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Cover
Chapter 1: Computational Approaches in Biomedical Nanoengineering: An Overview
1. 1.1 Introduction
2. 1.2 Nanobiotechnology in Disease Diagnosis
3. 1.3 Nanobiotechnology in Treatment
4. 1.4 Nanobiotechnology in Target‐specific Drug Delivery
5. 1.5 Computational Approaches
6. References
Chapter 2: Nanotechnology Applications – The Future Arrived Suddenly
1. 2.1 Introduction
2. 2.2 Nanotechnology: A Brief Approach
3. 2.3 Nanopanels: A Success of Nanotechnology in Industry
4. 2.4 Nanoelectronics: Improving the Life Standard
5. 2.5 Nanotechnology in Medicine: Friendly Efficient Healthcare
6. 2.6 Ethics and Nanotechnology
7. 2.7 Concluding Remarks
8. References
Chapter 3: Biosynthesized Nanobullets for Microbes and Biofilms
1. 3.1 Introduction
2. 3.2 Biosynthesized Nanoparticles
3. 3.3 Antimicrobial Potential of Nanoparticles nanoparticles"?> nanoparticles"?>
4. 3.4 Mechanism of Antimicrobial Action of Inorganic NPs
5. 3.5 Reactivity of NPs against Biofilms
6. 3.6 Nanosilver as Efficient Antimicrobial Agent
7. 3.7 NPs as Efficient Carrier of Traditional Antibiotics traditional antibiotics"?>
8. 3.8 Real‐life Applications of Antimicrobial Nanomaterials
9. 3.9 Conclusion and Future Prospects
10. References
Chapter 4: The Physics of Nanosensor Systems in Medicine and the Development of Physiological Monitoring Equipment
1. 4.1 Introduction
2. 4.2 Sensing Technology
3. 4.3 Sensor Design
4. 4.4 Discussion
5. References
Chapter 5: Nonlinear Multiphysical Laminar Nanofluid Bioconvection Flows: Models and Computation
1. 5.1 Introduction
2. 5.2 Numerical and Semi‐numerical Methods
3. 5.3 Multiphysical Nanofluid Bioconvection BVPs
4. 5.4 Conclusions/Future Directions in Nanofluid Bioconvection
5. References
Chapter 6: Exploring Nanotechnology Applications in Medicine
1. 6.1 Introduction
2. 6.2 Nanotechnology in Medicine
3. 6.3 Bone Tissue Engineering
4. References
Chapter 7: Microtubules: Nanobiomechanical Simulation
1. 7.1 Introduction
2. 7.2 Microtubules
3. 7.3 Review on Previous Researches
4. 7.4 Microtubule Dynamic Instability
5. References
Chapter 8: Simulation of Flowing Red Blood Cells with and without Nanoparticle Dispersion Using Particle‐based Numerical Methods
1. 8.1 Introduction
2. 8.2 Biomechanical Properties of RBCs
3. 8.3 RBC Membrane Models
4. 8.4 Numerical Simulations of RBC Motions in Capillaries
5. 8.5 Application of Particle‐based Methodsto Simulate RBC Motion
6. 8.6 Other Particle‐based Methods
7. 8.7 Nanoparticle Dispersion in RBC Suspension
8. 8.8 Advantages and Disadvantages of Particle‐based Methods
9. References
Chapter 9: Impact of Nanofluid in Medical Treatment by Mathematical Modeling
1. 9.1 Concept of Fluid Mechanics and Basic Laws
2. 9.2 Nanofluid and Properties properties"?>
3. 9.3 Newtonian and Non‐Newtonian Fluid
4. 9.4 Non‐Newtonian Fluid Flow Model for Blood
5. 9.5 Drug Delivery System
6. 9.6 Nanoparticles as Drug Carrier
7. 9.7 Importance of Shape and Material Properties of Nanoparticles
8. 9.8 Concentration Properties of Magnetic Nanoparticles
9. 9.9 Treatment by Incorporating Magnetic Source
10. 9.10 Treatment by Heat Transfer
11. 9.11 Boundary Layer Flow of Power Law Nanofluid
12. 9.12 Casson Nanofluid over a Stretching Surface
13. 9.13 Third‐grade Nanofluid Flow in a Channel
14. 9.14 Hydromagnetic, Irrotational, Laminar Flow of Non‐Newtonian Nanofluid Through a Channel
15. References
Chapter 10: Physiological Modeling and Simulation of Fluid Flows
1. 10.1 Introduction
2. 10.2 Physiological Modeling and Mathematical Formulation
3. 10.3 Solution of the Governing Equations
4. 10.4 Results and Discussion
5. 10.5 Conclusion
6. References
Index
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List of Tables

