrolyte are outside the IHP. The centers of these ions pass through the outer Helmholtz plane (OHP). The diffusion layer is a region other than OHP. The schematic diagram of the BDM model is shown in Fig. 4.3. When a voltage is applied to the capacitor, two layers of polarized ions are generated at the electrode interface. One layer is inside the solid electrode (on the surface of the crystal grain in contact with the electrolyte). The other layer of opposite polarity is formed by dissolved and solvated ions distributed in the electrolyte, which have moved to the polarized electrode. The two layers of polarized ions are separated by a single solvent molecule. The molecular monolayer forms the inner Helmholtz plane (IHP). It adheres to the electrode surface by physical adsorption and separates the oppositely polarized ions from each other to form a molecular dielectric. The amount of charge in the electrode matches the magnitude of the countercharge in the outer Helmholtz plane (OHP). This is the area where polarized electrolyte ions are collected near the IHP. This separation of the two layers of polarized ions passing through the double layer stores charges in the same way as a conventional capacitor. The double-layer charge forms an electrostatic field in the molecular IHP layer of solvent molecules, which corresponds to the strength of the applied voltage.

50

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.3 Schematic representation of a double layer on an electrode (BMD) model. 1. Inner Helmholtz plane, (IHP), 2. Outer Helmholtz plane (OHP), 3. Diffuse layer, 4. Solvated ions (cations), 5. Specifically adsorbed ions (redox ion, which contributes to the pseudocapacitance), 6. Molecules of the electrolyte solvent

In the electrolyte, the thickness depends on the size of solvent molecules and the movement and concentration of ions in the solvent. As described in the Debye length below, the range is 0.1–10 nm. The sum of the thickness is the total thickness of the double layer.

4.2 Debye Length At room temperature (20 C), the Debye length can consider in water the relation [30]. 0.304 κ −1 (nm) = √ I (M) where κ −1 is expressed in nanometers (nm) I is the ionic strength expressed in molar (M or mol/L).

4.2 Debye Length

51

Commonly speaking, for the contact between the metal electrode and the solution, the “thickness” of a charged layer in the metallic electrode, i.e., the average extension perpendicular to the surface, is about 0.1 nm, and mainly depends on the electron density because the atoms in solid electrodes are stationary. Because of the unparalleled ultra-thin properties of graphene, changes from the charge layer will be completely transferred to the channel material of graphene, which is unmatched by traditional bulk materials. Thus graphene-based FET sensor has a significant sensing level compared with the traditional bulk materials.

4.3 Graphene Field-Effect Transistor The field-effect transistor (FET) is one kind of transistor that can use the electric field to regulate the flow of current from the drain terminal to the source terminal. Normally, FETs contain three different terminals: source, drain, and gate between the source and drain terminal,the specific semiconductor channel is placed so that the current flow through this channel can be regulated by the variation of gate voltage. Generally speaking, because FET can control the source-to-drain current by changing the gate voltage, thereby changing the conductivity between the drain and the source. The graphene field-effect transistor (GFET) follows the typical structure of the FET device and the semiconductor channel between the source and drain is replaced with graphene to make a graphene field-effect transistor. Because the channel in the GFET uses graphene, which is a lattice of carbon atoms with a thickness of only one atom, therefore, any small changes that occur near the interface may be coupled with the carrier concentration of the local graphene, which will significantly affect the entire graphene channel, thus it has unprecedented sensitivity and can be used in various applications, such as photosensitive, magnetic sensing, gas sensing, and biological sensing. FETs control the flow of current by the application of a voltage to the gate, which in turn alters the conductivity between the drain and source. Graphene field-effect transistors (GFETs) take the typical FET device and insert a graphene channel tens of microns in size between the source and drain. Being graphene, a lattice of carbon atoms that is only one atom thick, the channels in GFETs have unprecedented sensitivity, which can be exploited in a wide variety of applications such as photosensing, magnetic sensing, gas sensing, and biosensing. Figure 4.4 shows the schematic of the typical graphene FET structure. For the biomolecules detections, the graphene channel is directly exposed so that it can sufficiently contact with the test solution to permit the binding and detection of biomolecules such as biomarker, nucleic, protein, virus, or other specific biomolecules [31]. When these specific biomolecules bind onto the graphene channel, these molecules will affect the double layer capacitance between the interface thus change the carrier density in the graphene side inducing the variation of the graphene conductivity. While the target biomolecules typically do not directly specifically bind with the bare graphene surface, the corresponding antibodies can be used to

52

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.4 shows the schematic of the typical graphene FET structure

modify the surface of the graphene. This processing gives the graphene surface the ability to specifically bind target biomolecules. Thus it makes specific detection of biomolecules possible.

4.4 Graphene Field-Effect Transistor Biosensors Graphene field-effect transistor (FET) biosensor for protein detection can be traced back to 2009, Ohno et al. first reported the electrolyte-gated graphene FET biosensor for bovine serum albumin (BSA) concentrate detection [10]. They using the exfoliated graphene to make the channel of the FET, basing the physical adsorption between the graphene and BSA, the concentration of the BSA can be quantitatively detected by the current of the Ids. This work confirmed that protein rapid quantitive detection is possible by the graphene FET biosensor. Almost after 10 years, although the structure of the graphene FET biosensor is not changing many crafts and manufacturing details have changed. In this section, we will review this novel craft and manufacture details. In the earliest days, graphene FET biosensor s were made based on nanosheets mechanically exfoliated from highly oriented pyrolytic graphite (HOPG) or natural graphite [10]. The atomic-thick graphene nanosheets are obtained by mechanical exfoliation to make the FET sensor channels, because high-quality single-layer graphene films could not be prepared in large quantities at that time. It was not until the CVD method of synthesizing graphene on copper foil was discovered and widely put into use that the mechanical exfoliate method was then gradually replaced. Up to now, graphene synthesized by CVD on the copper foil is still widely used in the research of graphene FET sensors.

4.4 Graphene Field-Effect Transistor Biosensors

53

The field-effect transistor (FET) is a type of transistor that uses an electric field to control the flow of current. For the normal FET, there are three terminals: source, gate, and drain. FETs control the flow of current by the application of a voltage to the gate, which in turn alters the conductivity between the drain and source. Normally, FETs use electrons or holes as charge carriers in their operation, but not both. Many different types of field-effect transistors exist. Graphene FET is one of the special FETs. Because graphene is a zero-bandgap semiconductor, gate voltage regulation cannot completely switch off the current from source to drain. Moreover, through gate voltage regulation, the carrier type can also be changed from the hole area to the electron area. For the graphene bio-FET sensor, not only the gate can regulate the current but also the graphene interface can also regulate the current through binding the specific molecules to the surface of graphene. When the target molecules are bound onto the surface of the graphene, the charge distribution at the interface of the graphene is regulated, this regulated principle is similar to the special gate for the graphene bio-FET sensor. Therefore, the target molecule concentration can be quantitively detected through observing the graphene FET biosensor current. The schematic diagram of the traditional graphene FET biosensor is shown in Fig. 4.5. The CVD graphene is transferred onto the SiO2 /Si substrate, and during the two sides of the graphene, there are the source and drain terminals made from metal. In some applications, for regulating the graphene conductivity better, the reference electrode is used as the top gate into the electrolyte solution. These are the most basic

Metal terminal SiO2 Si modificaƟon

D

Fig. 4.5 The schematic diagram of the traditional graphene FET biosensor

S

54

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.6 a The schematic diagram of the PBASE. b The schematic diagram of the TCPP

components of the graphene FET sensors. Moreover, in order to enable the graphene FET sensor to achieve certain specific functions, the surface of the graphene usually needs to be modified. For biosensing, the modification step is very important for the sensitivity and specificity of the sensor. It is mainly divided into three steps: 1. Modification of linkers 2. Modification of specific biomolecules 3. Blocking. For the modification of the linkers, the most commonly used linker is PBASE and AuNPs. Graphene is then chemically modified using 1-pyrenebutanoic acid succinimidyl ester (PBASE) through the Van der Waals force between the graphene and the pyrene backbone of PBASE molecule. This interaction is usually called π-stacking [32]. Then PBASE modified graphene is further modified with biomolecules through the NHS ester reaction from N-hydroxysuccinimide ester group of PBASE. The schematic diagram of the PBASE is shown in Fig. 4.6a. Otherwise, some studies show that the tetrakis(4-carboxyphenyl)porphyrin (TCPP) was used as a linker for surface modification of the graphene FET and the properties of the device were better than the graphene FET device modified with the conventional linker 1-pyrenebutanoic acid succinimidyl ester (PBASE) [13]. TCPP modification resulted in a higher density of receptor immunoglobulin E (IgE) aptamer molecules on the graphene FET. This study indicated that the TCPP may be a potential excellent linker than the PBASE. The schematic diagram of the TCPP is shown in Fig. 4.6b. Graphene is decorated with AuNPs, which is also a commonly used method for the modification of linkers [11]. The biocompatibility and high surface energy of Au allow it to bind to a large amount of protein without altering its activity and result in a more sensitive sensor. For the modification of specific biomolecules, there are several different types such as the antibody, nucleic acids, aptamer, and others. These different types of modification methods both have a unique property that they can bind with other specific biomolecules, such as the antibody can bind with its specified antigen, the nucleic acids can bind its corresponding nucleic acid chain based on the complementary base pairs. Actually, the function of the graphene FET biosensor is determined

4.4 Graphene Field-Effect Transistor Biosensors

55

by the modified specific biomolecules. As far as antibody modification is concerned, the antigen is specifically bound by the antibody and binds with high affinity to form an antigen–antibody complex, thereby realizing the detection of the specific antigen. Compared with aptamers, antibodies generally have higher affinity. Therefore, in some applications, graphene FET biosensors perhaps have higher sensitivity after being modified with antibodies than with aptamers. But generally speaking, aptamers usually have a simpler structure than antibodies thus aptamers are more suitable for large-scale production, and the aptamers are not easily affected by temperature, humidity, and other factors. Therefore, graphene FET biosensors modified with aptamers are more conducive to long-term storage. Moreover, aptamers have an innate ability to bind to any molecule they are targeted at, including cancer cells and bacteria. Thus, its application scale is wider than the antibodies theoretically. It can be said that the two methods have advantages for each other. Blocking is the last step of manufacturing processing. For the graphene FET biosensor, because the previous processing can not cover the entire surface of the graphene, the blocking processing can block the uncovered surface of the graphene after biomolecules modification that is to prevent the non-specific bind occurred at the uncovered area during the real-time detection. Because during real-time detection, the uncovered surface of the graphene will bind the other biomolecules through simple adsorption. It will induce the decrease the specificity during the real-time test. The BSA solution is the most commonly used for blocking processing, but some of the studies reported that the BSA solution may not the perfect blocking solution for the graphene [33] and proposed that ethanolamine is more suitable for the graphene FET biosensor [34]. Moreover, in some applications of the detection of nucleic acid, some studies indicate that guanine blocking is a useful potential method for the graphene surface [35]. The IV curve is the important characterization method for the graphene FET biosensor. The IV Test-Setting of the bare graphene FET is shown in Fig. 4.7a. The Vds is fixed at 0.1 V, 0.15 V, 0.20 V, 0.25 V, and 0.30 V respectively among each IV-Test. The Vgs is connected to the back gate (Si/SiO2 substrate). The Vgs is swept at the −50–50 V among each IV-Test. Based on the bare graphene FET biosensor, the different Vds of the IV curve is shown in Fig. 4.7b. When the back gate is connected to the negative voltage, due to the electric field effect, there are fewer electrons on the graphene surface, and the Fermi level drops. At this time, the Fermi level is in the valence band, and holes are the main carriers, dominating the graphene conductivity. When the back gate voltage gradually increases, due to the electric field effect, the electrons on the graphene surface gradually increase, and the valence band is gradually filled. When the back gate voltage moves to 0, the graphene Fermi level is still in the valence band, and holes still dominate the graphene conductivity. When the gate voltage is further increased to around 20 V, since graphene is a zero-bandgap semiconductor, the Fermi level is at the junction of the valence band and the conduction band. Based on the quantum tunneling effect, as well as the thermal effect, graphene can still conduct electricity at this time, and a small number of holes and electrons in the same amount simultaneously dominate

56

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.7 a The IV Test-Setting of the bare graphene FET in different Vgs and Vds. b The IV curve of the bare graphene FET in different Vgs and Vds

4.4 Graphene Field-Effect Transistor Biosensors

57

the conductivity of graphene. At this time, graphene has the least total amount of carriers and the lowest conductivity. The corresponding point at this time is usually called the Dirac point, and the voltage corresponding to this point is usually called the Dirac voltage of graphene. Note that when the graphene is in the region near its Dirac point, holes and electrons simultaneously dominate the graphene conduction, and the conductivity is the lowest at this time. When the back gate voltage is further increased, the Fermi level of graphene is further upper. At this time, the Fermi level has entered the conduction band, and electrons as the main carrier dominate the conductive properties of graphene. In addition, the conduction state of the graphene can be classified according to the types of the carrier, when the gate voltage is much smaller than the voltage of the Dirac point, the graphene is in the P zone (hole area). When the voltage is much greater than the voltage at the Dirac point, graphene is in the N zone (elec. area). When the voltage is near the Dirac voltage, the graphene is in the Dirac zone. Note that the slopes of the IV curves of the P and N zones are in opposite directions and equal in magnitude. This result implies that the carrier mobility (uelec. and uhole ) of holes and electrons is approximately equal. When the back gate voltage is 0, the graphene is in the P zone. Most of the bare graphene FETs made from the CVD graphene are the P-type (the Dirac point is on the right side of the Vgs). It indicates that the hole plays the Maxine carriers on bare graphene, and the conductivity of the graphene is regulated by Vgs and Vds at the same time, but whether it is changing Vds and Vds, the position of Dirac point cannot be changed. These results indicated that for the same graphene FET, the conductivity can be regulated by the Vds and Vgs. But the regulation of the Vds and Vgs can not change the position of the Dirac point. The position of the Dirac point may be related to the inherent properties of the interface graphene. Some of the studies reported that the changing of the Dirac point position can be regulated by the doping, the interface capacity effect, and other complex factors involved in the graphene interface [36–38]. In addition, we should note during the actual test that the pH value of the test solution and the ion concentration can also regulate the position of the Dirac point [10, 39]. For the GFET biosensor, the Dirac shift of graphene interface maintains high sensitivity for any modification and adsorption such as surface modification and molecules adsorption. In the following discussion, we first introduce the Dirac shift caused by graphene surface modification (AuNPs modification and PBASE modification) and then discuss the Dirac shift caused by molecular adsorption. Here I–V measurements are given for the clean and modified graphene. Vgs (back-gate) is swept at −10–60 V, Vds is fixed at 0.1 V, and the source terminal is grounded. A typical I–V curve for the clean and AuNP-modified graphene is shown in Fig. 4.8a. A typical I–V curve for the clean and PBASE-modified graphene is shown in Fig. 4.8b. For Fig. 4.8a, compared to the clean graphene, a large shift of the Dirac point toward lower voltages is observed in the case of AuNP-modified graphene. This shift in the Dirac point toward lower voltage indicates that the decoration of graphene with AuNPs decreases the extent of P-doping characteristics. This phenomenon can be attributed to differences in the work function between graphene and Au. The work

58

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.8 a I–V curves for clean and AuNPs modified graphene. b I–V curves for clean and PBASE modified graphene

function of graphene and Au is around 4.2 and 5.1 eV respectively. When the AuNPs are deposited on the surface of the graphene, the work function difference induces the electronic charge transfer from AuNPs to graphene. Note that the bare graphene is strong P-doped. (Because of many complex factors such as surface organic residues.) Figure 4.9 shows the details about the Dirac point shift after AuNPs modification. For Fig. 4.8b, a typical I–V curve for clean and PBASE modified (modified for 4 h) graphene is shown. A large shift of the Dirac point towards higher voltages is observed in the case of PBASE-modified graphene, this shift in the Dirac point toward lower voltage indicates that the decoration of graphene with PBASE increases the extent of P-doping characteristics. The possible reason is that pi–pi stacking fixes part of the free electrons of graphene, which leads to further enhancement of P-doped. This phenomenon has been observed and confirmed in many other published papers. Figure 4.9 shows the details about the Dirac point shift after PBASE modification. Besides, for molecular adsorption and binding, the Dirac shift of graphene will also occur. For the graphene–gas interface, some specific graphene gas sensors can be made based on the Dirac displacement produced by the interface adsorption and binding. For the graphene–liquid interface, specific graphene liquid sensing devices such as biosensors can also be fabricated based on the Dirac displacement generated by the interface adsorption and binding. At present, the electrical changes caused by the Dirac shift of graphene can be quickly and easily observed through a variety of electrical methods, there have been a large number of related researches on the sensing applications of graphene solid–liquid interface sensing and solid–gas interface sensing, showing huge application potential.

