Two-dimensional Metal Halide Perovskites: A Machine-generated Literature Overview - Rilegato

Rilegato

ISBN 10: 9819978297 ISBN 13: 9789819978298

Casa editrice: Springer Nature, 2024

Riassunto

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. 

It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Su questo libro

Informazioni sull'autore

Dr. John S. Colton completed his undergraduate studies at Brigham Young University, graduating with B.S. degrees in Physics and Mathematics in 1994. He received his Ph.D. in condensed matter physics from U.C. Berkeley in 2000. Later, he was a postdoctoral researcher at the Naval Research Laboratory in Washington, D.C. from 2001 to 2003, studying optically detected and controlled semiconductor spintronics. He joined the faculty at University of Wisconsin-La Crosse from 2003 to 2007, after which he returned again to Brigham Young University, as faculty. John’s research throughout his career has focused on studying semiconductors through optical methods. Since 2021, he has focused on hybrid organic-inorganic metal halide perovskites. In addition to research, John also enjoys teaching and has won several awards, including receiving a 2023 Outstanding Performance Award from BYU’s Department of Physics and Astronomy. John was also appointed as a 2017 BYU Alcuin Fellow and selectedas the 2006-2007 Wisconsin Teaching Fellow from UW-La Crosse. 

Dr. Kameron R. Hansen graduated from Brigham Young University with a B.S. in Physics and a minor in Mathematics, where  he studied quantum dots and wrote an undergraduate thesis on synthesis procedures to grow various types of quantum dots inside spherical proteins. Later, he completed his M.S. degree in chemical physics from Columbia University and was awarded an NSF Graduate Research Fellowship. He worked in Dr. Xiaoyang Zhu's laboratory, researching next generation semiconductors, such as metal-halide perovskites and monolayer transition metal dichalcogenides. Most recently, Kameron earned his Ph.D. in physical chemistry under the supervision of Dr. Luisa Whittaker Brooks (Utah, Chemistry) while closely collaborating with Dr. John S. Colton (BYU, Physics). His doctoral and postdoctoral research is multidisciplinary as he utilizes both experimental and computational approaches to study two-dimensionalperovskites.

Dalla quarta di copertina

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to thetopics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. 

It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9819978297
• ISBN 13: 9789819978298
• Rilegatura: Copertina rigida
• Numero di pagine: 228
• Redattore: Colton John S., Hansen Kameron R.
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania