Nanoscience and its Applications Alessandra L Da Róz Marystela Ferreira Fábio de Lima Leite Osvaldo N Oliveira Jr Tai ngay!!! Ban co the xoa dong chu nay!!! 16990153481601000000 William Andrew is an imprint of Elsevier The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2017 Elsevier Inc All rights reserved This English edition of Nanostructures by Osvaldo N Oliveira, Jr., Marystela Ferreira, Alessandra L Da Róz, Fabio de Lima Leite is published by arrangement with Elsevier Editora Ltda Originally published in the Portuguese language as Grandes Áreas Da Nanociência 1st edition (ISBN 9788535280906) © Copyright 2015 Elsevier Editora Ltda No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with 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injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-323-49780-0 For information on all William Andrew publications visit our website at https://www.elsevier.com/ Publisher: Matthew Deans Acquisition Editor: Simon Holt Editorial Project Manager: Charlotte Kent Production Project Manager: Lisa Jones Designer: Greg Harris Typeset by Thomson Digital List of Contributors Adriano Moraes Amarante Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Carolina de Castro Bueno Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Renata Pires Camargo Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Juliana Cancino-Bernardi Group of Nanomedicine and Nanotoxicology, Institute of Physics of São Carlos, University of São Paulo (Universidade de São Paulo—USP), São Carlos, São Paulo, Brazil Marco Roberto Cavallari Department of Electronic Systems Engineering, Polytechnic School of the USP, São Paulo, Brazil Richard André Cunha Institute of Chemistry, Federal University of Uberlandia—UFU, Uberlandia, Minas Gerais, Brazil Daiana Kotra Deda Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Fernando Josepetti Fonseca Department of Electronic Systems Engineering, Polytechnic School of the USP, São Paulo, Brazil Eduardo de Faria Franca Institute of Chemistry, Federal University of Uberlandia—UFU, Uberlandia, Minas Gerais, Brazil Luiz Carlos Gomide Freitas Chemistry Department, UFSCAR, São Carlos, São Paulo, Brazil ix x List of Contributors Pâmela Soto Garcia Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Jéssica Cristiane Magalhães Ierich Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Fabio de Lima Leite Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Valéria Spolon Marangoni Group of Nanomedicine and Nanotoxicology, Institute of Physics of São Carlos, University of São Paulo (Universidade de São Paulo—USP), São Carlos, São Paulo, Brazil Guedmiller Souza de Oliveira Nanoneurobiophysics Research Group, Department of Physics, Chemistry & Mathematics, Federal University of São Carlos, Sorocaba, São Paulo, Brazil Leonardo Giordano Paterno Institute of Chemistry, Darcy Ribeiro Campus, University of Brasilia, Brasilia, Brazil Gerson Dos Santos Department of Electronic Systems Engineering, Polytechnic School of the USP, São Paulo, Brazil Fabio Ruiz Simões Institute of Marine Sciences, Federal University of São Paulo, Santos, São Paulo, Brazil Clarice Steffens Regional Integrated University of Upper Uruguai and Missions, Erechim, Rio Grande Do Sul, Brazil Miguel Gustavo Xavier Center of Biological and Nature Sciences, Federal University of Acre, Rio Branco, Acre, Brazil Valtencir Zucolotto Group of Nanomedicine and Nanotoxicology, Institute of Physics of São Carlos, University of São Paulo (Universidade de São Paulo—USP), São Carlos, São Paulo, Brazil Nanomaterials: Solar Energy Conversion L.G Paterno I NS TI TUTE O F CHEM I S TRY, D A R C Y R I B E I R O C A MP U S , U N I V E R S I T Y O F B R A S I L I A , BRASILIA, BRAZIL CHAPTER OUTLINE 1.1 Introduction 1.2 Conversion of Solar Energy Into Electricity 1.2.1 Solar Spectrum and Photovoltaic Performance Parameters 1.2.2 Operating Principles of a Solar Cell 1.2.3 Organic Solar Cells 10 1.2.4 Dye-Sensitized Solar Cells 16 1.3 Photoelectrochemical Cells for the