Anmerkung: Intro -- Foreword -- Executive Summary -- Contents -- About the Authors -- Abbreviations -- 1 Introduction -- Abstract -- 1.1 Basic Concept and Definitions -- 1.2 Energy Efficiency and Climate Change Mitigation -- 1.3 Energy Efficiency, Global Energy Demand, and Environment -- 1.4 Energy Efficiency---Low Hanging Fruits -- 1.5 Energy Efficiency Barriers -- 1.6 Energy Efficiency Gap -- 1.7 Methodologies -- 1.8 Objective -- 1.9 Book Structure, Conclusions, and Recommendations -- References -- 2 Energy Efficiency Becomes First Fuel -- Abstract -- 2.1 History of Energy Efficiency---A Hidden Fuel -- 2.2 Energy Efficiency as the First Fuel -- 2.3 The First Fuel Never Runs Out -- 2.4 The Future Potential of the First Fuel -- 2.5 Challenge to the First Fuel -- 2.6 Applications of Energy Efficiency as Fuels -- 2.7 Summary -- References -- 3 Energy Efficiency Becomes First Tool for Climate Change Mitigation -- Abstract -- 3.1 Introduction -- 3.2 Analysis of GHG Reduction Target for China -- 3.2.1 China's Dream and Economic Development Outlook -- 3.2.2 China's Carbon Emissions Outlook -- 3.2.2.1 China's Historical Carbon Intensity -- 3.2.2.2 Business as Usual (Scenario 1) -- 3.2.2.3 Using Energy Efficiency as the First Fuel to Reduce Carbon (Scenario 2) -- China's Historical Energy Intensities -- Carbon Intensity Reduction with Energy Efficiency as the First Fuel -- 3.2.2.4 Enlarging Investments in Non-fossil Fuel Technologies (Scenario 3) -- 3.2.2.5 Using Energy Efficiency as the First Fuel and Enlarging Non-fossil Fuel to 20 % (Scenario 4) -- 3.3 Analysis of GHG Reduction Target for the USA -- 3.3.1 US EPA's Clean Air Act and Climate Change Mitigation Policies -- 3.3.2 GHG Emission Projection in the USA -- 3.4 Conclusions -- References -- 4 Market Barriers to Energy Efficiency -- Abstract -- 4.1 Introduction -- 4.2 Worldwide Subsidies to Fossil Energy. , 4.3 High Transaction Costs -- 4.4 Invisibility of Energy Efficiency Projects -- 4.5 Misplaced Incentives -- 4.6 Lack of Access to Financing -- 4.7 Regulatory Barrier -- 4.8 Gold Plating and Inseparability of Features -- 4.9 Externalities -- 4.10 Imperfect Information -- 4.11 Customer Inertia -- 4.12 Lack of Capacity in Developing Countries -- 4.13 Summary -- References -- 5 Overall Methodology in This Study -- Abstract -- 5.1 Methodological Framework -- 5.2 Data Used in This Study -- Reference -- 6 Energy Efficiency Policies -- Abstract -- 6.1 Introduction -- 6.2 Necessities for Public Policies Promoting Energy Efficiency -- 6.3 Sufficient Conditions for the Use of Policies for Energy Efficiency -- 6.4 Documentation of Global Energy Efficiency Policies -- 6.5 Energy Efficiency Policies to Benefit the Society -- 6.6 Government Policy and Regulation for Utilities -- 6.7 Examples of Energy Efficiency Policies -- 6.7.1 The USA -- 6.7.2 China -- 6.8 Monitoring and Evaluation of Government Policy Effectiveness -- 6.9 More Examples of Energy Policies by Countries -- 6.9.1 Energy Efficiency Policies -- 6.9.2 Energy Efficiency Labeling Programs -- 6.9.3 Appliance, Equipment, and Lighting Minimum Energy Performance Standards (MEPS) -- 6.9.4 Transport Fuel Efficiency Standards for Light-Duty Vehicles (LDV) and Heavy-Duty Vehicles (HDV) -- 6.9.5 Transport Fuel-Economy Labeling for LDV an HDV -- 6.9.6 Transport Fiscal Incentives for New Efficient Vehicles -- 6.9.7 Industrial Energy Management Programs -- 6.9.8 