4.4 Graphene Field-Effect Transistor Biosensors

59

Fig. 4.9 The details about the Dirac point shift after AuNPs modification and PBASE modification

60

4 Graphene Field-Effect Transistor Biosensor

4.5 Mechanism of the Graphene Field-Effect Transistor Biosensors When the target molecule is bound to the graphene interface, although two main mechanisms (direct charge transfer and electrostatic field effect) can both adjust the conductive properties of graphene, the electrostatic field effect plays a leading role in most GFET biosensor applications. Taking antigen–antibody interaction as an example, because the detection object is usually a biological molecule such as an antigen, which is not the conductor. Moreover, its charge is difficult to cross the antibody linker or the electrolyte double layer through direct charge transport then affecting the conductive properties of graphene. Thus the main mechanism is the electrostatic field effect of the electrolyte double layer. In the following sections, we will discuss based on this mechanism. In most biosensing applications, it is necessary to introduce receptor–ligand interaction as a tool to specifically recognize biomolecules. In order to identify specific ligands to be detected, the receptor needs to be modified on the graphene surface at first. For the specific biomolecules (ligand), the charges of the specific biomolecules could affect the charge distribution at the interface double layer between the graphene and the PBS solution to achieve the effect of regulating the conductivity of the graphene. The observation of the variation for the current Ids indicates that the specific biomolecules (ligand) in the solution bind onto the surface of the graphene leads to an interface capacitance variation at the double layer, thus changing the graphene carrier density (hole density). Based on σ = neu (σ represents the conductivity of the graphene, e represents the unit carrier charge, u represents the carrier mobility) that has mentioned mathematical formula in Chap. 2 and Ohm’s law, the current Ids for the graphene FET biosensor can be demonstrated in I = U ∗ σ (U represents the voltage between the interdigital electrode. U is 0.1 V during the realtime test.). Hence I = U ∗ neu (e is a constant, when the work point is far from the Dirac point, u can also be assumed to be constant [40]), thus current Ids and carrier concentration can be approximated as a linear relationship. Commonly based on the real-time current Ids variations upon addition of the different concentrations of specific biomolecule (ligand) solutions during the actual experiments, current change I and the concentration of the specific biomolecule (ligand) in solution C can be estimated using the Langmuir adsorption model I = (C*I max )/(C + Kd ), K d represents the dissociation constant, I max represents the maximum of the current change [41]. The corresponding Langmuir adsorption model has the potential to be used as the calibration curve for the quantitative detection of specific biomolecule (ligand) in a given sample. Note that the theoretical dissociation constant of the receptor–ligand interaction may be less than the estimated value. This difference in Kd value, which is in agreement with previous reports, is thought to be caused by binding of the receptor to the substrate surface [42–44], and the affinity of receptor–ligand interaction may also be affected when receptor is modified onto the linkers. In addition, the Langmuir adsorption model indicates that the current

4.5 Mechanism of the Graphene Field-Effect Transistor Biosensors

61

Ids and the concentration of the specific biomolecule (ligand) in the solution can be approximated as a non-linear relationship. Based on the mathematical model in Chap. 2 mentioned, we have known that current Ids and carrier density can be approximated as a linear relationship. Based on the actual experiments previously described, current Ids and the concentration of the specific biomolecule (ligand) in the solution can be approximated as a nonlinear relationship. Hence there must have a potential non-linear relationship between the concentration of the specific biomolecule (ligand) and carrier density. As we know that the surface adsorption phenomenon follows the Hill–Langmuir equation m given by θ = K dC+C m where θ represents the percentage of the surface cover [45]. C represents the concentration of the adsorbates. K d represents the dissociation constant, which represents the dissociation strength between the specific biomolecule (ligand) and the surface of the substrate. m represents the Hill coefficient. The m > 1 represents positively cooperative binding, i.e., if the specific molecule binds on the substrate, its affinity for other specific molecules increases. The m < 1 represents a negatively cooperative binding, i.e., if the specific molecule binds on the substrate, its affinity for other specific molecules decreases, and m = 1 represents non-cooperative (completely independent) binding, i.e., the substrate affinity for a particular molecule does not depend on the already bonded molecules. If assume that the change quantity of the carrier density on graphene (n) is S when the specific biomolecule (ligand) is saturated adsorption (θ = 1) and then assumed that the relationship between n and θ can be simply approximated to a linear relationship n = S · θ . Thus m the analytical expression of I will be given by I = U · S KdC+Cm eμ. When   m = 1, this analytical expression I = U · S KdC+C eμ can be unified with the I = (C*Imax )/(C + K d ), which is proposed by professor Yasuhide Ohno in 2009 [10]. The latter has been succeeded widely used for electrochemical biosensor nonlinear fitting. This result indicates that the relationship between the change quantity of the carrier density on graphene (n) and the percentage of the surface cover (θ ) can be simply approximated to a linear relationship n = S · θ . In addition, the potential non-linear relationship between the concentration of the biotinylated biomolecules (C) and the change  of the carrier density on graphene (n)  quantity Cm can be approximated to n = S Kd +Cm based on the Hill–Langmuir equation.

4.6 Biological Applications In this section, the applications of the graphene FET biosensor will be reviewed. At present, not only the protein, the nucleic acid target can be detected, but also the living cell, virus, and other target detection are also reported based on the graphene FET biosensor [8, 46–57]. These representative works will be listed as follows. For the protein target, it is the most common target for precise disease diagnosis. Many of the cancer biomarkers consist of protein, such as prostate-specific antigen

62

4 Graphene Field-Effect Transistor Biosensor

(PSA), carcinoembryonic antigen (CEA). The rapid detection of the cancer biomarker has become the potential application to the graphene FET biosensor field. Lin Zhou et al. demonstrated that label-free GFET biosensor based on antibody-modified [52]. Antibodies targeting carcinoembryonic antigen (Anti-CEA) were bond onto the surface of graphene using non-covalent modification. The bifunctional molecule, PBASE, which is composed of pyrene and a reactive succinimide ester group, interacts with graphene non-covalently via π-stacking. The succinimide ester group reacts with the amine group to initiate antibody surface immobilization, which was verified by Atomic Force Microscopy, X-ray Photoelectron Spectroscopy, and Electrochemical Impedance Spectroscopy. The resulting anti-CEA modified GFET sufficiently monitored the reaction between CEA protein and anti-CEA in real-time with high specificity, which revealed selective electrical detection of CEA with a limit of detection (LOD) of less than 100 pg/ml. The dissociation constant between anti-CEA and CEA protein was assessed to be 6.35 × 10−11 M, indicating the high sensitivity and affinity of anti-CEA-GFET. In addition,Kim et al. report reduced graphene oxide field-effect transistor (R-GO FET) biosensor for label-free ultrasensitive detection of a prostate cancer biomarker, prostate-specific antigen/α1-antichymotrypsin (PSAACT) complex [58]. This R-GO channel in the R-GO FET was manufactured by the reduction of graphene oxide nanosheets networked by a self-assembly process. Immunoreaction between PSA-ACT and PSA monoclonal antibodies on the RGO channel surface induced a sensitive linear response on the shift of the gate voltage, Vg, min. They also reported that R-GO FET can detect antibody–antigen interactions at the fM level, and its dynamic range exceeds Vg by 6 orders of magnitude. For the pH 6.2 and pH 7.4 analyte solutions, high association constants of 3.2 nM−1 and 4.2 nM−1 were obtained, respectively. In addition, they also confirmed that the R-GO FET biosensor also showed high specificity for other cancer biomarkers in PBS buffer and human serum. In addition, Kim et al. reported a biosensor based on reduced graphene oxide fieldeffect transistor (R-GO FET) for the detection of prostate cancer biomarkers, namely prostate-specific antigen/α1-antichymotrypsin (PSA-ACT) complex. The label-free ultra-sensitive detection [58]. The R-GO channel in this device is formed by reducing graphene oxide nanosheets that form a network through a self-assembly process. The immune response based on the PSA-ACT complex on the surface of the RGO channel and the PSA monoclonal antibody caused a linear response in the shift of the gate voltage Vg, min, in which the smallest conductivity appeared. R-GO FET can detect protein–protein interactions at the droplet level, and its dynamic range exceeds 6 orders of magnitude of Vg, and the minimum displacement is the sensitivity parameter. For the pH 6.2 and pH 7.4 analyte solutions, high association constants of 3.2 nM-1 and 4.2 nM-1 were obtained, respectively. The R-GO FET biosensor shows high specificity for other cancer biomarkers in phosphate buffer and human serum. mRNA is one kind of nucleic acid target, which plays an important role in disease diagnosis. Tian et al. used graphene field-effect transistor (GFET) biosensors modified with PNA and DNA probes for ultra-sensitive detection of specific RNA [59]. Compared with the DNA probe modified GFET sensor, the detection limit (LOD) of

4.6 Biological Applications

63

the PNA probe modified GFET sensor is reduced to 0.1 aM, which is three orders of magnitude lower. At the same time, they observed an excellent linear electrical response to RNA concentrations in a broad range from 0.1 aM to 1 pM for PNA probe-modified GFET and from 100 aM to 1 pM for DNA probe-modified GFET, respectively. They also claimed that both PNA and DNA probe-modified GFETs have great potential in the rapid quantitative detection of specific RNA. Compared with the DNA probe modified GFET biosensor, the PNA probe modified GFET sensor significantly reduces the detection time and speeds up the detection. In addition, the electrical response of the PNA probe-modified GFET biosensor to non-complementary RNA is almost negligible, showing that the sensor is highly specific for the detection of specific RNA. In addition, they also proved that the GFET sensor can also be applied to detect RNA in human serum, making it a promising method for detecting RNA in biomedical research and early clinical diagnosis in the future. For real-time live-cell monitoring, Priscilla et al. developed a graphene transistor array integrated with a micro-flow cytometer for “flow capture-release” sensing of malaria-infected red blood cells at the single-cell level [60]. They demonstrate that red blood cells infected with malaria cause highly sensitive capacitive coupling changes in the conductivity of graphene. Together with the characteristic conductance dwell time, specific microscopic information about the disease state can be obtained. For virus detection, Chen et al. have developed a reduced graphene oxide fieldeffect transistor (R-GO FET) biosensor for real-time detection of Ebola virus antigens [61]. Their biosensor takes advantage of the excellent semiconductor properties of graphene-based materials (R-GO) and can rapidly conduct high-sensitivity and specific detection for the Ebola glycoprotein. Ebola glycoprotein diluted with PBS buffer, human serum, and plasma was used to assess the feasibility of clinical application of this biosensor. These results indicate that their R-GO FET biosensor can successfully rapidly detect the Ebola virus and it is promising for Ebola virus clinical diagnosis in the future. In addition, Zhen Wang et al. develop a •OH FET sensor with a graphene channel functionalized by metal ion indicators for the free radical detection, and they claim that at the electrolyte/graphene interface, highly reactive •OH cuts the cysteamine to release the metal ions, resulting in surface charge de-doping and a current response. By this inner-cutting strategy, the •OH is selectively detected with a concentration down to 10−9 M [62]. In the past decade, GFETs based on various interactions have been extensively studied as shown in Fig. 4.10, including antigen–antibody, base pair, aptamer technology, concanavalin A, and avidin–biotin. Based on the significant advantages of biosensing, that is, the electronic properties of graphene are highly susceptible to a series of interface effects such as electrostatic force generated by long-range charge scatterers and local dielectric environment changes. This makes graphene extremely sensitive to the surface charge density at its interface. In my opinion, based on these properties, it will further apply widely in the future. Next sections, I will, respectively, introduce the prospects and developing directions based on my experience for the variety bio-interaction combined GFET.

64

4 Graphene Field-Effect Transistor Biosensor

Fig. 4.10 The research about GFET combing with various bio-interaction in the past decade

References 1. 1960: Metal Oxide Semiconductor (MOS) Transistor Demonstrated | The Silicon Engine | Computer History Museum. https://www.computerhistory.org/siliconengine/metal-oxide-sem iconductor-mos-transistor-demonstrated/. Accessed Sep 30, 2020 2. Park, J., Nguyen, H.H., Woubit, A., Kim, M.: Applications of field-effect transistor (FET)-type biosensors. Appl. Sci. Convergence Technol. 23(2), 61–71 (2014). https://doi.org/10.5757/ ASCT.2014.23.2.61 3. Clark, L.C., Lyons, C.: Electrode systems for continuous monitoring in cardiovascular surgery. Ann. N. Y. Acad. Sci. 102(1), 29–45 (1962). https://doi.org/10.1111/j.1749-6632.1962.tb1 3623.x 4. Bergveld, P.: The impact of MOSFET-based sensors. Sens. Actuat. 8(2), 109–127 (1985). https://doi.org/10.1016/0250-6874(85)87009-8 5. Toumazou, C., Georgiou, P.: Piet Bergveld—40 Years of ISFET technology: from neuronal sensing to DNA sequencing. Electron. Lett. 47(26), S7–S12 (2011). https://doi.org/10.1049/ el.2011.3231 6. Bergveld, P.: Development of an ion-sensitive solid-state device for neurophysiological measurements. IEEE Trans. Biomed. Eng. BME-17 (1), 70–71 (1970). https://doi.org/10.1109/ TBME.1970.4502688 7. Schöning, M.J., Poghossian, A.: Recent advances in biologically sensitive field-effect transistors (BioFETs). Analyst 127(9), 1137–1151 (2002). https://doi.org/10.1039/B204444G 8. Novoselov, K.S., Geim, A.K., Morozov, S.V., Jiang, D., Zhang, Y., Dubonos, S.V., Grigorieva, I.V., Firsov, A.A.: Electric field effect in atomically thin carbon films. Science 306(5696), 666–669 (2004). https://doi.org/10.1126/science.1102896 9. Schedin, F., Geim, A.K., Morozov, S.V., Hill, E.W., Blake, P., Katsnelson, M.I., Novoselov, K.S.: Detection of individual gas molecules adsorbed on graphene. Nat. Mater. 6(9), 652–655 (2007). https://doi.org/10.1038/nmat1967 10. Ohno, Y., Maehashi, K., Yamashiro, Y., Matsumoto, K.: Electrolyte-gated graphene field-effect transistors for detecting ph and protein adsorption. Nano Lett. 9(9), 3318–3322 (2009). https:// doi.org/10.1021/nl901596m 11. Mao, S., Lu, G., Yu, K., Bo, Z., Chen, J.: Specific protein detection using thermally reduced graphene oxide sheet decorated with gold nanoparticle-antibody conjugates. Adv. Mater. 22(32), 3521–3526 (2010). https://doi.org/10.1002/adma.201000520

References

65

12. Xu, S., Jiang, S., Zhang, C., Yue, W., Zou, Y., Wang, G., Liu, H., Zhang, X., Li, M., Zhu, Z., Wang, J.: Ultrasensitive label-free detection of DNA hybridization by sapphire-based graphene field-effect transistor biosensor. Appl. Surf. Sci. 427, 1114–1119 (2018). https://doi.org/10. 1016/j.apsusc.2017.09.113 13. Kawata, T., Ono, T., Kanai, Y., Ohno, Y., Maehashi, K., Inoue, K., Matsumoto, K.: Improved sensitivity of a graphene FET biosensor using porphyrin linkers. Jpn. J. Appl. Phys. 57(6), 065103 (2018). https://doi.org/10.7567/JJAP.57.065103 14. Tian, M., Xu, S., Zhang, J., Wang, X., Li, Z., Liu, H., Song, R., Yu, Z., Wang, J.: RNA detection based on graphene field-effect transistor biosensor. https://www.hindawi.com/journals/acmp/ 2018/8146765/. Accessed Sep 30, 2020. https://doi.org/10.1155/2018/8146765 15. Fenoy, G.E., Marmisollé, W.A., Azzaroni, O., Knoll, W.: Acetylcholine biosensor based on the electrochemical functionalization of graphene field-effect transistors. Biosens. Bioelectron. 148, 111796 (2020). https://doi.org/10.1016/j.bios.2019.111796 16. Sun, J., Xie, X., Xie, K., Xu, S., Jiang, S., Ren, J., Zhao, Y., Xu, H., Wang, J., Yue, W.: Magnetic graphene field-effect transistor biosensor for single-strand DNA detection. Nanoscale Res. Lett. 14(1), 248 (2019). https://doi.org/10.1186/s11671-019-3048-1 17. Bay, H.H., Vo, R., Dai, X., Hsu, H.-H., Mo, Z., Cao, S., Li, W., Omenetto, F.G., Jiang, X.: Hydrogel gate graphene field-effect transistors as multiplexed biosensors. Nano Lett. 19(4), 2620–2626 (2019). https://doi.org/10.1021/acs.nanolett.9b00431 18. Xiang, L., Wang, Z., Liu, Z., Weigum, S.E., Yu, Q., Chen, M.Y.: Inkjet-printed flexible biosensor based on graphene field effect transistor. IEEE Sens. J. 16(23), 8359–8364 (2016). https://doi. org/10.1109/JSEN.2016.2608719 19. Ping, J., Vishnubhotla, R., Vrudhula, A., Johnson, A.T.C.: Scalable production of highsensitivity, label-free DNA biosensors based on back-gated graphene field effect transistors. ACS Nano 10(9), 8700–8704 (2016). https://doi.org/10.1021/acsnano.6b04110 20. Campos, R., Borme, J., Guerreiro, J.R., Machado, G., Cerqueira, M.F., Petrovykh, D.Y., Alpuim, P.: Attomolar label-free detection of DNA hybridization with electrolyte-gated graphene field-effect transistors. ACS Sens. 4(2), 286–293 (2019). https://doi.org/10.1021/ acssensors.8b00344 21. Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., Kim, S.-J., Lee, J.-O., Kim, B.T., Park, E.C., Kim, S.I.: Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4), 5135–5142 (2020). https://doi.org/10.1021/acs nano.0c02823 22. Helmholtz, H.: Ueber Einige Gesetze Der Vertheilung Elektrischer Ströme in Körperlichen Leitern Mit Anwendung Auf Die Thierisch-Elektrischen Versuche. Ann. Phys. 165(6), 211–233 (1853). https://doi.org/10.1002/andp.18531650603 23. Wayback Machine. https://web.archive.org/web/20141222044332/http://services.eng.uts.edu. au/cempe/subjects_JGZ/eet/Capstone%20thesis_AN.pdf. Accessed Sep 30, 2020 24. Stern, O.: Zur Theorie Der Elektrolytischen Doppelschicht. Zeitschrift für Elektrochemie und angewandte physikalische Chemie 30(21–22), 508–516 (1924). https://doi.org/10.1002/bbpc. 192400182 25. Grahame, D.C.: The electrical double layer and the theory of electrocapillarity. Chem. Rev. 41(3), 441–501 (1947). https://doi.org/10.1021/cr60130a002 26. Bockris, J.O., Devanathan, M.A.V., Müller, K., Butler, J.A.V.: On the structure of charged interfaces. proceedings of the royal society of London. Series A. Math. Phys. Sci. 274(1356), 55–79 (1963). https://doi.org/10.1098/rspa.1963.0114 27. Srinivasan, S.: Fuel Cells: From Fundamentals to Applications; Springer US (2006). https:// doi.org/10.1007/0-387-35402-6 28. Conway, B.E.: Transition from “Supercapacitor” to “Battery” behavior in electrochemical energy storage. J. Electrochem. Soc. 138(6), 1539 (1991). https://doi.org/10.1149/1.2085829 29. The Nobel Prize in Chemistry 1992. https://www.nobelprize.org/prizes/chemistry/1992/sum mary/. Accessed Sep 30, 2020 30. Israelachvili, J.N.: Intermolecular and Surface Forces. Academic Press (2011)

