GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

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第一作者： Kiho,Matsubara

第一单位： Division of Molecular Science, Faculty of Science and Technology, Gunma University, 1-5-1 Tenjin, Kiryu, Gunma, 376-8515, Japan.

作者： Kiho,Matsubara ^[1] ; Kei,Takahashi ^[2] ; Takeshi,Matsuda ^[3] ; Yuji,Ueki ^[4] ; Noriaki,Seko ^[5] ; Ryohei,Kakuchi ^[5]

作者单位： Division of Molecular Science, Faculty of Science and Technology, Gunma University, 1-5-1 Tenjin, Kiryu, Gunma, 376-8515, Japan. ^[1] Faculty of Information Engineering, Fukuoka Institute of Technology, 3-30-1, Wajiro-higashi, Higashiku, Fukuoka, 811-0295, Japan. ^[2] School of Statistical Thinking, The Institute of Statistical Mathematics, Midoricyo10-3, Tachikawa-City, Tokyo, 190-8562, Japan. ^[3] Faculty of Management and Information, Hannan University, 5-4-33, Amami, Higashi, Matsubara, Osaka, 580-8502, Japan. ^[4] Department of Advanced Functional Materials Research, Takasaki Institute for Advanced Quantum Science, National Institutes for Quantum Science and Technology (QST), 1233 Watanuki-machi, Takasaki, Gunma, 370-1292, Japan. ^[5]

关键词 GFN-xTB emulsion conditions explicit solvation models machine learning radiation-induced graft polymerization

DOI 10.1002/cplu.202300480

PMID 37906113

发布时间 2024-04-18

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2024年89卷4期

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GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

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GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

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