Industrial MEPS for Electric Motors -- 6.9.9 Energy Utilities -- 6.10 Summary -- References -- 7 Energy Efficiency Cost-Effectiveness Test -- Abstract -- 7.1 Introduction -- 7.2 Methodology of Participant Analysis -- 7.2.1 Significance of the Analysis -- 7.2.2 Formulas -- 7.2.3 Parameters and Indictors of the Formulas. , 7.2.3.1 Present Value of Project Benefits (B) -- 7.2.3.2 Present Value of Project Costs (C) -- 7.2.3.3 Net Present Value -- 7.2.3.4 Benefit--cost ratio (BCR) -- 7.2.3.5 Internal Rate of Return (IRR) -- 7.2.3.6 Discounted Payback Period (DPP) -- 7.2.3.7 Payback Period (PP) -- 7.2.3.8 Discount Rate (D) -- 7.3 Application of the Cost-Effective Results -- 7.4 Summary -- References -- 8 Energy Efficiency Project Finance -- Abstract -- 8.1 Introduction -- 8.2 Financing Models -- 8.2.1 Dedicated Credit Lines -- 8.2.1.1 Definition of Dedicated Credit Lines -- 8.2.1.2 Objectives and Approaches of Dedicated Credit Lines -- 8.2.1.3 Structure of Dedicated Credit Lines -- 8.2.2 Risk-Sharing Facilities -- 8.2.2.1 Definition of Risk-Sharing Facilities -- 8.2.2.2 Objectives and Approaches of Risk-Sharing Facilities -- 8.2.2.3 Structure of the Risk-Sharing Mechanism -- 8.2.3 Energy Saving Performance Contracts -- 8.2.3.1 Definition of Energy Saving Performance Contracts -- 8.2.3.2 Objectives and Approaches of Energy Saving Performance Contracts -- 8.2.3.3 Structures of Energy Saving Performance Contracts -- 8.2.4 Leasing -- 8.2.4.1 Definition of Leasing -- 8.2.4.2 Objective of Leasing -- 8.2.4.3 Rationale of Leasing -- 8.2.4.4 Structures and Advantages of Leasing -- 8.2.5 Comparison of the Four Financial Models -- 8.3 Case Studies of Project Financing -- 8.3.1 Dedicated Credit Lines -- 8.3.1.1 China Energy Efficiency Financing Program -- 8.3.1.2 Thailand Energy Efficiency Revolving Fund -- 8.3.1.3 Indian KfW SME Credit Line -- 8.3.2 Risk-Sharing Facilities -- 8.3.2.1 IFC/GEF Commercializing Energy Efficiency Finance (CEEF) -- 8.3.2.2 IFC/GEF China Utility Energy Efficiency Program -- 8.3.2.3 World Bank China Energy Conservation II Program -- 8.3.3 Energy Saving Performance Contracts -- 8.3.3.1 World Bank/GEF Financed China Energy Conservation Project -- 8.3.4 Leasing. , 8.3.4.1 Turkey Commercializing Sustainable Energy Finance Program -- 8.4 Summary -- References -- 9 Energy Service Company Development -- Abstract -- 9.1 Introduction -- 9.2 Case Studies of GEF-Supported ESCO Projects -- 9.2.1 China -- 9.2.2 India -- 9.2.3 Ukraine -- 9.2.4 Brazil -- 9.3 Discussions -- 9.4 Conclusions -- References -- 10 Energy-Efficient Technologies -- Abstract -- 10.1 Introduction -- 10.2 Energy-Efficient Technologies in Lighting -- 10.2.1 Technology Development -- 10.2.2 Physical Principles and Performance Characteristics of SSL Technologies -- 10.2.2.1 Physical Principles and Types of LED -- 10.2.2.2 LED Efficiency -- 10.2.2.3 Longevity -- 10.2.2.4 Cost -- 10.2.2.5 The LED Outlook -- 10.3 Energy-Efficient Appliance: Refrigerator Technologies -- 10.3.1 Energy-Efficient Refrigerators -- 10.3.2 Efficiency Climbs with Computer Technologies -- 10.4 Energy-Efficient Vehicles -- 10.5 Energy-Efficient Electric Motors -- 10.5.1 Definition -- 10.5.2 Efficiency Values Used to Compare Motors -- 10.5.3 Use Energy-Efficient Motors -- 10.5.4 Cost-Effectiveness of Motors -- 10.5.5 Deal with Failed Motors -- 10.5.6 Motor Size to Consider -- 10.5.7 Operating Speed -- 10.5.8 Inrush