Table Of ContentSmart Sensors for Environmental and Medical Applications
IEEE Press
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Ekram Hossain, Editor in Chief
Jón Atli Benediktsson Bimal Bose David Alan Grier
Elya B. Joffe Xiaoou Li Peter Lian
Andreas Molisch Saeid Nahavandi Jeffrey Reed
Diomidis Spinellis Sarah Spurgeon Ahmet Murat Tekalp
Smart Sensors for Environmental and
Medical Applications
Edited by
Hamida Hallil and Hadi Heidari
IEEE Press Series on Sensors
Vladimir Lumelsky, Series Editor
© 2020 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress Cataloging‐in‐Publication Data
Names: Hallil, Hamida, 1981– editor. | Heidari, Hadi, editor.
Title: Smart sensors for environmental and medical applications / Hamida
Hallil, Hadi Heidari.
Description: Hoboken, New Jersey : Wiley-IEEE Press, 2020. | Series: IEEE
press series on sensors | Includes bibliographical references and index.
Identifiers: LCCN 2020011698 (print) | LCCN 2020011699 (ebook) | ISBN
9781119587347 (hardback) | ISBN 9781119587354 (adobe pdf) | ISBN
9781119587378 (epub)
Subjects: LCSH: Biosensors. | Medical instruments and apparatus.
Classification: LCC R857.B54 S64 2020 (print) | LCC R857.B54 (ebook) |
DDC 610.28/4–dc23
LC record available at https://lccn.loc.gov/2020011698
LC ebook record available at https://lccn.loc.gov/2020011699
Cover Design: Wiley
Cover Image: © toodtuphoto/Shutterstock
Set in 9.5/12.5pt STIXTwoText by SPi Global, Pondicherry, India
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
v
Contents
List of Contributors xi
Preface xiii
About the Editors xvii
1 Introduction 1
Hamida Hallil and Hadi Heidari
1.1 Overview 1
1.2 Sensors: History and Terminology 2
1.2.1 Definitions and General Characteristics 3
1.2.2 Influence Quantities 5
1.3 Smart Sensors for Environmental and Medical Applications 6
1.4 Outline 8
Reference 9
2 Field Effect Transistor Technologies for Biological
and Chemical Sensors 11
Anne-Claire Salaün, France Le Bihan, and Laurent Pichon
2.1 Introduction 11
2.2 FET Gas Sensors 12
2.2.1 Materials 12
2.2.1.1 Inorganic Semiconductors 12
2.2.1.2 Semiconductor Polymers 12
2.2.1.3 Nanostructured Materials 13
2.2.2 FET as Gas Sensors 13
2.2.2.1 Pioneering FET Gas Sensors 13
2.2.2.2 OFET Gas Sensors 13
2.2.2.3 Nanowires-Based FET Gas Sensors 14
2.3 Ion-Sensitive Field Effect Transistors Based Devices 18
2.3.1 Classical ISFET 18
2.3.2 Other Technologies 19
2.3.2.1 EGFET: Extended Gate FET 20
vi Contents
2.3.2.2 SGFET: Suspended Gate FFETs 20
2.3.2.3 DGFET: Dual-Gate FETs 20
2.3.2.4 Water Gating FET or Electrolyte Gated FET 21
2.3.2.5 Other FETs 23
2.3.3 BioFETs 23
2.3.3.1 General Considerations 23
2.3.3.2 DNA BioFET 23
2.3.3.3 Protein BioFET 25
2.3.3.4 Cells 25
2.4 Nano-Field Effect Transistors 25
2.4.1 Fabrication of Nano-Devices 25
2.4.1.1 Silicon Nano-Devices 25
2.4.1.2 Carbon Nanotubes Nano-Devices 28
2.4.2 Detection of Biochemical Particles by Nanostructures-Based FET 28
2.4.2.1 SiNW pH Sensor 29
2.4.2.2 DNA Detection Using SiNW-Based Sensor 30
2.4.2.3 Protein Detection 32
2.4.2.4 Detection of Bacteria and Viruses 33
References 34
3 Mammalian Cell-Based Electrochemical Sensor for Label-Free
Monitoring of Analytes 43
Md. Abdul Kafi, Mst. Khudishta Aktar, and Hadi Heidari
3.1 Introduction 43
3.2 State-of-the-Art Cell Chip Design and Fabrication 45
3.3 Substrate Functionalization Strategies at the Cell–Electrode Interface 48
3.4 Electrochemical Characterization of Cellular Redox 49
3.5 Application of Cell-Based Sensor 51
3.6 Prospects and Challenges of Cell-Based Sensor 54
3.7 Conclusion 56
References 56
4 Electronic Tongues 61
Flavio M. Shimizu, Maria Luisa Braunger, Antonio Riul, Jr.,
and Osvaldo N. Oliveira, Jr.