Chapter 01
1. Table 1.1 Recent machine learning approaches with applications.
Chapter 04
1. Table 4.1 ISFET response (sensitivity) and dynamic range for some of the most relevant chip materials.
Chapter 09
1. Table 9.1 Convergence of the series solution.
2. Table 9.2 Values of local Nusselt number of Cu–water nanofluid.

List of Illustrations

Chapter 01
1. Figure 1.1 Organ/tissue/cell/subcell levels.
2. Figure 1.2 Nanoparticle synthesis routes. Source: Sohail et al. (2017). Reproduced with permission of Elsevier.
3. Figure 1.3 Time scales for biological processes.
4. Figure 1.4 Schematic for the volatolomics detection.
5. Figure 1.5 Spectrometry results as depicted in Figure 1.4, when processed via artificially intelligent (AI) nanoarrays, lead to decision of solid or flexible sensors and other steps involved in decision making.
6. Figure 1.6 The pattern recognition step‐by‐step process.
7. Figure 1.7 Comparison of the patterns calculated from the different sets of sensors.
8. Figure 1.8 Finite element modeling of blood ferrohydrodynamics.
9. Figure 1.9 Finite element modeling of electrical activity in cardiac tissue.
10. Figure 1.10 Finite element modelling of fringe field effect in a micro device.
11. Figure 1.11 Penetrance functions (tables), broad‐sense heritability (h), total entropy (H), and interaction graphs for a dominant main effect model (a), a dominant‐by‐dominant interaction model (b), and a nonlinear interaction model based on the XOR function (c). Note that the entropy estimates in the interaction maps recapitulate the genetic models. Source: Moore et al. (2006). Reproduced with permission of Elsevier.
Chapter 03
1. Figure 3.1 Biological synthesis scheme of nanoparticles.
2. Figure 3.2 Schematic illustration of extracellular nanoparticle synthesis.
3. Figure 3.3 Schematic illustration of intracellular nanoparticle synthesis.
4. Figure 3.4 TEM images of (a) zinc nanoparticles synthesized from Sargassum muticum (b) AgNPs synthesized by Caulerpa racemosa.
5. Figure 3.5 TEM image of AuNPs synthesized by Aspergillus terreus.
6. Figure 3.6 (a, b) SEM images. (c, d) TEM images of Yarrowia lipolytica (NCYC 789) synthesized AgNPs.
7. Figure 3.7 TEM images of AgNPs synthesized employing (a) Streptomyces sp. DPUA 1549. (b) Streptomyces sp. DPUA 1747. (c) Streptomyces sp. DPUA 1748.
8. Figure 3.8 General scheme of phytoreduction of metal ions for nanoparticle synthesis.
9. Figure 3.9 TEM images of (a) AgNPs synthesized from Folgers coffee. (b) AgNPs synthesized from Azadirachta indica aqueous leaf extract.
10. Figure 3.10 Mechanism of NP interaction with biofilms.
11. Figure 3.11 Schematic representation of various mechanisms of AgNP toxicity.
12. Figure 3.12 Pathway I: Synergistic effect of AgNPs + antibiotics. Pathway II: Antibacterial action of AgNPs. Pathway III: Antibacterial action of antibiotics (Deng et al. 2016).
13. Figure 3.13 Zn–CuO nanoparticle antibiofilm action.
Chapter 04
1. Figure 4.1 Outline of the biomedical sensing concept using nanoscale technologies for the detection of physical and chemical characteristics in a fluid or solid medium.
2. Figure 4.2 (a) Generalized outline of the ISFET pH sensor based on semiconductor technology with silver/silver chloride reference for self‐calibration. (b) Example of the Sentron semiconductor design used in a needle electrode for in situ assessment of acidity in tissue, biological media, or environmental monitoring. (c) Latest advances in pH sensor design with smaller footprint.
3. Figure 4.3 (a) Schematic representation of the full Wheatstone bridge pressure sensor design. (b) MEMS configuration of the pressure detection mechanism by means of shear strain. (c) The application of shear strain on the MEMS semiconductor foundation under applied pressure, illustrated by the deformation of the resistor arms. (d) In order to provide the means for reliable pressure assessment, the sensor requires a balance, which is achieved by sealing the backside of the MEMS fingers in a box.