Production of Solar Fuels 23 1.4 Conclusions and Perspectives 28 References 30 1.1 Introduction The increase in population density and economic growth in many parts of the world since 1980 has maintained a strong pace because of the availability of 15 terawatts (TW) of energy—our current consumption—at accessible prices However, even the most optimistic forecasts cannot ensure that this scenario will persist in the coming decades A large part of the world’s energy consumption depends on fossil fuels, especially oil However, extraction of “cheap oil” may reach its peak in the next few years and then decline Therefore, production of energy from alternative sources is indispensable to maintain sustainable global economic growth and the perspective of the consumption of an additional 15 TW after 2050 [1] We must also note in this new planning effort that the alternative sources of energy production must be clean, as gas emissions from fossil fuels have negatively contributed to the quality of life on the planet because of global temperature increases and air pollution [2] In view of the imminent collapse of the current energy production system, it is necessary to seek alternative sources of energy production Within the current scenario of scientific and technological development and the urgent demand for 30 TW by 2050 [1], there are at Nanoscience and its Applications Copyright © 2017 Elsevier Inc All rights reserved 2 Nanoscience and its Applications least three alternative options for energy production [3]: (1) burning of fossil fuel associated with CO2 sequestration, (2) nuclear energy, and (3) renewable energy In option (1), emissions of greenhouse gases (GHGs), especially CO2, can already be controlled by emission certificates known as carbon credits [4] One ton of CO2 is equal to one carbon credit In practical terms, the Kyoto Protocol of 1999 establishes the maximum level of GHGs that a certain country can emit from its industrial activities If a certain industry or country does not reach the established goals, it becomes a buyer of carbon credits equivalent to the excess emissions By contrast, countries whose emissions are lower than the preestablished limit can sell their “excess” credits on the international market However, this initiative is still controversial, mainly because, to many, it implies a discount on the penalty from the excessive emission of GHGs Option (2) is very attractive because it is a clean type of energy, with high production efficiency and zero GHG emission [5] Many European countries, as well as the United States and Japan, produce and consume electricity from nuclear plants [6] However, the risk of accidents is constant, especially after the catastrophic events of Chernobyl (1986) [7] and Fukushima (2011) [8], and the improper use of nuclear technology for nonpeaceful purposes raises concerns Energy production from renewable sources (3) is undoubtedly the most promising alternative [9] The different technologies in this group, such as solar, wind, hydroelectric, and geothermal, are absolutely clean in terms of GHG emissions Theoretically, energy production from the burning of biomass, such as wood, ethanol, and biodiesel, creates no net CO2 emission because it is a closed cycle (the amount of CO2 sequestered from the atmosphere during photosynthesis and transformed into biomass is the same amount released to the atmosphere when the biomass is combusted with O2) In practical terms, the balance is slightly negative because land management, transportation, and processing use noncounterbalanced sources Still, this balance is much less negative than the one associated with the burning of fossil fuels The conversion of solar energy into electricity is one of the most promising ways to produce clean and cheap energy from a renewable source [3,9,10] Silicon-based solar cells, already manufactured in the 1950s for military and space applications, have been available for civilian use for at least three decades, mainly in buildings and