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  1. 原著論文

Machine Learning Approach for Prediction of the Grafting Yield in Radiation-Induced Graft Polymerization

https://repo.qst.go.jp/records/84792
https://repo.qst.go.jp/records/84792
9fff72c0-983d-4d25-a152-4afcb7f40154
Item type 学術雑誌論文 / Journal Article(1)
公開日 2021-08-16
タイトル
タイトル Machine Learning Approach for Prediction of the Grafting Yield in Radiation-Induced Graft Polymerization
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
アクセス権
アクセス権 metadata only access
アクセス権URI http://purl.org/coar/access_right/c_14cb
著者 Yuuji, Ueki

× Yuuji, Ueki

WEKO 1023103

Yuuji, Ueki

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Noriaki, Seko

× Noriaki, Seko

WEKO 1023104

Noriaki, Seko

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Yasunari, Maekawa

× Yasunari, Maekawa

WEKO 1023105

Yasunari, Maekawa

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Yuuji, Ueki

× Yuuji, Ueki

WEKO 1023106

en Yuuji, Ueki

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Noriaki, Seko

× Noriaki, Seko

WEKO 1023107

en Noriaki, Seko

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Yasunari, Maekawa

× Yasunari, Maekawa

WEKO 1023108

en Yasunari, Maekawa

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抄録
内容記述タイプ Abstract
内容記述 Grafting yields for the radiation-induced graft polymerization of a methacrylate ester monomer to give a polyethylene-coated polypropylene nonwoven fabric were predicted as an objective variable by a machine learning approach. The degrees of grafting were obtained from actual experiments. Monomer structure information, atomic charge information, atomic NMR shift information, and infrared absorption wavenumber information, derived from density functional theory calculations, were adopted as explanatory variables of a grafting yield prediction model. Among machine learning algorithms as a prediction model on the grafting yield, XGBoost and random forest models showed higher prediction accuracy, compared to a multiple linear regression model. The prediction accuracies of the various algorithm decreased in the order: XGBoost > random forest > multiple linear regression/LASSO > decision tree > multiple linear regression. The monomer polarizability and the O2 NMR shift were found to be important explanatory variables for predicting the grafting yield in the XGBoost model. This is probably because the polarizability, which represents a miscibility indicator of the monomer to the trunk polymer, and the O2 NMR shift, which represents a diffusivity indicator of the monomer into the trunk polymer, remarkably reflect the difference in the substituent structure of the methacrylate ester monomers.
書誌情報 Applied Materials Today

巻 25, p. 101158-1-101158-9, 発行日 2021-09
出版者
出版者 Elsevier
ISSN
収録物識別子タイプ ISSN
収録物識別子 2352-9407
DOI
識別子タイプ DOI
関連識別子 10.1016/j.apmt.2021.101158
関連サイト
識別子タイプ URI
関連識別子 https://www.sciencedirect.com/science/article/abs/pii/S2352940721002225?via%3Dihub
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