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Fully virtual exploration of cellulose solvents
http://hdl.handle.net/10458/0002001521
http://hdl.handle.net/10458/000200152151cf8bdc-6c46-4a32-9e40-2dbbed664bb6
| 名前 / ファイル | ライセンス | アクション |
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||||
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| 公開日 | 2025-07-21 | |||||||||||||||||||
| タイトル | ||||||||||||||||||||
| タイトル | Fully virtual exploration of cellulose solvents | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| キーワード | Cellulose | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Ionic liquid | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Solubility prediction | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Dissolution mechanism | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Molecular dynamics | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Machine learning | |||||||||||||||||||
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| 言語 | en | |||||||||||||||||||
| キーワード | Computer-aided molecular design | |||||||||||||||||||
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| 資源タイプ | journal article | |||||||||||||||||||
| アクセス権 | ||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||
| 著者 |
Kaneko, Yu
× Kaneko, Yu
× Okiba, Satoshi
× Isobe, Yutaka
× Arai, Takashi
× 宇都, 卓也
WEKO
21867
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| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | Cellulose is a very important renewable resource because of its abundance in nature. However, it is known to have poor solubility and processability owing to its high crystallinity. In the present study, molecular dynamics (MD) simulations and machine learning (ML) were used to design solvents with high cellulose dissolving power by gaining deep mechanistic insights into the dissolution mechanism. High-throughput MD simulations were performed to evaluate the number of hydrogen bonds in cellulose crystals after dissolution, as the cellulose solubility, in various imidazolium-based ionic liquids (3,200 molecules) generated by ML models. Furthermore, the cation–anion distribution in the ionic liquid and the interaction energy between the cellulose molecular chains in the crystal after dissolution were used as metrics to characterize the cellulose solubility to improve the prediction performance of ML models. Several functional groups (methyl, ethyl, allyl, chloro, fluoro, and methoxymethyl groups) on the side chain of the imidazolium cation were identified to be effective for increasing the cellulose solubility, and a new series of powerful cellulose solvents is proposed. | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 書誌情報 |
en : ChemRxiv 発行日 2025-04-02 |
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| 出版者 | American Chemical Society (ACS) | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
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| 関連タイプ | isVersionOf | |||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | https://doi.org/10.26434/chemrxiv-2025-fhkh3 | |||||||||||||||||||
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| 出版タイプ | VoR | |||||||||||||||||||
