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Cover
Title Page
List of Contributors
Preface
Acknowledgments
Section 1: Introduction to Biosensors, Recognition Elements, Biomarkers, and Nanomaterials
1. 1 General Introduction to Biosensors and Recognition Receptors
  1. 1.1 Introduction to Biosensors
  2. 1.2 Enzyme‐Based Biosensors
  3. 1.3 DNA‐ and RNA‐Based Biosensors
  4. 1.4 Antibody‐Based Biosensors
  5. 1.5 Aptasensors
  6. 1.6 Peptide‐Based Biosensors
  7. 1.7 MIP‐Based Biosensors
  8. 1.8 Conclusions
  9. Acknowledgment
  10. References
2. 2 Biomarkers in Health Care
  1. 2.1 Introduction
  2. 2.2 Biomarkers
  3. 2.3 Biological Samples and Biomarkers
  4. 2.4 Personalized Health and Point‐of‐Care Technology
  5. 2.5 Use of Biomarkers in Biosensing Technology
  6. 2.6 Biomarkers in Disease Diagnosis
  7. 2.7 Conclusions
  8. References
3. 3 The Use of Nanomaterials and Microfluidics in Medical Diagnostics
  1. 3.1 Introduction
  2. 3.2 Nanomaterials in Medical Diagnostics (Bottom‐Up Approach)
  3. 3.3 Application of Microfluidic Devices in Clinical Diagnostics (Top‐Down Approach)
  4. 3.4 Integration of Microfluidics with Nanomaterials
  5. 3.5 Future Perspectives of Nanomaterial and Microfluidic‐Based Diagnostics
  6. References
Section 2: Biosensor Platforms for Disease Detection and Diagnostics
1. 4 SPR‐Based Biosensor Technologies in Disease Detection and Diagnostics
  1. 4.1 Introduction
  2. 4.2 Basic Theoretical Principles
  3. 4.3 SPR Applications in Disease Detection and Diagnostics
  4. 4.4 Conclusions
  5. Acknowledgment
  6. References
2. 5 Piezoelectric‐Based Biosensor Technologies in Disease Detection and Diagnostics
  1. 5.1 Introduction
  2. 5.2 QCM Biosensors
  3. 5.3 Disease Diagnosis Using QCM Biosensors
  4. 5.4 Conclusions
  5. Acknowledgment
  6. References
3. 6 Electrochemical‐Based Biosensor Technologies in Disease Detection and Diagnostics
  1. 6.1 Introduction
  2. 6.2 Electrochemical Biosensors: Definitions, Principles, and Classifications
  3. 6.3 Biomarkers in Clinical Applications
  4. 6.4 Conclusions
  5. References
4. 7 MEMS‐Based Cell Counting Methods
  1. 7.1 Introduction
  2. 7.2 MEMS‐Based Cell Counting Methods
  3. 7.3 Electrical and Electrochemical Cell Counting Methods
  4. 7.4 Gravimetric Cell Counting Methods
  5. 7.5 Conclusion and Comments
  6. References
5. 8 Lab‐on‐a‐Chip Platforms for Disease Detection and Diagnosis
  1. 8.1 Introduction
  2. 8.2 Continuous Flow Platforms
  3. 8.3 Paper‐Based LOC Platforms
  4. 8.4 Droplet‐Based LOC Platforms
  5. 8.5 Digital Microfluidic‐Based LOC Platforms
  6. 8.6 CD‐Based LOC Platforms
  7. 8.7 Wearable LOC Platforms
  8. 8.8 Conclusion and Outlook
  9. References
Section 3: Nanomaterial’s Applications in Biosensors and Medical Diagnosis
1. 9 Applications of Quantum Dots in Biosensors and Diagnostics
  1. 9.1 Introduction
  2. 9.2 Quantum Dots: Optical Properties, Synthesis, and Surface Chemistry
  3. 9.3 Biosensor Applications of QDs
  4. 9.4 Other Biological Applications of QDs
  5. 9.5 Water Solubility and Cytotoxicity
  6. 9.6 Conclusion
  7. Acknowledgment
  8. References