4.1 Introduction 61
4.2 General Applications of E-tongues 63
4.3 Bioelectronic Tongues (bETs) 65
4.4 New Design of Electrodes or Measurement Systems 66
4.5 Challenges and Outlook 73
Acknowledgments 73
References 74
Contents vii
5 Monitoring of Food Spoilage Using
Polydiacetylene‐ and Liposome‐Based Sensors 81
Max Weston, Federico Mazur, and Rona Chandrawati
5.1 Introduction 81
5.2 Polydiacetylene for Visual Detection of Food Spoilage 82
5.2.1 Contaminant Detection 83
5.2.2 Freshness Indicators 85
5.2.3 Challenges, Trends, and Industrial Applicability
in the Food Industry 87
5.3 Liposomes 88
5.3.1 Pathogen Detection 88
5.3.1.1 Escherichia coli 88
5.3.1.2 Salmonella spp. 90
5.3.1.3 Other Bacterium 90
5.3.1.4 Viruses, Pesticides, and Toxins 91
5.3.2 Stability of Liposome‐Based Sensors 93
5.3.3 Industrial Applicability of Liposomes 93
5.4 Conclusions 94
References 94
6 Chemical Sensors Based on Metal Oxides 103
K. S. Shalini Devi, Aadhav Anantharamakrishnan,
Uma Maheswari Krishnan, and Jatinder Yakhmi
6.1 Introduction 103
6.2 Classes of MOx-Based Chemical Sensors 104
6.3 Synthesis of MOx Structures 104
6.4 Mechanism of Sensing by MOx 105
6.5 Factors Influencing Sensing Performance 106
6.6 Applications of MOx-Based Chemical Sensors 109
6.6.1 MOx Sensors for Environmental Monitoring 109
6.6.2 MOx Sensors in Clinical Diagnosis 112
6.6.3 MOx Sensors in Pharmaceutical Analysis 113
6.6.4 MOx-Based Sensors in Food Analysis 116
6.6.5 MOx Sensors in Agriculture 117
6.6.6 MOx Sensors for Hazard Analysis 117
6.6.7 Flexible Sensors Based on MOx 118
6.6.8 MOx-Based Lab-on-a-Chip Sensors 118
6.7 Concluding Remarks 119
Acknowledgment 119
References 120
viii Contents
7 Metal Oxide Gas Sensor Electronic Interfaces 129
Zeinab Hijazi, Daniele D. Caviglia, and Maurizio Valle
7.1 General Introduction 129
7.1.1 Gas Sensing System 129
7.1.2 Gas Sensing Technologies 130
7.2 MOX Gas Sensors 131
7.2.1 Principle of Operation 131
7.2.2 Assessment of Available MOX-Based Gas Sensors 132
7.3 System Requirements and Literature Review 134
7.3.1 System Requirements 134
7.3.2 Wide Range Resistance Interface Review 136
7.4 Resistance to Time/Frequency Conversion Architecture 137
7.4.1 Electronic Circuit Description 137
7.4.2 Specifications for Each Building Block to Preserve
High Linearity 138
7.4.2.1 Resistance to Current Conversion (R-to-I) 138
7.4.2.2 Switches 141
7.4.2.3 Current to Voltage Conversion (I-to-V) 141
7.4.2.4 Voltage to Time/Period (V-to-T) Conversion 141
7.5 Power Consumption 141
7.5.1 Power Consumption of MOX Gas Sensor 141
7.5.2 Low Power Operating Mode 142
7.5.3 Power Consumption at Circuit Level 142
7.6 Conclusion 143
References 143
8 Smart and Intelligent E-nose for Sensitive and
Selective Chemical Sensing Applications 149
Saakshi Dhanekar
8.1 Introduction 149
8.1.1 The Human Olfactory System 150
8.1.2 The Artificial Olfactory System 150
8.1.2.1 Sensor Array 151
8.1.2.2 Multivariate Data Analysis 152
8.1.2.3 Pattern Recognition Methods 153
8.2 What Is an Electronic Nose? 154
8.3 Applications of E-nose 155
8.3.1 Key Applications of E-nose 155
8.3.2 E-nose for Chemical Sensing 155
8.4 Types of E-nose 157
8.5 Examples of E-nose 158
8.6 Improvements and Challenges 165
Contents ix
8.7 Conclusion 165
References 166
9 Odor Sensing System 173
Takamichi Nakamoto and Muis Muthadi
9.1 Introduction 173
9.2 Odor Biosensor 174
9.3 Prediction of Odor Impression Using Deep Learning 176
9.4 Establishment of Odor‐Source Localization Strategy Using
Computational Fluid Dynamics 181
9.4.1 Background of Odor‐Source Localization 181
9.4.2 Sensor Model with Response Delay 182
9.4.3 Simulation of Testing Environment Using CFD 183
9.4.4 Simulation of Biologically Inspired Odor‐Source Localization 185
9.4.4.1 Odor Plume Tracking Strategy 185
9.4.4.2 Result 186
9.4.5 Summary of Odor Source Localization Strategy 187
9.5 Conclusion 188
Acknowledgments 189
References 189
10 Microwave Chemical Sensors 193
Hamida Hallil and Corinne Dejous
10.1 Interests of Electromagnetic Transducer Gas Sensors at Microwave
Frequencies 193
10.2 Operating Principle 193
10.2.1 Electromagnetic Transducers 193
10.2.2 The Case of Microwave Transducers 195
10.3 Theory of Microwave Transducers: Design, Methodology,
and Approach 196
10.4 Microwave Structure‐Based Chemical Sensor 200
10.4.1 Manufacturing Techniques 200
10.4.2 Chemical Microwave Sensors 200
10.4.3 Wireless Interrogation Schemes 204
10.5 Multivariate Data Analysis and Machine Learning
for Targeted Species Identification 207
10.6 Conclusion and Prospects 209
Acknowledgments 210
References 210
Index 217
