| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-03-06 |
| タイトル |
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タイトル |
Computationally complemented insights into new generation solvents for radiation-induced graft polymerization |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Kiho Matsubara
韮塚 徹
Kei Takahashi
Takeshi Matsuda
Mironu Kuroiwa
Omichi Masaaki
Seko Noriaki
Ryohei Kakuchi
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
The adoption of next-generation solvents has lagged behind that of conventional solvents, primarily due to the operational and economic advantages associated with the latter. These advantages stem from the well-established body of knowledge regarding their chemical applications. This disparity creates a reinforcing cycle, where the widespread use of conventional solvents limits the integration of next-generation alternatives. To address this issue, this study focuses on optimizing solvents in radiation-induced graft polymerization (RIGP) processes to advance sustainable materials chemistry. By leveraging machine learning approaches, the study provides practical guidelines for facilitating the transition from conventional to next-generation solvents, effectively transferring chemical insights from established systems to emerging alternatives. |
| 書誌情報 |
Materials Today Chemistry
発行日 2025-03
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| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1016/j.mtchem.2025.102610 |
