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Conversion of Carbon Dioxide into Hydrocarbons Vol. 1 Catalysis / / edited by Inamuddin, Abdullah M. Asiri, Eric Lichtfouse
| Conversion of Carbon Dioxide into Hydrocarbons Vol. 1 Catalysis / / edited by Inamuddin, Abdullah M. Asiri, Eric Lichtfouse |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (X, 211 p. 53 illus., 43 illus. in color.) |
| Disciplina | 546.6812 |
| Collana | Environmental Chemistry for a Sustainable World |
| Soggetto topico |
Environmental chemistry
Catalysis Pollution Analytical chemistry Electrochemistry Environmental Chemistry Analytical Chemistry |
| ISBN | 3-030-28622-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Chapter 1. Conversion of carbon dioxide into liquid hydrocarbons in the presence of a cobalt-containing catalysts -- Chapter 2. Conversion of carbon dioxide using lead/composite/oxides electrodes into formate/formic acid -- Chapter 3. Thermo-chemical conversion of carbon dioxide to carbon monoxide by reverse water-gas shift reaction over ceria-based catalysts -- Chapter 4. Photocatalytic systems for carbon dioxide conversion to hydrocarbons -- Chapter 5. Electrochemical reduction of carbon dioxide to methanol using metal-organic frameworks and non-metal-organic frameworks catalysts -- Chapter 6. Photocatalytic conversion of carbon dioxide into hydrocarbons -- Chapter 7. Electrocatalytic production of methanol from carbon dioxide. |
| Record Nr. | UNINA-9910768464703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Conversion of Carbon Dioxide into Hydrocarbons Vol. 2 Technology / / edited by Inamuddin, Abdullah M. Asiri, Eric Lichtfouse
| Conversion of Carbon Dioxide into Hydrocarbons Vol. 2 Technology / / edited by Inamuddin, Abdullah M. Asiri, Eric Lichtfouse |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XI, 202 p. 45 illus., 28 illus. in color.) |
| Disciplina | 577.14 |
| Collana | Environmental Chemistry for a Sustainable World |
| Soggetto topico |
Environmental chemistry
Catalysis Pollution Analytical chemistry Electrochemistry Environmental Chemistry Analytical Chemistry |
| ISBN | 3-030-28638-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- 1. Use of carbon dioxide in polymer synthesis (Annalisa Abdel Azim, Alessandro Cordara, Beatrice Battaglino, Angela Re) -- 2. Biological conversion of carbon dioxide into volatile organic compounds (Ihana Aguiar Severo, Pricila Nass Pinheiro, Karem Rodrigues Vieira, Leila Queiroz Zepka, Eduardo Jacob-Lopes) -- 3. Application of metal organic frameworks in carbon dioxide conversion to methanol (Tamer Zaki) -- 4. Conversion of Carbon Dioxide into Formic Acid (Umesh Fegade and Ganesh Jethave) -- 5. Selective hydrogenation of carbon dioxide into methanol (Pham Minh, Roger, Parkhomenko, L'Hospital, Rego de Vasconcelos, Ro, Mahajan, Chen, Singh, N. Vo) -- 6. Conversion of carbon dioxide into formaldehyde (Trinh Duy Nguyen, Thuan Van Tran, Sharanjit Singh, Pham T. T. Phuong, Long Giang Bach, Sonil Nanda, Dai-Viet N. Vo) -- 7. A Short Review on Production of Syngas via Glycerol Dry Reforming (Sumaiya Zainal Abidin, Asmida Ideris, Nurul Ainirazali, Mazni Ismail). |
| Record Nr. | UNINA-9910767542603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Drug design using machine learning / / edited by Inamuddin, [and three others]
| Drug design using machine learning / / edited by Inamuddin, [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (378 pages) |
| Disciplina | 929.605 |
| Soggetto topico | Computer-aided design |
| 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-9910643043703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Drug design using machine learning / / edited by Inamuddin, [and three others]
| Drug design using machine learning / / edited by Inamuddin, [and three others] |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , ℗2022 |
| Descrizione fisica | 1 online resource (378 pages) |
| Disciplina | 929.605 |
| Soggetto topico | Computer-aided design |
| 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-9910830147803321 |
| Hoboken, NJ : , : Wiley, , ℗2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
E-waste Recycling and Management : Present Scenarios and Environmental Issues / editors Anish Khan, Inamuddin, Abdullah M. Asiri
