This book explores the fundamental properties of a wide range of energy storage and conversion materials, covering mainstream theoretical and experimental studies and their applications in green energy. It presents a thorough investigation of diverse physical, chemical, and material properties of rechargeable batteries, supercapacitors, solar cells, and fuel cells, covering the development of theoretical simulations, machine learning, high-resolution experimental measurements, and excellent device performance.
• Covers potential energy storage (rechargeable batteries and supercapacitors) and energy conversion (solar cells and fuel cells) materials.
• Develops theoretical predictions and experimental observations under a unified quasi-particle framework.
• Illustrate up-to-date calculation results and experimental measurements.
• Describes successful synthesis, fabrication, and measurements, as well as potential applications and near future challenges.
Promoting a deep understanding of the basic science, application engineering, and commercial products, this work is appropriate for senior graduate students and researchers in materials, chemical, and energy engineering and related disciplines.
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Ngoc Thanh Thuy Tran obtained her Ph.D. in Physics in 2017 from the National Cheng Kung University (NCKU), Taiwan. Afterward, she began to work as a postdoctoral researcher and then an assistant researcher at Hierarchical Green-Energy Materials (Hi-GEM) Research Center, NCKU. Her scientific interest is focused on the fundamental (electronic, magnetic, and thermodynamic) properties of 2D materials and rechargeable battery materials by means of the first-principles calculations.
Jeng-Shiung Jan is a professor in the Department of Chemical Engineering, NCKU, Taiwan. He received his PhD in Chemical Engineering in 2006 from Texas A&M University and conducted postdoctoral research at Georgia Institute of Technology. His current research focuses on studying the synthesis of functional polymers and nanomaterials. He received several awards, including Outstanding Professor in Academic Research from LCY Education Foundation and Lai Zaide Professor Award.
Wen-Dung Hsu is a professor in the Department of Materials Science and Engineering, NCKU, Taiwan. His expertise is utilizing computational materials science methods including first-principle calculations, molecular dynamics simulations, Monte-Carlo methods, and finite-element methods to study materials issues. His research interests are mechanical properties of materials from atomic to macro scale, lithium-ion battery, solid-oxide fuel cell, ferroelectrics, solid catalyst design for biodiesel, and processing design for single-crystal growth.
Ming-Fa Lin is a distinguished professor in the Department of Physics, NCKU, Taiwan. He received his PhD in physics in 1993 from the National Tsing-Hua University, Taiwan. His main scientific interests focus on essential properties of carbon-related materials and low-dimensional systems. He is a member of American Physical Society, American Chemical Society, and the Physical Society of Republic of China (Taiwan).
Jow-Lay Huang is working as a chair professor in the Department of Materials Science and Engineering, NCKU, Taiwan. He is the director of the Hierarchical Green-Energy Materials (Hi-GEM) Research Center, NCKU. He received his PhD in Materials Science and Engineering in 1983 from University of Utah. His research interest includes the fabrication, development and application of ceramic nanocomposites, piezo-phototronic thin films for photodetector devices, piezoelectric thin films for high frequency devices, metal oxide/graphene and SiCx nanocomposites as anode materials for lithium-ion battery, and 2D nanocrystal materials for photoelectrochemical application.
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Taschenbuch. Condizione: Neu. Neuware - This book explores energy storage and conversion materials, covering mainstream theoretical and experimental studies and applications. It investigates properties of rechargeable batteries, supercapacitors, solar cells, and fuel cells, theoretical simulations, machine learning, high-resolution experimental measurements, and device performance. Codice articolo 9781032434223
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