徳島大学 教育・研究者情報データベース(EDB)

1. 徳島大学.大学院社会産業理工学研究部.理工学域.応用化学系.物質合成化学分野(2017年4月1日〜)

1. (英) Kamiike Ryota

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2. 平野 朋広([徳島大学.大学院社会産業理工学研究部.理工学域.応用化学系.物質合成化学分野]/[徳島大学.理工学部.理工学科.応用化学システムコース.物質合成化学講座])

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3. 右手 浩一

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(英) Statistical determination of chemical composition and blending fraction of copolymers by multivariate analysis of 1H NMR spectra of binary blends of the copolymers

(英) 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.

(日) アクリロニトリル，スチレンおよびα-メチルスチレンのうちの2つのモノマーからなる2元共重合体を合成し，その2元ブレンドの1H NMRスペクトルの多変量解析により，ブレンドパラメータを推定した．変数を段階的に最適化することにより，NMRシグナルの帰属なしに，共重合体成分の組成とブレンド比率を精度よく推定できることを見出した．