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Aerospace Polymeric Materials
Aerospace Polymeric Materials
Autore Inamuddin <1980->
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (281 pages)
Altri autori (Persone) AltalhiTariq A
AdnanSayed Mohammed
Soggetto genere / forma Electronic books.
ISBN 1-119-90526-5
1-119-90525-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Tuning Aerogel Properties for Aerospace Applications -- 1.1 Introduction -- 1.2 Synthesis -- 1.3 Aerospace Missions -- 1.3.1 Stardust Mission -- 1.3.2 MARS Pathfinder Mission -- 1.3.3 Hypersonic Inflatable Aerodynamic Decelerator -- 1.3.4 Mars Science Laboratory -- 1.3.5 Cryogenic Fluid Containment -- 1.4 Property Tuning of Aerogels -- 1.4.1 During Synthesis -- 1.4.2 Post-Synthesis -- 1.4.3 Aerogel Composites -- 1.5 Tuning Properties for Aerospace Applications -- 1.5.1 Thermal Conductivity -- 1.5.1.1 Minimizing Solid Conductivity -- 1.5.1.2 Modification of IR Absorption Properties -- 1.5.1.3 Minimizing Gaseous Conductivity -- 1.5.2 Mechanical Property -- 1.5.3 Optical Transmittance -- 1.6 Conclusion and Future Prospects -- Acknowledgments -- References -- 2 Welding of Polymeric Materials in Aircrafts -- 2.1 Introduction -- 2.2 Major Polymer Welding Methods Applied in Aviation -- 2.2.1 Hot Gas Welding -- 2.2.2 Hot Plate Welding -- 2.2.3 Extrusion Welding -- 2.2.4 Infrared Welding -- 2.2.5 Laser Welding -- 2.2.6 Vibration Welding -- 2.2.7 Friction Welding -- 2.2.8 Friction Stir Welding -- 2.2.9 Friction Stir Spot Welding -- 2.2.10 Ultrasonic Welding -- 2.2.11 Resistance Implant Welding -- 2.2.12 Induction Welding -- 2.2.13 Dielectric Welding -- 2.2.14 Microwave Welding -- 2.3 Conclusion -- References -- 3 Carbon Nanostructures for Reinforcement of Polymers in Mechanical and Aerospace Engineering -- 3.1 Introduction -- 3.2 Common Carbon Nanoparticles -- 3.2.1 Graphene -- 3.2.2 Carbon Nanotubes -- 3.2.3 Fullerenes -- 3.3 Modeling and Mechanical Properties of Carbon Nanoparticles -- 3.4 Modeling of Carbon Nanoparticles Reinforced Polymers -- 3.5 Preparation of Carbon Nanoparticles Reinforced Polymers.
3.6 Mechanical Properties of Carbon Nanoparticles Reinforced Polymers -- 3.6.1 Graphene Family/Polymer -- 3.6.1.1 Graphite Nanosheets/Polymer -- 3.6.1.2 Graphene and Graphene Oxide/Polymer -- 3.6.2 CNT/Polymer -- 3.6.3 Fullerene/Polymer -- 3.7 Application of Carbon Nanoparticles Reinforced Polymers in Mechanical and Aerospace Engineering -- 3.8 Conclusions -- References -- 4 Self-Healing Carbon Fiber-Reinforced Polymers for Aerospace Applications -- 4.1 General Principle of Self-Healing Composites -- 4.1.1 Extrinsic Healing -- 4.1.2 Intrinsic Self-Healing -- 4.2 Self-Healing Carbon Fiber-Reinforced Polymers -- 4.2.1 Carbon Fiber-Reinforced Polymers (CFRPs) -- 4.2.2 Healing Efficiency -- 4.3 Manufacturing Techniques -- 4.4 Recent Development of Carbon Fiber-Reinforced Polymers in Aerospace Applications -- 4.4.1 Engines -- 4.4.2 Fuselage -- 4.4.3 Aerostructure -- 4.4.4 Coating -- 4.4.5 Other Application -- 4.5 Disposal and Recycling of Self-Healing Carbon Fiber-Reinforced Polymers -- 4.6 Conclusion and Future Challenges -- References -- 5 Advanced Polymeric Materials for Aerospace Applications -- 5.1 Introduction -- 5.2 Types of Advanced Polymers -- 5.2.1 Copolymers -- 5.2.2 Polymer Matrix Composite -- 5.2.3 Properties of Reinforced Materials -- 5.3 Thermoplastics -- 5.4 Thermosetting -- 5.5 Polymeric Nanocomposites -- 5.6 Glass Fiber -- 5.7 Polycarbonates -- 5.8 Applications -- 5.9 Conclusion -- References -- 6 Self-Healing Composite Materials -- 6.1 Introduction -- 6.2 Self-Healing Mechanism -- 6.3 Types of Self-Healing Coatings -- 6.3.1 Passive Self-Healing for External Techniques -- 6.3.1.1 Microencapsulation -- 6.3.1.2 Hollow-Fiber Approach -- 6.3.1.3 Microvascular Network -- 6.3.2 Active Self-Healing Methodology Based on Intrinsic -- 6.3.2.1 Shape Memory Polymers (SMPs) -- 6.3.2.2 Reversible Polymers.
