Sensors for Next-Generation Electronic Systems and Technologies

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Acknowledgements
Preface
About the editors
Contributors
Chapter 1: Fabrication and study of fluidic MEMS device for toxic heavy metal ion sensing in water
	1.1 Introduction
	1.2 Design of micromixer device
		1.2.1 Simulation of Herringbone (HB) structure micromixer device
	1.3 Experimental
		1.3.1 Design of HB bent micromixer device
		1.3.2 Fabricated HB bent micromixer device
		1.3.3 Preparation of gold nanofluids
	1.4 Sample fluid preparation
		1.4.1 Detection process of metal ions using microfluidic device
	1.5 Results and discussion
		1.5.1 FTIR studies of sensing fluids
		1.5.2 Fluorescence studies
	1.6 Colorimetric method based heavy metal ions detection
		1.6.1 Colorimetric analysis in conventional tube
		1.6.2 Colorimetric analysis in microfluidic device
	1.7 Conclusion
	References
Chapter 2: The review of micro-electromechanical systems-based biosensor: A cellular base perspective
	2.1 Introduction
	2.2 The prologue of biological cells
		2.2.1 The prologue of biological cell
		2.2.2 Cell cycle and division
		2.2.3 Mathematical modeling of Single cell
		2.2.4 Growth model
		2.2.5 Cancer prognosis
	2.3 Techniques involved in detection of biophysical properties of the cell
		2.3.1 Coulter devices
		2.3.2 Fluorescent-based techniques
		2.3.3 Flow cytometry
		2.3.4 Mass spectrometry
		2.3.5 Surface plasmon resonance
	2.4 Micro electro mechanical systems based mass sensors
		2.4.1 Technique of mass sensing using resonant mass sensor
		2.4.2 Cantilever mechanics
	2.5 Mass sensors reported in literature
		2.5.1 Pedestal mass sensor
		2.5.2 Suspended micro-resonating channel (SMR)
	2.6 Fractal MEMS structures
		2.6.1 Fractal tree geometry realization
	References
Chapter 3: MEMS-based electrochemical gas sensor
	3.1 Introduction
	3.2 Gas sensors classification
		3.2.1 Mass-sensitive gas sensors
		3.2.2 Optical gas sensors
		3.2.3 Thermometric sensor
		3.2.4 Electrochemical sensors
	3.3 Fabrication materials
		3.3.1 Metal oxide semiconductors
		3.3.2 Graphene
		3.3.3 Composite materials (CM)
	3.4 MEMS electrochemical gas sensor
	3.5 Structure
	3.6 Fabrication of MEMS gas sensors
	3.7 Application
	3.8 Conclusion
	References
Chapter 4: Electrochemical biosensors
	4.1 Introduction
	4.2 Classifying of biosensors
	4.3 Principles of electrochemical biosensors
		4.3.1 Potentiometric method
		4.3.2 Voltammetric method
		4.3.3 Impedance method
		4.3.4 Amperometric method
		4.3.5 Electrochemical biosensor assay strategy: labeled vs. label-free
		4.3.6 Biocatalytic/affinity electrochemical biosensors
			4.3.6.1 Biocatalytic biosensors
			4.3.6.2 Affinity biosensors
	4.4 Electrochemical lab-on-a-chip systems
		4.4.1 Design of LOC systems
		4.4.2 Materials and fabrication
		4.4.3 Microfluidics
			4.4.3.1 The physics of microfluidics
			4.4.3.2 Classification of microfluidic platforms
				4.4.3.2.1 Channel microfluidics
				4.4.3.2.2 Digital microfluidics (DMF)
				4.4.3.2.3 Paper-based microfluidics
	4.5 Wearable electrochemical biosensor
		4.5.1 Target biofluids for the wearable electrochemical biosensors
			4.5.1.1 Saliva analysis
			4.5.1.2 Tear analysis
			4.5.1.3 Sweat analysis
			4.5.1.4 Interstitial Fluid (ISF) analysis
		4.5.2 Template and non-template fabrication methods
	4.6 Healthcare applications
		4.6.1 Cancer detection
		4.6.2 Infectious detection
		4.6.3 Cardiac detection
	4.7 Conclusion
	References
Chapter 5: Graphene/carbon nanotubes-based biosensors for glucose, cholesterol, and dopamine detection
	5.1 Introduction to carbon nanomaterials
		5.1.1 Carbon nanotubes
		5.1.2 Structure
		5.1.3 Properties
		5.1.4 Synthesis and functionalization of CNTs
		5.1.5 Purification of CNTs
		5.1.6 Graphene
	5.2 Graphene synthesis methods
		5.2.1 Chemical vapor deposition techniques
		5.2.2 Exfoliation
		5.2.3 Carbon nanomaterials for electrochemical detection
		5.2.4 Glucose biosensors