| E-waste Recycling and Management : Present Scenarios and Environmental Issues / editors Anish Khan, Inamuddin, Abdullah M. Asiri |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | XIII, 235 p. : ill. ; 24 cm |
| Disciplina |
660(Ingegneria chimica e tecnologie connesse)
668.4192(Tecnologia dei rifiuti) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0237442 |
| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
E-waste Recycling and Management : Present Scenarios and Environmental Issues / / edited by Anish Khan, Inamuddin, Abdullah M. Asiri
| E-waste Recycling and Management : Present Scenarios and Environmental Issues / / edited by Anish Khan, Inamuddin, Abdullah M. Asiri |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (244 pages) |
| Disciplina |
621.38150286
628.4 |
| Collana | Environmental Chemistry for a Sustainable World |
| Soggetto topico |
Waste management
Chemical engineering Environmental management Waste Management/Waste Technology Industrial Chemistry/Chemical Engineering Environmental Management |
| ISBN | 3-030-14184-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Solution and Challenges in recycling waste cathode-ray tube -- 2. Reconfigurable recycling systems of e-waste -- 3. An Economic Assessment of Present and Future Electronic Waste Streams: Japan’s Experience -- 4. Recent technologies in electronic waste management -- 5. Recycling challenges for electronic consumer products to e-waste: A developing countries perspective -- 6. Chemical recycling of electronic waste for clean fuel production -- 7. Management of electrical and electronic equipment in European Union countries: a comparison -- 8. E-waste management from macroscopic to microscopic scale -- 9. Recycling processes for the recovery of metal from e-waste of the LED industry -- 10. E-waste management and the conservation of geochemical scarce resources -- 11. Sustainable electronic waste management: Implications on environmental and human health -- 12. E-waste and their implications on the environmental and human health. |
| Record Nr. | UNINA-9910366639203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Electroceramics for High Performance Supercapicitors / / edited by Inamuddin, Tariq Altalhi and Sayed Mohammed Adnan
| Electroceramics for High Performance Supercapicitors / / edited by Inamuddin, Tariq Altalhi and Sayed Mohammed Adnan |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (0 pages) |
| Disciplina | 621.381 |
| Soggetto topico |
Electronic ceramics
Supercapacitors |
| ISBN |
1-394-16716-4
1-394-16715-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Lead-Free Energy Storage Ceramics -- 1.1 Introduction -- 1.2 Dielectric Capacitor and Energy Storage -- 1.3 Energy Storage of Dielectric Ceramics Free of Lead -- 1.4 Conclusion and Outlooks -- Acknowledgments -- References -- Chapter 2 Lead-Based Ceramics for High-Performance Supercapacitors -- 2.1 Introduction -- 2.2 General Idea of Ceramics for Supercapacitors -- 2.2.1 Metallic Oxide Ceramics for Supercapacitors -- 2.2.2 Binary Metal Oxides -- 2.2.2.1 Ceramics of Spinal Oxide Material -- 2.2.2.2 Barium Titanate Ceramics -- 2.2.3 Multimetal Oxidized Ceramics -- 2.2.4 Metal Hydroxide-Type Ceramics -- 2.3 Principle Involved in Electroceramics -- 2.3.1 Electrostatic Capacitor -- 2.4 Lead-Based Ceramics -- 2.4.1 Lead-Based Ferroelectrics -- 2.4.2 Lead-Based Relaxor Ferroelectrics -- 2.4.3 Lead-Based Anti-Ferroelectrics -- 2.5 Characteristics of Lead-Based Ceramics -- 2.5.1 Characteristics of Lead Zirconate Titanate -- 2.5.2 Characteristics of Lead Magnesium Niobate -- 2.5.3 Characteristics of Lead Zinc Niobate -- 2.6 Conclusion and Perspectives -- 2.6.1 Up-to-Date Sintering and Molding Process -- 2.6.2 Microscopical and Flexible Ceramics Electrode Materials -- 2.6.3 Improvement of Efficiency of the Ceramic Electrode Materials -- References -- Chapter 3 Ceramic Films for High-Performance Supercapacitors -- 3.1 Introduction -- 3.2 Energy Storage Principles -- 3.3 Factors Optimizing Energy Density -- 3.3.1 The Intrinsic Band Gap (Eg) -- 3.3.2 Electrical Microstructure -- 3.3.3 Density and Grain Size -- 3.4 Ceramics for Supercapacitors -- 3.4.1 Metal Oxide Ceramics -- 3.4.2 Multielemental Oxides -- 3.5 Conclusions and Outlook -- References -- Chapter 4 Ceramic Multilayers and Films for High-Performance Supercapacitors -- 4.1 Introduction.