66

4 Graphene Field-Effect Transistor Biosensor

31. Kuila, T., Bose, S., Khanra, P., Mishra, A.K., Kim, N.H., Lee, J.H.: Recent advances in graphene-based biosensors. Biosens. Bioelectron. 26(12), 4637–4648 (2011). https://doi.org/ 10.1016/j.bios.2011.05.039 32. Huang, X., Qi, X., Boey, F., Zhang, H.: Graphene-based composites. Chem. Soc. Rev. 41(2), 666–686 (2012). https://doi.org/10.1039/C1CS15078B 33. Fernandes, E., Cabral, P.D., Campos, R., Machado, G., Cerqueira, M.F., Sousa, C., Freitas, P.P., Borme, J., Petrovykh, D.Y., Alpuim, P.: Functionalization of single-layer graphene for immunoassays. Appl. Surf. Sci. 480, 709–716 (2019). https://doi.org/10.1016/j.apsusc.2019. 03.004 34. Huang, Y., Dong, X., Shi, Y., Ming Li, C., Li, L.-J., Chen, P.: Nanoelectronic biosensors based on CVD grown graphene. Nanoscale 2(8), 1485–1488 (2010). https://doi.org/10.1039/C0N R00142B 35. Park, C.S., Yoon, H., Kwon, O.S.: Graphene-based nanoelectronic biosensors. J. Ind. Eng. Chem. 38, 13–22 (2016). https://doi.org/10.1016/j.jiec.2016.04.021 36. Du, X., Guo, H., Jin, Y., Jin, Q., Zhao, J.: Electrochemistry investigation on the graphene/electrolyte interface. Electroanalysis 27(12), 2760–2765 (2015). https://doi.org/10. 1002/elan.201500302 37. Shimatani, M., Ogawa, S., Fujisawa, D., Okuda, S., Kanai, Y., Ono, T., Matsumoto, K.: Giant dirac point shift of graphene phototransistors by doped silicon substrate current. AIP Adv. 6(3), 035113 (2016). https://doi.org/10.1063/1.4944622 38. Fang, Z., Wang, Y., Liu, Z., Schlather, A., Ajayan, P.M., Koppens, F.H.L., Nordlander, P., Halas, N.J.: Plasmon-induced doping of graphene. ACS Nano 6(11), 10222–10228 (2012). https:// doi.org/10.1021/nn304028b 39. Maehashi, K., Sofue, Y., Okamoto, S., Ohno, Y., Inoue, K., Matsumoto, K.: Selective ion sensors based on ionophore-modified graphene field-effect transistors. Sens. Actuat. B: Chem. 187, 45–49 (2013). https://doi.org/10.1016/j.snb.2012.09.033 40. Goldsmith, B.R., Locascio, L., Gao, Y., Lerner, M., Walker, A., Lerner, J., Kyaw, J., Shue, A., Afsahi, S., Pan, D., Nokes, J., Barron, F.: Digital biosensing by foundry-fabricated graphene sensors. Scientific Reports 9(1), 434 (2019). https://doi.org/10.1038/s41598-019-38700-w 41. Pan, L.-H., Kuo, S.-H., Lin, T.-Y., Lin, C.-W., Fang, P.-Y., Yang, H.-W.: An electrochemical biosensor to simultaneously detect VEGF and PSA for early prostate cancer diagnosis based on Graphene Oxide/SsDNA/PLLA nanoparticles. Biosens. Bioelectron. 89, 598–605 (2017). https://doi.org/10.1016/j.bios.2016.01.077 42. Neish, C.S., Martin, I.L., Henderson, R.M., Edwardson, J.M.: Direct visualization of ligandprotein interactions using atomic force microscopy. Br. J. Pharmacol. 135(8), 1943–1950 (2002). https://doi.org/10.1038/sj.bjp.0704660 43. Wayment, J.R., Harris, J.M.: Biotin-avidin binding kinetics measured by single-molecule imaging. Anal. Chem. 81(1), 336–342 (2009). https://doi.org/10.1021/ac801818t 44. Nguyen, T.T., Sly, K.L., Conboy, J.C.: Comparison of the energetics of avidin, streptavidin, Neutravidin, and anti-biotin antibody binding to biotinylated lipid bilayer examined by secondharmonic generation. Anal. Chem. 84(1), 201–208 (2012). https://doi.org/10.1021/ac202375n 45. Zhu, Y., Hao, Y., Adogla, E.A., Yan, J., Li, D., Xu, K., Wang, Q., Hone, J., Lin, Q.: A Graphenebased affinity Nanosensor for detection of low-charge and low-molecular-weight molecules. Nanoscale 8(11), 5815–5819 (2016). https://doi.org/10.1039/C5NR08866F 46. Cai, B., Huang, L., Zhang, H., Sun, Z., Zhang, Z., Zhang, G.-J.: Gold nanoparticles-decorated graphene field-effect transistor biosensor for femtomolar MicroRNA detection. Biosens. Bioelectron. 74, 329–334 (2015). https://doi.org/10.1016/j.bios.2015.06.068 47. Ohno, Y., Maehashi, K., Matsumoto, K.: Label-Free biosensors based on aptamer-modified graphene field-effect transistors. J. Am. Chem. Soc. 132(51), 18012–18013 (2010). https://doi. org/10.1021/ja108127r 48. Wang, Z., Yi, K., Lin, Q., Yang, L., Chen, X., Chen, H., Liu, Y., Wei, D.: Free radical sensors based on inner-cutting graphene field-effect transistors. Nature Commun. 10(1), 1–10 (2019). https://doi.org/10.1038/s41467-019-09573-4

References

67

49. Xu, S., Zhan, J., Man, B., Jiang, S., Yue, W., Gao, S., Guo, C., Liu, H., Li, Z., Wang, J., Zhou, Y.: Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor. Nature Communications 8(1), 1–10 (2017). https://doi.org/ 10.1038/ncomms14902 50. Yang, Y., Yang, X., Zou, X., Wu, S., Wan, D., Cao, A., Liao, L., Yuan, Q., Duan, X.: Ultrafine Graphene Nanomesh with large On/Off ratio for high-performance flexible biosensors. Adv. Func. Mater. 27(19), 1604096 (2017). https://doi.org/10.1002/adfm.201604096 51. Yu, Y., Li, Y.-T., Jin, D., Yang, F., Wu, D., Xiao, M.-M., Zhang, H., Zhang, Z.-Y., Zhang, G.-J.: Electrical and label-free quantification of exosomes with a reduced graphene oxide field effect transistor biosensor. Anal. Chem. 91(16), 10679–10686 (2019). https://doi.org/10.1021/acs. analchem.9b01950 52. Zhou, L., Mao, H., Wu, C., Tang, L., Wu, Z., Sun, H., Zhang, H., Zhou, H., Jia, C., Jin, Q., Chen, X., Zhao, J.: Label-free graphene biosensor targeting cancer molecules based on non-covalent modification. Biosens. Bioelectron. 87, 701–707 (2017). https://doi.org/10.1016/j.bios.2016. 09.025 53. Matsumoto, K.: Frontiers of Graphene and Carbon Nanotubes: Devices and Applications; Springer (2015) 54. Kwong Hong Tsang, D., Lieberthal, T.J., Watts, C., Dunlop, I.E., Ramadan, S., del Rio Hernandez, A.E., Klein, N.: Chemically functionalised graphene FET biosensor for the labelfree sensing of exosomes. Scientific Reports 9 (1), 13946 (2019). https://doi.org/10.1038/s41 598-019-50412-9 55. Taniguchi, Y., Miki, T., Ohno, Y., Nagase, M., Arakawa, Y., Yasuzawa, M.: Observation of the interaction between avidin and Iminobiotin using a Graphene FET on a SiC substrate. Jpn. J. Appl. Phys. 58 (SD), SDDD02 (2019). https://doi.org/10.7567/1347-4065/ab0544 56. Nozaki, R., Ikuta, T., Ueno, K., Tsukakoshi, K., Ikebukuro, K., Maehashi, K.: Ethanol detection at the parts per billion level with single-stranded-DNA-modified graphene field-effect transistors. Physica Status Solidi (b) 257(2), 1900376 (2020). https://doi.org/10.1002/pssb.201 900376 57. Parate, K., Rangnekar, S.V., Jing, D., Mendivelso-Perez, D.L., Ding, S., Secor, E.B., Smith, E.A., Hostetter, J.M., Hersam, M.C., Claussen, J.C.: Aerosol-Jet-Printed Graphene immunosensor for label-free cytokine monitoring in serum. ACS Appl. Mater. Interfaces. 12(7), 8592–8603 (2020). https://doi.org/10.1021/acsami.9b22183 58. Kim, D.-J., Sohn, I.Y., Jung, J.-H., Yoon, O.J., Lee, N.-E., Park, J.-S.: Reduced graphene oxide field-effect transistor for label-free femtomolar protein detection. Biosens. Bioelectron. 41, 621–626 (2013). https://doi.org/10.1016/j.bios.2012.09.040 59. Tian, M., Qiao, M., Shen, C., Meng, F., Frank, L.A., Krasitskaya, V.V., Wang, T., Zhang, X., Song, R., Li, Y., Liu, J., Xu, S., Wang, J.: Highly-Sensitive Graphene field effect transistor biosensor using PNA and DNA probes for RNA detection. Appl. Surf. Sci. 527, 146839 (2020). https://doi.org/10.1016/j.apsusc.2020.146839 60. Ang, P.K., Li, A., Jaiswal, M., Wang, Y., Hou, H.W., Thong, J.T.L., Lim, C.T., Loh, K.P.: Flow sensing of single cell by graphene transistor in a microfluidic channel. Nano Lett. 11(12), 5240–5246 (2011). https://doi.org/10.1021/nl202579k 61. Wang, Z., Yi, K., Lin, Q., Yang, L., Chen, X., Chen, H., Liu, Y., Wei, D.: Free radical sensors based on inner-cutting graphene field-effect transistors. Nature Commun. 10(1), 1544 (2019). https://doi.org/10.1038/s41467-019-09573-4 62. Chen, Y., Ren, R., Pu, H., Guo, X.; Chang, J., Zhou, G., Mao, S., Kron, M., Chen, J.: Fieldeffect transistor biosensor for rapid detection of ebola antigen. Sci Rep 7 (2017). https://doi. org/10.1038/s41598-017-11387-7

Chapter 5

Graphene FET Biosensor Based on the Avidin–Biotin Technology

Abstract Avidin-biotin interaction is the strongest known protein-ligand interaction and is widely used in the field of biomedicine, including immunochromatography and various biological detection technologies. Because avidin or streptavidin can specifically bind to four biotinylated molecules efficiently and quickly, and the biotinylated molecules will not change their inherent characteristics. At present, this avidin-biotin interaction has been widely used in the field of biomedical detection to amplify biological signals for specific molecules. Combined with the graphene fieldeffect transistor biosensor, using the avidin-biotin interaction, the concentration of specific molecules in the test solution is expected to be amplified by the biotin-avidin interaction, which helps to increase the limit of detection for the biosensor. Besides, based on the avidin-biotin interaction, the graphene field-effect transistor biosensor is expected to rapidly quantitative detect free biotin and biotinylated molecules. This chapter mainly introduces the graphene field-effect transistor biosensor from the perspectives of device preparation, surface modification, sensitivity, and specificity, and discusses the possible situations that the sensor may encounter in actual detection. Keywords Avidin · Biotin · Graphene FET · Avidin-biotin interaction Avidin–biotin technology is widely used in different types of ELISA (enzyme-linked immunosorbent assay) kits, polymer-based detection and labeled immunosensor s for the detection of different bio-markers linked to different diseases such as cancer and influenza. In this section, we introduced the employing avidin-biotin technology in the graphene FET biosensor and demonstrated the specific detection of the biotinylated biomolecule in the sub-pico molar (pM) range. The sensing performance of graphene FET biosensor was characterized by the real-time two-terminal electrical current measurement upon injection of analyte solution into a silicone pool preattached onto the graphene channel. Since the Avidin–biotin technology has strong affinity and specificity, any biotinylated biomolecules are hopping for rapid detecting with ultra-low concentration level through this sensing platform. Thus the present graphene FET biosensor is expected to be a breakthrough in biomedical analysis. It can be used as a potential common platform for the rapid and point of care detection of different biomolecules and biomarkers linked to different diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_5

69

70

5 Graphene FET Biosensor Based …

5.1 Background The strong interaction between avidin and biotin has been widely exploited in many applications such as protein and nucleic acid detection, immobilization, and purification methods [1–3]. Avidin produced in the oviducts of reptiles, birds, and amphibians is a tetrameric biotin-binding protein and deposited in the egg white. Some research reported that Dimeric members of the avidin family are also found in some bacteria [4]. The avidin makes up approximately 0.05% of total protein (approximately 1800 μg per egg) in chicken egg white. Avidin contains four identical subunits (homotetramer), each of which can specifically bind biotin (vitamin B7, vitamin H) with high affinity [5]. Biotin involves in a wide range of metabolic processes, both in humans and other organisms, primarily related to the utilization of fats, carbohydrates, and amino acids [6]. Because of its small size, biotin is a useful label for many proteins and nucleotides without changing the original properties of the proteins or nucleotides. The process of labeling a protein or nucleotide with biotin is known as biotinylation. It is also important that biotin protein ligases can attach biotin to specific lysine residues in vitro or in living cells [7]. The interaction between avidin and biotin is known as the most specific and strongest non-covalent interaction (Kd = 10−15 M) between a protein and ligand [5]. The exceptionally strong affinity of avidin for biotin arises from hydrophobic interactions of biotin and aromatic amino acids arranged in the binding pocket of avidin and a multiple hydrogen bonding between heteroatoms in the ureido ring of biotin and asparagine, serine, tyrosine, and threonine residues in avidin [7]. Due to the strong interaction, avidin–biotin complex is robust and stable against temperature, pH, harsh organic solvents and denaturing reagents. For the unique properties of avidin–biotin system, it has been used in many enzyme-linked immunosorbent assay (ELISA) for the purpose of different types of medical applications such as cancer diagnosis [8–11]. In addition, the other fantastic applications such as labeled immunosensors [12], polymer-based detection systems [13] are also based on the avidin–biotin system. To date, most of the biomolecule and biomarker detections are based on the ELISA, where the quantitative detection is achieved through the measurement of intensity of transmitted light by spectrophotometry. Experimentally, the specific conjugation of biomolecules is realized in terms of optical signals, which are then converted into electrical signals by spectrophotometry for quantitative reading. The direct conversion of biological conjugation into electrical signals is of great significance in clinical diagnosis, especially for the development of simple, easy to use and low-cost sensing devices. The direct electrochemical detection methods possess not only the advantages of simplicity, fast responses, and ease of use but also promising for miniaturization of the diagnostics instruments into low-cost microscale dimensions [14]. Of course, the present status of ELISA detection methods in medical diagnosis is unshakeable because of its widespread application and commercialization of the kits. Hence other methods of biomolecules detection including field-effect transistor (FET)-based sensor might not immediately be the alternative to the ELISA but to the complementary to ELISA as an initial screening method for dealing with large-scale epidemic situation.

5.1 Background

71

Biosensors are comprised of mainly two components. A bio-recognition molecule or receptor such as antibody or antigen (or capture molecule) determines the specificity, and a signal transducer such as graphene determines the sensitivity of the sensor [15]. The extremely high carrier mobility (200,000 cm2 V−1 s−1 ), ambipolar transfer characteristics [16, 17], physical flexibility and robustness in ambient conditions make the graphene the most ideal material for different types of ultra-sensitive graphene field-effect transistor (GFET) sensors including biosensor. Because of the compatibility, sensitivity, and the advancement of the state of art nanofabrication technique, GFET sensor has drawn much attention as the most promising approach to point-of-care medical diagnostics for rapid, sensitive, specific, low-cost detection and quantification of biomarkers [8]. To date, a number of studies have been reported on the potential applications of GFET in biosensors [18–30]. The detection of biomolecules including cancer marker s and RNA s by GFET biosensors has been reported using various types of acceptor/receptor design [18–26]. Ohno et al. have reported the electrolyte gated GFET for pH and protein detection [27]. The aptamermodified GFET has been reported for label-free detection of immunoglobulin (IgE) protein [20]. Recently, GFET has been shown to detect ethanol at ppb level. Deana et al. have reported the label free sensing of exosomes using functionalized graphenebased FET [28]. Seo et al. have reported the rapid detection of COVID-19 causative virus (SARS-CoV-2) using GFET [31]. Aerosol-jet-printed graphene-based sensors were used for label-free cytokine monitoring in serum and food safety [32, 33]. Because of the easy attachment of avidin on solid surface, avidin immobilized on graphene can be utilized as a prove to detect biotin and biotinylated protein in the form of electrical signal allowing the real-time point of care diagnosis. As a common linker of the graphene FET biosensor, PBASE is usually used to chemically modify the surface of the graphene through the Van der Waals force between the graphene and the pyrene backbone of the PBASE molecule. Then PBASE modified graphene is further modified with avidin through the NHS ester reaction between the lysine residue of avidin and N-hydroxysuccinimide ester group of PBASE. Otherwise, graphene sheets decorated with metal nanoparticles (MNPs) such as gold nanoparticles (AuNPs) are excellent materials for biosensors platforms because of the bio-compatibility of AuNPs and the further enhancement of the carrier mobility of the graphene [34, 35]. Recently, AuNPs decorated reduced graphene oxide FET has been reported for the label-free detection of miRNA using peptide nucleic acid as bio-recognition molecule [36, 37]. Those studies reported to date utilize a surface-immobilized recognition probe to selectively interact with a biological analyte in solution, i.e., the GFET device is designed for the detection of a specific biological analyte. Indeed, the binding capability of the bio-recognition molecule limits the application of the sensor, i.e., if the bio-recognition molecule can be tailored to bind different biomolecules with high specificity, then the same sensing platform can be used for different target analytes. It is thus of great importance to develop a universal point of care sensing platform that can be used for a wide range of detection purposes simply by labeling or linking the biomolecule to be detected.