for power generation at remote locations The cost is decreasing with the increasing production scale and the corresponding reduction of the price of silicon [11] In addition, research in the field of solar cells increased in the 1990s as a result of discoveries in the area of nanotechnology Today, third-generation devices are being developed that use nanomaterials to convert solar light into electricity at significantly lower costs than those of conventional silicon cells [3] Thirdgeneration solar cells, such as organic solar cells (OSCs) and dye-sensitized solar cells (DSSCs or dye cells), are a reality, are being produced at pilot scale by small companies, and will be commercially available in the near future [12,13] Nanotechnology research has also enabled the development of photoelectrochemical cells for artificial photosynthesis, whose efficiency is still low, but with a high potential to increase to levels that would make the cells commercially interesting [14] The use of nanomaterials is decisive to fully achieve energy conversion in these new devices In addition, the use of nanomaterials reduces the cost and the environmental impact of cell production to make such devices even more promising Chapter • Nanomaterials: Solar Energy Conversion Initially, this chapter will present the principles of solar conversion and the operation of solar cells, with a focus on OSCs and DSSCs Then, the role of nanomaterials in the different parts of each device and in their operation is discussed The most recent data (until 2012) on the conversion performance of third-generation cells are provided The last topic consists of a brief description of photoelectrochemical cells to produce solar fuels, considering that this subject is correlated to the previous ones However, it is not the intention of this chapter to present a thorough literature review on this last subject, and the reader is encouraged to consult the references listed at the end of the chapter 1.2 Conversion of Solar Energy Into Electricity 1.2.1 Solar Spectrum and Photovoltaic Performance Parameters The Sun is the most abundant and sustainable source of energy available on the planet The Earth receives close to 120,000 TW of energy from the Sun every year, an amount 104 larger than the current global demand [14] The photons that reach the Earth as solar light are distributed across different wavelengths and depend on variables, such as latitude, time of day, and atmospheric conditions This distribution, known as the solar spectrum, is shown in Fig. 1.1 [15] The spectrum shows the solar incidence power per area per wavelength (W m−2 nm−1), also known as irradiance, considering a bandwidth of 1 nm (∆λ) [16] The terms AM0, AM1.0, and AM1.5 refer to solar spectra calculated according to different ASTM standards, appropriate for each type of application [17] For example, spectra AM1.0 and AM1.5 are calculated according to standard ASTM G173 and are used as reference standards for terrestrial applications, whereas spectrum AM0, based on standard ASTM E 490, is used in FIGURE 1.1 Solar spectrum expressed in W m−2 nm−1 according to AM0 and AM1.5 standards Adapted from http://org ntnu.no/solarcells [15] 4 Nanoscience and its Applications FIGURE 1.2 Schematics of the different forms of solar radiation incident on the Earth and the respective standards to calculate the solar spectrum Adapted from http://org.ntnu.no/solarcells [15] satellites As shown schematically in Fig. 1.2, the calculation of the spectra considers specific geographic and atmospheric variables (i.e., the angle of incidence on the planet, the air density, and other parameters) The reproduction of the solar spectrum in the laboratory, according to the established standards, is fundamental to developing photovoltaic cells because it allows for comparison and certification of the performances of devices developed by different manufacturers and those still in development The performance of an illuminated solar cell is assessed by photovoltaic