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ID 117324
著者
Kamiike, Ryota Tokushima University|Nippon A&L
キーワード
Copolymer blend
NMR
Multivariate analysis
資料タイプ
学術雑誌論文
抄録
A chemometric approach for the quantitative structural analysis of binary blends of copolymers was conducted. Three types of copolymers were synthesized by radical emulsion copolymerization of two out of three monomers—acrylonitrile, styrene, and α-methylstyrene—to prepare three series of binary blends of these copolymers. Partial least-squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) regression were conducted with datasets in which the 1H nuclear magnetic resonance (NMR) spectral matrix of the binary blends (explanatory variables) is combined with the blending parameter matrix (objective variables) of the binary blends. The blending parameters, such as chemical compositions and mole fractions of the component copolymers, were successfully predicted without any assignments of the 1H NMR signals through stepwise optimization of the objective and explanatory variables. LASSO regression exhibited higher accuracy than PLS regression, suggesting that the variable selection in LASSO regression was responsible for the improvement in the quantitative prediction.
掲載誌名
Polymer
ISSN
00323861
cat書誌ID
AA11537383
出版者
Elsevier
256
開始ページ
125207
発行日
2022-08-08
権利情報
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
EDB ID
出版社版DOI
出版社版URL
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言語
eng
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著者版
部局
理工学系