72

5 Graphene FET Biosensor Based …

5.2 Biotinylated Biomolecules Detection In biochemistry, biotinylation is the process of covalent bonding of biotin to nucleic acids, proteins, or other molecules. The process of biotinylation is specific and rapid, and due to the small size of biotin (molecular weight = 244.31 g/mol), the process of biotin is unlikely to interfere with the original function of the modified molecule. Biotin can bind to streptavidin and avidin with high affinity, rapid opening rate, and high specificity. This interaction is usually used in many fields of biotechnology to separate and detect target biotinylated molecules. The combination of biotin and avidin has certain resistance to extreme pH, heat, and proteolysis, which allows this interaction to quickly capture biotinylated molecules in various complex environments with high specificity, thereby detecting target biomolecules. In this section, the graphene FET biosensor based on avidin–biotin technology will be explained.

5.2.1 Device Fabrication The commercially available SiO2 /Si wafer (4-inch 285 nm) was cut into the desired size (typically 1 cm × 1 cm). Then the cut SiO2 /Si was used as the substrate platform for the fabrication of interdigital electrode. For the fabrication of the interdigital electrodes, the commercially available shadow mask was carefully attached to the SiO2 /Si surface. As the electrode materials, chromium (Cr) and gold (Au) were used. The subsequent deposition of Cr and Au with desired thickness was achieved by the electron beam vacuum evaporation deposition technique. Figure 5.1 shows the fullscale optical image of the interdigital electrodes fabricated on a SiO2 /Si substrate after electrode deposition. Transferring graphene onto the interdigital electrodes is a crucial step for the fabrication of the sensor device. For transferring the graphene on the interdigital electrodes, a thin layer of PMMA was deposited on the graphene on copper foil Fig. 5.1 The full-scale optical image of the interdigital electrodes fabricated on a SiO2 /Si substrate

5.2 Biotinylated Biomolecules Detection

73

using PMMA in acetone solution. The deposition of PMMA solution was done using a spin coater followed by 5 min of heating at 90 °C to evaporate the acetone solvent. Then this PMMA/graphene on the copper foil was cut into 5 mm × 5 mm size so that the interdigital electrodes can be fully covered. For etching of the copper foil, the cut PMMA/graphene on copper foil was placed on ammonium peroxodisulfate aqueous solution until the copper fully is fully dissolved. This etching process lefts the PMMA/graphene film floating on the surface of the solution. Then the PMMA/graphene film was carefully transferred on the ultra-pure water, held for 30 min for removal of ammonium peroxodisulfate adhered to the film. Then the clean PMMA/graphene film was transferred onto the surface of the interdigitated electrode supported on SiO2 /Si substrate. The transferred PMMA/graphene film on the interdigital electrodes was then dried in air for 30 min at room temperature and then heated at 60 °C for 30 min in an oven. Finally, the PMMA attached with graphene was removed with boiling acetone and isopropyl alcohol (IPA), which left the graphene attached to the interdigital electrodes [38]. For the preparation of GFET, it is inseparable from semiconductor technology. First, a specific electrode shape needs to be prepared by photolithography on the substrate. If the small channel size (less than 1 micron) of the graphene FET is required, electron beam lithography or maskless photolithography can be used. Figure 5.2 shows electron beam lithography equipment and maskless lithography equipment, they can produce the nano-scale size of the channel, but it takes a long time. These equipment are very suitable for GFET research in nano-level applications. Ordinary UV mask lithography technology is sufficient for most current applications, particularly some of the GFET applications are not the severe requirements in the size of the channel. Figure 5.3 shows a typical UV mask lithography platform. After the specific electrode shape is prepared by photolithography, it needs to be deposited to prepare the metal electrode. At present, there are many deposition methods such as thermal evaporation, sputtering, and electron beam evaporation. In order to ensure the uniformity and adhesion of the coating, electron beam

Fig. 5.2 a The image of the electron beam lithography equipment. b The image of the maskless lithography equipment

74

5 Graphene FET Biosensor Based …

Fig. 5.3 The image of the typical UV mask lithography platform

evaporation is recommended. In addition, in order to better remove the photoresist, it is recommended to use high-pressure jet lift-off equipment. Figure 5.4 shows the electron beam evaporation equipment and high-pressure jet lift-off equipment. After the photoresist is removed, annealing (inert gas protection) is required in usual to release the stress on the interface between the electrode and the substrate. The thermal annealing equipment is the same as shown in Fig. 5.5b. Although photolithography provides higher manufacturing accuracy and better uniformity; however, the photolithography process is complicated, so if the requirements for the channel are at the 100 μm level, the substrate can be directly covered with the shadow mask then used to deposit the electrode. As a simple method, it can both avoid the photoresist contaminates the surface of the substrate and simplify the processing, particularly, the absolute cleanliness of the substrate surface is very important for two-dimensional material such as graphene, their properties are very easy to be affected with the substrate. Our sample is manufactured with this method. The commercially available SiO2 /Si wafer (4-inch 285 nm) was cut into the desired size (typically 1 cm × 1 cm) by the dicing saw equipment shown in Fig. 5.5a. The cut SiO2 /Si shown in Fig. 5.5b was used as the substrate platform for the fabrication of the interdigital electrode. For the fabrication of the interdigital electrodes, the commercially available shadow mask was carefully attached to the SiO2 /Si surface. As the electrode materials, chromium (Cr) and gold (Au) were used. The subsequent deposition of Cr and Au with desired thickness was achieved by the electron beam

5.2 Biotinylated Biomolecules Detection

75

Fig. 5.4 a The image of electron beam evaporation equipment. b The image of the high-pressure jet lift-off equipment

Fig. 5.5 a The image of the dicing saw equipment. b The image of the cut SiO2 /Si wafer

76

5 Graphene FET Biosensor Based …

Fig. 5.6 a The full-scale optical image of the interdigital electrodes fabricated on a SiO2 /Si substrate. b The amplified optical image of the interdigital electrodes fabricated on a SiO2/Si substrate. The electrode width and the electrode gap are both 200 microns

vacuum evaporation deposition technique. Figure 5.6 shows the full-scale optical image of the interdigital electrodes fabricated on a SiO2 /Si substrate after electrode deposition. Because our GFET device uses CVD graphene to make the channel, transferring of CVD graphene onto the interdigital electrodes is a crucial step for the fabrication of the GFET. For transferring the graphene on the interdigital electrodes, a thin layer of PMMA was deposited on the graphene on copper foil using PMMA in acetone solution. The deposition of PMMA solution was done using a spin coater followed by 5 min of heating at 90 °C to evaporate the acetone solvent. Then this PMMA/graphene on the copper foil was cut into 5 mm × 5 mm size so that the interdigital electrodes can be fully covered. For etching of the copper foil, the cut PMMA/graphene on copper foil was placed on ammonium peroxodisulfate aqueous solution until the copper fully is fully dissolved. This etching process lefts the PMMA/graphene film floating on the surface of the solution. Then the PMMA/graphene film was carefully transferred on the ultra-pure water, held for 30 min for the removal of ammonium peroxodisulfate adhered to the film. Then the clean PMMA/graphene film was transferred onto the surface of the interdigitated electrode supported on SiO2 /Si substrate. The transferred PMMA/graphene film on the interdigital electrodes was then dried in air for 30 min at room temperature and then heated at 60 °C for 30 min in an oven. Finally, the PMMA attached with graphene was removed with boiling acetone and isopropyl alcohol (IPA), which left the graphene attached to the interdigital electrodes [38]. Note that some reports have shown that hydrogen annealing can better remove residual organic matter on the graphene surface, but this may destroy the integrity of the graphene surface. Figure 5.7 shows the processing flow of the GFET chip fabrication.

5.2 Biotinylated Biomolecules Detection

77 PMMA Graphene Copper film

Etching soluƟon PMMA e

Graphene

Water

SiO2 (285 nm)

Cr/Au (25 nm/50 nm)

e

Graphene

Si

E-BEAM evaporaƟon

PMMA

Graphene transfer

Graphene

PMMA removing

Fig. 5.7 The processing flow of the GFET chip fabrication

5.2.2 Graphene Modification Graphene modification is the key bridge step connected with the GFET chip fabrication and the actual detection. The graphene surface functionalized by the modification process enables the GFET to achieve specific functions. Therefore, the graphene surface modification of GFET is a very important process. For the nanoparticle modification shown in Fig. 5.8a, the transferred graphene on SiO2 /Si substrate was directly immersed into the as-prepared AuNPs solution during the AuNP synthesis at room temperature. The AuNPs were synthesized by the reduction of 1 mM HAuCl4 solution by slow addition of sodium borohydride (NaBH4). Thus homogeneous decoration of AuNPs (25–70 nm) on graphene transferred on SiO2 /Si substrate was obtained. To facilitate the real-time measurement upon the addition of liquid analytes, a pre-designed liquid pool made with a silicone sheet was fixed on the top of the AuNPs decorated graphene. There are many methods for the characterization of gold nanoparticles on graphene, for the microscopy methods such as atomic force microscope (AFM) and scanning electron microscope (SEM), and for the chemical detection methods such as X-ray photoelectron spectroscopy (XPS) [38]. For the PBASE modification shown in Fig. 5.8b, the surface modification with PBASE is optimized by incubating the graphene on interdigital electrodes device in the dry dimethylformamide (DMF) solution of 50 mM PBASE for 4 h at room temperature individually. Then the PBASE modified graphene is washed with methanol three times and dried with rotary pump. Then a pre-designed liquid pool made with silicone sheet is constructed on the top of the PBASE modified graphene so that

78

5 Graphene FET Biosensor Based …

Fig. 5.8 a The image of AuNPs surface-modified processing. b The image of PBASE surfacemodified processing

the desired bio-solution to be tested can be injected into this test pool [38]. After PBASE is modified onto the surface of the graphene, the PBASE modification can usually be characterized by observing the increase of the D (1350 cm−1 ) and D’ band (1620 cm−1 ) peaks of the Raman spectrum [38]. The avidin was immobilized on modified graphene by injecting 100 μl of 1 mg/ml avidin-PBS solution into the pool, and holding for 1 h at room temperature. Then the device is washed with 100 μl PBS three times. To prevent the non-specific binding of the residual surface regions (regions unmodified by avidin) to other molecules, which may affect the signal during actual testing of the sensor device, the remaining unmodified surface regions of the graphene were blocked by injecting 100 μl of 0.01 mg/ml BSA-PBS solution into the pool and holding for 1 h at room temperature followed by washing with 100 μl PBS three times. For the nanoparticle modification, the schematic image of the biotin ylated molecules capturing is shown in Fig. 5.9.

5.2.3 Quantitative Detection For the quantitative detection, the biotin ylated biomolecule solution to be tested is prepared to add into the test pool. Figure 5.10 shows the optical image for the GFET biosensor during the real-time detection. During this processing, the current value

5.2 Biotinylated Biomolecules Detection

79

Fig. 5.9 The schematic image of the biotin ylated molecules capturing

Fig. 5.10 The optical image for the graphene FET biosensor during the real-time detection

of the Ids is monitored the whole time. Note that for avoiding the interference of the current Ids signal, the top-gate (reference electrode) is not recommended. It is observed that based on the concentration of biotinylated biomolecule solution, the stable current value Ids gradually varies. In common, the current Ids value quickly remains stable, the response time is less than 1 min. In addition, when the test solution is added to the pool, there will be a significant characteristic that occurs a quick sharp current alter, then gradually return to a stable. Note that the spikes in Ids vs time curve upon addition of the solutions are the signature of an interfacial capacitance that previously mentioned, it is disrupted by the fresh solution added and

80

5 Graphene FET Biosensor Based …

then recovers with time. Hence by monitoring the sharp current drops upon addition of the test solution and subsequent recovery to stable Ids, the limit of detection for biotinylated biomolecule can be estimated to the concentration of pM level. Based on the avidin–biotin technology, the value of detection limit is lower than that of the reported value in the case of pristine GFET biosensor [38].

5.2.4 Specificity of the Sensor One of the key challenges in the practical applicability of a biosensor is the specific detection of the target molecule or marker. Avidin is a glycoprotein that contains four identical subunits of 16,400 Daltons each, giving an intact molecular weight of approximately 66,000 [39]. Each subunit contains one binding site for biotin. The tetrameric protein is highly basic, having a pI of about 10. Tryptophan and lysine residues in each subunit are known to be involved in forming the binding pocket [40]. Once the biotin is bound to avidin, the avidin–biotin complex became much robust against breakdown. A minimum of 6–8 M guanidine at pH 1.5 is required for inducing complete dissociation of the avidin–biotin complex [41, 42]. Such robust nature of avidin–biotin complex against any denaturing agent makes the complex a very useful in bioconjugate chemistry. Even biotinylated molecules and avidin can also bind together and make the complex under extreme conditions. It can be said that the specificity of the avidin–biotin interaction is similar to that of antibody–antigen or receptor-ligand recognition, but with much higher affinity. Indeed, the specific binding of biotinylated molecules with avidin cannot be prevented by variations in buffer salt, pH, the presence of denaturants or detergents, and extremes of temperature [43]. Because of the high pI and carbohydrate content, natural avidin has tendency to bind nonspecifically with components other than biotin, hence, it shows some disadvantages to using for sensing purposes. However, this disadvantage of tendency to non-specific binding has been eliminated in the chemically modified avidin, such as NeutrAvidin used in the present study, through deglycosylation and reducing its pI through covalent modification of charged residues. Hence the present avidinimmobilized graphene FET platform is expected to exhibit high specificity.

5.2.5 Exogenous Biotin Interferences In the case of blood sample analysis, one of the main disadvantages of the avidinbiotin technology is the interference of exogenous biotin present in the blood serum, which can range from 0.12 to 0.36 nM [6]. Indeed, normal intake of biotin from various foods and milk poses little effect on the avidin/biotin-based immunoassays. However, overconsumption of biotin (daily doses 100–300 mg) can significantly affect the avidin–biotin-based immunoassays. Biotin interferences have been noted in immunoassays designed for thyroid markers, drugs, hormones, cancer marker s,

5.2 Biotinylated Biomolecules Detection

81

the biomarker for cardiac function (β-human chorionic gonadotropin), etc [44]. Since the sensitivity of the present graphene FET platform lies in the pM range, the lowering or removal of biotin content in the target sample should be considered before testing the blood sample. There are different methods that can be used for lowering or preremoving the biotin from the blood serum. For example, biotin can be removed from blood serum by interacting the serum with avidin/streptavidin immobilized on an insoluble matrix such as magnetic particles, polymers (e.g. agarose beads), silica, etc [45].

5.2.6 Comparative Sensitivity and Practical Applicability To date, a number of studies have reported on the utilization of avidin–biotin interaction in sensing technology [46–50]. The detection limits ranged from 2 pM to 84 nM have been reported using various detection methods such as competitive immunoassay, electrochemical, cyclic voltammetry, and immunoaffinity chromatography as listed in Table 5.1. The graphene FET biosensor based on avidin–biotin technology presented here shows the highest sensitivity and the minimum time requirement for real-time detection. Similar to the biotin-avidin system used in the different types of conventional ELISA kits [51–53], the present graphene FET biosensor is expected to be utilized for the detection of antigen-antibody, hormone-receptor, nucleic acid system in body fluid, tissue and cell, and other bioactive macromolecules through the judicious Table 5.1 Avidin–biotin technology used in different biosensors. The limit of detection and time required for data acquisition compared to the present study are indicated Platform

Material

Method

Limit of detection

Time

References

Beads and gold nanoparticles

Anti-biotin antibody

Competitive immunoassay

2 pM

15 min

[46]

Boron-doped diamond electrode

Captavidin

Electrochemical sensing

1 nM

10 min

[48]

Boron-doped diamond electrode

Streptavidin

Electrochemical sensing

5 nM

10 min

[47]

Electrochemical magneto biosensor

Streptavidin

Cyclic voltammetry

84 nM

40 min

[49]

Sepharose beads

Anti-biotin antibody

Immunoaffinity chromatographic

41 pM

30 min

[50]

Graphene FET biosensor

Streptavidin (neutravidin)

Current Ids 0.4 pM real-time monitor

1 min

[38]

82

5 Graphene FET Biosensor Based …

choice of biotinylation. As the common sensing platform, because of the avidin’s strong affinity for biotinylated macromolecule with high specificity, the different biotinylated proteins, nucleotides, etc., can be quantitatively detected by monitoring the current (Ids) response upon injection on to the device.