2. 10 Applications of Molecularly Imprinted Nanostructures in Biosensors and Medical Diagnosis
  1. 10.1 Introduction
  2. 10.2 Molecular Imprinted Polymers
  3. 10.3 Imprinting Approaches
  4. 10.4 Molecularly Imprinted Nanostructures
  5. 10.5 MIP Biosensors in Medical Diagnosis
  6. 10.6 Diagnostic Applications of MIP Nanostructures
  7. 10.7 Conclusions
  8. References
3. 11 Smart Nanomaterials
  1. 11.1 Introduction
  2. 11.2 Metal Nanoparticles
  3. 11.3 Magnetic Nanoparticles
  4. 11.4 Carbon Nanotubes
  5. 11.5 Graphene
  6. 11.6 Nanostructured Metal Oxides
  7. 11.7 Nanostructured Hydrogels
  8. 11.8 Nanostructured Conducting Polymers
  9. 11.9 Conclusions and Future Trends
  10. Acknowledgment
  11. References
4. 12 Applications of Magnetic Nanomaterials in Biosensors and Diagnostics
  1. 12.1 Introduction
  2. 12.2 MNP‐Based Biosensors for Disease Detection
  3. 12.3 MNPs in Cancer Diagnosis and Therapy
  4. 12.4 Cellular Applications of MNPs in Biosensing, Imaging, and Therapy
  5. 12.5 Conclusions
  6. Acknowledgment
  7. References
5. 13 Graphene Applications in Biosensors and Diagnostics
  1. 13.1 Introduction
  2. 13.2 Graphene and Biosensors
  3. 13.3 Medical Applications of Graphene
  4. 13.4 Conclusions
  5. Acknowledgment
  6. References
Section 4: Organ‐Specific Health Care Applications for Disease Cases Using Biosensors
1. 14 Optical Biosensors and Applications to Drug Discovery for Cancer Cases
  1. 14.1 Introduction
  2. 14.2 Biosensor Technology and Coupling Chemistries
  3. 14.3 Optical Biosensors for Drug Discovery
  4. 14.4 Computational Simulations and New Approaches for Drug–Receptor Interactions
  5. 14.5 Conclusions
  6. Acknowledgment
  7. References
2. 15 Biosensors for Detection of Anticancer Drug–DNA Interactions
  1. 15.1 Introduction
  2. 15.2 Voltammetric Techniques
  3. 15.3 Optical Techniques
  4. 15.4 Electrochemical Impedance Spectroscopy Technique
  5. 15.5 QCM Technique
  6. 15.6 Conclusions
  7. Acknowledgments
  8. References
Index
End User License Agreement

List of Tables

Chapter 02
1. Table 2.1 Main biological sample, sampling techniques, and key properties.
2. Table 2.2 Selected analyte tests available in POCT format that have been waived by Clinical Laboratory Improvement Amendments of 1988.
3. Table 2.3 Clinically important biomarkers and associated disease states.
Chapter 04
1. Table 4.1 Advantages and disadvantages of SPR biosensors.
2. Table 4.2 Detection of pathogens using biomimetic SPR sensors.
Chapter 05
1. Table 5.1 Literature related to QCM immunosensor and the limits of detection achieved for some pathogenic bacteria.
Chapter 06
1. Table 6.1 FDA‐approved serum and urine biomarkers for cancer diagnosis, prognosis, and therapy selection.
2. Table 6.2 Biosensor for cancer biomarkers detection.
3. Table 6.3 Biomarkers for cardiovascular diseases.
4. Table 6.4 Electrochemical biosensor for cardiac biomarkers detection.
5. Table 6.5 Electrochemical biosensor for autoimmune and infectious disease biomarkers detection.
Chapter 12
1. Table 12.1 Various applications of MNPs in biosensors for the detection of target biological compounds in different samples.