6.4 Research Areas of Self-Healing Materials -- 6.5 Aerospace Applications of Polymer Composite Self-Healing Materials -- 6.5.1 Aircraft Fuselage and Structure -- 6.5.2 Coatings -- 6.6 Conclusion -- References -- 7 Conducting Polymer Composites for Antistatic Application in Aerospace -- 7.1 Introduction -- 7.2 Conducting Polymer Composites (CPCs) for Antistatic Application in Aerospace -- 7.3 Conducting Polymer Nanocomposites (CPNCs) for Antistatic Application in Aerospace -- 7.4 Conclusions -- References -- 8 Electroactive Polymeric Shape Memory Composites for Aerospace Application -- 8.1 Introduction -- 8.1.1 Electroactive Polymer -- 8.1.1.1 Electronic EAPs -- 8.1.1.2 Dielectric Elastomer Actuators (DEAs) -- 8.1.1.3 Piezoelectric Polymer -- 8.1.1.4 Ferroelectric EAPs -- 8.1.2 Ionic Polymers -- 8.1.2.1 Carbon Nanotube (CNT) Actuators -- 8.1.2.2 Ionic Polymer Metal Composites -- 8.1.2.3 Carbon Nanotubes -- 8.1.2.4 Ionic Polymer Gels -- 8.2 Shape-Memory Polymers (SMPs) -- 8.2.1 Properties of Shape Memory Polymers -- 8.2.1.1 Classification of SMPs by Stimulus Response -- 8.2.2 Shape Memory Polymer Composites -- 8.2.3 Electroactive Shape Memory Polymers -- 8.2.4 Applications of Electroactive Shape Memory Polymer Composites in Aerospace -- 8.2.5 Hybrid Electroactive Morphing Wings -- 8.2.6 Paper-Thin CNT -- 8.2.7 SMPC Hinges -- 8.2.8 SMPC Booms -- 8.2.9 Foldable SMPC Truss Booms -- 8.2.9.1 Coilable SMPC Truss Booms -- 8.2.9.2 SMPC STEM Booms -- 8.2.10 SMPC Reflector Antennas -- 8.2.11 Expandable Lunar Habitat -- 8.2.12 Super Wire -- References -- 9 Polymer Nanocomposite Dielectrics for High-Temperature Applications -- 9.1 Introduction -- 9.1.1 Polymer Nanocomposite Dielectrics (PNCD) -- 9.2 Crucial Factor in Framing the High-Temperature Polymer Nanocomposite Dielectric Materials -- 9.2.1 Dielectric Permittivity -- 9.2.2 Thermal Stability.
9.3 Application of Polymer Nanocomposite Dielectric at Elevated Temperature and Their Progress -- 9.4 Conclusion -- References -- 10 Self-Healable Conductive and Polymeric Composite Materials -- 10.1 Introduction -- 10.2 Self-Healing Materials -- 10.2.1 Self-Healing Polymers -- 10.2.2 Self-Healing Polymer Composite Materials -- 10.3 Mechanically-Induced Self-Healing Materials -- 10.3.1 Self-Healing Induction Grounded on Gel -- 10.3.2 Self-Healing Induction Based on Crystals -- 10.3.3 Self-Healing Induction Based on Corrosion Inhibitors -- 10.4 Self-Healing Elastomers and Reversible Materials -- 10.5 Self-Healing Conductive Materials -- 10.5.1 Self-Healing Conductive Polymers -- 10.5.2 Self-Healing Conductive Capsules -- 10.5.3 Self-Healing Conductive Liquids -- 10.5.4 Self-Healing Conductive Composites -- 10.5.5 Self-Healing Conductive Coating -- 10.6 Conclusion and Future Prospects -- References -- Index -- EULA.