4. Figure 4.4 Conceptual description of the wavelength‐dependent interferometric sensing technology applied to pressure sensing in a Fabry–Pérot cavity.
5. Figure 4.5 (a) Concept outline of the ISFET sensor technology, with specific applications in the detection of H⁺ ions and hence measurement of the activity of the ions that constitute the pH of the medium surrounding the ISFET. (b) Voltage difference (x‐axis with respect to pH under constant current (y‐axis)).
6. Figure 4.6 Graphical representation of the carbon nanotube concept introduced by the group of Cees Dekker, applied to a MOSFET semiconductor design, connecting the source to the drain.
7. Figure 4.7 The modified nanotube design as proposed by the group of Zhilong Zhen, using an electronic connection between the nanotube and the gate of the field‐effect transistor, using a specific ionophore at the gate to enhance electivity and sensitivity.
8. Figure 4.8 A snapshot in time for the current through a nanotube under the influence of a target analyte.
Chapter 05
1. Figure 5.1 Examples of multiphysical nano‐bioconvection flows.
2. Figure 5.2 Physical model and coordinate system.
3. Figure 5.3 Concept of a nanofluid.
4. Figure 5.4 Nanofluid Jeffery–Hamel flow (diverging nozzle).
5. Figure 5.5 Numerical/semi‐numerical techniques considered.
6. Figure 5.6 Procedure for solving nano‐bioconvection nonlinear boundary value problems.
7. Figure 5.7 Flow geometry.
8. Figure 5.8 Effect of unsteadiness (S) and suction/injection (f _w) on the nanoparticle concentration profiles for α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
9. Figure 5.9 Effect of unsteadiness (S) and suction/injection (f _w) on the motile microorganism density number profiles for α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
10. Figure 5.10 Effect of disk stretching (α) and suction/injection (f _w) on the microorganism profiles for S = − 0.5, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
11. Figure 5.11 Effect of suction/injection (f _w) on the radial local skin friction for S = − 0.5, α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
12. Figure 5.12 Effect of suction/injection (f _w) on the circumferential local skin friction for S = − 0.5, α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
13. Figure 5.13 Effect of suction/injection (f _w) on the local Nusselt number for S = − 0.5, α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
14. Figure 5.14 Effect of suction/injection (f _w) on the local Sherwood number for S = − 0.5, α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
15. Figure 5.15 Effect of suction/injection (f _w) on the motile microorganism number for S = − 0.5, α = 0.2, Nb = Nt = 10⁻⁷, Le = Lb = Pe = 1, Nd = 0.4, and Pr = 0.7.
Chapter 06
1. Figure 6.1 Schematic illustration of nanoplatforms used for biomedical purposes.
2. Figure 6.2 Structure of the blood–brain barrier.
3. Figure 6.3 Hierarchical structural organization of the bone: (a) cortical and cancellous bone; (b) osteons with haversian systems; (c) lamellae; (d) collagen fiber assemblies of collagen fibrils; (e) bone mineral crystals, collagen molecules, and non‐collagenous proteins.
4. Figure 6.4 Cartilage types and distribution. Source: McKinley (2014). Reproduced with permission of McGraw‐Hill.
5. Figure 6.5 Cross section of a cracked trapezoidal silicon nanobeam.
Chapter 07
1. Figure 7.1 Eukaryotic cells (www.withcarbon.com).
2. Figure 7.2 Single, double, and triple columns of microtubules.
3. Figure 7.3 Microtubule with A lattice and B lattice.
4. Figure 7.4 Docking procedure.
5. Figure 7.5 Type of microtubules according to number of protofilaments and star helix number.
6. Figure 7.6 Applied dimension for molecular mechanics modeling.
7. Figure 7.7 Added mass coefficient for symmetric geometries.
8. Figure 7.8 Molecular mechanics model of microtubule.
9. Figure 7.9 Graphical abstract of nanobiomechanical model.
Chapter 08
1. Figure 8.1 Blood and its constituents.
2. Figure 8.2 Schematic illustration of a three‐dimensional (a), an axisymmetric (b), and a two‐dimensional (c) capsule enclosed by a membrane developing in‐plane tension τ and the transverse shear tension q and elastic bending moments m .