2. Table 12.2 Different biomolecules and coating agents utilized in the manufacturing of magnetic nanoparticles.
3. Table 12.3 Different general features of iron oxide‐based MNPs synthesized using different chemical techniques.
Chapter 14
1. Table 14.1 Key features of Biacore 8K SPR system and Biacore 4000 as potential high‐throughput drug discovery tools.
2. Table 14.2 Developers of label‐free optical sensor platforms.
3. Table 14.3 Characteristics of MASS‐1 (Sierra Sensors, Hamburg, Germany) surface plasmon resonance biosensor.
4. Table 14.4 Examples of SPR‐based drug discovery works from the literature.
Chapter 15
1. Table 15.1 Summary of the recent biosensor studies of interactions between anticancer drugs and nucleic acids.

List of Illustrations

Chapter 01
1. Figure 1.1 Schematic of a second‐generation biosensor.
2. Figure 1.2 Schematic of interstrand binding in DNA.
3. Figure 1.3 Schematic of an antibody‐based immunosensor.
4. Figure 1.4 Scheme for the systematic evolution of ligands by exponential (SELEX) enrichment process.
5. Figure 1.5 Schematic representation of the imprinting process.
Chapter 02
1. Figure 2.1 Schematic of an ideal POC diagnostic device. The ideal POC device would be able to quantitatively detect several analytes, within minutes, at ultrasensitive femtomolar sensitivity. Sample handling, preparation, detection, and electronics for displaying the diagnostic result would all be included.
2. Figure 2.2 Schematic of a typical biosensor.
Chapter 03
1. Figure 3.1 Classification of nanomaterials for use in medical diagnostics.
Chapter 04
1. Figure 4.1 Applications of SPR including sample targets, measurable parameters, and reactions that facilitate measurement.
2. Figure 4.2 Generic principle of SPR. The receptor of interest is immobilized to a polymer matrix using a well‐established surface chemistry. The analyte is then passed through a flow cell over the receptor‐derivatized matrix. Any change in mass following the interaction between the receptor and the analyte is detected as a change in the angle of the incident light needed to generate the surface plasmon resonance phenomenon at the gold polymer interface (a). This is measured as an energy or reflectance dip as a function of pixels, which translates to response units (RU) over time. The RU change is directly proportional to molecular mass change, and so binding kinetics and stoichiometry can be measured in real time without any label (b).
3. Figure 4.3 Interrogation modes for commercial surface plasmon resonance (SPR)‐based instruments.
4. Figure 4.4 Light‐scattering images and microabsorption spectra of HaCaT noncancerous cells, HOC cancerous cells, and HSC cancerous cells after incubation with unconjugated colloidal gold nanoparticles. The images display that the particles are inside the cells in the cytoplasm region but do not seem to adsorb strongly on the nuclei of the cells. The absorption spectra were measured for 25 different single cells of each kind. They show that nanoparticles have an SPR absorption maximum around 548 nm, independent of the cell type. The broad long wavelength tails in the absorption spectra suggest the presence of aggregates. It also shows that no specific difference is observed in either the scattering images or the absorption spectra of the gold nanoparticles in the cancerous and the noncancerous cells.
5. Figure 4.5 Schematic illustration of the AuNP‐enhanced SPR biosensor with an aptamer–antibody sandwich assay.
6. Figure 4.6 Synthesis of an adenovirus‐specific biomimetic receptor and its use in an SPR‐based biosensor for the real‐time detection of viruses.
Chapter 05
1. Figure 5.1 Experimental apparatus for a piezoelectric system.
2. Figure 5.2 The principle of (a) QCM and (b) a sensorgram of an interaction of antibody and antigen showing the establishment of an initial baseline, association, dissociation, and regeneration phases.
3. Figure 5.3 Biosensor detection principle with representation of the gold electrode surface (a); EIS measurements showing a signal change in the Nyquist plot before (i) and after (ii) PSA binding (b); QCM‐D measurements showing frequency and dissipation responses before (i) and after (ii) PSA binding (c).
4. Figure 5.4 Schematic representation of a flow‐type QCM immunosensor system. a, Buffer reservoir; b, micro‐dispensing pump; c, injector; d, flow‐through cell; d1, acryl holder; d2, O‐ring; d3, QCM; d4, joint; e, disposal basin; f, oscillator module; g, quartz crystal analyzer; h, PC.
Chapter 06
1. Figure 6.1 Schematic representation of electrochemical biosensors development: (a) catalytic biosensor; (b) affinity biosensor: (1) label‐free approaches and (2) label approach.