Record Nr. UNINA-9910590093903321
Inamuddin <1980->  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Drug Design Using Machine Learning
Drug Design Using Machine Learning
Autore Inamuddin
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (378 pages)
Altri autori (Persone) AltalhiTariq A
CruzJorddy N
RefatMoamen Salah El-Deen
Soggetto genere / forma Electronic books.
ISBN 1-394-16725-3
1-394-16724-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Molecular Recognition and Machine Learning to Predict Protein-Ligand Interactions -- 1.1 Introduction -- 1.1.1 Molecular Recognition -- 1.2 Molecular Docking -- 1.2.1 Conformational Search Algorithm -- 1.2.2 Scoring Function with Conventional Methods -- 1.3 Machine Learning -- 1.3.1 Machine Learning in Molecular Docking -- 1.3.2 Machine Learning Challenges in Molecular Docking -- 1.4 Conclusions -- References -- 2 Machine Learning Approaches to Improve Prediction of Target-Drug Interactions -- 2.1 Machine Learning Revolutionizing Drug Discovery -- 2.1.1 Introduction -- 2.1.2 Virtual Screening and Rational Drug Design -- 2.1.3 Small Organic Molecules and Peptides as Drugs -- 2.2 A Brief Summary of Machine Learning Models -- 2.2.1 Support Vector Machines (SVM) -- 2.2.2 Random Forests (RF) -- 2.2.3 Gradient Boosting Decision Tree -- 2.2.4 K-Nearest Neighbor (KNN) -- 2.2.5 Neural Network and Deep Learning -- 2.2.6 Gaussian Process Regression -- 2.2.7 Evaluating Regression Methods -- 2.2.8 Evaluating Classification Methods -- 2.3 Target Validation -- 2.3.1 Ligand Binding Site Prediction (LBS) -- 2.3.2 Classical Approaches -- 2.3.3 Machine Learning Approaches -- 2.3.3.1 SVM-Based Approaches -- 2.3.3.2 Random Forest-Based Approaches -- 2.3.3.3 Deep Learning-Based Approaches -- 2.4 Lead Discovery -- 2.4.1 The Relevance of Predict Binding Affinity -- 2.4.2 The Concept of Docking -- 2.4.3 The Scoring Function -- 2.4.4 Developing of Novels Scoring Functions by Machine Learning -- 2.4.4.1 Random Forests -- 2.4.4.2 Support Vector Machines -- 2.4.4.3 Neural Networks -- 2.4.4.4 Gradient Boosting Decision Tree -- 2.5 Lead Optimization -- 2.5.1 QSAR and Proteochemometrics -- 2.5.2 Machine Learning Algorithms in Deriving Descriptors.
2.6 Peptides in Pharmaceuticals -- 2.6.1 Peptide Natural and Synthetic Sources -- 2.6.2 Applications and Market for Peptides-Based Drugs -- 2.6.3 Challenges to Become a Peptide Into a Drug -- 2.6.4 Improving Peptide Drug Development Using Machine Learning Techniques -- 2.7 Conclusions -- References -- 3 Machine Learning Applications in Rational Drug Discovery -- 3.1 Introduction -- 3.2 The Drug Development and Approval Process -- 3.3 Human-AI Partnership -- 3.4 AI in Understanding the Pathway to Assess the Side Effects -- 3.4.1 Traditional Versus New Strategies in Drug Discovery -- 3.4.2 Target Identification and Authentication -- 3.4.3 Searching the Hit and Lead Molecules with the Help of AI -- 3.4.4 Discretion of a Population for Medical Trials Using AI -- 3.5 Predicting the Side Effects Using AI -- 3.6 AI for Polypharmacology and Repurposing -- 3.7 The Challenge of Keeping Drugs Safe -- 3.8 Conclusion -- Resources -- References -- 4 Deep Learning for the Selection of Multiple Analogs -- 4.1 Introduction -- 4.2 Goals of Analog Design -- 4.3 Deep Learning in Drug Discovery -- 4.4 Chloroquine Analogs -- 4.5 Deep Learning in Medical Field -- 4.5.1 Scientific Study of Skin Diseases -- 4.5.2 Anatomical Laparoscopy -- 4.5.3 Angiography -- 4.5.4 Interpretation of Wound -- 4.5.5 Molecular Docking -- 4.5.6 Breast Cancer Detection -- 4.5.7 Polycystic Organs -- 4.5.8 Bone Tissue -- 4.5.9 Interaction Drug-Target -- 4.5.10 Pancreatic Issue Prediction -- 4.5.11 Prediction of Carcinoma in Cells -- 4.5.12 Determining Parkinson's -- 4.5.13 Segregating Cells -- 4.6 Conclusion -- References -- 5 Drug Repurposing Based on Machine Learning -- 5.1 Introduction -- 5.2 Computational Drug Repositioning Strategies -- 5.2.1 Drug-Based Strategies -- 5.2.2 Disease-Based Strategies -- 5.3 Machine Learning.