		5.2.5 CNT and graphene-based glucose biosensor
		5.2.6 Non-enzymatic glucose biosensors
		5.2.7 Cholesterol biosensors
		5.2.8 Dopamine biosensors
		5.2.9 Computational studies on graphene and carbon nanotube-based biosensors
		5.2.10 Challenges in modeling and simulation of graphene and CNTs for sensing applications
		5.2.11 Challenges and future trends
	References
Chapter 6: Transition metal dichalcogenide based surface plasmon resonance for bio-sensing
	6.1 Introduction
		6.1.1 Basics of the surface plasmon resonance sensor
		6.1.2 Interrogation approaches
		6.1.3 Surface plasmon resonance sensor performance parameter
	6.2 Two-dimensional materials
	6.3 Biosensor
		6.3.1 Optical biosensor
	6.4 Advanced materials-based surface plasmon resonance biosensor
		6.4.1 Graphene-based sensors
		6.4.2 TMDs based sensors
	6.5 Conclusion
	References
Chapter 7: Graphene and carbon nanotube-based sensors
	7.1 Introduction
	7.2 Classification of graphene and its derivatives
		7.2.1 Graphene
		7.2.2 Graphene oxide
		7.2.3 Reduced graphene oxide
	7.3 Graphene based sensors
		7.3.1 Graphene based SERS sensor
		7.3.2 Graphene based electrochemical sensor
		7.3.3 Graphene based fluorescence sensor
		7.3.4 Graphene based FET sensor
	7.4 Classification of carbon nanotubes
		7.4.1 Multi–walled carbon nanotubes
		7.4.2 Single–walled carbon nanotubes
	7.5 Application of carbon nanotubes–based sensors
		7.5.1 Carbon nanotubes-based SERS sensors
		7.5.2 Carbon nanotubes-based electrochemical sensors
		7.5.3 Carbon nanotubes-based fluorescence sensors
		7.5.4 Carbon nanotubes-based FET sensor
	7.6 Conclusion
	Acknowledgements
	References
Chapter 8: Intelligent flow sensor using artificial neural networks
	8.1 Introduction: background of flow measurement and taxonomy
	8.2 Theory of the control valve
	8.3 The multilayer perceptron neural network
	8.4 FPGA implementation
	8.5 FPGA implementation of the sigmoid function
	8.6 FPGA Implementation of the MLP neural network
	8.7 Discussion
	8.8 Summary
	Bibliography
Chapter 9: Smart sensor systems for military and aerospace applications
	9.1 Introduction
	9.2 Requirement for an ideal design of smart sensor systems
	9.3 HEMT for RADAR application
	9.4 Photodetectors in military applications
		9.4.1 Photoresistors
		9.4.2 Photodiodes
		9.4.3 Infrared sensors
	9.5 Solar blind photodetectors for military applications
	9.6 Infrared photodetectors as night vision devices
	9.7 Conclusion
	References
Chapter 10: Magnetic biosensors: Need and progress
	10.1 Basics of biosensors
	10.2 Existing technologies of biosensing
		10.2.1 Hemagglutination Assay (HA)
		10.2.2 Enzyme-linked immunosorbent assay (ELISA)
		10.2.3 Polymerase Chain Reaction (PCR)
	10.3 Advantages and limitations of the existing methods
		10.3.1 Characteristics of a biosensor
		10.3.2 Selectivity
		10.3.3 Reproducibility
		10.3.4 Stability
		10.3.5 Sensitivity
		10.3.6 Linearity
	10.4 Principles of magnetic biosensing
		10.4.1 Magnetic nanoparticles
		10.4.2 Classification of MNPs based on their magnetic nature
		10.4.3 Need for magnetic nanosensors
	10.5 Types of biorecognition elements
		10.5.1 Enzymatic biosensors
		10.5.2 Biosensors using DNA and RNA
		10.5.3 Biosensors using antibody
		10.5.4 Aptasensors
		10.5.5 Peptide based molecular sensors
	10.6 Principles of magnetic nanosensors
		10.6.1 Magnetoresistance
		10.6.2 The Hall effect sensors
		10.6.3 Anisotropic Magnetoresistance Sensors (AMR)
		10.6.4 Giant Magnetoresistance Sensors (GMR)
		10.6.5 Tunneling Magneto Resistance (TMR)
		10.6.6 Magnetic nanosensors for biomedical applications
		10.6.7 Magnetic Relaxation Immunoassays (MARIA) using Fluxgate Sensor
		10.6.8 Planar Hall Magnetoresistive (PHR) aptasensor for thrombin detection
		10.6.9 Anisotropic Magnetoresistance (AMR) biosensors
		10.6.10 Eigen Diagnosis Platform (EDP) using giant magnetoresistance biosensors
		10.6.11 Tunneling Magnetoresistance (TMR) biosensors
		10.7 Conclusion
	References
Index