parameters, such as the power produced per illuminated cell area (Pout, in W·cm−2), open-circuit voltage (Voc, in V), short-circuit current density (Jsc, in mA·cm−2), fill factor (FF), and overall conversion efficiency (η) [16] All these parameters depend initially on the power of the light incident on the cell (Pin), given by Eq 1.1 [16]: hc Φ ( λ )d λ λ λ Pin = ∫ (1.1) where h is the Planck constant (4.14 × 10−15 eV·s), c is the speed of light (3.0 × 108 m·s−1), Φ(λ) is the flux of photons corrected for reflection and absorption before impacting the cell (cm−2 s−1 per ∆λ), and λ is the wavelength of the incident light The open-circuit voltage corresponds to the voltage between the terminals (electrodes) of the illuminated cell when the terminals are open (infinite resistance) The short-circuit current density corresponds to the condition in which the cell’s terminals are connected to a zero-resistance load The short-circuit current density grows with the intensity of the incident light because the number of photons (and thus the number of electrons) also increases with intensity Since the current usually increases with the active area of the solar cell, the current is conventionally expressed in terms of current density, J (current/area) When a load is connected to a solar cell, the current decreases and a voltage is developed when the electrodes are charged The resultant current can be interpreted as a Chapter • Nanomaterials: Solar Energy Conversion superposition of the short-circuit current caused by the absorption of photons and a dark current caused by the voltage generated by the load that flows in the opposite direction Considering that solar cells usually consist of a p–n (p–n junction: junction of p-type and n-type semiconductors) or D–A [D–A junction: junction of an electron donor material (D) and an electron acceptor material (A)] junction, they can be treated as diodes For an ideal diode, the dark current density (Jdark) is given by [16] J dark (V ) = J (e qV /kBT − 1) (1.2) where J0 is the current density at 0 K, q is the electron charge (1.6 × 10−19 C), V is the voltage between the electrodes of the cell, kB is the Boltzmann constant (8.7 × 10−5 eV·K−1), and T is the absolute temperature The resultant current can be explained as a superposition of the short-circuit current and the dark current [16]: J = J sc − J (e qV /kBT − 1) (1.3) The open-circuit voltage is defined at J = 0, which means that the currents cancel and no current flows through the cell, which is the open-circuit condition The resultant expression is given by [16]: Voc = kBT J sc + 1 ln J0 q (1.4) The performance parameters of a solar cell are determined experimentally from a current–voltage curve (J × V), schematically represented in Fig. 1.3, when the cell is subjected to standard operating conditions, in other words, illumination according to standard AM1.5, under an irradiating flux of 100 W·cm−2 and a temperature of 25°C FIGURE 1.3 Current density–voltage (J × V) curve of an illuminated solar cell 6 Nanoscience and its Applications The power density produced by the cell (Pout) (Fig. 1.3, gray area) is given by the product of the current density (J, in mA·cm−2) and the corresponding operating voltage (V, in V) as per Eq 1.5 [16]: Pout = JV (1.5) The maximum power density (Pmax) is given by Pmax = J maxVmax (1.6) Based on Fig. 1.3, it can be concluded that the maximum power produced by an illuminated solar cell is between V = 0 (short circuit) and V = Voc (open circuit), or Vmax The corresponding current density is given by Jmax The conversion efficiency of the cell (η) is given by the ratio between the maximum power and the incident power [16]: η= J maxVmax Pin (1.7) The behavior of an ideal solar cell may be represented by a J × V curve of rectangular shape (represented in the graph by dashed lines) where the current density produced is maximum, constant, and equal to Jsc up to the Voc value However, not all of the incident power is converted into energy by the