References 1. Guesdon, J.L., Ternynck, T., Avrameas, S.: The use of avidin-biotin interaction in immunoenzymatic techniques. J. Histochem. Cytochem. 27(8), 1131–1139 (1979). https://doi.org/10.1177/ 27.8.90074 2. Hsu, S., Raine, L., Fanger, H.: The use of antiavidin antibody and avidin-biotin-peroxidase complex in immunoperoxidase technics. Am. J. Clin. Pathol. 75(6), 816–821 (1981). https:// doi.org/10.1093/ajcp/75.6.816 3. Hsu, S.M., Raine, L., Fanger, H.: Use of avidin-biotin-peroxidase complex (ABC) in immunoperoxidase techniques: a comparison between ABC and unlabeled antibody (PAP) procedures. J. Histochem. Cytochem. 29(4), 577–580 (1981). https://doi.org/10.1177/29.4. 6166661 4. Helppolainen, S.H., Nurminen, K.P., Määttä, J.A.E., Halling, K.K., Slotte, J.P., Huhtala, T., Liimatainen, T., Ylä-Herttuala, S., Airenne, K.J., Närvänen, A., Jänis, J., Vainiotalo, P., Valjakka, J., Kulomaa, M.S., Nordlund, H.R.: Rhizavidin from Rhizobium Etli: the first natural dimer in the avidin protein family. Biochem. J. 405(3), 397–405 (2007). https://doi.org/10.1042/ BJ20070076 5. Green, N.: avidin. 1. the use of [14c]biotin for kinetic studies and for assay. Biochem J 89(3), 585–591 (1963). https://doi.org/10.1042/bj0890585 6. Zempleni, J., Wijeratne, S.S.K., Hassan, Y.I.: Biotin. BioFactors 35(1), 36–46 (2009). https:// doi.org/10.1002/biof.8 7. Chivers, C.E., Koner, A.L., Lowe, E.D., Howarth, M.: How the biotin–streptavidin interaction was made even stronger: investigation via crystallography and a chimaeric tetramer. Biochem. J. 435(1), 55–63 (2011). https://doi.org/10.1042/BJ20101593 8. Ben Aissa, A., Herrera-Chacon, A., Pupin, R.R., Sotomayor, M.D.P.T., Pividori, M.I.: Magnetic molecularly imprinted polymer for the isolation and detection of biotin and biotinylated biomolecules. Biosens. Bioelectron. 88, 101–108 (2017). https://doi.org/10.1016/j.bios.2016. 07.096 9. Gutiérrez-Zúñiga, G.G., Hernández-López, J.L.: Sensitivity improvement of a sandwich-type ELISA immunosensor for the detection of different prostate-specific antigen isoforms in human serum using electrochemical impedance spectroscopy and an ordered and hierarchically organized interfacial supramolecular architecture. Anal. Chim. Acta 902, 97–106 (2016). https:// doi.org/10.1016/j.aca.2015.10.042 10. Lakshmipriya, T., Gopinath, S.C.B., Tang, T.-H.: Biotin-streptavidin competition mediates sensitive detection of biomolecules in enzyme linked immunosorbent assay. PLoS One 11(3) (2016). https://doi.org/10.1371/journal.pone.0151153 11. Yang, Y.-C., Tseng, W.-L.: 1,4-benzenediboronic-acid-induced aggregation of gold nanoparticles: application to hydrogen peroxide detection and biotin–avidin-mediated immunoassay with naked-eye detection. Anal. Chem. 88(10), 5355–5362 (2016). https://doi.org/10.1021/acs. analchem.6b00668 12. Yang, X., Yuan, R., Chai, Y., Zhuo, Y., Mao, L., Yuan, S.: Ru(Bpy)32+ -doped silica nanoparticles labeling for a sandwich-type electrochemiluminescence immunosensor. Biosens. Bioelectron. 25(7), 1851–1855 (2010). https://doi.org/10.1016/j.bios.2009.12.027

References

83

13. Ballesta-Claver, J., Ametis-Cabello, J., Morales-Sanfrutos, J., Megía-Fernández, A., ValenciaMirón, M.C., Santoyo-González, F., Capitán-Vallvey, L.F.: Electrochemiluminescent disposable cholesterol biosensor based on avidin-biotin assembling with the electroformed luminescent conducting polymer poly(Luminol-Biotinylated Pyrrole). Anal. Chim. Acta 754, 91–98 (2012). https://doi.org/10.1016/j.aca.2012.10.006 14. Gao, W., Jeanneret, S., Yuan, D., Cherubini, T., Wang, L., Xie, X., Bakker, E.: Electrogenerated chemiluminescence for chronopotentiometric sensors. Anal. Chem. 91(7), 4889–4895 (2019). https://doi.org/10.1021/acs.analchem.9b00787 15. Bhalla, N., Jolly, P., Formisano, N., Estrela, P.: Introduction to biosensors. Essays Biochem. 60(1), 1–8 (2016). https://doi.org/10.1042/EBC20150001 16. Chen, B., Huang, H., Ma, X., Huang, L., Zhang, Z., Peng, L.-M.: How good can CVD-grown monolayer graphene be? Nanoscale 6(24), 15255–15261 (2014). https://doi.org/10.1039/C4N R05664G 17. Geim, A.K., Novoselov, K.S.: The rise of Graphene. In: Nanoscience and Technology; CoPublished with Macmillan Publishers Ltd, UK, pp 11–19 (2009). https://doi.org/10.1142/978 9814287005_0002 18. Novoselov, K.S., Geim, A.K., Morozov, S.V., Jiang, D., Zhang, Y., Dubonos, S.V., Grigorieva, I.V., Firsov, A.A.: Electric field effect in atomically thin carbon films. Science 306(5696), 666–669 (2004). https://doi.org/10.1126/science.1102896 19. Afsahi, S., Lerner, M.B., Goldstein, J.M., Lee, J., Tang, X., Bagarozzi, D.A., Pan, D., Locascio, L., Walker, A., Barron, F., Goldsmith, B.R.: Novel graphene-based biosensor for early detection of Zika virus infection. Biosens. Bioelectron. 100, 85–88 (2018). https://doi.org/10.1016/j.bios. 2017.08.051 20. Cai, B., Huang, L., Zhang, H., Sun, Z., Zhang, Z., Zhang, G.-J.: Gold nanoparticles-decorated graphene field-effect transistor biosensor for femtomolar MicroRNA detection. Biosens. Bioelectron. 74, 329–334 (2015). https://doi.org/10.1016/j.bios.2015.06.068 21. Ohno, Y., Maehashi, K., Matsumoto, K.: Label-Free biosensors based on aptamer-modified graphene field-effect transistors. J. Am. Chem. Soc. 132(51), 18012–18013 (2010). https://doi. org/10.1021/ja108127r 22. Pan, L.-H., Kuo, S.-H., Lin, T.-Y., Lin, C.-W., Fang, P.-Y., Yang, H.-W.: An electrochemical biosensor to simultaneously detect VEGF and PSA for early prostate cancer diagnosis based on graphene Oxide/SsDNA/PLLA nanoparticles. Biosens. Bioelectron. 89, 598–605 (2017). https://doi.org/10.1016/j.bios.2016.01.077 23. Wang, Z., Yi, K., Lin, Q., Yang, L., Chen, X., Chen, H., Liu, Y., Wei, D.: Free radical sensors based on inner-cutting graphene field-effect transistors. Nature Commun. 10(1), 1–10 (2019). https://doi.org/10.1038/s41467-019-09573-4 24. Xu, S., Zhan, J., Man, B., Jiang, S., Yue, W., Gao, S., Guo, C., Liu, H., Li, Z., Wang, J., Zhou, Y.: Real-time reliable determination of binding kinetics of DNA hybridization using a multichannel graphene biosensor. Nature Commun. 8(1), 1–10 (2017). https://doi.org/10.1038/nco mms14902 25. Yang, Y., Yang, X., Zou, X., Wu, S., Wan, D., Cao, A., Liao, L., Yuan, Q., Duan, X.: Ultrafine graphene nanomesh with large on/off ratio for high-performance flexible biosensors. Adv. Func. Mater. 27(19), 1604096 (2017). https://doi.org/10.1002/adfm.201604096 26. Yu, Y., Li, Y.-T., Jin, D., Yang, F., Wu, D., Xiao, M.-M., Zhang, H., Zhang, Z.-Y., Zhang, G.-J.: Electrical and label-free quantification of exosomes with a reduced graphene oxide field effect transistor biosensor. Anal. Chem. 91(16), 10679–10686 (2019). https://doi.org/10.1021/acs. analchem.9b01950 27. Zhou, L., Mao, H., Wu, C., Tang, L., Wu, Z., Sun, H., Zhang, H., Zhou, H., Jia, C., Jin, Q., Chen, X., Zhao, J.: Label-free graphene biosensor targeting cancer molecules based on non-covalent modification. Biosens. Bioelectron. 87, 701–707 (2017). https://doi.org/10.1016/j.bios.2016. 09.025 28. Matsumoto, K.: Frontiers of Graphene and Carbon Nanotubes: Devices and Applications. Springer (2015)

84

5 Graphene FET Biosensor Based …

29. Kwong Hong Tsang, D., Lieberthal, T.J., Watts, C., Dunlop, I.E., Ramadan, S., del Rio Hernandez, A.E., Klein, N.: Chemically functionalised graphene FET biosensor for the labelfree sensing of exosomes. Scientific Reports 9(1), 13946 (2019). https://doi.org/10.1038/s41 598-019-50412-9 30. Taniguchi, Y., Miki, T., Ohno, Y., Nagase, M., Arakawa, Y., Yasuzawa, M.: Observation of the interaction between avidin and Iminobiotin using a Graphene FET on a SiC substrate. Jpn. J. Appl. Phys. 58(SD), SDDD02 (2019). https://doi.org/10.7567/1347-4065/ab0544 31. Nozaki, R., Ikuta, T., Ueno, K., Tsukakoshi, K., Ikebukuro, K., Maehashi, K.: Ethanol detection at the parts per billion level with single-stranded-dna-modified graphene field-effect transistors. Physica Status Solidi (b) 257(2), 1900376 (2020). https://doi.org/10.1002/pssb.201900376 32. Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., Kim, S.-J., Lee, J.-O., Kim, B.T., Park, E.C., Kim, S.I.: Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4), 5135–5142 (2020). https://doi.org/10.1021/acs nano.0c02823 33. Parate, K., Rangnekar, S.V., Jing, D., Mendivelso-Perez, D.L., Ding, S., Secor, E.B., Smith, E.A., Hostetter, J.M., Hersam, M.C., Claussen, J.C.: Aerosol-Jet-Printed graphene immunosensor for label-free cytokine monitoring in serum. ACS Appl. Mater. Interfaces. 12(7), 8592–8603 (2020). https://doi.org/10.1021/acsami.9b22183 34. Gole, A., Dash, C., Ramakrishnan, V., Sainkar, S.R., Mandale, A.B., Rao, M., Sastry, M.: Pepsin−gold colloid conjugates: preparation, characterization, and enzymatic activity. Langmuir 17(5), 1674–1679 (2001). https://doi.org/10.1021/la001164w 35. Gole, A., Vyas, S., Phadtare, S., Lachke, A., Sastry, M.: Studies on the formation of bioconjugates of endoglucanase with colloidal gold. Colloids Surf., B 25(2), 129–138 (2002). https:// doi.org/10.1016/S0927-7765(01)00301-0 36. Dreaden, E.C., Alkilany, A.M., Huang, X., Murphy, C.J., El-Sayed, M.A.: The golden age: gold nanoparticles for biomedicine. Chem. Soc. Rev. 41(7), 2740–2779 (2012). https://doi.org/ 10.1039/C1CS15237H 37. Hossain, M.Z., Shimizu, N.: Covalent immobilization of gold nanoparticles on graphene. J. Phys. Chem. C 123(6), 3512–3516 (2019). https://doi.org/10.1021/acs.jpcc.8b09619 38. Wang, S., Hossain, M.Z., Shinozuka, K., Shimizu, N., Kitada, S., Suzuki, T., Ichige, R., Kuwana, A., Kobayashi, H.: Graphene field-effect transistor biosensor for detection of biotin with ultrahigh sensitivity and specificity. Biosens. Bioelectron. 165, 112363 (2020). https://doi.org/10. 1016/j.bios.2020.112363 39. Green, N.M.: Avidin. In: Anfinsen, C.B., Edsall, J.T., Richards, F.M (eds.) Advances in Protein Chemistry. Academic Press, Vol. 29, pp 85–133 (1975). https://doi.org/10.1016/S0065-323 3(08)60411-8 40. Gitlin, G., Bayer, E.A., Wilchek, M.: Studies on the biotin-binding site of avidin. lysine residues involved in the active site. Biochem J 242(3), 923–926 (1987). https://doi.org/10.1042/bj2 420923 41. Cuatrecasas, P., Wilchek, M.: Single-step purification of avidin from egg white by affinity chromatography on biocytin-sepharose columns. Biochem. Biophys. Res. Commun. 33(2), 235–239 (1968). https://doi.org/10.1016/0006-291X(68)90774-2 42. Bodanszky, A., Bodanszky, M.: Sepharose-avidin column for the binding of biotin or biotincontaining peptides. Experientia 26(3), 327 (1970). https://doi.org/10.1007/BF01900128 43. Ross, S.E., Carson, S.D., Fink, L.M.: Biotechniques 4, 350–354 (1986) 44. Luong, J.H.T., Male, K.B., Glennon, J.D.: Biotin interference in immunoassays based on biotinstrept(Avidin) chemistry: an emerging threat. Biotechnol. Adv. 37(5), 634–641 (2019). https:// doi.org/10.1016/j.biotechadv.2019.03.007 45. Colon, P.J., Greene, D.N.: Biotin interference in clinical immunoassays. J Appl Lab Med 2(6), 941–951 (2018). https://doi.org/10.1373/jalm.2017.024257 46. Lin, W.-Z., Chen, Y.-H., Liang, C.-K., Liu, C.-C., Hou, S.-Y.: A competitive immunoassay for biotin detection using magnetic beads and gold nanoparticle probes. Food Chem. 271, 440–444 (2019). https://doi.org/10.1016/j.foodchem.2018.07.152

References

85

47. Buzid, A., McGlacken, G.P., Glennon, J.D., Luong, J.H.T.: Electrochemical sensing of biotin using nafion-modified boron-doped diamond electrode. ACS Omega 3(7), 7776–7782 (2018). https://doi.org/10.1021/acsomega.8b01209 48. Buzid, A., Hayes, P.E., Glennon, J.D., Luong, J.H.T.: Captavidin as a regenerable biorecognition element on boron-doped diamond for biotin sensing. Anal. Chim. Acta 1059, 42–48 (2019). https://doi.org/10.1016/j.aca.2019.01.058 49. Kergaravat, S.V., Gómez, G.A., Fabiano, S.N., Laube Chávez, T.I., Pividori, M.I., Hernández, S.R.: Biotin determination in food supplements by an electrochemical magneto biosensor. Talanta 97, 484–490 (2012). https://doi.org/10.1016/j.talanta.2012.05.003 50. Ho, J.A., Hung, C.-H.: Using liposomal fluorescent biolabels to develop an immunoaffinity chromatographic biosensing system for biotin. Anal. Chem. 80(16), 6405–6409 (2008). https:// doi.org/10.1021/ac800850w 51. Carinelli, S., Xufré, C., Alegret, S., Martí, M., Pividori, M.I.: CD4 quantification based on magneto ELISA for AIDS diagnosis in low resource settings. Talanta 160, 36–45 (2016). https://doi.org/10.1016/j.talanta.2016.06.055 52. Safaei, T.S., Mohamadi, R.M., Sargent, E.H., Kelley, S.O.: In situ electrochemical elisa for specific identification of captured cancer Cells. ACS Appl. Mater. Interfaces. 7(26), 14165– 14169 (2015). https://doi.org/10.1021/acsami.5b02404 53. Tauriello, D.V.F., Palomo-Ponce, S., Stork, D., Berenguer-Llergo, A., Badia-Ramentol, J., Iglesias, M., Sevillano, M., Ibiza, S., Cañellas, A., Hernando-Momblona, X., Byrom, D., Matarin, J.A., Calon, A., Rivas, E.I., Nebreda, A.R., Riera, A., Attolini, C.S.-O., Batlle, E.: TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature 554(7693), 538–543 (2018). https://doi.org/10.1038/nature25492

Chapter 6

Graphene FET Biosensor Based on the Antigen–Antibody Interaction

Abstract Antigen-antibody interaction is the most commonly used tool in biomedical detection and has the most extensive applications. generally speaking, antigens include proteins, bacteria, viruses, and many other targets. These targets can specifically bind to specific antibodies. for biomedical detection, neither hematological testing nor histological testing can easily conduct without antigen-antibody interaction. The graphene field-effect transistor biosensor combined with antigen-antibody interaction is the earliest research field for graphene biosensing. This chapter first introduces the progress that has been made in recent years in the field of graphene field-effect transistor combined with antigen-antibody interaction. Then the implementation methods and future prospects of graphene field-effect transistor combined with antigen-antibody interaction to detect specific biomarkers including cancer markers are mainly introduced. Keywords Graphene FET · Antigen-antibody interaction · Tumor marker · Biomarker Antigen–antibody interaction (antigen-antibody reaction) as a kind of significant immunic interaction is one of the most significant fundamental reactions in our body. It can protect the human body from complex external factors such as pathogens or chemical toxins. The antigen in the blood has specificity and high affinity and combines with the antibody to form an antigen–antibody complex. The complex is then transported to the cell system to destroy or inactivate it [1]. For the different kinds of antigen, there are also different types of the specific antibody to bind its corresponding antigen. Because antigens are bound to antibodies through several weak and non-covalent interactions and their combination, such as hydrogen bonds, electrostatic interactions, hydrophobic interactions, and Van der Waals forces. Therefore, the specificity of binding depends on the specific chemical composition of each antibody. The epitope is located in the variable region of the polypeptide chain and is recognized by the paratope of the antibody. This variable domain is highly variable and contains a unique amino acid sequence within each antibody. Currently, the antigen–antibody interaction has been widely used in quantification of the antibody for the purposes of the clinical diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_6

87

88

6 Graphene FET Biosensor Based …

Fig. 6.1 The schematic image of the graphene FET biosensor based on the antigen–antibody interaction

Because the antigen–antibody interaction has been widely applied to the traditional diagnosis approaches, combined with the antigen–antibody interaction technology has been deeply researched for the field of the Graphene FET biosensor. Generally speaking, the antigen should be placed on the surface of the graphene first. Then, based on the antigen–antibody interaction, the functionalized graphene can bind the specific antigens to the graphene surface. Finally, the concentration of a specific antibody can be quantified by the graphene FET based on the principle of the electrical double layer. Commonly, the binding methods for binding antibodies to the surface of the graphene include covalent and non-covalent methods. The covalent binding includes PBASE and TCPP, and for the non-covalent methods, the AuNPs decoration method is also used. The schematic image of the graphene FET biosensor based on the antigen–antibody interaction is shown in Fig. 6.1. Because antibodies are biomolecules composed of proteins, they are easily affected by temperature and other complex environmental factors. Therefore, after modification on the surface of graphene, it is difficult to maintain the original affinity with the antigen. Current studies have shown that after surface modification, the affinity is likely to decrease. At the same time, biosensor s made from antigens are extremely susceptible to the influence of time and temperature. Excessive time, extremely low or extremely high temperatures will cause the antibody to lose activity, thereby losing its affinity for the antigen. But antigen–antibody technology, as the most mature biometric tool so far, seems to be the easiest to be combined with GFET. Most of the existing diagnostic methods based on antigen–antibody technology can be transplanted to the graphene FET biosensing platform, which makes GFET based on antigen–antibody technology a promising research direction.