2. Figure 6.2 Schematic representation of immunosensor development for CA125 detection: direct adsorption (ISA) and covalent amine bond formation (ISB). (a) Cystamine‐modified gold electrode, (b) covalent immobilization of Au–Ag NPs, (c) covalent immobilization of anti‐CA125 on citrate stabilized Au–Ag NPs for immunosensor A (ISA), (d) amine functionalization of immobilized Au–Ag NPs, and (e) covalent immobilization of anti‐CA125 on amine functionalized Au–Ag NPs for immunosensor B (ISB).
3. Figure 6.3 Representation of immunosensors development and approaches for CA125 and CEA determination.
4. Figure 6.4 Molecular‐imprinted polymer (MIP)‐based electrochemical biosensor for myoglobin determination. (a) Formation of a polymer layer on bare gold chip with carboxylated poly(vinyl chloride) (PVC‐COOH). (b) Covalent attachment of myoglobin protein on the activated surface. (c) Imprinting of the protein template. (d) Removal of the template from polymer matrix and obtaining MIP for myoglobin detection.
5. Figure 6.5 Immunosensor development (a) and signal acquisition (b) for determination C‐reacted protein in serum samples.
6. Figure 6.6 Scheme of immunosensor development for MPO_active (route 3a) and MPO_mass (route 3b) detections.
7. Figure 6.7 Molecular beacon‐based electrochemical biosensor for hepatitis B virus determination.
Chapter 07
1. Figure 7.1 The fluorescence Jablonski diagram. (1) Excitation of atoms to higher energy states . (2) Dissipation of some energy as heat or other background processes. (3) Emission of photons (luminescence) and return of the atoms to the initial state S_i.
2. Figure 7.2 Schematic of the subcomponents and processes that constitute an integrated portable micro‐cytometer reader, substance packs, and disposable microfluidic chip.
3. Figure 7.3 Lensless shadow image (a) and fluorescence image of the fluorescent microspheres (b); lensless shadow image (c) and fluorescence image of L929 cells (d), as well the composition (e) of both type pictures; and the bright filed (f) and fluorescent microscopy images for the same L929 sample (g).
4. Figure 7.4 Wide field‐of‐view fluorescence imaging of the GFP cells. (a) The 3.7 mm × 3.5 mm image. (b1, c1, d1, and e1) Cropped images of typical cells in (a), including G1 (b1), G2 (c1), metaphase (d1), and anaphase (e1) (arrows) (b2, c2, d2, and e2). The same cells as imaged by a conventional microscope with a 20×/0.4 NA objective.
5. Figure 7.5 LUCAS images for (a) monocytes, (b) NIH‐3T3 fibroblasts, (c) red blood cells. Each image has next to it a zoomed version of a cell and its comparison with a low NA microscope image. (d) Computer‐assisted automated detection of the dynamic location of the red blood cells. A total of 41 cells are counted within a field of view of 1.4 mm × 1.4 mm.
6. Figure 7.6 Illustration of a Coulter counter.
7. Figure 7.7 Dimensions of a cell in an aperture.
8. Figure 7.8 (a) Schematics of the MEMS Coulter counter. (b) A magnified view of the focusing and detection electrodes, and the Coulter channel.
9. Figure 7.9 Effect of mass loading on cantilever.
10. Figure 7.10 Illustration of mass, spring, and damping system. m denotes the resonating mass, B is damping constant, and k is spring constant.
11. Figure 7.11 Generation of drive force of resonator.
12. Figure 7.12 Electrical model of thermal actuators.
13. Figure 7.13 Spring, mass, and damping system whose motion will be sensed electrostatically.
14. Figure 7.14 Cantilever vibrating in z direction.
15. Figure 7.15 A resonant sensor with uniform mass sensitivity.
16. Figure 7.16 (a) SMR system proposed in Ref. [30] is illustrated. (b) Immobilized molecules increase the mass of the channel and create a change in resonance frequency. (c) During particle flow, resonance frequency changes according to position of the particle. Exact mass of the particle can be found by the peak resonance frequency shift.