5.4 Data Resources Used for Computational Drug Repositioning Through Machine Learning Techniques -- 5.5 Machine Learning Approaches Used for Drug Repurposing -- 5.5.1 Network-Based Approaches -- 5.5.2 Text Mining-Based Approaches -- 5.5.3 Semantics-Based Approaches -- 5.6 Drugs Repurposing Through Machine Learning-Case Studies -- 5.6.1 Psychiatric Disorders -- 5.6.2 Alzheimer's Disease -- 5.6.3 Drug Repurposing for Cancer -- 5.6.4 COVID-19 -- 5.6.5 Herbal Drugs -- 5.7 Conclusion -- References -- 6 Recent Advances in Drug Design With Machine Learning -- 6.1 Introduction -- 6.2 Categorization of Machine Learning Tasks -- 6.2.1 Supervised Learning -- 6.2.2 Unsupervised Learning -- 6.2.3 Semisupervised Learning -- 6.2.4 Reinforcement Learning -- 6.3 Machine Language-Mediated Predictive Models in Drug Design -- 6.3.1 Quantitative Structure-Activity Relationship Models (QSAR) -- 6.3.2 Quantitative Structure-Property Relationship Models (QSPR) -- 6.3.3 Quantitative Structure Toxicity Relationship Models (QSTR) -- 6.3.4 Quantitative Structure Biodegradability Relationship Models (QSBR) -- 6.4 Machine Learning Models -- 6.4.1 Artificial Neural Networks (ANNs) -- 6.4.2 Self-Organizing Map (SOM) -- 6.4.3 Multilayer Perceptrons (MLPs) -- 6.4.4 Counter Propagation Neural Networks (CPNN) -- 6.4.5 Bayesian Neural Networks (BNNs) -- 6.4.6 Support Vector Machines (SVMs) -- 6.4.7 Naive Bayesian Classifier -- 6.4.8 K Nearest Neighbors (KNN) -- 6.4.9 Ensemble Methods -- 6.4.9.1 Boosting -- 6.4.9.2 Bagging -- 6.4.10 Random Forest -- 6.4.11 Deep Learning -- 6.4.12 Synthetic Minority Oversampling Technique -- 6.5 Machine Learning and Docking -- 6.5.1 Scoring Power -- 6.5.2 Ranking Power -- 6.5.3 Docking Power -- 6.5.4 Predicting Docking Score Using Machine Learning -- 6.6 Machine Learning in Chemoinformatics.