6.1 Tumor Marker For cancer diagnosis, developing a novel platform for rapid quantitative detection is as important as finding more efficient cancer marker s for specific cancers. Graphene FET biosensor as a novel type of detection platform, most of these researches

6.1 Tumor Marker

89

focused on the quantification of the tumor marker. In the traditional clinical diagnosis of cancer, the quantification method of the tumor marker has been an important diagnosis factor during the whole clinical processing. Currently, several tumor markers have been used to identify in oncology to help detect or diagnose the presence of cancer, each of which may indicate a specific disease course. Elevated levels of tumor markers may indicate the presence of cancer; however, there may be other reasons for elevated levels (such as false-positive values), such as alpha-fetoprotein (AFP) for the germ cell tumor, hepatocellular carcinoma, prostate-specific antigen (PSA) for prostate cancer [2]. Based on the GFET, the concentration of these specific tumor markers is hopefully to rapid qualification. Currently, many of the researchers have shown that the GFET based on the antigen–antibody interaction has huge applied potential in tumor marker qualification. Dae Hoon Kim et al. developed a biosensor that uses G-FET to detect alpha-fetoprotein (AFP) in phosphate-buffered brine (PBS) solution. Furthermore, this anti-AFP immobilized G-FET biosensor can detect AFP at a concentration of 0.1 ng mL−1 in PBS [3]. As a self-protective layer of graphene, the nano-DBSA film has a certain protective effect, which can prevent surface pollution caused by lithography processing. Lin Zhou et al. made a GFET sensor composed of nano-DBSA film immobilized anti-cancer embryonic antigen monoclonal antibody (anti-CEA mAb) on EDC and Sulfo-NHS-activated graphene channels. This modified GFET biosensor showed good specificity and high sensitivity to target matter (CEA) at an ultra-low concentration of 337.58 fg mL−1 [4]. Based on the immune reaction of PSA-ACT complex with PSA monoclonal antibody on the surface of reduced graphene oxide (R-GO) channel, Duck-Jin Kim et al. obtained high binding constants of 3.2 nM−1 and 4.2 nM−1 in analytical solutions at pH 6.2 and pH 7.4, respectively. The reduced graphene oxide field-effect transistor (R-GO FET) biosensor demonstrated high specificity of cancer biomarkers in phosphate-buffered saline solutions and human serum. The R-GO FET biosensor has certain application value and high sensitivity in detecting prostate-specific antigen/α1-antichymotrypsin (PSA-ACT) complex and unlabeled prostate cancer biomarkers [5].

6.2 Other Biomarkers Not only the tumor marker-related researches have been reported but also some of the biomarker-related researches based on the antigen–antibody interaction. Yong-Min Lei et al. reported the platinum nanoparticles (PtNPs)-decorated reduced graphene oxide (R-GO) field-effect transistor (FET) biosensor coupled with a microfilter system for label-free and highly sensitive detection of brain natriuretic peptide (BNP) in whole blood about the heart failure diagnosis. Their biosensors can achieve a lower detection limit of 100 fM. The rGO FET sensor is cast from rGO onto a prefabricated FET chip and then modified with PTNPs on the graphene surface. After anti-BNP was combined with the surface of PTNPs, the FET biosensor immobilized by anti-BNP could successfully detect BNP [6]. Nur Nasyifa Mohd Maidin et al. reported the role

90

6 Graphene FET Biosensor Based …

Table 6.1 The main researches about the biomarker Target

Disease

Platform

Sensitivity

References

Alpha-fetoprotein (AFP)

Cancer

Graphene FET

0.1 ng mL−1

[3]

Carcinoembryonic antigen (CEA) Cancer

Graphene FET

mL−1

[4]

Prostate-specific antigen/α1-antichymotrypsin (PSA-ACT)

Cancer

Reduced 100 fg mL−1 graphene oxide FET

[5]

Brain natriuretic peptide (BNP)

Heart failure

Reduced 100 fM graphene oxide FET

[6]

Cortisol hormone

Stress

Graphene FET

Amyloid-β (Aβ) and tau protein

Alzheimer’s Reduced 10−1 pg mL−1 [8] disease graphene oxide FET

337 fg

10 pg mL−1

[7]

of graphene-based electrolytic gate field-effect transistors (EGFET) in the detection of cortisol hormones. Cortisol antibody was immobilized on the graphene surface for specific and sensitive detection of cortisol target hormone and ethanolamine was used to prevent non-specific binding. They also claimed that the 10 pg mL−1 of the cortisol can be detected by their biosensors [7]. Dongsung Park et al. demonstrated hypersensitivity and multiplexing of R-GO FETs for biomarkers of Alzheimer’s disease (AD) (Aβ1-42 and T-Tau) in the biological fluid. Furthermore, based on antigen–antibody interactions, they proposed a broad log-linear range for the detection of lower fM levels in 10−1 –105 pg mL−1 and a fM level limit of detection in biofluids (human plasma and artificial cerebrospinal fluid) and phosphate buffer saline (PBS) [8]. In summary, based on the antigen-antibody interaction, the FET biosensor can be used to rapidly detect the specific biomarker with ultrasensitive. Table 6.1 listed the main research using the antigen–antibody interaction about the biomarker detection based on the GFET. Such as previously mentioned, the Debye length determines the active length of the electric field effect from the target biomolecules. Thus, we hoping that the target biomolecules can more close to the graphene channel so that the maximize the electric field effect of a single target molecule. But for the antigen– antibody interaction, the bulk of the antibody (nm level) modified on the graphene channel is relatively large compared with the Debye length in the test solution. Therefore, this may cause the antigen to be far away from the graphene channel when the antibody binds to the antigen, which makes the detection effect unsatisfactory. Currently, using the antigen-binding fragment (Fab) instead of the antibody to bind the surface of the graphene perhaps will become an available approach. It is a region on an antibody that binds to antigens. Compared with the whole antibody, the bulk of Fab has a small size that will bring more ideal detection effect. In addition, if only from the perspective of the Debye length in the solution, the ion concentration in the solution to be tested can be reduced to make the Debye length longer. However, the concentration of the target molecule may decrease as the solution is diluted during the actual test process. Therefore, finding the best adaptation point between

6.2 Other Biomarkers

91

Fig. 6.2 The schematic image of the compound nanosensor of the graphene and nanowire

the dilution of the solution and the concentration of the target biomolecule to be tested may be the focus of future research. Finally, some compound nanosensors made from a variety of nanomaterials are also hopeful to improve this problem. Here I propose a new preliminary idea for the first time that the compound nanosensor of the graphene and nanowire. The schematic image is shown in Fig. 6.2, place the nanowire on top of graphene, and prepare electrodes only on both ends of the nanowire. Nanowire s are used as the sensing part, and graphene is used as the capturing part of specific biomolecules. When graphene captures a specific molecule because the target molecule is closer to the upper nanowire, a more ideal detection effect may be achieved, and because there is no hard link between the nanowire and the graphene interface, the water molecules in the solution will penetrate then form an insulating layer at the interface (modified graphene and nanowires are hydrophilic), so that the graphene will hardly affect the electrical properties of nanowires.

References 1. Antigen-Antibody Interaction. Wikipedia (2020) 2. Tumor Marker. Wikipedia (2020) 3. Kim, D.H., Oh, H.G., Park, W.H., Jeon, D.C., Lim, K.M., Kim, H.J., Jang, B.K., Song, K.S.: Detection of Alpha-fetoprotein in hepatocellular carcinoma patient plasma with graphene fieldeffect transistor. Sensors 18(11), 4032 (2018). https://doi.org/10.3390/s18114032 4. Zhou, L., Wang, K., Sun, H., Zhao, S., Chen, X., Qian, D., Mao, H., Zhao, J.: Novel graphene biosensor based on the functionalization of multifunctional nano-bovine serum albumin for the highly sensitive detection of cancer biomarkers. Nano-Micro Lett. 11(1), 20 (2019). https://doi. org/10.1007/s40820-019-0250-8 5. Kim, D.-J., Sohn, I.Y., Jung, J.-H., Yoon, O.J., Lee, N.-E., Park, J.-S.: Reduced graphene oxide field-effect transistor for label-free femtomolar protein detection. Biosens. Bioelectron. 41, 621– 626 (2013). https://doi.org/10.1016/j.bios.2012.09.040

92

6 Graphene FET Biosensor Based …

6. Lei, Y.-M., Xiao, M.-M., Li, Y.-T., Xu, L., Zhang, H., Zhang, Z.-Y., Zhang, G.-J.: Detection of heart failure-related biomarker in whole blood with graphene field effect transistor biosensor. Biosens. Bioelectron. 91, 1–7 (2017). https://doi.org/10.1016/j.bios.2016.12.018 7. Maidin, N.N.M., Rahim, R.A., Halim, N.H.A., Abidin, A.S.Z., Ahmad, N.A., Lockman, Z.: Interaction of graphene electrolyte gate field-effect transistor for detection of cortisol biomarker. AIP Conf. Proc. 2045(1), 020022 (2018). https://doi.org/10.1063/1.5080835 8. Park, D., Kim, J.H., Kim, H.J., Lee, D., Lee, D.S., Yoon, D.S., Hwang, K.S.: Multiplexed femtomolar detection of alzheimer’s disease biomarkers in biofluids using a reduced graphene oxide field-effect transistor. Biosens. Bioelectron. 167, 112505 (2020). https://doi.org/10.1016/ j.bios.2020.112505

Chapter 7

Graphene FET Biosensor Based on the Base Pair

Abstract The rapid quantitative detection of DNA/RNA fragments occupies an important position in biomedical detection. Relying on the principle of base complementary pairing, any specific target (DNA/RNA) can be efficiently identified. At present, based on complementary base pairing the biomedical detection methods such as PCRs and NATs have been widely used in practical clinical detection applications. For example, many special miRNA/mRNA have been found that can be used as tumor markers. Referred to the quantitative detection result of the specific tumor markers, it has great reference value for predicting the cancer stage of the patient and for evaluating the prognosis. In addition, the rapid quantitative detection of viruses and other foreign DNA and RNA is also helpful to provide a rapid diagnosis basis for influenza, Ebola virus, and other epidemics. For graphene field-effect transistors, due to their excellent characteristics of rapid response, the target DNA/RNA in the sample to be tested is expected to be quickly quantitatively detected. This provides a new platform for rapid quantitative detection of DNA/RNA. This chapter first introduces classic cases of graphene field-effect transistors combined with base complementary pairing principles to detect specific DNA/RNA. Finally, a novel biosensing scheme is proposed based on the graphene field-effect transistor to rapidly quantitive detect COVID-19 RNA fragments. Keywords DNA · RNA · Base pair · COVID-19 · Graphene FET Base pairs (BPs) are the basic building blocks of the DNA/RNA double helix, the folded structures that make up DNA and RNA. BP consists of two bases bonded by hydrogen bonds, including thymine (T), cytosine (C), adenine (A), guanine (G), and uracil (U). It is the basic unit of double-stranded nucleic acid [1]. Based on the specific hydrogen bonding patterns, (guanine-cytosine, adenine-thymine, and adenine-uracil) allow the double-stranded nucleic acids to tightly bind together and maintain a regular helical structure. At present, the nucleic acid test (NAT) has become the widest applied approach for the early diagnosis of a disease based on base-pairing technology [2]. It is different from antigen/antibody detection because the antigens/antibodies generally require a

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_7

93

94

7 Graphene FET Biosensor Based …

Table 7.1 The recent main researches about the nucleic acid detection Target

Linker

Probe

Platform

Sensitivity

References

DNA

PBASE

DNA

Graphene FET

10 pM

[3]

DNA/RNA

PBASE

DNA

Crumpled graphene FET

600 zM

[4]

miRNA

AuNPs

PNA

R-GO FET

10 fM

[5]

further long time for them to start appearing in the bloodstream. Thus through the detection of a particular nucleic acid sequence, it is to rapidly identify a virus or bacteria in humans. Currently, most nucleic acid detection techniques rely on the specificity of base pairing to capture complementary strands of DNA or RNA target molecules through single-stranded probes or primers. Therefore, the design of probe chain is of great significance to improve the specificity and sensitivity of nucleic acid detection. Recent researches have reported some works about graphene FET biosensor based on the base pair for DNA/RNA detection. Shicai Xu et al. demonstrated that the timeand concentration-dependent DNA hybridization kinetics could be accurately and sensitively measured by pattering graphene single crystal regions into multiple channels based on base pairs with DNA detection limits of 10 pM [3]. Michael Taeyoung Hwang et al. used FETs with morphed graphene monolayer with millimeter-scale channels to detect nucleic acids and demonstrated that they could detect ultrahigh sensitivity of 600 zM and 20 aM, 18 and 600 nucleic acid molecules, respectively, in the buffer and human serum samples [4]. Besides, Bingjie Cai et al. claimed that reduced graphene oxide (R-GO) suspension was poured onto the sensor surface, and then gold nanoparticles (AuNPs) were modified onto the surface of R-GO. Then fixing the polypeptide nucleic acid (PNA) probe on the surface of AuNPs, PNAmiRNA hybridization was conducted for detection. It was found that the developed FET biosensor could reach a detection limit as low as 10 fM. Based on AuNPs modified GFET biosensors, their devices can be used to detect miRNAs with high sensitivity, selectivity, and no labeling [5]. All of these fantastic works with highly sensitive and selective have shown that GFET biosensor based on base pair for the detection of the nucleic acid may have huge potential for the actual clinical diagnosis in near future. Table 7.1 listed the recent works about the nucleic acid detection. Generally speaking, for the detection of the nucleic acid based on the graphene FET, the probe nucleic acid chain should be linked onto the surface of the graphene at first. The commonly linker contains the PBASE and AuNPs. Some of the works reported that using the Xeno nucleic acids (XNA) instead of the probe nucleic acid may be a better approach because generally, the XNA is more stable than traditional nucleic acids. Finally, through the base-pairing principle, the specific nucleic acids could be bound onto the surface of the graphene, thus conduct quantification detection.

7.1 COVID-19 Detection

95

7.1 COVID-19 Detection Since 2019, the COVID-19 has widely spread induced the global pandemic. For the COVID-19 detection based on the graphene FET, Giwan Seo et al. demonstrated that SARS-CoV-2 in clinical samples can be detected by a novel GFET biosensor. Their sensor is coated with specific antibodies against SARS-CoV-2 spines on graphene sheets of FET, and their instrument can detect SARS-CoV-2 spike protein in phosphate buffer saline at a concentration of 1 fg/mL and in a clinical transport medium of 100 fg/mL [6]. However, there are no related works reported the nucleic acid s test for the COVID-19 based on the graphene FET biosensor until now. Here, I propose a new scheme for detecting specific partial sequences of COVID-19 nucleic acid as follows. As we all know, COVID-19 is an RNA virus. The current traditional detection method is to detect specific fragments of COVID-19 in human blood/saliva through nucleic acid test ing. However, nucleic acid detection usually requires amplification, which may require longer time and more specialized equipment. The application of graphene FET biosensor for ultra-sensitive nucleic acid fragment detection for COVID-19 is expected to break the time limit of traditional detection methods and bring the possibility of large-scale census. Generally speaking, the specific probe-DNA is modified onto the surface of the graphene at first which is to activate the surface of the graphene. Then the remaining parts of the graphene surface should be blocked. Basically, the BSA and the ethanolamine are always used for this processing. Finally, based on the principle

Fig. 7.1 The schematic image of the whole processing for the COVID-19 nucleic acid detection

96

7 Graphene FET Biosensor Based …

of the base-pairing, the target RNA sequence of the COVID-19 could be rapidly quantitively detected. The schematic image of the whole processing is shown in Fig. 7.1. In addition, because the design of probe strands is very important for detection. Here I give a potential probe sequence (AAGGATCAGTGCCAAGCTCGTCGCC) that can be used in the design. It corresponds to the 701–725 of the original sequence of the COVID-19.