Current -- 10.5.9 Periodic Maintenance -- 10.6 Conclusions and Looking Ahead -- References -- 11 Energy-Efficient Urban Transport -- Abstract -- 11.1 Introduction -- 11.2 Barriers to Urban Transport System Efficiency -- 11.3 Government Policy Role to Remove Barriers -- 11.3.1 Public and Private Partnership in Financing Transport Projects -- 11.3.2 Examples of Project Financing in the Urban Transport Sector -- 11.3.3 Replacement of Energy and Transport with Information Technology -- 11.4 Conclusions and Looking Ahead -- References -- 12 Case Studies -- 12.1 Case Study Paper 1: Raising China's Motor Efficiency -- 12.1.1 Abstract -- 12.1.2 Introduction. , 12.1.3 Methodology and Approaches in This Case Study -- 12.1.4 Intelligent Motor Controllers -- 12.1.5 Study in China -- 12.1.5.1 Projection of Power Consumption by Large Induction Motors by 2020 -- 12.1.5.2 Assumptions of Business as Usual and Energy Efficiency Scenarios -- 12.1.5.3 Analysis Results -- 12.1.5.4 Environment Benefits -- 12.1.5.5 Cost-Benefit Assessments -- 12.1.5.6 National Investment Assessment -- 12.1.5.7 Financial Assessment -- 12.1.5.8 Barriers to Investing in IMCs -- 12.1.5.9 Policy Recommendations -- 12.1.6 Conclusions -- 12.2 Case Study Paper 2: Investing in Boiler Steam Systems -- 12.2.1 Abstract -- 12.2.2 Introduction -- 12.2.3 China -- 12.2.3.1 Design of the GEF Project -- 12.2.3.2 GEF Finance -- 12.2.3.3 Project Implementation -- 12.2.3.4 Achievement of Objective and Outputs -- 12.2.3.5 Global Environment Benefits -- 12.2.4 Vietnam -- 12.2.4.1 Design of an Industrial Boiler Project -- 12.2.4.2 Global Environmental Benefit -- 12.2.5 Russia -- 12.2.5.1 Barriers to Industrial System Energy Efficiency -- 12.2.5.2 Policy and Strategy Recommendations -- 12.2.6 Conclusions -- References -- 13 Conclusions and Further Studies -- Abstract -- 13.1 Conclusions -- 13.2 Future Studies in This Area -- Erratum to: Energy Efficiency -- Glossary of Terms.

Anmerkung: Intro -- Preface -- Contents -- Editors and Contributors -- 1 Navigation, Routing and Nature-Inspired Optimization -- 1 Introduction -- 2 Navigation in Animals -- 3 Navigation, Routing and Optimization -- 3.1 Optimization -- 3.2 Travelling Salesman Problem -- 3.3 Routing Problems -- 4 Nature-Inspired Algorithms for Optimization -- 4.1 Deterministic or Stochastic -- 4.2 Genetic Algorithms -- 4.3 Ant Colony Optimization -- 4.4 Particle Swarm Optimization -- 4.5 Firefly Algorithm -- 4.6 Cuckoo Search -- 4.7 Bat Algorithm -- 4.8 Flower Pollination Algorithm -- 4.9 Other Algorithms -- 5 Algorithmic Characteristics -- 5.1 Characteristics -- 5.2 Discretization and Solution Representations -- 6 Conclusions -- References -- 2 Navigation and Navigation Algorithms -- 1 Navigation Introduction -- 1.1 Navigation Origin -- 1.2 Navigation Definition -- 2 Development of Navigation -- 2.1 Initial Germination Stage -- 2.2 Low-Speed Development Stage -- 2.3 Prosperity and Active Stage -- 2.4 Blooming Stage -- 3 Navigation Algorithms -- 3.1 Ecosystem Simulation Algorithm -- 3.2 Swarm Intelligence Algorithm -- 3.3 Evolutionary Algorithm -- 3.4 Artificial Intelligence Algorithm -- 4 Development Tendency of Navigation Algorithms -- 4.1 Development Status of Navigation Algorithms -- 4.2 Development Tendency of Navigation Algorithm -- 5 Application of Navigation Algorithm -- 5.1 Application of Aviation Navigation Algorithm -- 5.2 Application of Land