Li Y. Applied Metallomics. From Life Sciences to Environmental Sciences 2024

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Li Y. Applied Metallomics. From Life Sciences to Environmental Sciences 2024

Textbook in PDF format Applied Metallomics: A groundbreaking survey of a field that unites the sciences. The metallome of a cellular compartment, such as an enzyme, is the variety and arrangement of its metal ions. Metallomics is the multidisciplinary study of the metallome and its many important interactions with biological molecules and systems. It exists at the intersection of biochemistry and materials science, offering crucial insights into biological processes in which iron, for instance, plays a pivotal role. Applied Metallomics is an up-to-the-minute overview of research developments in metallomics, offering both analysis and applications in a vast array of scientific and industrial areas. Moving freely between material science, environmental science, health science, and more, it offers a comprehensive survey of this interdisciplinary research area. As the field of metallomics continues to develop and its applications expand, this book will only be a need of the hour. Applied Metallomics readers will also find: Detailed treatment of nanometallomics, environmetallomics, agrometallomics, and many more. Coverage of machine learning and artificial intelligence techniques with applications in metallomics. An author team with vast international research experiences. Applied Metallomics is ideal for researchers in many areas touched by metallomics, that include chemistry, biochemistry, biotechnology, bioinorganic chemistry, and more. Introduction Nanometallomics Environmetallomics Agrometallomics Metrometallomics Medimetallomics and Clinimetallomics Matermetallomics Archaeometallomics Metallomics in Toxicology Pathometallomics: Taking Neurodegenerative Disease as an Example Oncometallomics: Metallomics in Cancer Studies Bio-elementomics Methodology and Tools for Metallomics ICP-MS for Single-Cell Analysis in Metallomics Novel ICP-MS-based Techniques for Metallomics Machine Learning for Data Mining in Metallomics