3. Figure 8.3 Tensions acting along one line segment.
4. Figure 8.4 Schematic diagram of the WLC RBC membrane model with bending response. (a) 2D triangulated network of springs to model the membrane shear response. (b) Determination of the instantaneous angle between two adjacent triangles sharing the common edge j to model the membrane‐bending response.
5. Figure 8.5 Schematic diagram of the spring‐ and damper‐based membrane model employed by Secomb et al. (2007).
6. Figure 8.6 Particles inside of the influence domain contribute to determine the field function value of ith particle.
7. Figure 8.7 Discrete velocity models: (a) D2Q9 and (b) D3Q15.
8. Figure 8.8 Three different modes (tank‐treading, tumbling, and trembling motions from top to bottom) of RBC in a shear flow.
9. Figure 8.9 (a) Axisymmetric and asymmetric motions of an RBC in a tube flow. Source: Figure 2 of Kaoui et al. (2009). Reproduced with permission of American Physical Society. The reduced volume is defined as the ratio of the averaged RBC volume to the volume of a sphere with the same diameter as the RBC. (b) Single RBC flowing in a stenotic tube.
10. Figure 8.10 Rouleaux structures formed due to aggregation interactions between RBCs under shear flow.
11. Figure 8.11 RBCs suspending in a tube flow.
12. Figure 8.12 Deformation of five RBCs when they flow in a stenosed capillary.
13. Figure 8.13 Single RBC motion in a stenotic channel.
14. Figure 8.14 Contact modes in a doublet: (a) flat contact, (b) sigmoid contact, and (c) relaxed sigmoid contact.
15. Figure 8.15 RBC motion along with platelets in a Poiseuille flow.
16. Figure 8.16 Shape evolution of an RBC in the constricted tube.
Chapter 09
1. Figure 9.1 Viscosity of Newtonian, shear thinning, and shear thickening fluids.
2. Figure 9.2 Structure of the arteries and veins.
3. Figure 9.3 Carbon‐based nanoparticles.
4. Figure 9.4 Gold nanoparticles.
5. Figure 9.5 Ceramic nanoparticles.
6. Figure 9.6 Velocity and temperature profile for different values of φ.
7. Figure 9.7 Effect of nanoparticle volume fraction (φ) on (a) velocity profile. (b) Temperature profile. (c) concentration profile.
8. Figure 9.8 Skin friction coefficient for different values of φ.
9. Figure 9.9 Geometry of the problem.
10. Figure 9.10 Temperature and concentration profile for different values N _t and N _b .
11. Figure 9.11Figure 9.11 Velocity profile for different values of (a) thermophoretic constant N _t , (b) magnetic parameter M, (c) Grashof number Gr, and (d) Brinkman number Br.
12. Figure 9.12 Velocity profile for different values of (a) N _b , (b) ΓΓ, (c) C, and (d) Λ.
13. Figure 9.13 Geometry of the problem.
14. Figure 9.14 Temperature profile for different values of β and N _t .
15. Figure 9.15Figure 9.15 Temperature profile for different values of N _b and Pr.
16. Figure 9.16Figure 9.16 Concentration profile for different values of γ and N _b .
17. Figure 9.17Figure 9.17 Pressure rise for different values of k and M.
18. Figure 9.18 Pressure rise for different values of Gr _T and Gr _F .
Chapter 10
1. Figure 10.1 Geometry of the flow problem.
2. Figure 10.2 Physiology of the human oviduct.
3. Figure 10.3 The behavior of axial velocity against the radial distance for change in (a) Eyring–Powell parameter M keeping N = 1, ξ = 6, k = 1.5, ε = 0.08, ϕ = 0.13, (b) amplitude ratio ϕ keeping M = N = 1, ξ = 7, k = 1.5, ε = 0.08, (c) metachronal wave parameter ε keeping M = N = 1, ξ = 5, k = 1.5, ϕ = 0.13, and (d) pressure gradient ξ keeping N = 1, k = 1.5, ε = 0.08, ϕ = 0.13.
4. Figure 10.4 The behavior of axial velocity against the axial distance for change in (a) Eyring–Powell parameter M keeping N = 1, ξ = 6, k = 1.5, ε = 0.08, ϕ = 0.13, (b) amplitude ratio ϕ keeping M = N = 1, ξ = 7, k = 1.5, ε = 0.08, (c) metachronal wave parameter ε keeping M = N = 1, ξ = 5, k = 1.5, ϕ = 0.13, and (d) pressure gradient ξ keeping N = 1, k = 1.5, ε = 0.08, ϕ = 0.13.