References 1. Base Pair. Wikipedia (2020) 2. Nucleic Acid Test. Wikipedia (2020) 3. Xu, S., Zhan, J., Man, B., Jiang, S., Yue, W., Gao, S., Guo, C., Liu, H., Li, Z., Wang, J., Zhou, Y.: Real-time reliable determination of binding kinetics of DNA hybridization using a multichannel graphene biosensor. Nature Commun. 8(1), 14902 (2017). https://doi.org/10.1038/nco mms14902 4. Hwang, M.T., Heiranian, M., Kim, Y., You, S., Leem, J., Taqieddin, A., Faramarzi, V., Jing, Y., Park, I., van der Zande, A.M., Nam, S., Aluru, N.R., Bashir, R.: Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors. Nature Commun. 11(1), 1543 (2020). https://doi.org/10.1038/s41467-020-15330-9 5. Cai, B., Huang, L., Zhang, H., Sun, Z., Zhang, Z., Zhang, G.-J.: Gold nanoparticles-decorated graphene field-effect transistor biosensor for Femtomolar MicroRNA detection. Biosens. Bioelectron. 74, 329–334 (2015). https://doi.org/10.1016/j.bios.2015.06.068 6. Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., Kim, S.-J., Lee, J.-O., Kim, B.T., Park, E.C., Kim, S.I.: Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4), 5135–5142 (2020). https://doi.org/10.1021/acs nano.0c02823

Chapter 8

Graphene FET Biosensor Based on the Aptamer Technology

Abstract Aptamer technology is an emerging technology in biomedical testing. It can be divided into nucleic acid aptamers and protein aptamers. This chapter mainly focuses on aptamers in aptamer technology. Based on SELEX technology, specific nucleic acid strands are screened out to target different targets (binding objects). This makes the aptamer technology broadly adaptable to almost all foreign objects, especially some targets which is still no corresponding antibody. Similarly, with the antigen-antibody technogy,the designated nucleic acid chain screened by the SELEX technology can bind the specific target with high-sensitivity and specificity. It may have a wider application prospect in the biomedical testing field compared with antigen-antibody technology. Besides, compared with antibodies composed of amino acids, aptamers composed of nucleic acids are more stable, which enables biosensing devices based on this technology to have milder storage and transportation conditions, thus facilitates future practical applications. This chapter briefly describes the graphene field-effect transistor biosensor based on aptamer technology, including its specific implementation and preparation methods. Keywords Aptamer · Graphene FET · Biosensor Aptamers are oligonucleotides or peptide molecules that can bind to specific target molecules. Here, we mainly discuss nucleic acid aptamers. This kind of aptamers usually consists of short oligonucleotide chains [1]. An aptamer is a nucleic acid (next-generation antibody simulator) that can produce antibodies against a specific target through in vitro selection or other similar methods (from small entities (such as heavy metal ions) to large entities (such as cells)) [2], especially suitable for targets for which there is no specific antibody. At the molecular level, aptamers bind to homologous targets through various noncovalent interactions, such as hydrophobicity, electrostatic interaction, and induced fitting. Aptamers are of great value in biotechnology and detection applications because they have molecular recognition properties equivalent to ordinary antibodies, are easy to prepare and store, and are not easily affected by other environmental factors. In addition, compared to antibodies made of amino acids, aptamers (aptamer nucleic acids) are made of oligonucleotides (DNA, RNA, XNA), so theoretically they are not easy to lose their activity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_8

97

98

8 Graphene FET Biosensor Based on the Aptamer Technology

Table 8.1 The main researches about the Graphene FET biosensor based on the aptamer technology Target

Linker

Probe

Platform

Sensitivity

References

Immunoglobulin PBASE E

DNA-aptamer Graphene 47 nM FET

[3]

Escherichia coli

The pyrene tag (pyrene phosphoramidite)

DNA-aptamer Graphene 100 CFU mL−1 [4] FET

Hg2+

1,5-diaminonaphthalene DNA-aptamer Graphene 40 pM (DAN) through Schiff FET base reaction

[5]

As of 2020, the graphene FET biosensor based on aptamer technology has been researched for 10 years. Yasuhide Ohno et al. first reported this technology in 2010. They demonstrated an unlabeled immunosensor based on an aptamer-modified GFET. Atomic force microscopy confirmed that immunoglobulin E (IgE) aptamers with a height of about 3 nm have been successfully immobilized on the surface of graphene. The aptamer-modified GFET can selectively detect IgE protein electrically. According to the dependence of the drain current change on the IgE concentration, the disintegration constant is estimated to be 47 nM, which indicates that GFET has a good affinity and may be used in biosensors [3]. They demonstrated that a labelfree immunosensor based on an aptamer-modified graphene field-effect transistor (G-FET). Immunoglobulin E (IgE) aptamers with an approximate height of 3 nm were successfully immobilized on a graphene surface, as confirmed by atomic force microscopy. The aptamer-modified G-FET showed selective electrical detection of IgE protein. From the dependence of the drain current variation on the IgE concentration, the dissociation constant was estimated to be 47 nM, indicating good affinity and the potential for G-FETs to be used in biological sensors [3]. Guangfu Wu et al. reported that based on graphene FET biosensor with the aid of pyrene-tagged DNA aptamers, which exhibit excellent selectivity, affinity, and stability for Escherichia coli (E. coli) detection. It is the first time that the change of the carrier density in the probe-modified graphene due to the attachment of E. coli is discussed theoretically and also verified experimentally. They confirmed the low detection limit of 100 CFU mL−1 for E. coli detection [4]. JiaWei Tu et al. reported a GFET array biosensor (6 × 6 GFETs on chip) and applied it to the quantitative detection of Hg2+ based on single-stranded DNA (ssDNA) aptamer. The biosensor shows excellent selectivity to Hg2+ in mixed solutions containing a variety of metal ions. The results show that the biosensor has a lower detection limit (40 pM), a wider detection range (100 pM–100 nM) and a shorter response time (less than 1 s) [5]. These pioneering studies are listed in Table 8.1. In principle, the graphene FET combined with aptamer technology is similar to the antigen–antibody interaction that has been demonstrated in the previous section. Figure 8.1 shows the schematic image of the graphene FET biosensor based on aptamer technology. Here we mainly discuss the aptamer surface-modified method on graphene. The most common method is to carry out amination or thiolation at the end of aptamer, after which they can be combined with commonly used linker s

8 Graphene FET Biosensor Based on the Aptamer Technology

99

Fig. 8.1 The schematic image of the graphene FET biosensor based on aptamer technology

PBASE or AuNPs in graphene FET. In addition, there are many link methods, such as the previously mentioned Schiff base reaction [5]. Because compared to antibodies, aptamer has a smaller volume, which makes the distance between the object and the graphene interface closer, which may bring better detection results. In addition, it can be used to specifically identify the test objects in which there is no related specific antibody through SELEX technology. This makes aptamer possible to have a wider range of applications than antibodies.

References 1. Aptamer. Wikipedia (2020) 2. Kaur, H., Shorie, M.: Nanomaterial based aptasensors for clinical and environmental diagnostic applications. Nanoscale Adv. 1(6), 2123–2138 (2019). https://doi.org/10.1039/C9NA00153K 3. Ohno, Y., Maehashi, K., Matsumoto, K.: Label-free biosensors based on aptamer-modified graphene field-effect transistors. J. Am. Chem. Soc. 132(51), 18012–18013 (2010). https://doi. org/10.1021/ja108127r 4. Wu, G., Dai, Z., Tang, X., Lin, Z., Lo, P.K., Meyyappan, M., Lai, K.W.C.: Graphene fieldeffect transistors for the sensitive and selective detection of escherichia coli using pyrene-tagged dna aptamer. Adv. Healthcare Mater. 6(19), 1700736 (2017). https://doi.org/10.1002/adhm.201 700736 5. Tu, J., Gan, Y., Liang, T., Hu, Q., Wang, Q., Ren, T., Sun, Q., Wan, H., Wang, P.: Graphene FET array biosensor based on SsDNA aptamer for ultrasensitive Hg2 + detection in environmental pollutants. front. Chem., 6 (2018). https://doi.org/10.3389/fchem.2018.00333

Chapter 9

Graphene FET Biosensor Based on the Concanavalin A

Abstract Concanavalin A (ConA) is a particular protein that can specifically bind to various sugars, glycoproteins, and some structures of glycolipids (mainly internal and non-reducing terminal α-D-mannosyl and α-D-glucosyl groups). Unlike the other biological interactions mentioned above, ConA protein only specifically adsorbs target with glycosyl groups such as monosaccharides, glycans, glycoproteins, cell surface with glycosyl groups, etc. This allows the interaction between ConA and targets (with glycosyl groups) to be described separately as a special type of biological interaction. In actual biomedical clinical detection applications, glycoproteins, cell surfaces, microbial surfaces, virus surfaces, etc., all contain glycosyl groups, which makes it possible to use ConA protein for specific detection. This chapter introduces the graphene field-effect transistor biosensor combined with ConA for rapid detection of targets contained glycosyl groups. Then discusses two different detection modes that are adsorption mode detection and dissociation mode detection. Unlike the biological interactions described previously, ConA protein has a wide range of glycosyl group binding ability, which makes it possible to use ConA in many glycosyl group-containing targets. But at the same time, it should be noted that the binding of ConA to targets is not one-to-one, so competitive adsorption issues should be considered in practical applications. Keywords Concanavalin A · ConA · Glycosyl group · Monosaccharides · Glycans · Glycoproteins · Graphene FET Concanavalin A (ConA) is a lectin (carbohydrate-binding protein), a member of the legume lectin family, and is extracted from Jack bean (Canavalia ensiformis). Because ConA can specifically bind to various sugars, glycoproteins, and some structures of glycolipids (mainly internal and non-reducing terminal α-D-mannosyl and α-Dglucosyl groups). Therefore, ConA has great potential for efficient recognition of sugars, glycoproteins, and glycolipids [1, 2]. Note that ConA needs to bind with metal ions (Mn2+ and a Ca2+ ) to obtain affinity for sugars. ConA has been considered that it is a useful tool applied in the solid-phase immobilization of glycoenzymes, especially these difficult to immobilize by traditional covalent coupling. Based on the ConA coupling matrix, this type of enzyme can be immobilized efficiently, but the ConA coupling matrix will not lose © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_9

101

102

9 Graphene FET Biosensor Based on the Concanavalin A

the activity and/or stability of the enzyme. In addition, this kind of non-covalent coupling mechanism can be easily dissociated by competing with other sugars or at acidic pH. Based on the above advantages, many of the reports have demonstrated that ConA can interact with diverse receptors containing mannose carbohydrates, particularly rhodopsin, insulin-receptor, immunoglobulins. In addition, many of the cells and microbes have been proved that can interact with the ConA through surface immunoglobulins, such as cancerous cells, muscle cells, B-lymphocytes, Escherichia coli, and Bacillus subtilis [3]. Because of its extensive binding capabilities, some studies have already applied it for the graphene biosensor at present. Xiaojian Li et al. reported that an electrochemiluminescence (ECL) biosensor was developed for the detection of Con A, and they claimed that under the optimization of determination conditions, a linear response range for Con A from 0.5 pg mL−1 to 100 ng mL−1 was obtained, and the detection limit was calculated to be 0.18 pg mL−1 (S/N = 3) [4]. Juanjuan Zhang et al. reported that they designed a highly sensitive ECL biosensor for the detection of ConA based on glucose oxidase (GOx) as a recognition element by carbohydrate–lectin biospecific interaction, and poly(ethylenimine) (PEI) reduced graphene and hollow gold nanoparticles (HAuNPs) as supporting matrix and signal amplifier. They found a linear relationship between ECL signal strength and the logarithm of ConA concentration, with a linear range of 1.0–20 ng/mL and a detection limit of 0.31 ng/mL (signal to noise ratio =3) [5]. Besides, Chun-Fang Huang et al. claimed that based on ConA as a model protein, they developed a novel surface plasmon resonance (SPR) sensor for sensitive detection ConA. Their SPR sensor successfully fulfilled the sensitive detection of ConA in the range of 1.0–20.0 μg mL−1 with a detection limit of 0.39 μg mL−1 [6]. Although many of the ConA detection-related researches have been reported; however, it may be difficult if the ConA is fixed to detect biomolecules with glycome groups. Since many of the biomolecules are both containing glycome, thus how to maintain the high specificity for the target biomolecules that is the difficult point during the real detection. Thus for glycome biomolecule detection with the graphene FET biosensor based on the ConA, the specificity should be noted. In addition, the affinity of the ConA should be activated through the metal ions; however, some of the reports have shown that the metal ions will also disturb the signal of the graphene FET biosensing. It is a disturbing factor during target biomolecule detection. Thus the introduced volume and concentration of the metal ions solution should be severely controlled. At present, there is almost no related research on ConA-based graphene FET biosensing technology. The following will list my vision for the future application of graphene FET based on ConA technology. My vision can be divided into two types: adsorption and dissociation, and they will be described as follows, respectively.

9.1 Adsorption

103

9.1 Adsorption Generally speaking, the adsorption method is that fixes ConA on the surface of the graphene FET biosensor at first then relies on the adsorption capacity of ConA for glycome-based molecules, microorganisms, cells, etc. for specific detection. The immobilization of ConA on the graphene surface includes covalent bonding and noncovalent bonding. Among them, covalent bonding may be an ideal fixing method for the adsorption method, because if a non-covalent bonding method is used for fixing, there may be a risk that ConA binds to the target molecule and breaks off the graphene surface during the test. The immobilization of ConA on the graphene surface includes covalent bonding and non-covalent bonding. Among them, covalent bonding may be an ideal fixing approach for the adsorption method, because if a non-covalent bonding method is used for fixing, there may be a risk during the test that the target molecule bind to the ConA then separates from the surface. For covalent bonding, there are lots of approaches such as N-Hydroxysuccinimide (NHS) ester that has been usually applied as a linker, so that ConA can be bound to the graphene surface. Because this kind of structure can firmly fix ConA on the surface of graphene so that we can expect that the quantitative detection of glycome-containing biomolecules, microbes, and cells is possible through this structure in the near future. The schematic image of the adsorption approach is shown in Fig. 9.1.

9.2 Dissociation In general, for the dissociation method, it is first necessary to immobilize ConA molecules on the graphene surface in a non-covalent manner. For example, first, modify the chitosan onto the surface of graphene, and then rely on the electrostatic effect and glycosyl binding effect between chitosan and ConA molecules, ConA molecules can be immobilized on chitosan in a non-covalent manner. Later, when the target molecule with glycosyl groups is added, the target molecule will occur the competitive adsorption with the ConA-chitosan complex, resulting in a certain dissociation of the ConA-chitosan complex. The original non-covalently modified ConA molecules will partly dissociate from the graphene chitosan surface due to the addition of target molecules with glycosyl groups. Because the dissociation ratio is related to the concentration of the target molecule and the affinity of the target molecule for the ConA molecule, thus it is possible to quantify the concentration of the target molecule based on the graphene FET biosensor. The schematic image of the dissociation approach is shown in Fig. 9.2.

104

9 Graphene FET Biosensor Based on the Concanavalin A

Fig. 9.1 a The schematic image of the adsorption approach before target binding. b The schematic image of the adsorption approach after target binding

Comparing these two methods, we can find that the adsorption method binds the target molecules in the test solution to the graphene surface at first, and then changes the electric double layer capacitance through the binding of the target molecules, thereby changing the Ids of the graphene FET. But for the dissociation method is to occur, the competitive adsorption between the target molecule in the test solution and the ConA that has been non-covalently bound onto the graphene in previous, so that the ConA is partly separated from the graphene surface into the solution. After the ConA is separated from the graphene surface, this processing will change the original electric double layer capacitance, which in turn changes the Ids of the graphene FET. For the adsorption method, it is more necessary to consider whether the adsorption of the target molecule is sufficient to change the electric double-layer capacitance, which depends on the isoelectric point (pI) of the target molecule, and a series of complex factors. For the dissociation method, we need to intentionally focus on the affinity of the target molecule with ConA. In short, based on my experience, I think

9.2 Dissociation

105

Fig. 9.2 a The schematic image of the dissociation approach before target molecules introduced. b The schematic image of the dissociation approach after target molecules introduced

the adsorption method may be more suitable for biomacromolecules, microbes, and cells. But for the dissociation method, I think it is more suitable for micromolecules, such as oligosaccharides.

References 1. Liener, I.: The Lectins: Properties, Functions, and Applications in Biology and Medicine. Elsevier (2012) 2. Sumner, J.B., Gralën, N., Eriksson-Quensel, I.-B.: The molecular weights of urease, canavalin, concanavalin a and concanavalin B. Science 87(2261), 395–396 (1938). https://doi.org/10.1126/ science.87.2261.395 3. Concanavalin A. Wikipedia (2020)

106

9 Graphene FET Biosensor Based on the Concanavalin A

4. Li, X., Wang, Y., Shi, L., Ma, H., Zhang, Y., Du, B., Wu, D., Wei, Q.: A novel ECL biosensor for the detection of concanavalin a based on glucose functionalized NiCo2 S4 nanoparticles-grown on carboxylic graphene as quenching probe. Biosensors Bioelectron. 96, 113–120 (2017). https:// doi.org/10.1016/j.bios.2017.04.050 5. Zhang, J., Chen, S., Ruo, Y., Zhong, X., Wu, X.: An ultrasensitive Electrochemiluminescent biosensor for the detection of concanavalin a based on poly(Ethylenimine) reduced graphene oxide and hollow gold nanoparticles. Anal. Bioanal. Chem. 407(2), 447–453 (2015). https://doi. org/10.1007/s00216-014-8290-x 6. Huang, C.-F., Yao, G.-H., Liang, R.-P., Qiu, J.-D.: Graphene oxide and dextran capped gold nanoparticles based surface plasmon resonance sensor for sensitive detection of concanavalin a. Biosens. Bioelectron. 50, 305–310 (2013). https://doi.org/10.1016/j.bios.2013.07.002

Chapter 10

Challenges and Outlook

Abstract Since the graphene field-effect transistor biosensor was reported in 2009, it has been widely developed around various biological interactions. Series exciting research results are reported gradually in the past decade, which also indicate that its great potential for practical clinical testing applications. However, there are still many problems that need to be improved, such as how to improve the quality of the graphene film, and how to standardize the graphene transfer and the surface modification process. In addition, from the perspective of industrialization, how to reduce signal disturbance, how to integrate with semiconductor technology and a series of engineering problems also need time to solve. This chapter focuses on the current problems facing industrialization, including graphene quality problems, standardization problems, etc., and proposes improvement suggestions. Keywords Graphene FET · Graphene transfer · Surface modification · Standardization · Signal interference Although, the graphene FET biosensor shows much fantastic potential application expects at present. At the same time, there are also lots of challenges. Such as integration with semiconductor technology, the standardization of transfer and modification, and the signal of the graphene FET biosensor are easy to be interfered by the ultraviolet and other factors. In this section, these challenges will be illustrated respectively, and at the end of this section, the outlook of the graphene FET biosensor and the potential application in the future will also be demonstrated based on my opinion. To our knowledge, when showing their readings of the source-drain current, which is used as the biosensor signal, it is evident that there are substantially different current levels in the experiences. The origin factors of differences have lots of aspects. For the CVD graphene, it is polycrystalline not like the exfoliated graphene from the graphite. The exfoliated graphene from the graphite is almost monocrystalline but the CVD graphene is polycrystalline. Because of the irregular grain boundaries, the conductivity of each part of the graphene on the channel is both different, so for the channel material itself, it is impossible to guarantee two graphene FET with the same conductivity. Figure 10.1a shows the optical image for the CVD graphene on copper foil. It shows the micron-scale grain boundaries of CVD graphene. Figure 10.1b shows the SEM image of the CVD graphene on copper foil. It shows that there are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_10

107

108

10 Challenges and Outlook

Fi. 10.1 a The optical image of the CVD graphene on copper foil. b The SEM image of the CVD graphene on copper foil

randomly distributed multilayer graphene island core structures with darker colors inside the grain boundaries. It will also interrupt the conductivity of the graphene FET biosensor. However, as far as the current level of technology is concerned, it is still very difficult to grow single-crystal graphene comparable to silicon wafer level. It is conceivable that if graphene as a new type of two-dimensional material is further widely used in several sensing applications, the core problem is how to prepare high-quality monocrystalline graphene in my opinion. At present, some studies have shown the method of preparing single-crystal graphene films by the CVD method [1]. They report that the growth of millimeter-sized hexagonal single-crystal graphene and graphene films joined from such grains on Pt by ambient-pressure chemical vapor deposition. I believe that the problem of CVD preparation of large-size single-crystal graphene will be solved in the future.