cell, so in actual situations the J × V curve deviates from the ideal rectangular shape The fill factor term (FF) is introduced to measure how close to the ideal behavior a photovoltaic cell operates The FF is given by [16] FF = J mVm J scVoc (1.8) By definition, FF ≤ Thus, the overall conversion efficiency can be expressed using the FF value [16]: η= J scVoc FF Pin (1.9) In addition to these, another important performance parameter is the quantum efficiency, which measures how many electrons capable of performing work are generated by each 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force curves vs depth, ideal elastic/plastic material, 130 principles, 126–128 theoretical considerations regarding force curves, 123, 129 theoretical models for analysis of force curves, 129 Atomic nuclei in nuclear magnetic resonance (NMR) spectroscopy, 204 B Benzene molecule, 181 different representations, 181 Biodegradable nanoparticles, 103–104 Bioinformatics, 107 Biological structure databases, 187 Biomarkers, 71, 76, 168 Biomolecules, 182 Biomolecule structure databases, 189 5,5-Bis-(7-dodecyl-9H-fluoren-2-yl)-2, 2-bithiophene (DDFTTF), 55 6,13-Bis[triisopropylsilylethynyl] (TIPS) pentacene, 52 Bradley theory, 132 Bromocriptine, 104 C caBIG See Cancer Biomedical Informatics Grid Cancer Biomedical Informatics Grid (caBIG), 113 C and C++ languages, 189 Cantilever sensors, 123 device for AFM measurements and nanoscale sensing (tip) composed of, 126 Capacitors, 37 Carbohydrates, 23 Carbon credits, Carbon nanotubes, 76, 98 Carbon sp2 hybridization, in ethylene molecule, 36 Cell-based impedance sensor (CIS), 175 CFM See Chemical force microscopy Characteristic curves, modeling of, 44 Charge carriers, 53 Charge transport in organic devices, process of, 40–42 Chemical energy, 23, 24 Chemical force microscopy (CFM), 133 overview, 133 Chemical modification, 208 Chloroauric acid, 80 Chlorobenzene, 15 221 222 Index Chronoamperograms (CAs) of AgZEGE electrode, 162 Chronoamperometry, 161–163 CIS See Cell-based impedance sensor Clostridium difficile (PDB ID2N6J), 203 Cobaloxime, 27 Cobalt (Co(OH)2), 26 Cobalt(II/III) complexes, 18, 27 Cobalt phthalocyanine, 27 Computational molecular modeling, 180, 207 Computational neuroscience, 108 Conduction band (CB), 35 Conductive polymers, 18 Conductometry, 157 Conductors, 10, 35 Conjugated polymers, 10, 36 Contact angle measurements, 135 Controlled drug delivery, 74–77 See also Nanomedicine Conventional electrochemical cell, in a system of three electrodes, 161 Core-shells, medical applications, 74 Coulombic attraction, Coulomb potential, 193 Counter electrode (or auxiliary electrode), 161 Cyclic voltammetry, 163–165 D Dark current density, Debye-Huckel equation, 199 Dendrimers, 105–106 containing molecules of a pharmaceutical, 106 Dendron, 105 Density functional theory (DFT), 197 Derjaguin-Muller-Toporov (DMT) theory, 130 Dexamethasone, 107 DFT See Density functional theory Dielectric constant, 44 Differential pulse voltammetry (DPV), 165–167 Diodes, 37 DMT See Derjaguin-Muller-Toporov DNAdamage, 83 DNAintercalating agent, 77 Doping, Doxorubicin, 105 DPV See Differential pulse voltammetry Drug delivery, 71 DSSCs See Dye-sensitized solar cells Dual-polarization interferometry, 135 Dye-sensitized solar cells (DSSCs), 16, 17 consists of, 16 development of, 16 overall conversion efficiency, 16 performance, 22 schematics of, 19 structural formula of dyes used in, 17 E Elastic sphere deformation, 130 Electricity, Electroanalytical methods, 157 dynamic methods, 157 interfacial methods, 157 noninterfacial methods, 157 principles, 157 static methods, 157 Electrochemical cell, in system of two electrodes, 160 Electrochemical impedance spectroscopy, 171–172 Electrochemical methods, 157 subdivisions, 159 Electrochemical sensors, 155, 159 application in everyday lives, 155 general diagram, 159 historical perspective, 156 Electrolyte, 18, 22 Electron energy levels of the n and p semiconductors, Electronic tongue, 172–175 Energy levels of inorganic