6.7 Challenges and Limitations for Machine Learning in Drug Discovery -- 6.8 Conclusion and Future Perspectives -- References -- 7 Loading of Drugs in Biodegradable Polymers Using Supercritical Fluid Technology -- 7.1 Introduction -- 7.2 Supercritical Fluid Technology -- 7.2.1 Supercritical Fluids -- 7.2.2 Physicochemical Properties -- 7.2.3 Carbon Dioxide -- 7.3 Biodegradable Polymers -- 7.3.1 Main Biologically-Derived Polymers Used With SCF Technologies -- 7.3.1.1 Cellulose -- 7.3.1.2 Chitosan -- 7.3.1.3 Alginate -- 7.3.1.4 Collagen -- 7.3.2 Main Synthetic Polymers Used With SCF Technologies -- 7.3.2.1 Polylactic Acid (PLA) -- 7.3.2.2 Poly (Lactic-co-Glycolic Acid) (PLGA) -- 7.3.2.3 Polycaprolactone (PCL) -- 7.3.2.4 Poly (Vinyl Alcohol) (PVA) -- 7.4 Drug Delivery -- 7.4.1 Types of Drugs -- 7.4.2 Influence of Experimental Conditions on the Drug Loading -- 7.5 Conclusion -- Acknowledgments -- References -- 8 Neural Network for Screening Active Sites on Proteins -- 8.1 Introduction -- 8.2 Structural Proteomics -- 8.2.1 PPIs -- 8.2.2 Active Sites in Proteins -- 8.3 Gist Techniques to Study the Active Sites on Proteins -- 8.3.1 In Vitro -- 8.3.1.1 Affinity Purification -- 8.3.1.2 Affinity Chromatography -- 8.3.1.3 Coimmunoprecipitation -- 8.3.1.4 Protein Arrays -- 8.3.1.5 Protein Fragment Complementation -- 8.3.1.6 Phage Display -- 8.3.1.7 X-Ray Crystallography -- 8.3.1.8 Nuclear Magnetic Resonance Spectroscopy (NMR) -- 8.3.2 In Vivo -- 8.3.2.1 In-Silico Two-Hybrid -- 8.3.3 In-Silico and Neural Network -- 8.3.3.1 Data Base -- 8.3.3.2 Sequence-Based Approaches -- 8.3.3.3 Structure-Based Approaches -- 8.3.3.4 Phylogenetic Tree -- 8.3.3.5 Gene Fusion -- 8.4 Neural Networking Algorithms to Study Active Sites on Proteins -- 8.4.1 PDBSiteScan Program -- 8.4.2 Patterns in Nonhomologous Tertiary Structures (PINTS) -- 8.4.3 Genetic Active Site Search (GASS).
8.4.4 Site Map -- 8.4.5 Computed Atlas of Surface Topography of Proteins (CASTp) -- 8.5 Conclusion -- References -- 9 Protein Redesign and Engineering Using Machine Learning -- 9.1 Introduction -- 9.2 Designing Sequence-Function Model Through Machine Learning -- 9.2.1 Training of Model and Evaluation -- 9.2.2 Representation of Proteins by Vector -- 9.2.3 Guiding Exploration by Employing Sequence-Function Prediction -- 9.3 Features Based on Energy -- 9.4 Features Based on Structure -- 9.5 Prediction of Thermostability of Protein with Single Point Mutations -- 9.6 Selection of Features -- 9.6.1 Extraction of Features -- 9.7 Force Field and Score Function -- 9.8 Machine Learning for Prediction of Hot Spots -- 9.8.1 Support Vector Machines -- 9.8.2 Nearest Neighbor -- 9.8.3 Decision Trees -- 9.8.4 Neural Networks -- 9.8.5 Bayesian Networks -- 9.8.6 Ensemble Learning -- 9.9 Deep Learning-Neural Network in Computational Protein Designing -- 9.10 Machine Learning in Engineering of Proteins -- 9.11 Conclusion -- References -- 10 Role of Transcriptomics and Artificial Intelligence Approaches for the Selection of Bioactive Compounds -- 10.1 Introduction -- 10.2 Types of Bioactive Compounds -- 10.2.1 Phenolic Acids -- 10.2.2 Stilbenes -- 10.2.3 Ellagitannins -- 10.2.4 Flavonoids -- 10.2.5 Proanthocyanidin -- 10.2.6 Vitamins -- 10.2.7 Bioactive Peptides -- 10.3 Transcriptomics Approaches for the Selection of Bioactive Compounds -- 10.3.1 Hybrid Transcriptome Sequencing -- 10.3.2 Microarray -- 10.3.3 RNA-Seq -- 10.4 Artificial Intelligence Approaches for the Selection of Bioactive Compounds -- 10.4.1 Machines Learning (ML) Approach for the Selection of Bioactive Compounds -- 10.4.1.1 Evolution of Machine Learning to Deep Learning -- 10.4.1.2 Virtual Screening -- 10.4.1.3 Recent Advances in Machine Learning -- 10.4.1.4 Deep Learning.
10.4.2 De Novo Synthesis of Bioactive Compounds.
Record Nr. UNINA-9910623989503321
Inamuddin  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of Bioplastics and Biocomposites Engineering Applications
Handbook of Bioplastics and Biocomposites Engineering Applications
Autore Inamuddin
Edizione [2nd ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (683 pages)
Disciplina 620.192323
Altri autori (Persone) AltalhiTariq A
Soggetto genere / forma Electronic books.