Navigation Algorithm -- 5.3 Application of Sea Surface Navigation Algorithm -- 5.4 Application of Underwater Navigation Algorithm -- References -- 3 Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities -- 1 Introduction -- 2 Problem Statement -- 3 Recent Advances in Vehicle Routing Problem -- 3.1 Vehicle Routing Problem and Genetic Algorithms. , 3.2 Vehicle Routing Problem and Tabu Search -- 3.3 Vehicle Routing Problem and Simulated Annealing -- 3.4 Vehicle Routing Problem and Particle Swarm Optimization -- 3.5 Vehicle Routing Problem and Artificial Bee Colony -- 3.6 Vehicle Routing Problem and Ant Colony Optimization -- 3.7 Vehicle Routing Problem and Cuckoo Search -- 3.8 Vehicle Routing Problem and Imperialist Competitive Algorithm -- 3.9 Vehicle Routing Problem and Bat Algorithm -- 3.10 Vehicle Routing Problem and Firefly Algorithm -- 3.11 Vehicle Routing Problem and Other Nature-Inspired Metaheuristics -- 4 Challenges and Research Opportunities -- 5 Conclusions -- References -- 4 Review of Tour Generation for Solving Traveling Salesman Problems -- 1 Introduction -- 2 Traveling Salesman Problem (TSP) -- 2.1 History -- 2.2 Definitions -- 2.3 Applications -- 3 Best Tour Generation -- 3.1 Tour Construction -- 3.2 Tour Improvement -- 4 TSP Solution Space -- 4.1 Search Space Model -- 4.2 Constraints -- 5 Search Methods -- 6 Example: Discrete Cuckoo Search -- 6.1 First Layer: Construct a Solution -- 6.2 Second Layer:Improving the Solution -- 6.3 Third Layer: Local Optimum Escaping Methods -- 6.4 Fourth Layer: Discrete Cuckoo Search -- 7 Conclusion -- References -- 5 Flow Shop Scheduling By Nature-Inspired Algorithms -- 1 Introduction -- 2 Problem Definition -- 3 Literature Review -- 3.1 Ant Colony Optimization (ACO) -- 3.2 African Wild Dog Algorithm -- 3.3 Artificial Bee Colony (ABC) -- 3.4 Bacterial Foraging Optimization Algorithm (BFOA) -- 3.5 Bat Algorithm (BA) -- 3.6 Cuckoo Search (CS) -- 3.7 Crow Search Algorithm (CSA) -- 3.8 Firefly Algorithm (FA) -- 3.9 Flower Pollination Algorithm (FPA) -- 3.10 Fruit Fly Optimization Algorithm (FFO) -- 3.11 Gray Wolf Optimization (GWO) Algorithm -- 3.12 Invasive Weed Optimization (IWO) Algorithm -- 3.13 Migrating Birds Optimization (MBO) Algorithm. , 3.14 Monkey Search Algorithm -- 3.15 Particle Swarm Optimization (PSO) -- 3.16 Rhinoceros Search Algorithm (RSA) -- 3.17 Sheep Flock Heredity Algorithm (SFHA) -- 3.18 Shuffled Frog Leaping Algorithm (SFLA) -- 3.19 Water Wave Optimization (WWO) Algorithm -- 3.20 Whale Optimization Algorithm (WOA) -- 4 Future Direction of Research -- 5 Conclusions -- References -- 6 Mobile Robot Path Planning Using a Flower Pollination Algorithm-Based Approach -- 1 Introduction -- 1.1 Multi-robot Path Planning Approach -- 1.2 Soft Computing-Based Approaches -- 1.3 Challenges in the Application of Artificial Intelligent Approaches -- 2 Flower Pollination Algorithm -- 2.1 Basic Principle -- 2.2 Proposed Approach for Robot Path Planning -- 3 Results and Discussions -- 4 Conclusions for the Book Chapter -- References -- 7 Smartphone Indoor Localization Using Bio-inspired Modeling -- 1 Introduction -- 2 Background -- 2.1 Indoor Localization with Smartphones -- 2.2 Bio-inspired Computing: An Overview -- 3 Literature Review -- 3.1 Localization Using Bio-inspired Techniques -- 3.2 Smartphone Indoor Navigation Using Bio-inspired Techniques -- 4 Indoor Localization Using Artificial