semiconductors in aqueous medium, 24 Energy production from renewable sources, system, alternative options, EQE See External quantum efficiency Equivalent circuit of a solar cell, Index 223 ExPASy See Expert Protein Analysis System Expert Protein Analysis System (ExPASy), 190 External quantum efficiency (EQE), 6, 60 F Fe3O4 nanoparticles, 81 Fibroblast cells, 84 Field effect transistors (FETs), 37, 45 Fill factor (FF), 4, Flavonoids, 20 Fluorescence spectroscopy, 135 Fluorine-doped tin oxide (FTO), 16 Force curve See Atomic force spectroscopy Fossil fuels, associated with CO2 sequestration, Fourier transform infrared spectroscopy (FTIR), 135 Free-induction decay (FID) signal, 204 FTO/semiconductor interface, 22 Fuel cells, 23 Fullerene, 12, 99–100 structure, 99 G Gallium arsenide, Gas emissions, Genetic algorithms, 196 GHGs See Greenhouse gases Global temperature, Glutaraldehyde, 134 Gold nanoparticles, 80 Graphite, 18 Greenhouse gases (GHGs), H Haloperidol, 105 Health insurance companies, 146 Hematite (α-Fe2O3), 24, 25 cross-section of a hematite film grown by chemical vapor deposition on, 27 photoanode, photoelectrochemical cell for water oxidation based on, 25 Hertz theory, 130, 131 1,2-Hexadecanodiol, 81 Highest occupied molecular orbital (HOMO), 13, 35, 190 High mobility organic molecules, 52 Hole transport layers (HTL), 57 HTL See Hole transport layers I Ideal solar cell, 6, Illuminated solar cell, current density-voltage curve, Immobilizations, 135 Incident photon to current efficiency (IPCE), Inorganic semiconductors, Insulators, 35 Internal quantum efficiency (IQE), Ion selective electrode (ISE), 172 Ion-sensitive field effect transistor (ISFET), 55 ISE See Ion selective electrode ISFET See Ion-sensitive field effect transistor Itraconazole, 107 J JMol software package, 189 Johnson-Kendall- Roberts (JKR) theories, 130, 133 K Kyoto Protocol, 1999, L Lennard-Jones (or van der Waals) potential, 192 Light emitting diodes (LEDs), 37 Light scattering, 59 Liposomes, 102–103 single-layer, pharmaceuticals incorporated, 102 Liquid electrolyte, 22 Lowest unoccupied molecular orbital (LUMO), 13, 35, 190 LUMO See Lowest unoccupied molecular orbital (LUMO) 224 Index M Magnetite, 74 and core-shells medical applications of, 74 medical applications of, 74 Medical nanoinformatics, 115 Mesoporous system, 21 Metal-free dyes, 20 Metal-insulator-semiconductor (MIS) structure, 44 Metal-oxide-semiconductor capacitor, 45 Metal-oxide-semiconductor FET (MOSFET) technology, 44 Metal-to-ligand charge transfer (MLCT), 19 Micelles, 104–105 Microcantilever deflection, 140 Microcantilever sensors, 137 applications for, 141–145 deflections of cantilever sensor functionalized with a polyimide layer and exposed to, 142 nanomechanical microcantilever responses in, 142 operation modes, 138–139 schematic representation of operation modes, 138 theoretical considerations, 140–141 Microelectromechanical systems (MEMS), 137 Mobility, 47 Modeling of the characteristic curves, 44–49 field effect mobility, 49–52 organic TFT-based sensors, 53–55 Molecular computer modeling methods, 180 classical methods, 190 genetic algorithms, 196 molecular docking, 195 molecular dynamics, 190 molecular dynamics and related potentials, 191–193 molecular dynamics simulation methods, 193–195 receptor-ligand molecular docking, 195 hybrid methods (quantum mechanics/ molecular mechanics), 197 applications of QM/MM in molecular systems, 200 electrostatic interactions, 199 overview, 197 programs for using the QM/MM methodology, 198 simulation of biomolecular systems, 197 theoretical foundations and mathematical description, 198 Molecular docking, 195, 207 Molecular dynamics (MD) simulations, 189 Molecular imaging, 71, 72 Molecular memories, 37 Monomers chemical structure, from the principal semiconducting polymers, 36 Multiple sclerosis (MS), 93 N Nano(bio)informatics, 111 Nanobiosensors, 123, 133, 207 computational methods applied to the development of, 209–210 Nanocomputers, improve sensory input to brain, 