4.2 Fundamentals of Energy Storage in Electroceramics -- 4.2.1 Electrostatic Capacitors -- 4.2.2 Important Factors Designed for Assessing Energy Storage Characteristics -- 4.3 Important Factors for Maximizing Energy Density -- 4.3.1 Intrinsic Band Gap -- 4.3.2 Electrical Microstructure -- 4.4 Different Types of Electroceramics Capacitors for Energy Storage -- 4.4.1 Pb-Doped Ceramics -- 4.4.1.1 Pb-Doped RFEs -- 4.4.1.2 Lead-Doped Antiferroelectrics -- 4.4.2 Pb-Free Ceramics -- 4.4.2.1 BaTiO3-Based Ceramics -- 4.4.2.2 K0.5Na0.5NbO3-Doped Ceramics -- 4.4.2.3 Na0.5Bi0.5TiO3-Doped Ceramics -- 4.4.2.4 AgNbO3-Based Ceramics -- 4.5 Application of Electroceramics Supercapacitor -- 4.6 Conclusion -- References -- Chapter 5 Superconductors for Energy Storage -- 5.1 Introduction -- 5.1.1 Background -- 5.1.2 Superconducting Properties -- 5.1.3 Synthetic Methodology -- 5.2 Low-Temperature Superconductors -- 5.2.1 Nb-Ti-Based LTS -- 5.2.2 Nb3Sn-Based LTS -- 5.3 High-Temperature Superconductors -- 5.3.1 Cuprate-Based HTS -- 5.3.2 Iron-Based Pnictides (Pn) and Chalcogenides (Ch) as HTS -- 5.3.3 MgB2-Based HTS -- 5.3.4 Hydrides-Based HTS -- 5.4 Superconductors in Energy Applications -- 5.4.1 Superconducting Magnetic Energy Storage -- 5.4.1.1 Use of SMES in the Power Grid: Flexible AC Transmission System (FACTS) -- 5.4.1.2 Use of SMES as Fault Current Limiters -- 5.4.2 Use of Superconductors in Accelerator System -- 5.4.3 Use of Superconductors in Fusion Technologies -- 5.4.4 Challenges Faced During Superconducting Energy Storage -- 5.5 Conclusion -- Acknowledgments -- References -- Chapter 6 Key Factors for Optimizing Energy Density in High-Performance Supercapacitors -- 6.1 Supercapacitor -- 6.2 Electric Double-Layer Capacitor -- 6.3 Pseudo-Capacitor -- 6.4 Hybrid Supercapacitor -- 6.4.1 Electrochemical Performance -- 6.4.2 Capacitance -- 6.4.3 Specific Capacitance. 6.4.4 Energy Density -- 6.4.5 Power Density -- 6.4.6 Cyclic Stability -- 6.5 The Energy Density of Supercapacitor -- 6.5.1 Optimization of High Energy Density -- 6.5.1.1 Pore Size -- 6.5.1.2 Surface Area -- 6.5.1.3 Grain Size -- 6.5.1.4 Functional Groups -- 6.5.1.5 Band Gap -- 6.5.2 Effect of Voltage -- 6.5.3 Asymmetric Supercapacitors -- 6.5.4 Negative Electrode Materials -- 6.5.5 Positive Electrode Materials -- 6.5.6 Battery-Supercapacitor Hybrid (Bsh) Device -- 6.5.6.1 Lithium-Ion BSH -- 6.5.6.2 Na-Ion BSH -- 6.5.6.3 Acidic BSH -- 6.5.6.4 Alkaline BSH -- 6.6 Future Outlook -- 6.7 Conclusion -- References -- Chapter 7 Optimization of Anti-Ferroelectrics -- 7.1 Introduction -- 7.2 Energy Storage Properties -- 7.3 Antiferroelectric for Energy Storage -- 7.3.1 Lead-Based Antiferroelectric -- 7.3.2 Lead-Free Antiferroelectric -- 7.3.3 Challenges -- 7.4 Explosive Energy Conversion -- 7.5 Energy Storage and High-Power Capacitors -- 7.6 Thermal-Electric Energy Interconversion -- 7.7 Optimization -- 7.7.1 Phase Structure Engineering -- 7.7.1.1 Planning Phase in a Structural Engineering Project -- 