10.1 Standardization of Transfer and Modification Graphene transfer is the most difficult processing during the graphene FET manufacture. The graphene film has to transfer to the substrate from the liquid solution, it is so thin that is very easy to be destroyed during the transfer processing. The transfer of CVD graphene first requires spin-coating a layer of PMMA film on the surface of the graphene copper foil, then etch the copper foil with the oxidant, and finally transfer the graphene to the substrate in the solution. This series of complicated steps are difficult to full automation. At present, the transfer steps of graphene are basically done manually, which brings great difficulties to the standardization of graphene

10.1 Standardization of Transfer and Modification

109

FET biosensor. Therefore, it is easy to cause graphene to form wrinkles and creases on the substrate during the graphene transfer process. The surface defects caused by these subtle wrinkles and creases made the difference in conductivity between CVD graphene FET sensors, which is also the reason for the conductivity difference. Otherwise, modification processing is also the main reason for the conductivity difference. But at present, the modification steps of ELISA and other biological applications of immunohistochemistry can be well standardized to narrow the differences between individuals. So I think that with the gradual deepening of the application of graphene FET biosensor, the standardization of the process will be well resolved, including the use of more advanced automatic analyzers. In addition, the adhesion between the electrode and the substrate is also an aspect that needs attention (during the GFET biosensor test in solution, low adhesion may cause the electrode to fall off). For example, if epitaxial graphene is used as a substrate, the low adhesion between the interdigital electrode and the substrate may cause the electrode of the GFET biosensor to fall during the test (strong hydrophilicity of the electrode material). The current ideal improvement plan is to first deposit electrodes on the substrate (to ensure strong adhesion between the electrodes and the substrate), and then transfer the CVD graphene to the electrodes. However, the thickness of the deposited electrode should be controlled as thin as possible so that the graphene can better adhere to the surface of the electrode and the substrate through Van der Waals force. When using epitaxial graphene as the substrate, the specific hydrophobic layer can be deposited on the surface of the electrode to shield the electrode from its strong hydrophilicity.

10.2 Signal Interference The signal interference of graphene biosensing is multifaceted. The most common one is the electric signal jitter caused by vibration. In addition, since graphene is almost transparent, if the substrate is SiO2 /Si, then electromagnetic radiation such as ultraviolet rays can easily pass through the insulating layer to cause a similar gate effect on the Si layer, which affects the conductivity of graphene [2]. Note that if the linker with the fluorescent effects such as PBASE is used as the linker, it may also absorb electromagnetic radiation such as ultraviolet rays, thereby affecting the biosensing signal. Therefore, the sensor should try to avoid using it under strong electromagnetic radiation. From the perspective of the device structure, it is recommended to use only the back-gate grounding method for real-time testing. Because in the real-time test process, the change of the current Ids in the ideal state is only determined by the number of molecules adsorbed on the graphene channel. If the gate is given to the voltage, any small fluctuations in the gate voltage will affect the current Ids during the real-time test. In addition, if the reference electrode is introduced as the top gate, any small mechanical vibration and many other complex factors will significantly

110

10 Challenges and Outlook

disturb the current Ids during the real-time test. Meanwhile, the reference electrode will introduce new ions in the test solution. Thus, according to my experience, for real-time testing, the back-gate grounding method is recommended.

10.3 Outlook Compared with traditional clinical detection methods, although GFET biosensor cannot detect single nucleic acid molecules such as PCR through amplification in terms of sensitivity, its sensitivity to specific targets can currently reach the sub-fM level, which is close to that of ELISA. But in terms of detection speed, because GFET biosensor can directly convert biological signals into current signals, and relying on the super-large contact surface between the graphene channel and the solution to be tested, the detection of GFET biosensor can accomplish within 1 min. This is unmatched by traditional methods (even the rapid ELISA technique usually requires more than 1 h). At present, as a credible technology in clinical detection, the position of traditional detection methods (such as ELISA and PCR) in medicine is unshakable. We proposed the novel GFET biosensor that is not intended to alternative these traditional detection methods but to complement each other based on the advantage of rapid detection which traditional detection methods do not possess. At the same time, this novel GFET biosensor also has a certain sensitivity and selectivity. Because this sensor has a huge potential to perform rapid detection, it may be more suitable as a kind of the initial screening method, thereby quickly introducing a preliminary diagnostic opinion from the test results of the GFET biosensor. Then according to the positive or negative detected result, further traditional detection methods such as ELISA and PCR are gradually considered. This may be useful for large-scale epidemic diseases such as COVID-19 (Tables 10.1, 10.2, 10.3 and 10.4). Although graphene FET biosensors still have some thorny issues at present, compared with traditional bulk materials, 2D materials, especially graphene, have shown excellent performance in biosensing and other applications, so I think graphene in the future FET biosensors are promising, but further research is still needed. For example, how to manufacture GFET biosensors in large-scale industrial production, and how to integrate with the existing semiconductor technology (Based on system-on-a-chip technology, GFET biosensors should be further integrated with the analog-digital converter and microprocessor). The research of graphene applications has received more and more attention worldwide, and many research centers have been established in the past two decades. The main locations of these research centers are concentrated in China and the United States. Finally, these major graphene research centers will be introduced.

10.3 Outlook

111

Table 10.1 The main research center in China China Shenyang Institute of Metal Science

Cheng Huiming group http://www.imr.cas.cn/

Central South University

Liu Yanping group http://www.yplab.cn/

Harbin Institute of Technology

Key laboratory of Micro-systems and Micro-structures Manufacturing http://mmme.hit.edu.cn/

Shanghai Institute of Microsystem and Information Technology

Mao Hongju group http://english.sim.cas.cn/

Xiamen University

Graphene Industry and Engineering Research Institute of Xiamen University https://gieri-en.xmu.edu.cn/en/

Fudan University

Wei Dacheng group http://www.weigroupfudan.com/

Table 10.2 The main research center in Japan Japan Osaka University

Kobayashi Yoshihiro group http://www.ap.eng.osaka-u.ac.jp/nanomaterial/ index.html

Nagoya University

Ohno Yutaka group https://nanoflex.jp/public-j/member_ohno.html

Tokyo University of Agriculture and Technology

Maehashi Kenzo group http://web.tuat.ac.jp/~maehashi/index.html

Saitama University

Oeno Keiji group http://surface-www.chem.saitama-u.ac.jp/wiki/

National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba

Nanomaterials Research Institute https://unit.aist.go.jp/nmri/index_en.html

National Institute for Materials Science (NIMS)

Advanced Low-Dimensional Nanomaterials Group https://www.nims.go.jp/1Dnanomaterials/eng lish/index.html

112

10 Challenges and Outlook

Table 10.3 The main research center in the United States United States Massachusetts Institute of Technology

Jarillo-Herrero Group http://jarilloherrero.mit.edu/

Harvard University

Charles M. Lieber group http://cml.harvard.edu/

Columbia University

Qiao Lin group https://biomems.me.columbia.edu/

University of California, Los Angeles

Duan Xiangfeng group http://xduan.chem.ucla.edu/

University of Michigan

MICROTECHNOLOGY LAB https://www.egr.msu.edu/mems/

University of Pennsylvania

Charlie Johnson Group http://nanophys.seas.upenn.edu/

Table 10.4 The main research center in other countries Other countries University of Manchester

The University of Manchester National Graphene Institute https://www.graphene.manchester.ac.uk/

University of Cambridge

Cambridge Graphene Centre https://www.graphene.cam.ac.uk/

University of Oxford

Nanostructured Materials Group Department of Materials http://nsm.materials.ox.ac.uk/Main/HomePage

Vienna University of Technology

Thomas Mueller group https://www.graphenelabs.at/home

The National University of Singapore

The Graphene Research Centre (GRC) https://graphene.nus.edu.sg/

Nanyang Technological University

Liu group https://www.ntu.edu.sg/home/z.liu/index.html

References 1. Gao, L., Ren, W., Xu, H., Jin, L., Wang, Z., Ma, T., Ma, L.-P., Zhang, Z., Fu, Q., Peng, L.-M., Bao, X., Cheng, H.-M.: Repeated growth and bubbling transfer of graphene with millimetre-size single-crystal grains using platinum. Nature Commun. 3(1), 699 (2012). https://doi.org/10.1038/ ncomms1702 2. Iqbal, M.Z., Siddique, S., Anwar, N.: Influence of electron beam and ultraviolet irradiations on graphene field effect transistors. Optical Mater. 72, 496–500 (2017). https://doi.org/10.1016/j. optmat.2017.06.039

Chapter 11

Conclusions and Future Works

Abstract Compared with other traditional clinical detection methods, the core advantage of graphene field-effect transistor biosensor is its unparalleled testing speed. At the same time, it can also maintain high sensitivity and specificity. From the perspective of future applications, the graphene field-effect transistor biosensor may be more suitable as a preliminary rapid detection solution. Large-scale prescreening can be conducted so that it can rapidly provide preliminary diagnostic results. As a novel rapid quantitative detection platform, it can make up for the shortcomings of traditional detection methods in large-scale rapid detection. Believe that with the gradual deepening of the research of graphene field-effect transistor biosensors, its potential value in the field of biological detection will become more and more outstanding in the future. Keywords Graphene · Graphene FET · Biosensor · Rapid detection

11.1 Conclusions Based on excellent electrical properties, chemically stability, and compatibility with different organic macromolecules, graphene has become an ideal platform for binding receptor molecules and biosensor sensors. With the development of advanced nanofabrication equipment, GFET biosensors have become the most potential biosensing and biological detection technology for rapid, low-cost, and ultrasensitive quantification of biomolecules. Although the invention and development of the graphene FET biosensor have only gone through 10 years, it has sufficiently merged with multiple biological technologies, and many different types of graphene FET biosensors have been developed. Current research has shown that based on graphene FET biosensors, virus es, bacteria, cells, and many different types of biomolecules can be ultrasensitive rapid quantitative detection. Its detection limit has approached the fM level, and through a series of blocking methods, the graphene FET biosensor has shown ultra-high specificity, and compared with traditional detection methods, it can perform rapid quantification, thereby providing a potential basis for rapid judgment for clinical diagnosis. This is unmatched by traditional detection methods. In particular, it provides a potentially effective procedure for large-scale © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1_11

113

114

11 Conclusions and Future Works

investigations of epidemics in the population. All these exciting results indicated the great prospects of this technology in biosensing applications. Here I would like to emphasize again that compared with traditional clinical detection methods, such as ELISA and PCR, the key advantage of GFET biosensors lies in the rapid detection and the high sensitivity. From the perspective of detection speed, GFET biosensors can quickly respond to the concentration of the test solution within 1 min, because GFET biosensors can directly convert biological signals into electrical signals. However, traditional detection methods such as PCR and ELISA, even the rapid detection procedure also takes 1 h. GFET biosensor has unparalleled rapid detection performance (1 min < < 1 h). From the perspective of detection sensitivity, although the current GFET biosensor cannot perform single molecular-level detection like PCR, its detection sensitivity is close to the detection accuracy of ELISA (sub-pM-level) and is expected to be further improved in the future. Besides, it may not require complex detection equipment, which makes it highly portable. As a novel biosensing platform, the GFET biosensor is a good complementary detection method for traditional clinical detection methods. As an emerging biosensing platform, it may be more suitable for rapid screening of large-scale epidemics and is expected to complement the current traditional clinical detection methods that cannot achieve large-scale rapid detection.

11.2 Future Works Although this novel sensor has great potential in the clinical application field, there are still some problems that need to be solved before it is put into practical application. For example, the safety of graphene nanomaterials has not yet been clarified, and how the graphene FET biosensor can be fused with current semiconductor technology for large-scale preparation. In addition, it may be necessary to develop special signal acquisition and analysis equipment for graphene FET biosensors. But these problems will not substantially affect the sensing applications of graphene FET biosensor, and there are some signs that these problems are gradually improving. So I believe that the prospect of graphene biosensors is worthy of being expected. Furthermore, in order to improve the detection effect, avoiding the noise signal, and the standardization of graphene transfer and graphene modification should be noted. These experimental details may also affect the detection results.

Index

A Adsorption detection, vii Alpha-fetoprotein, 89, 90 Amyloid-β (Aβ) and tau protein, 90 Antibody, 54, 62, 71, 80, 81, 87–90, 93, 98, 99 Antigen, 54, 61, 71, 80, 81, 87–90, 93, 98 Antigen-antibody interaction, vi, 87–90, 98 Aptamer, 46, 54, 71, 97–99 Aptamer technology, 98, 99 Atomic Force Microscope (AFM), 30, 77 AuNPs, 54, 71, 77, 88, 94, 99, 102 Avidin, 69–71, 78, 80, 81 Avidin-biotin technology, 69, 80, 81

Chemical vapor deposition, 30 Chitosan, 103 ConA-chitosan complex, 103 Cortisol hormone, 90 COVID-19, 47, 71, 95, 96 COVID-19 detection, 95

D Debye length, 50, 90 Diffuse Layer (DL), 7, 47, 48, 50 Dirac point, 22, 57, 60 Dissociation detection, vii DNA, 9, 11, 46, 94, 95, 97, 98 DNA sequencing, 9, 11 Double layer, 7, 47, 50, 51, 60, 88, 104 Drug delivery, 9

B Base pair, 54, 93, 94 BDM model, 49 BioFETs, 46 Biological detection, 13 Biomedical applications, 9 Biosensor, 31, 37, 45–47, 52–55, 60–62, 69, 71, 78–81, 88–90, 93–95, 98, 99, 102, 103, 107, 109, 114 Biotin, 69–71, 78–81 Blocking, 55, 113 Bovine Serum Albumin (BSA), 46, 52, 55, 78, 95 Brain natriuretic peptide, 89, 90

E Early diagnosis, 93 Electrical Double Layer (EDL), 7, 47 Electrical theoretical basis, vi Electrochemiluminescence, 102 ELISA, 69, 70, 81, 109 Epitaxial growth, 37 Escherichia coli, 98, 102 Etching transfer method, 33 Ethanolamine, 55, 90, 95

C Cancer marker, 71, 80, 88 Carcinoembryonic antigen, 62, 90 Cell imaging, 11

F Field-effect transistor, 1, 29, 45, 46, 51–53, 60, 62, 71, 98 Free radical detection, 63

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Wang et al., Graphene Field-Effect Transistor Biosensors, https://doi.org/10.1007/978-981-16-1212-1

115

116 G Glucose oxidase, 102 Gold nanoparticles, 71, 77, 81, 102 Graphene, 1, 2, 6, 7, 9–12, 14, 15, 21, 22, 24–39, 45–48, 51–55, 57, 60–63, 69, 71, 72, 77–81, 88–91, 94, 95, 98, 99, 102–104, 107, 108, 114 Graphene biosafety, 14 Graphene conductive ink, 2 Graphene electrical characteristics, 21 Graphene FET biosensor, 37, 46, 47, 52–55, 60–62, 69, 71, 78, 79, 81, 88, 95, 98, 99, 102, 103, 107–109, 114 Graphene field-effect transistor, 29, 45, 62, 71, 98 Graphene flexible sensing, 6 Graphene manufacture, 29 Graphene nanogenerator, 6 Graphene supercapacitor, 4 Guanine blocking, 55

H Hall device, 26 Helmholtz plane, 50 Hill-Langmuir equation, 61

I Immunoglobulin E, 98 Interface capacitance, 60 Isoelectric point, 104

L Labeled immunosensor, 69, 70 Langmuir adsorption model, 60 Large-scale census, 95 Large-scale epidemic detection, vi Limit of detection, 62, 80, 81 Linker, 46, 54, 60, 71, 94, 98, 103 Low-temperature electrical characteristics, 27

M Mechanical exfoliation method, 29 Metal ions, 63, 101, 102 Microbes, 102, 103, 105 Microbial fuel cells, 5 MiRNA, 71, 94 Moiré pattern, 27 Monocrystalline, 107, 108

Index N Nanoscale graphene oxide, 10 Nanowire, 91 N-Hydroxysuccinimide (NHS) ester, 103 Non-covalent interaction, 70 Nucleic acid, 54, 55, 61, 70, 71, 81, 93–95, 97 Nucleic acid sequence, 94 Nucleic Acid Test (NAT), 93, 95

O Oligonucleotides, 97

P PBASE, 46, 54, 62, 71, 88, 94, 98, 99 Percentage of the surface cover, 61 Point-of-care testing, vi Polycrystalline, 107 Polymer-based detection, 69, 70 Polypeptide nucleic acid (PNA), 94 Poly(methyl methacrylate) (PMMA), 36, 37, 72, 108 Preparation method, 29 Probe sequence, 96 Prostate specific antigen/α1-antichymotrypsin, 90

R Raman spectrum, 78 Reduced Graphene Oxide (RGO), 39, 89 RNA, 71, 94–97 RNA virus, 95 Room-temperature electrical characteristics, 22

S SARS-CoV-2, 71 Scanning Electron Microscope (SEM), 77, 107, 108 Schiff base reaction, 98, 99 SELEX technology, 99 Sensing platform, 6, 26, 69, 71, 82, 88 SiC, 27, 37–39 Signal interference, 109 Stacking, 10, 54, 62 Standardization of transfer and modification, 108 Stern model, 48 Surface modification, 54, 77, 88

Index T Target RNA sequence, 96 Tetrakis(4-carboxyphenyl)porphyrin (TCPP), 46, 54, 88 Thermal applications, 7 Tumer therapy, 12

117 V Vibration, 36 Virus, 61, 71, 94, 95, 113

X Xeno nucleic acids (XNA), 94, 97 X-ray Photoelectron Spectroscopy (XPS), 37, 38, 77