| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-02-26 |
| タイトル |
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タイトル |
Bayesian optimization of radical polymerization reactions in a flow synthesis system |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Polymer |
| キーワード |
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主題Scheme |
Other |
|
主題 |
flow synthesis |
| キーワード |
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主題Scheme |
Other |
|
主題 |
radical polymerization |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Bayesian optimization |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Styrene |
| キーワード |
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主題Scheme |
Other |
|
主題 |
methyl methacrylate |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
高須賀, 聖五
Ito, Sho
Oikawa, Shunto
原嶋, 庸介
高山, 大鑑
Nag, Aniruddha
Wakiuchi, Araki
安藤, 剛
Sugawara, Tetsunori
Hatanaka, Miho
宮尾, 知幸
松原, 崇充
Ohnishi, Yu-ya
網代, 広治
藤井, 幹也
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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 |
Proportions of monomers in a copolymer will greatly affect the properties of materials. However, due to a phenomenon known as composition drift, the proportions of monomers in a copolymer can deviate from the value expected from the raw monomer ratio because of differences in monomer reactivity. It is therefore necessary to optimize the polymerization process to account for such composition drift. In the present study, styrene-methyl methacrylate copolymers were generated using a flow synthesis system and the processing variables were tuned employing Bayesian optimization (BO) to obtain a target composition. First trials of BO with generation of four candidate points per cycle, completed the optimization within five cycles. Subsequent Bayesian Optimization (BO) trial, using 40 points per cycle, identified several sets of processing conditions that could achieve the desired copolymer composition, accompanied by variations in other physical properties. To optimize the monomer composition ratio in the polymer, it was discovered from a data science perspective that the solvent-to-monomer ratio was as crucial as the styrene proportions. The role of each variable in the radical polymerization reaction was elucidated by assessing the extensive array of processing conditions while evaluating several broad trends. The proposed model confirms that specific monomer proportions can be produced in a copolymer using machine learning while investigating the reaction mechanism. In the future, the use of multi-objective BO to fine-tune the processing conditions is expected to allow optimization of the copolymer composition together with adjustment of physical properties. |
| 書誌情報 |
en : Science and Technology of Advanced Materials: Methods
巻 4,
号 1,
ページ数 14,
発行日 2024-11-28
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| 出版者 |
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出版者 |
Taylor and Francis |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2766-0400 |
| 出版者版DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1080/27660400.2024.2425178 |
| 出版者版URI |
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|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
URI |
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関連識別子 |
https://www.tandfonline.com/doi/full/10.1080/27660400.2024.2425178 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc/4.0/ |
|
権利情報 |
© 2024 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), whichpermits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has beenpublished allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
| 著者版フラグ |
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出版タイプ |
VoR |
| 助成情報 |
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助成機関名 |
New Energy and Industrial Technology Development Organization (NEDO) |
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研究課題番号 |
JPNP14004 |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
JP21K20537 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K20537/ |
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研究課題名 |
異種材料データの統合による未知材料創出 |
fulltext (8.5 MB)