17. Figure 7.17 Laterally resonating structure for cell sensing.
18. Figure 7.18 Air trapping method.
Chapter 08
1. Figure 8.1 Automated on‐chip DNA purification. (a–e) The workflow summarizing the on‐chip DNA isolation protocol. (f) Picture of the parallel DNA purifier system that has a two‐layer structure for fluid flow and valve actuation. Fluid channels and control channels have widths of 100 and 200 µm, respectively.
2. Figure 8.2 CTC isolation chip. Whole blood mixed with antibody‐tagged magnetic beads is sent to the channel in parallel with buffer solution. Deterministic lateral displacement compartment of the chip enables the separation of white blood cells (WBCs) and CTCs from the whole blood. Then, WBCs and CTCs are brought on a single line after passing asymmetric focusing elements utilizing inertial microfluidics. Finally, magnetic force is applied over centrally aligned cells that force magnetically labeled CTCs to a different outlet, completing the isolation process.
3. Figure 8.3 Agilent’s capillary electrophoresis chip. (a) Sample is delivered to the junction area. (b) Small amount of sample is metered into the detection channel. (c) DNA is electrophoretically separated. (d) Separated DNA is fluorescently detected.
4. Figure 8.4 (a) Complete single‐stranded DNA is cut into small pieces, and every piece is bonded on a single bead for amplification to cover all surfaces of the bead where it is placed on a small pH sensitive well. (b) Nucleotides are flowed through the chip; if there is a conjugate base in the well, then it is incorporated, releasing H⁺ ions. Changing pH, resulting from H⁺ ions, is detected by CMOS sensor underneath the well. (c) Signals obtained from each well are encoded to obtain the DNA sequence.
5. Figure 8.5 Illustration of a single‐layer microfluidic paper‐based device fabricated by photolithography.
6. Figure 8.6 Different detection methods used in a paper‐based platform. (a) Colorimetric detection of glucose and protein.(b) Electrochemical detection of three analytes implemented on a single device.
7. Figure 8.7 A collection of droplet unit operations: (a) droplet generation [46], (b) mixing and generation [47], (c) fusion [48], (d) incubation, (e) storage [49], (f) detection, (g) sorting, and (h) re‐injection [46].
8. Figure 8.8 Detection of mutated DNA using digital PCR in picoliter microdroplets. (a) An overview of the system showing the production, mixing, and collection of droplets containing DNA, PCR reagents, and TaqMan probes. (b) Thermocycling and amplification of the emulsion. (c) Reinjection of droplets and analysis of the fluorescence signal of individual droplets.
9. Figure 8.9 A sample digital microfluidic platform system that is capable of working with multiple droplets without requiring any channels, pumps, or valves.
10. Figure 8.10 Digital microfluidic platforms: (a) Close system and (b) open system. (c) 2D schematic illustration of digital microfluidic chip illustrating dispensing, merging, splitting, and mixing units.
11. Figure 8.11 (a) Photograph of a CD microfluidic cartridge unit, reaction wells are filled with dye solution. (b) Schematic of the microfluidic design showing some critical components such as inlet, metering chambers, burst valves, and reaction wells.
Chapter 09
1. Figure 9.1 Relation of quantum dot size with emission peak using single wavelength excitation.
2. Figure 9.2 Quantum dots (QDs) are a heterogeneous group of materials. Biological fate and toxicity depend on QD physicochemical properties. Shape, core composition, size, and shell composition can be manipulated during QD synthesis. Post‐synthesis surface ligands are added to solubilize and target the particles. An additional coating can further protect the QD core from oxidation. Surface chemistry influences the quantum dot’s propensity to aggregate, particularly in biological solutions.
3. Figure 9.3 (a) Principle of target‐recycled nonenzymatic amplification and (b) schematic of QD‐labeled strip biosensor for amplification product detection.
4. Figure 9.4 (a) Fluorescence graphs of the immune complexes detected using different concentrations of CYRFA 21‐1, NSE, and CEA. (b) Standard curves for the detection of CYRFA 21‐1, NSE, and CEA in the multiplexed assay (using four‐parameter equation fitting). (c) The linear range of the plot in (b).