ISBN 1-119-16014-6
1-119-16018-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Bioplastics, Synthesis and Process Technology -- Chapter 1 An Introduction to Engineering Applications of Bioplastics -- 1.1 Introduction -- 1.2 Classification of Bioplastics -- 1.3 Physical Properties -- 1.3.1 Rheological Properties -- 1.3.2 Optical Properties -- 1.3.3 Mechanical and Thermal Properties -- 1.3.4 Electrical Properties -- 1.4 Applications of Bioplastics in Engineering -- 1.4.1 Bioplastics Applications in Sensors -- 1.4.2 Bioplastics Applications in Energy Sector -- 1.4.3 Bioplastics Applications in Bioengineering -- 1.4.4 Bioplastics Applications in "Green" Electronics -- 1.5 Conclusions -- Acknowledgement -- Dedication -- References -- Chapter 2 Biobased Materials: Types and Sources -- 2.1 Introduction -- 2.2 Biodegradable Biobased Material -- 2.2.1 Polysaccharides -- 2.2.2 Starch -- 2.2.3 Polylactic Acid -- 2.2.4 Cellulose -- 2.2.5 Esters -- 2.2.6 Ether -- 2.2.7 Chitosan -- 2.2.8 Alginate -- 2.2.9 Proteins -- 2.2.10 Gluten -- 2.2.11 Gelatine -- 2.2.12 Casein -- 2.2.13 Lipid -- 2.2.14 Polyhydroxyalkanoates (PHA) -- 2.3 Nonbiodegradable Biobased Material -- 2.3.1 Polyethylene (PE) -- 2.3.2 Polyethylene Terephthalate (PET) -- 2.3.3 Polyamide (PA) -- 2.4 Conclusion -- Acknowledgment -- References -- Chapter 3 Bioplastic From Renewable Biomass -- 3.1 Introduction -- 3.2 Plastics and Bioplastics -- 3.2.1 Plastics -- 3.2.2 Bioplastics -- 3.3 Classification of Bioplastics -- 3.4 Bioplastic Production -- 3.4.1 Biowaste to Bioplastic -- 3.4.1.1 Lipid Rich Waste -- 3.4.2 Milk Industry Waste -- 3.4.3 Sugar Industry Waste -- 3.4.4 Spent Coffee Beans Waste -- 3.4.5 Bioplastic Agro-Forestry Residue -- 3.4.6 Bioplastic from Microorganism -- 3.4.7 Biomass-Based Polymers -- 3.4.7.1 Biomass-Based Monomers for Polymerization Process -- 3.5 Characterization of Bioplastics.
3.6 Applications of Bioplastics -- 3.6.1 Food Packaging -- 3.6.2 Agricultural Applications -- 3.6.3 Biomedical Applications -- 3.7 Bioplastic Waste Management Strategies -- 3.7.1 Recycling of Poly(Lactic Acid ) (PLA) -- 3.7.1.1 Mechanical Recycling of PLA -- 3.7.1.2 Chemical Recycling of PLA -- 3.7.2 Recycling of Poly Hydroxy Alkanoates (PHAs) -- 3.7.3 Landfill -- 3.7.4 Incineration -- 3.7.5 Composting -- 3.7.6 Anaerobic Digestion -- 3.7.6.1 Anaerobic Digestion of Poly(Hydroxyalkanoates) -- 3.7.6.2 Anaerobic Digestion of Poly(Lactic Acid) -- 3.8 Conclusions and Future Prospects -- References -- Chapter 4 Modeling of Natural Fiber-Based Biocomposites -- 4.1 Introduction -- 4.2 Generality of Biocomposites -- 4.2.1 Natural Matrix -- 4.2.2 Natural Reinforcement -- 4.2.3 Natural Fiber Classification -- 4.2.4 Biocomposites Processing -- 4.2.4.1 Extrusion and Injection -- 4.2.4.2 Compression Molding -- 4.2.5 RTM-Resin Transfer Molding -- 4.2.6 Hand Lay-Up Technique -- 4.3 Parameters Affecting the Biocomposites Properties -- 4.3.1 Fiber's Aspect Ratio -- 4.3.2 Fiber/Matrix Interfacial Adhesion -- 4.3.3 Fibers Orientation and Dispersion -- 4.3.3.1 Short Fibers Orientation -- 4.3.3.2 Fiber's Orientation in Simple Shear Flow -- 4.3.3.3 Fiber's Orientation in Elongational Flow -- 4.4 Process Molding of Biocomposites -- 4.4.1 Unidirectional Fibers -- 4.4.1.1 Classical Laminate Theory -- 4.4.1.2 Rule of Mixture -- 4.4.1.3 Halpin-Tsai Model -- 4.4.1.4 Hui-Shia Model -- 4.4.2 Random Fibers -- 4.4.2.1 Hirsch Model -- 4.4.2.2 Self-Consistent Approach (Modified Hirsch Model) -- 4.4.2.3 Tsai-Pagano Model -- 4.5 Conclusion -- References -- Chapter 5 Process Modeling in Biocomposites -- 5.1 Introduction -- 5.2 Biopolymer Composites -- 5.2.1 Natural Fiber-Based Biopolymer Composites -- 5.2.2 Applications of Biopolymer Composites -- 5.2.3 Properties of Biopolymer Composites.