108 Nanodrugs, 72 Nanofabrication technologies, 123 Nanogels, 101, 102 Nanoinformatics, 111 education, 113 Nanomaterials, applied to diagnosis and therapy, 72 in medicine, 72 synthesis, for application in nanomedicine, 79 biofunctionalization of nanomaterials, 81–82 core-shell-type structures, 81 gold nanoparticles, 80 magnetic nanoparticles, 80 Nanomedicine, 71, 100, 111 computational resources in, 107 Nanoneurobiophysics, 93, 207 Nanoneuropharmacology, 93, 94, 100 controlled delivery systems applied to CNS diseases, 100–101 Index 225 Nanoneuroscience, 93–95 AFM, 95 clinical, 97 quantum dots, 96 Nanoparticle ontology (NPO), 114 Nanoparticles in photothermal and photodynamic therapy, 77–78 for upconversion, 78 imaging of cancer cells, 78–79 with various ligands, 82 Nanopharmacology, 93, 94 Nanosensors, 122, 123, 134 See also Nanobiosensors fundamental components, 122 limitation in application, 145 social and ethical challenge, 146 types/application, 124 Nanospheres and nanocapsules with incorporated pharmaceuticals, 103 Nanosponge, 106–107 with incorporated medication, 107 Nanostructured sensors, 121 Nanotechnology biomedical studies, 207 research, Nanotoxicology, 82–83 in vitro assays, 84 in vivo studies, 84–85 Nanotubes, 98 National Cancer Institute, 114 National Institute for Occupational Safety and Health (NIOSH), 113 Nernst equation, 158 Neurobiophysics, 108, 111 Neuroinformatics, 108, 110 combines neuroscience and informatics research, 108 objective of, 110 platform, 110 Neuromyelitis optica (NMO), 93 Neuronal activity, 111 Neuroprotection, 95, 98, 99 Neuroregeneration, 98 Neuroscience, 95, 110 Newton’s equations, 193, 198 Nitric oxide (NO), 76 NMR See Nuclear magnetic resonance Normal (or standard) hydrogen electrode (NHE or SHE), 160 Novel nanomaterials development, for use in medicine, 73 Nuclear energy, Nuclear magnetic resonance (NMR), 188, 205 O Oleic acid, 81 Open-circuit voltage, 4, Optoelectronics, 44 Oregon Nanoscience and Microtechnologies Institute, 113 Organic electronic systems, 38 Organic films, techniques for making, 38–40 Organic light emitting diodes, 37, 56 electrooptic characterization of, 57–60 structure of thin films in OLEDs and typical materials used, 56–57 Organic materials, for nanoelectronics, 35 Organic semiconductors, 18 applied in TFTs, 54 sp2 hybridization, 35 Organic solar cells (OSCs), 2, 10, 37 absorbing materials for, 11 absorption efficiency, 14 for bilayer heterojunction, 14 carrier mobility, 15 current density generated by, 14 dissociation efficiency, 15 efficiency, 15 FF and the overall conversion efficiency, 15 J × V curves subjected to irradiation prepared with fullerenes, 16 module manufactured by, 12 photovoltaic parameters, 13 schematics of photoinduced electron transfer process from, 13 Organic TFT-based sensors See Organic thin film transistors (OTFTs) 226 Index Organic thin film transistors (OTFTs), 37, 42, 43, 53–55 based gas sensor, 55 organic dielectrics promising materials, 44 organic insulators for modifying dielectric semiconductor interface in, 44 structure of the TFT, 42–44 Organic transistor, 48, 50 See also Organic thin film transistors (OTFTs) Oscillator monolayers, 37 OSCs See Organic solar cells (OSCs) OTFTs See Organic thin film transistors (OTFTs) Overpotential, 160 Oxidation-reduction potentials, 19, 24 P Paclitaxel, 107 Parkinson’s disease, 111 p-FET transistor, 45 Pharmaceutical development, 207 computational molecular modeling in, 207 Photoanode, 18 Photocatalytic systems, 24 Photoelectrochemical cells, 2, 24 for production of solar fuels, 23 Photoexcited dye, 18 Photons, Photopic response function, according to the CIE 1924, 58 Photosensitive agent formulations, 78 Photosensitizer dye, 22 Photosensitizers, 20 Photosynthesis, 23 Photothermal therapies, 72 Photovoltaic cells, Photovoltaic conversion process, Photovoltaic performance parameters, Piezoelectric displacement, 126 Plasma enhanced chemical vapor deposition (PECVD), 43 Plasmon absorption band of nanoparticles, 77 Platinum, 