7.7.1.2 Design Phase -- 7.7.1.3 Construction Phase -- 7.7.2 Grain Size Engineering -- 7.7.3 Domain Engineering -- 7.7.3.1 Phase -- 7.7.3.2 Domain Analysis -- 7.7.3.3 Domain Design -- 7.7.4 Doping -- 7.8 Conclusion -- References -- Chapter 8 Super Capacitive Performance Assessment of Mixed Ferromagnetic Iron and Cobalt Oxides and Their Polymer Composites -- 8.1 Introduction -- 8.1.1 Electrolyte -- 8.1.2 Separator -- 8.1.3 Current Collector -- 8.1.4 Supercapacitor Electrode Materials -- 8.2 Ferromagnetic Electrode Materials -- 8.3 Mixed Ferromagnetic Iron and Cobalt Oxides -- 8.4 Conclusion -- References -- Chapter 9 Transition Metal Oxides with Broaden Potential Window for High-Performance Supercapacitors -- 9.1 Introduction of Transition Metal Oxides (TMOs). 9.2 Redox-Based Materials -- 9.3 Conducting Polymers -- 9.4 Electroactive Metal Oxides or Transition Metal Oxides (TMOs) as Electrodes for SCs -- 9.4.1 MnO2 as Electrode Material for SCs -- 9.4.2 Pseudo-Capacitive Behavior of á-MnO2 by Cation Insertion -- 9.4.3 Na0.5MnO2 Nanosheet Assembled Nanowall Arrays for ASCs -- 9.4.4 FeOx/FeOOH Material as Negative Electrode -- 9.4.5 Carbon-Stabilized Fe3O4@C Nanorod Arrays as an Efficient Anode for SCs -- 9.4.6 Electrochemical Performance of Fe3O4 and Fe3O4@C NRAs as Anode -- 9.4.7 Construction of Na0.5MnO2//Fe3O4@C ASC and Electrochemical Performance -- 9.4.8 Highly Efficient NiCo2S4@Fe2O3//MnO2 ASC -- 9.4.9 Bi2O3 as Negative Electrode with Broaden Potential Window -- 9.5 Conclusion -- References -- Chapter 10 Aqueous Redox-Active Electrolytes -- 10.1 Introduction -- 10.2 Electrolyte Requirements for High-Performance Supercapacitors -- 10.2.1 Conductivity -- 10.2.2 Salt Effect -- 10.2.3 Solvent Effect -- 10.2.4 Electrochemical Stability -- 10.2.5 Thermal Stability -- 10.3 Effect of the Electrolyte on Supercapacitor Performance -- 10.3.1 Aqueous Electrolytes -- 10.3.2 Acidic Electrolytes -- 10.3.2.1 Sulfuric Acid Electrolyte-Based EDLC and Pseudocapacitors -- 10.3.2.2 H2SO4 Electrolyte-Based Hybrid Supercapacitors -- 10.3.3 Alkaline Electrolytes -- 10.3.3.1 Alkaline Electrolyte-Based EDLC and Pseudocapacitors -- 10.3.3.2 Alkaline Electrolyte-Based Hybrid Supercapacitors -- 10.3.4 Neutral Electrolyte -- 10.3.4.1 Neutral Electrolyte-Based EDLC and Pseudocapacitors -- 10.3.4.2 Neutral Electrolyte-Based Hybrid Supercapacitors -- 10.4 Conclusion and Future Research Directions -- References -- Chapter 11 Strategies for Improving Energy Storage Properties -- 11.1 Introduction -- 11.2 Result and Discussion -- 11.2.1 Solid-State Batteries -- 11.2.2 Ultracapacitor -- 11.2.3 Flywheels. 