5.3 Classification of Biocomposites -- 5.3.1 PLA Biocomposites -- 5.3.2 Nanobiocomposites -- 5.3.3 Hybrid Biocomposites -- 5.3.4 Natural Fiber-Based Composites -- 5.4 Process Modeling of Biocomposite Models -- 5.4.1 Compression Moulding -- 5.4.2 Injection Moulding -- 5.4.3 Extrusion Method -- 5.5 Formulation of Models -- 5.5.1 Types of Model -- 5.6 Conclusion -- References -- Chapter 6 Microbial Technology in Bioplastic Production and Engineering -- 6.1 Introduction -- 6.2 Fundamental Principles of Microbial Bioplastic Production -- 6.3 Bioplastics Obtained Directly from Microorganisms -- 6.3.1 PHA -- 6.3.2 Poly (ƒÁ-Glutamic Acid) (PGA) -- 6.4 Bioplastics from Microbial Monomers -- 6.4.1 Bioplastics from Aliphatic Monomers -- 6.4.1.1 PLA -- 6.4.1.2 Poly (Butylene Succinate) -- 6.4.1.3 Biopolyamides (Nylons) -- 6.4.1.4 1, 3-Propanediol (PDO) -- 6.4.2 Bioplastics from Aromatic Monomers -- 6.5 Lignocellulosic Biomass for Bioplastic Production -- 6.6 Conclusion -- References -- Chapter 7 Synthesis of Green Bioplastics -- 7.1 Introduction -- 7.2 Bioplastic -- 7.2.1 Polyhydroxyalkanoates (PHAs) -- 7.2.2 Poly(lactic acid) (PLA) -- 7.2.3 Cellulose -- 7.2.4 Starch -- 7.3 Renewable Raw Material to Produce Bioplastic -- 7.3.1 Raw Material from Agriculture -- 7.3.2 Organic Waste as Resources for Bioplastic Production -- 7.3.3 Algae as Resources for Bioplastic Production -- 7.3.4 Wastewater as Resources for Bioplastic Production -- 7.4 Bioplastics Applications -- 7.4.1 Food Industry -- 7.4.2 Agricultural Applications -- 7.4.3 Medical Applications -- 7.4.4 Other Applications -- 7.5 Conclusions -- References -- Chapter 8 Natural Oil-Based Sustainable Materials for a Green Strategy -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Entropy Methodology -- 8.2.2 Copras Methodology -- 8.3 Conclusions -- References.