Neural Networks -- 4.1 System Model -- 4.2 Research Goal -- 4.3 Radiomap Modeling Using Artificial Neural Networks -- 5 Experimental Evaluation -- 5.1 Datasets and Evaluation Metrics -- 5.2 Evaluation Results -- 6 Conclusions and Future Challenges -- References -- 8 A New Obstacle Avoidance Technique Based on the Directional Bat Algorithm for Path Planning and Navigation of Autonomous Overhead Traveling Cranes -- 1 Introduction -- 2 BA, dBA and Variants -- 2.1 The Standard Bat Algorithm -- 2.2 Recent Advance in Improving the Bat Algorithm -- 2.3 The Directional Bat Algorithm -- 3 The Proposed Strategy for OTC Autonomous Path Planning -- 4 Simulation, Results and Discussions -- 5 Conclusions. , Appendix -- References -- 9 Natural Heuristic Methods for Underwater Vehicle Path Planning -- 1 Path Planning of Underwater Vehicle -- 1.1 Path Planning -- 1.2 Objective Optimization -- 1.3 Main Processes -- 1.4 The Key to the Problems -- 2 Characteristics of Underwater Path Planning -- 2.1 Safe Navigation Factors -- 2.2 Hidden Navigation Factors -- 2.3 Marine Environmental Factors -- 3 Intelligent Path Planning Algorithm -- 3.1 Neural Network Method -- 3.2 Fuzzy Logic Method -- 3.3 Genetic Algorithm -- 3.4 Ant Colony Algorithm -- 4 Firefly Algorithm -- 4.1 Bionics Principle -- 4.2 Algorithm Description -- 4.3 Algorithm Flow -- 4.4 Performance Analysis -- 4.5 Algorithm Improvement -- 5 Route Planning Based on Firefly Algorithm -- 5.1 Environmental Modeling -- 5.2 Route Expression -- 5.3 Evaluation Function -- 5.4 Process Design -- 5.5 Simulation -- References.

Anmerkung: Engineering the Bioelectronic Interface -- Contents -- Chapter 1 Communication with the Mononuclear Molybdoenzymes: Emerging Opportunities and Applications in Redox Enzyme Biosensors -- 1.1 Introduction - the Three Mo Enzyme Families -- 1.2 Mechanism -- 1.3 Amperometric Biosensors -- 1.4 Emerging Applications of Mo Enzymes in Sensing -- 1.4.1 Xanthine Oxidase Family -- 1.5 Sulfite Oxidase Family -- 1.5.1 Sulfite Oxidoreductase -- 1.6 DMSO Reductase Family -- 1.6.1 DMSO Reductase -- 1.6.2 Nitrate Reductase -- 1.6.3 Arsenite Oxidase -- 1.6.4 Chlorate and Perchlorate Reductase -- 1.7 Conclusions -- References -- Chapter 2 Scanning Probe Analyses at the Bioelectronic Interface -- 2.1 Introduction -- 2.1.1 Scanning Probe Microscopy -- 2.1.2 SPM Applications at the Biomolecular Interface -- 2.1.3 Summary -- 2.2 Bioelectronic Analyses -- 2.2.1 Electrode Surface Considerations -- 2.2.2 AFM Imaging Case Studies -- 2.2.3 The Direct Imaging of Electrochemistry and Enzyme Activity -- 2.2.4 Spectroscopic Assessment Electrodebiomolecule Electronic Coupling -- 2.3 Summary -- References -- Chapter 3 Electrical Interfacing of Redox Enzymes with Electrodes by Surface Reconstitution of Bioelectrocatalytic Nanostructures -- 3.1 Introduction -- 3.2 Reconstituted Enzyme Electrodes in Monolayer Configurations -- 3.3 Electrical Wiring of Redox Proteins with Electrodes by their Reconstitution on Cofactor-Functionalised Metallic Nanoparticles (NPs) or Carbon Nanotubes (CNTs) -- 3.4 Reconstitution of apo-Enzymes in Thin Films of Redox Polymers -- 3.5 Design of Electrically Contacted Enzyme Electrodes by the Crossing of Surface-confined Cofactor-enzyme Affinity Complexes -- 3.6 Reconstituted Enzyme Electrodes for Biofuel Cell Applications -- 3.7 Conclusions and Perspectives -- Acknowledgement -- References -- Chapter 4 