18, 24 PLGApolymeric nanoparticles, 84 Poisson ratio, 138 trans-Polyacetylene doped with iodine, 36 Polyaniline (PANI) complex, 13 Polyanion poly(styrene sodium sulfonate) (PSS), 22 Poly(3,4-ethylenedioxytiophene)poly(styrenesulfonic acid), 13, 37 Polyethylene glycol (PEG), 134 Poly(ethylene oxide), 21 Polyfluorene (PF), 56 Poly(3-hexylthiophene) regioregularities in, 49 Polylactic acid (PLA), 75 Poly(lactic-co-glycolic acid) (PLGA), 75 Poly (2-methoxy- 5-(3,7-dimethyloctyloxy)-1, 4-phenylenevinylene) (MDMO-PPV), 43 Polymer electrolytes, 18 Polypyridine complexes of ruthenium(II), 19 Polythiophene, 54 Polytriarylamine transistor (PTAA), 55 Polyvinylcarbazole (PVK), 56 Poole-Frenkel model, 15 Poole-Frenkel phenomena, 48 Positive threshold voltage, 44 Potentiometry, 158 Power density, 5, maximum, Power efficiency, 57 Power luminous efficiency, 58 Principal organic conductors chemical structure of, 38 Protein Data Bank (PDB), 188 Proteins, 182 modeling by homology, 205 structure, 183 structure visualization, 188 types and biomolecule representations, 184–185 p-type semiconductors, 18 Python language, 189, 190 Q QM/MM approximation and methodology, 197, 198 Index 227 Quantitative structure-activity relationship (QSAR), 113 Quantum dots, 16, 97 administered to rats and visualization of their fluorescence, 96 R Radio frequency identification devices (RFIDs), 37 Rational pharmaceutical design, computational molecular modeling in, 207 Receptor-ligand molecular docking technique, 195 Receptor-ligand system, 196 Reference electrode, 160 types and composition, and potential, 161 Renewable energy, Reticuloendothelial defense system, 105 Ritonavir, 104 Rivastigmine, 104 Ruthenium complexes, 20 Ruthenium(II) complexes photosensitizers, 20 incident photon to current efficiency (IPCE) curves of photosensitizers, 20 S Saturated calomel electrode (SCE), 160 Saturated silver/silver chloride electrode, 160 Scanning electron microscopy (SEM), 93 Scanning tunneling microscope (STM), 156 Semiconductor/dielectric interface, 47 Semiconductor oxide, 21 Semiconductors, 7, DSSCs with, 20 organic, 18 applied in TFTs, 54 sp2 hybridization, 35 Sensors, 122 Srr also specific sensors Short-circuit current density, 4, Silicon, See also Microcantilever sensors cells, hydrogenated amorphous, 37 Silicon dioxide (SiO2), 42 Silicon microcantilevers, 143 Silicon nitride tip covered by a thin organic methylterminated layer, 136 covered by biotin, 136 Silver nanoparticles, 84 Single-wall CNTs (SWCNTs), 84 Sintering process, 21 Sneddon theory, 131 deformation/contact force, 132 Sodium yttrium fluoride doped with ytterbium and erbium, 79 Solar cells, See also Dye-sensitized solar cells constructed from organic absorbers, operating principles, photoelectrochemical cells for production of, 23–27 Solar energy, 2, 24 into electricity, conversion of, Solar fuels, Solar radiation incident, Solar spectrum, Square wave voltammetry, 167–170 Stereo-radian photon flux, 59 Structural characterization, 201 nuclear magnetic resonance, 203–205 protein modeling by homology, 205–206 x-ray diffraction, 201–203 Structure-based rational pharmaceutical design, 208 flowchart of, 208 Surface plasmon resonance, 135 Swiss-PDB Viewer, 190 Synthetic organic dyes, 16, 20 T Tacrine, 104 Tamoxifen, 107 Tetracyanoquinodimethane (TCNQ), 52 Tetraethylorthosilicate (TEOS), 81 Tetrakis(hydroxymethyl) phosphonium chloride (THPC), 81 Theoretical-computational methods, 207 228 Index Three-dimensional reconstruction resources and tools of neuron morphology, 112 scientific applications of, 110 Threshold voltage, 47 Tin oxide, 13 TiO2 mesoporous films, 19, 21 Toluene, 15 Torsion angle, 191 Transportation, V Velocity-Verlet algorithm, 194 Visual molecular dynamics (VMD), 189 VMD See Visual molecular dynamics Voltammetric methods, 163 W Water-splitting reaction, 23 Working electrode, 161 X X-ray diffractometry (XRD), 181, 200–202 X-ray photoelectron spectroscopy (XPS), 135 XRD See X-ray diffractometry Xylella fastidiosa, genome, 109