Part II: Applications of Bioplastics in Health and Hygiene -- Chapter 9 Biomedical Applications of Bioplastics -- 9.1 Introduction -- 9.2 Synthesis of Bioplastics -- 9.2.1 Starch-Based Bioplastics -- 9.2.2 Cellulose-Based Bioplastics -- 9.2.3 Chitin and Chitosan -- 9.2.4 Polyhydroxyalkanoates (PHA) -- 9.2.5 Polylactic Acid (PLA) -- 9.2.6 Bioplastics from Microalgae -- 9.3 Properties of Bioplastics -- 9.3.1 Material Strength -- 9.3.2 Electrical, Mechanical, and Optical Behavior of Bioplastic -- 9.4 Biological Properties of Bioplastics -- 9.5 Biomedical Applications of Bioplastics -- 9.5.1 Antimicrobial Property -- 9.5.2 Biocontrol Agents -- 9.5.3 Pharmaceutical Applications of Bioplastics -- 9.5.4 Implantation -- 9.5.5 Tissue Engineering Applications -- 9.5.6 Memory Enhancer -- 9.6 Limitations -- 9.7 Conclusion -- References -- Chapter 10 Applications of Bioplastics in Hygiene Cosmetic -- 10.1 Introduction -- 10.2 The Need to Find an Alternative to Plastic -- 10.3 Bioplastics -- 10.3.1 Characteristic of Bioplastics -- 10.3.2 Types (Classification) -- 10.3.3 Uses of Bioplastics -- 10.4 Resources of Bioplastic -- 10.4.1 Polysaccharides -- 10.4.2 Starch or Amylum -- 10.4.3 Cellulose -- 10.4.3.1 Source of Cellulose -- 10.5 Use of Biodegradable Materials in Packaging -- 10.6 Bionanocomposite -- 10.7 Hygiene Cosmetic Packaging -- 10.8 Conclusion -- References -- Chapter 11 Biodegradable Polymers in Drug Delivery -- 11.1 Introduction -- 11.2 Biodegradable Polymer (BP) -- 11.2.1 Natural -- 11.2.1.1 Polysaccharides -- 11.2.1.2 Proteins -- 11.2.2 Synthetic -- 11.2.2.1 Polyesters -- 11.2.2.2 Polyanhydrides -- 11.2.2.3 Polycarbonates -- 11.2.2.4 Polyphosphazenes -- 11.2.2.5 Polyurethanes -- 11.3 Device Types -- 11.3.1 Three-Dimensional Printing Devices -- 11.3.1.1 Implants -- 11.3.1.2 Tablets -- 11.3.1.3 Microneedles -- 11.3.1.4 Nanofibers -- 11.3.2 Nanocarriers.
11.3.2.1 Nanoparticles -- 11.3.2.2 Dendrimers -- 11.3.2.3 Hydrogels -- 11.4 Applications -- 11.4.1 Intravenous -- 11.4.2 Transdermal -- 11.4.3 Oral -- 11.4.4 Ocular -- 11.5 Existing Materials in the Market -- 11.6 Conclusions and Future Projections -- References -- Chapter 12 Microorganism-Derived Bioplastics for Clinical Applications -- 12.1 Introduction -- 12.2 Types of Bioplastics -- 12.2.1 Poly(3-hydroxybutyrate) (PHB) -- 12.2.2 Polyhydroxyalkanoate -- 12.2.3 Poly-Lactic Acid -- 12.2.4 Poly Lactic-co-Glycolic Acid (PLGA) -- 12.2.5 Poly (.-caprolactone) (PCL) -- 12.3 Properties of Bioplastics -- 12.3.1 Physiochemical, Mechanical, and Biological Properties of Bioplastics -- 12.3.1.1 Polylactic Acid -- 12.3.1.2 Poly Lactic-co-Glycolic Acid -- 12.3.1.3 Polycaprolactone -- 12.3.1.4 Polyhydroxyalkanoates -- 12.3.1.5 Polyethylene Glycol (PEG) -- 12.4 Applications -- 12.4.1 Tissue Engineering -- 12.4.2 Drug Delivery System -- 12.4.3 Implants and Prostheses -- 12.5 Conclusion -- References -- Chapter 13 Biomedical Applications of Biocomposites Derived From Cellulose -- 13.1 Introduction -- 13.2 Importance of Cellulose in the Field of Biocomposite -- 13.3 Classification of Cellulose -- 13.4 Synthesis of Cellulose in Different Form -- 13.4.1 Mechanical Extraction -- 13.4.2 Electrochemical Method -- 13.4.3 Chemical Extraction -- 13.4.4 Enzymatic Hydrolysis -- 13.4.5 Bacterial Production of Cellulose -- 13.5 Formation of Biocomposite Using Different Form of Cellulose -- 13.6 Biocomposites Derived from Cellulose and Their Application -- 13.6.1 Tissue Engineering -- 13.6.2 Wound Dressing -- 13.6.3 Drug Delivery -- 13.6.4 Dental Applications -- 13.6.5 Other Applications -- 13.7 Conclusion -- References -- Chapter 14 Biobased Materials for Biomedical Engineering -- 14.1 Introduction -- 14.2 Biomaterials.
14.3 Biobased Materials for Implants and Tissue Engineering.
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Newark : , : John Wiley & Sons, Incorporated, , 2022
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