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This book presents recent developments and fascinating attributes of electronic-nose (E-nose) technology based on advanced nanostructured materials that are compatible with modern semiconductor fabrication technology. The new trend in these nanostructured materials includes metal oxide nanostructures, 2D materials, graphene, and other carbon-based nanostructures. These versatile materials with remarkable electronic, mechanical, electrical, thermal properties, and potential e-nose in a wide range of fields are discussed in the book. Close attention is given to problems connected with stability, selectivity, and functionalizing of e-nose devices and deposited materials. The field of sensors is indeed in fast and consistent growth. This book consolidates information on each and every aspect of these novel materials, their gas sensing mechanisms, engineering, and e-nose devices covering the entire spectrum of up-to-date literature citations, current market, and patents in a very detailed manner. Moreover, in the past few years, tremendous advances have been made in experimental and theoretical studies on various properties of these materials. In summary, this book makes an attempt to provide in-depth study of recent advances and focuses on current problems related to selectivity and stability of sensor devices. The book is interesting to all materials professionals and researchers from universities and national laboratories working in the field of nanostructured materials-based gas sensors.

Nanoscience. --- Materials. --- Detectors. --- Nanotechnology. --- Condensed matter. --- Nanophysics. --- Sensors and biosensors. --- Nanoengineering. --- Two-dimensional Materials.

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This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Optics. --- Machine learning. --- Materials. --- Detectors. --- Tumor markers. --- Photonics. --- Optical engineering. --- Optics and Photonics. --- Machine Learning. --- Sensors and biosensors. --- Tumour Biomarkers. --- Photonics and Optical Engineering. --- Photonic Devices. --- Artificial Intelligence --- Materials --- Oncology --- Microwaves --- Optics --- Computers --- Technology & Engineering --- Medical --- Science

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This book provides a unique approach to understand the Nuclear Physics, especially from the experimental end. The highlight of this book is that special care has been taken to provide more experimental information, considering real experimental data which has been published in several journals. Special experimental focus is given to methodologies involving: offline gamma counting and online particle detection. The book provides information about recent developments in accelerators, overview of the detectors and concise information of associated electronics, data acquisition systems and computers for data analysis.

Measuring methods in physics --- Nuclear physics --- Physics --- Applied physical engineering --- Chemical technology --- meetmethoden --- toegepaste wetenschappen --- meettechniek --- ingenieurswetenschappen --- fysica --- atoomfysica

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This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Optics. Quantum optics --- Electronics --- Applied physical engineering --- Programming --- photonics --- programmeren (informatica) --- optica --- Optics. --- Machine learning. --- Materials. --- Detectors. --- Tumor markers. --- Photonics. --- Optical engineering. --- Optics and Photonics. --- Machine Learning. --- Sensors and biosensors. --- Tumour Biomarkers. --- Photonics and Optical Engineering. --- Photonic Devices. --- Artificial Intelligence --- Materials --- Oncology --- Microwaves --- Optics --- Computers --- Technology & Engineering --- Medical --- Science