11.2.4 Pumped Hydroelectric Storage Dams -- 11.2.5 Rail Energy Storage -- 11.2.6 Compressed Storage of Air -- 11.2.7 Liquid Air Energy Storage -- 11.2.8 Pumped Heat Electrical Storage -- 11.2.9 Redox Flow Batteries -- 11.2.10 Superconducting Magnetic Energy Storage -- 11.2.11 Methane -- 11.3 Energy Storage Systems Applications -- 11.3.1 Mills -- 11.3.2 Homes -- 11.3.3 Power Stations and Grid Electricity -- 11.3.4 Air Conditioning -- 11.3.5 Transportation -- 11.3.6 Electronics -- 11.4 Energy Storage Systems Economics -- 11.5 Impacts on Environment by Electricity Storage -- 11.6 Future Prospective -- 11.7 Conclusion -- References -- Chapter 12 State-of-the-Art in Electroceramics for Energy Storage -- 12.1 Introduction -- 12.2 Electroceramics for Energy-Storing Devices -- 12.2.1 Bulk-Based Ceramics -- 12.2.2 Lead-Free Ceramics -- 12.3 Ceramic Multilayers and Films -- 12.4 Ceramic Films for Energy Storage in Capacitors -- 12.5 Conclusion -- References -- Chapter 13 Lead-Free Ceramics for High Performance Supercapacitors -- 13.1 Introduction -- 13.2 Ceramics -- 13.2.1 General Classification of Ceramics -- 13.2.1.1 Ceramic-Based Capacitors -- 13.3 Types of Ceramic Capacitors -- 13.4 Overview of Ceramics for Supercapacitors -- 13.4.1 Metal Oxide Ceramics for Supercapacitors -- 13.4.2 Multi-Elemental Oxide Ceramics for Supercapacitors -- 13.4.2.1 Spinel Oxide Ceramics -- 13.5 Lead-Based Ceramics -- 13.6 Lead-Free Ceramics -- 13.6.1 Analysis of Pb-Free Hybrid Materials for Energy Conversion -- 13.7 Comparison of Pb-Based Ceramics and Pb-Free Ceramics -- 13.8 Conclusion -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910747099803321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Electrospun materials and their allied applications / / edited by Inamuddin [and three others]
| Electrospun materials and their allied applications / / edited by Inamuddin [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Srivener Publishing : , : Wiley, , [2020] |
| Descrizione fisica | 1 online resource (541 pages) |
| Disciplina | 620.197 |
| Soggetto topico | Fibers |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-65513-7
1-5231-3695-2 1-119-65510-2 1-119-65503-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555077803321 |
| Hoboken, New Jersey : , : Srivener Publishing : , : Wiley, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Electrospun materials and their allied applications / / edited by Inamuddin [and three others]
| Electrospun materials and their allied applications / / edited by Inamuddin [and three others] |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2020] |
| Descrizione fisica | 1 online resource (541 pages) |
| Disciplina | 620.197 |
| Altri autori (Persone) | AhamedMohd Imran |
| Soggetto topico | Fibers |
| ISBN |
1-119-65513-7
1-5231-3695-2 1-119-65510-2 1-119-65503-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Electrospinning fabrication strategies : From conventional to advanced approaches / JR Dias, Alexandra I. F. Alves, Carolina A. Marzia-Ferreira and Nuno M. Alves -- History, basics, and parameters of electrospinning technique / Aysel Kantürk Figen -- Physical characterization of electrospun fibers / Anushka Purabgola and Balasubramanian Kandasubramanian. |
| Record Nr. | UNINA-9910677175103321 |
| Hoboken, NJ : , : Wiley, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