5. Figure 9.5 Breast tumor targeting with QD‐PEG‐P. (a) Intravital whole‐body fluorescent imaging (ventral view) of MCF10CA1a breast tumor‐bearing nude mice at 28 h after intravenous injection of PBS, QD‐PEG, or QD‐PEG‐P. Images were taken under 700 nm channel of LI‐COR Pearl Impulse small animal imaging system. White arrows show the position of MCF10CA1a tumors (circled in black). (b) Ex vivo CW and TG imaging of the tissues harvested from the mice in (a) after the in vivo imaging. B, brain; H, heart; K, kidney; Li, liver; Lu, lung; Sp, spleen; T, tumor.
Chapter 10
1. Figure 10.1 Schematic presentation of the molecular imprinting process.
2. Figure 10.2 Virus detection using molecularly imprinted nanopolymer‐functionalized SPR biosensor.
Chapter 11
1. Figure 11.1 Schematic of a biosensor.
2. Figure 11.2 Glucose biosensor containing AuNPs. (a, b) Deposition of LbL multilayers containing polyvinyl sulfonate/PAMAM‐Au. (c) After deposition of three bilayers, an ITO‐(PVS/PAMAM‐Au)₃@CoHCF electrode was prepared by potential cycling. (d) Immobilizing enzyme using bovine serum albumin (BSA), glutaraldehyde, and glucose oxidase (GOx).
3. Figure 11.3 Schematics of used detection formats: direct detection (a), sandwich assays with amplification by detection antibody (b), and MNP‐dAb without (c) and with (d) applied magnetic field. Detection format consisting of preincubating MNP‐dAb with βhCG followed by sandwich assay upon applied magnetic field gradient (e).
4. Figure 11.4 NMR‐filter system for bacterial concentration and detection. (a) The system consists of a microcoil and a membrane filter integrated with a microfluidic channel. The microcoil is used for NMR measurements; the membrane filter concentrates bacteria inside the NMR detection chamber to achieve high detection sensitivity. (b) A prototype device with two measurement sites. The NMR detection volume was approximately 1 μL.
5. Figure 11.5 Schematic of (a) graphene and single‐walled carbon nanotube (SWCNT) and (b) few layer graphene and multiwalled carbon nanotube (MWCNT) structures.
6. Figure 11.6 (a) SEM image of the well‐aligned MWCNTs and (b) current–time response obtained on increasing the glucose concentration from 2.0 µmol L⁻¹ to 11 mmol L⁻¹ at (A) GCE and (B) MWCNT electrodes. Shown in the inset is the dependence of the current response versus the concentration of glucose at (a) GCE and (b) MWCNT electrodes.
7. Figure 11.7 Schematic of a label‐free electrochemical aptasensor for thrombin detection prepared on a single‐walled carbon nanotube (SWCNT)‐casted glassy carbon electrode (GCE) and the EC′ reaction mechanism. Inset: the electrocatalytic current of Ru(bpy)₃²⁺ with (solid) and without (dashed) probe TBA that contains guanine.
8. Figure 11.8 (a) Representative Nyquist diagrams of 1.0 mmol L⁻¹ [Fe(CN)₆]^3−/4− in 0.1 mol L⁻¹ KCl recorded at a ssDNA/Fe₃O₄/CNTs/CPE and after the hybridization reaction with different concentrations of the BCR/ABL fusion gene target sequence: (a) ssDNA/Fe₃O₄/CNTs/CPE, (b) 1.0 × 10⁻¹⁵, (c) 1.0 × 10⁻¹⁴, (d) 1.0 × 10⁻¹³, (e) 1.0 × 10⁻¹², (f) 1.0 × 10⁻¹¹, (g) 1.0 × 10⁻¹⁰, and (h) 1.0 × 10⁻⁹ mol L⁻¹. (b) The plot of ∆R_ct versus the logarithm of the BCR/ABL fusion gene target sequence concentrations.