Single-wall Carbon Nanotube Forests in Biosensors. , 4.1 Unique Properties of Carbon Nanotubes -- 4.1.1 Introduction -- 4.1.2 Electrocatalytic Properties -- 4.2 Biosensors Using Non-oriented Carbon Nanotube Electrodes -- 4.3 Biosensors Utilising Vertically Aligned Carbon Nanotube Forests -- 4.3.1 CNT Forest Fabrication -- 4.3.2 Biosensor Applications of SWNT Forests -- 4.4 Outlook for the Future -- References -- Chapter 5 Activating Redox Enzymes through Immobilisation and Wiring -- 5.1 Introduction -- 5.2 Protein Complexes -- 5.2.1 Co-crystallisation -- 5.2.2 Covalent Complexes -- 5.3 Electron Transfer at Electrodes -- 5.3.1 Voltammetry -- 5.3.2 Chronoamperometry -- 5.4 Surface Preparation -- 5.4.1 Carbon -- 5.4.2 Gold -- 5.4.3 Other Methods -- 5.5 Immobilisation -- 5.5.1 Direct Immobilisation -- 5.5.2 Wires -- 5.5.3 Wiring Proteins -- 5.6 Conclusion -- References -- Chapter 6 Cytochromes P450: Tailoring a Class of Enzymes for Biosensing -- 6.1 Introduction -- 6.2 Structure-function of Bacterial and Human Cytochromes P450 -- 6.3 The Need for Electrons: the Cytochrome P450 Catalytic Cycle -- 6.4 Human Cytochromes P450 and Drug Metabolism -- 6.5 Protein Engineering of P450s to Improve or Expand their Catalytic Properties -- 6.5.1 Directed Evolution of Cytochrome P450 Enzymes -- 6.5.2 Rational Design of Cytochrome P450 Enzymes -- 6.6 Interfacing Cytochromes P450 to Electrodes -- 6.6.1 Immobilisation on Unmodified Electrodes -- 6.6.2 Immobilisation with Surfactants, Polymers and Gold Nanoparticles -- 6.6.3 Immobilisation by Covalent Linkage on Gold Electrodes: Use of Spacers -- 6.6.4 Protein Engineering to Control Protein Immobilisation and Catalytic Turnover on Electrode Surfaces -- 6.7 Conclusions -- References -- Chapter 7 Label-free Field Effect Protein Sensing -- 7.1 Interfacial Protein Detection -- 7.2 Protein Microarrays -- 7.2.1 Array Substrates -- 7.2.2 Surface Chemistry and Immobilisation. , 7.2.3 Capture Biomolecules -- 7.2.4 Detection Tools -- 7.2.5 Ultrasensitive Protein Detection -- 7.3 Label-free Field Effect Protein Detection -- 7.3.1 Field Effect Transistor (FET) based Protein Sensing -- 7.3.2 Capacitance/Impedance Label-free Protein Sensing -- 7.3.3 Nanoscale Devices for Label-free Field Effect Protein Sensing -- 7.4 Conclusions -- References -- Chapter 8 Biological and Clinical Applications of Biosensors -- 8.1 Biosensing for Pure Biological Research -- 8.1.1 The Challenges of ''Omics'' and ''Systems'' Approaches -- 8.1.2 Biological Complexity -- 8.1.3 The Types of Device Required -- 8.2 Biosensing for Clinical Applications -- 8.2.1 The Clinical Problems - Diagnosis, Prognosis, Personalised Medicine -- 8.2.2 Biosensors for Clinical Applications -- 8.3 Further Reading -- References -- Subject Index.

Anmerkung: Ebook , Chapter 1: The Mononuclear Molybdoenzymes: potential applications in redox enzyme biosensors-- Chapter 2: Scanning Probe Analyses at the Bioelectronic Interface-- Chapter 3: Electrical Interfacing of Redox Enzymes with Electrodes by the Surface Reconstitution of Apo-Enzymes-- Chapter 4: Single-wall carbon nanotube forests in biosensors-- Chapter 5: Energizing redox enzymes through substrate surface preparation and wiring-- Chapter 6: Cytochromes P450s: Tayloring a class of enzymes for biosensing-- Chapter 7: Label free field effect protein sensing-- Chapter 8: Biological and Clinical Applications of Protein Biosensors.