5. Figure 10.5 The behavior of temperature profile against the radial distance for change in (a) metachronal wave parameter ε keeping N _t = 2, ξ = 1, k = 1.5, ϕ = 0.13, (b) amplitude ratio ϕ keeping N _t = 1, ξ = 1, k = 1.5, ε = 0.08, and (c) thermophoretic parameter N _t keeping ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08.
6. Figure 10.6 The behavior of temperature profile against the axial distance for change in (a) metachronal wave parameter ε keeping N _t = 2, ξ = 1, k = 1.5, ϕ = 0.13, (b) amplitude ratio ϕ keeping N _t = 1, ξ = 1, k = 1.5, ε = 0.08, and (c) thermophoretic parameter N _t keeping ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08.
7. Figure 10.7 The behavior of concentration profile against the radial distance for change in (a) metachronal wave parameter ε keeping N _b = 1, N _t = 2, ξ = 1, k = 1.5, ϕ = 0.13, (b) amplitude ratio φ keeping N _b = 1, N _t = 2, ξ = 1, k = 1.5, ε = 0.08, (c) thermophoretic parameter N _t keeping N _b = 1, ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08, and (d) Brownian motion parameter N _b keeping N _t = 2, ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08.
8. Figure 10.8 The behavior of concentration profile against the axial distance for change in (a) metachronal wave parameter ε keeping N _b = 1, N _t = 2, ξ = 1, k = 1.5, ϕ = 0.13, (b) amplitude ratio ϕ keeping N _b = 1, N _t = 2, ξ = 1, k = 1.5, ε = 0.08, (c) thermophoretic parameter N _t keeping N _b = 1, ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08, and (d) Brownian motion parameter N _b keeping N _t = 2, ϕ = 0.13, ξ = 1, k = 1.5, ε = 0.08.
9. Figure 10.9 The axial flow behavior via vector plots for Eyring–Powell parameter at (a) M = 0, (b) M = 1, (c) M = 2, and (d) M = 3 keeping N = 1, ξ = 6, k = 1.5, ε = 0.08, ϕ = 0.13.
10. Figure 10.10 The axial flow behavior via vector plots for pressure gradient at entrance of the tube for (a) ξ = 6, (b) ξ = 7, (c) ξ = 8, and (d) ξ = 9 keeping M = N = 1, k = 1.5, ε = 0.08, ϕ = 0.13.
11. Figure 10.11 The axial flow behavior of vector plots for amplitude ratio at (a) ϕ = 0.00, (b) ϕ = 0.05, (c) ϕ = 0.10, and (d) ϕ = 0.15 keeping M = N = 1, ξ = 7, k = 1.5, ε = 0.08.
12. Figure 10.12 The behavior of streamlines for Eyring–Powell parameter at (a) M = 0, (b) M = 1, (c) M = 2, and (d) M = 3 keeping N = 1, ξ = 6, k = 1.5, ε = 0.08, ϕ = 0.13.
13. Figure 10.13 The behavior of streamlines for amplitude ratio at (a) ϕ = 0.00, (b) ϕ = 0.05, (c) ϕ = 0.10, and (d) ϕ = 0.15 keeping M = N = 1, ξ = 7, k = 1.5, ε = 0.08.
14. Figure 10.14 The behavior of streamlines for pressure gradient at entrance of the tube for (a) ξ = 6, (b) ξ = 7, (c) ξ = 8, and (d) ξ = 9 keeping M = N = 1, k = 1.5, ε = 0.08, ϕ = 0.13.
15. Figure 10.15 The behavior of pressure gradient against the axial distance for change in (a) Eyring–Powell parameter M keeping N = 1, ξ = 6, k = 1.5, ε = 0.08, ϕ = 0.13, (b) amplitude ratio ϕ keeping M = N = 1, ξ = 7, k = 1.5, ε = 0.08, (c) metachronal wave parameter ε keeping M = N = 1, ξ = 5, k = 1.5, ϕ = 0.13, and (d) pressure gradient ξ keeping N = 1, k = 1.5, ε = 0.08, ϕ = 0.13.

Computational Approaches in Biomedical Nano-Engineering

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