9. Figure 11.9 (a) Cyclic voltammograms of CuO NWs/CF with or without nitrogen bubbling; (b) anti‐interference property of the CuO NWs/CF electrode with initial addition of 1.0 mmol L⁻¹ glucose and 0.1 mmol L⁻¹ ascorbic acid (AA), uric acid (UA), dopamine (DA), 0.5 mmol L⁻¹ cysteine, 0.1 mmol L⁻¹ sodium chloride (NaCl), and then again 1.0 mmol L⁻¹ glucose, followed by addition of 0.05 mmol L⁻¹ lactose, sucrose, and maltose and lastly of 1.0 mmol L⁻¹ glucose; (c) reproducibility of six CuO NWs/CF electrodes for detection of 0.5 mmol L⁻¹ glucose; the inset shows the repeatability of CuO NWs/CF electrode for detecting 0.5 mmol L⁻¹ glucose for eight times; (d) the stability measurement of CuO NWs/CF electrode for 15 days.
10. Figure 11.10 Schematic of the mechanism proposed by Marioli and Kuwana, M^* = Zn, Co.
11. Figure 11.11 (a) Schematic of the steps involved in the synthesis of the spotted nanoflower (NF) DNA bioelectrode. (b) FESEM image of low magnification revealing the flower‐like ZnO nanostructure possessing hexagonally shaped tips, which demonstrate the high crystallinity of the prepared ZnO nanowire ends. (c and d) Low‐ and high‐magnification images of spotted NFs indicate that radially oriented NFs have an average length of 2–3 µm and a diameter of approximately 100 nm.
12. Figure 11.12 (a) Impedance spectra of (i) spotted NF, (ii) spotted NF/p‐DNA (probe), and (iii) spotted NF/p‐DNA/t‐DNA (duplex) bioelectrode; the inset shows the Randles equivalent circuit, where the parameters R_a, R_ct, and CPE represent the bulk solution resistance, charge transfer resistance, and constant phase element, respectively. (b) Impedimetric response curve of spotted NF/p‐DNA hybridized with different concentrations of complementary target DNA (i–viii), 10 μM to 100 fm. (c) Imaginary part showing the overall impedance, which decreases, and the peak frequency, which is shifted toward the higher frequencies as the concentration of complementary DNA decreases. (d) The gain curve of spotted NF/p‐DNA hybridized at different concentrations.
13. Figure 11.13 Comparison of volumes of a topological gel (polyrotaxane gel) in as‐prepared (a), dried (b), and swelling equilibrium (c) states.
14. Figure 11.14 Classification of hydrogels based on different properties.
15. Figure 11.15 Schematic of the general sensing mechanism of a CPH‐based electrode platform. (a) PtNPs and enzymes were loaded onto hierarchically 3D porous PAni hydrogel matrices to form PAni hydrogel/PtNP hybrid electrodes. (b) The PtNP‐catalyzed sensing process of the biosensor based on PAni/PtNP/enzyme hybrid films.
16. Figure 11.16 Schematic of four strategies for selective saccharide sensing via multivalent boronic acid–saccharide interactions, exemplified by glucose sensing. (a) Synthetic diboronic acids that form 1 : 1 cyclic boronate esters with glucose, (b) boronic acid‐containing polymers that bind glucose with two of the pendant boronic acid moieties, (c) aggregation of simple boronic acids via non‐covalent interactions to allow multivalent glucose binding, and (d) boronic acid‐conjugated nanomaterials as multivalent scaffolds. Note that the aggregates shown in (c) can be aggregates or saccharide binding altered aggregates of boronic acid or saccharide binding induced aggregates of boronic acid.
17. Figure 11.17 (a) Fluorescence intensity changes of three different types of hydrogels, A, B, and C, with acrylamide concentration ratio of 1 : 10 : 100 in response to different titers of AIV H5N1 (2⁰, 2², 2⁴, and 2⁶ HAU). The means and error bars (standard deviation) were calculated based on three replicates. (b) Two different reaction mechanisms based on the size‐dependent property of the aptamer–hydrogel embedded with ssDNA₂‐QD conjugates upon target binding for hydrogels A, B, and C.
18. Figure 11.18 Structures of the most common conducting polymers.
19. Figure 11.19 Schematic of the mechanism of the soft‐template synthesis of different conducting polymer nanostructures: (a) micelles acted as soft templates in the formation of nanotubes. Micelles were formed by the self‐assembly of dopants, and polymerization was carried out on the surface of the micelles; (b) nanowires formed by the protection of dopants. The polymerization was carried out inside the micelles; (c) monomer droplets acted as soft templates in the formation of microsphere; and (d) polymerization on the substrate producing aligned nanowire arrays. Nanowires were protected by the dopants, and polymerization was carried out on the tips of nanowires.
20. Figure 11.20 SEM images of (a) granular PPy . Cl (scale bar, 200 nm), (b) PPy . Cl nanoclips (scale bar, 1 µm; inset, digital picture of paper clips), (c) PAni HCl nanoclips (scale bar, 1 µm), and (d) PEDOT Cl nanoclips (scale bar, 1 µm).
Chapter 12
1. Figure 12.1 Schematic illustration of the strategy for dopamine (DA) detection using dithiobis(sulfosuccinimidylpropionate)‐modified gold nanoparticles (DTSSP‐AuNPs) and Fe₃O₄ magnetic particles (MPs) (a) and the interaction between DA, Fe₃O₄, and DTSSP‐AuNPs (b).
Chapter 13
1. Figure 13.1 A visual depiction of the structure of a layered microscopic segment of graphene.
2. Figure 13.2 A schematic showing the conventional methods commonly used for the synthesis of graphene along with their key features and the possible applications.
3. Figure 13.3 Amperometric responses of modified electrode to successive additions of 0.1 mM L‐cysteine, 0.1 mM tyrosine, 0.1 M glucose, 0.1 mM BSA, 0.01 mM UA, and 0.01 mM AA in 0.1 M phosphate buffer of pH 7.0 at 0.7 V.
4. Figure 13.4 Schematic synthesis of graphene–Au hybrid nanosheets.
5. Figure 13.5 (a) The biosensing of cholesterol ester with enzyme‐integrated rGO–nPd‐based biosensor is illustrated. Amperometric (b) response obtained for the sensing of cholesterol ester. Corresponding calibration plot is shown in (c).
6. Figure 13.6 (a) DPVs of 1 mM AA and 200 μM UA mixture (black line), 1 mM AA, 200 μM UA, and 0.1 μM DA in a ternary mixture. The line with two peak points represents the data for 200 μM UA + 1 mM AA. The other line with three peak points are for 0.1 μM DA + 200 μM UA + 1 mM AA. (b) DPV responses of different concentrations of DA in PBS solution (pH = 7.0) containing 1 mM AA and 200 μM UA. The bottom line shows the data for blank. The height of the peak increases with the increased DA concentration.
7. Figure 13.7 Schematic representation of the Aβab‐MNG platform construction.
8. Figure 13.8 Schematic of synchronous electrosynthesis of PXa–ERGO for DNA EIS detection.
9. Figure 13.9 Schematic diagram of SPR principle. Light is directed through a prism of high RI into a surface layer with low RI (sample). At a particular angle total internal refection of the impinging light occurs. Although the light does not enter into the sample medium, the intensity at the interfacial boundary is not equal to zero. The photon energy from the light is transferred to the metal electrons, causing them to oscillate and produce surface‐bound plasmons. This produces an exponential evanescent wave that penetrates a defined distance (~100 nm) into the low index medium, resulting in a characterized decrease in reflected light intensity.
10. Figure 13.10 Principle of graphene‐based fiber optic SPR biosensor and experimental setup.
Chapter 14
1. Figure 14.1 Stages of drug discovery process.
2. Figure 14.2 Covalent coupling methods to immobilize receptors onto the sensor surface. (a) Water‐soluble EDC‐mediated coupling. (b) Amino‐presenting surfaces. (c) Salicylhydroxamic acid‐derivatized surfaces.
3. Figure 14.3 Non‐covalent coupling methods to immobilize receptors onto the sensor surface.
Chapter 15
1. Figure 15.1 Schematic presentation of biosensing of anticancer drug–DNA interactions.

Biosensors and Nanotechnology

Applications in Health Care Diagnostics

List of Contributors

Preface

Acknowledgments

Section 1
Introduction to Biosensors, Recognition Elements, Biomarkers, and Nanomaterials