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SSRN 2024年

作者： Hirano, Tomohiro Kamiike, Ryota Ute, Koichi Department of Applied Chemistry Tokushima University 2-1 Minamijosanjima Tokushima770-8506 Japan R&D Center Nippon A&L Inc. 2-10-2 Kikumoto Ehime Niihama792-0801 Japan Research Cluster on Development of Data-driven Prediction System for Polymeric Materials Tokushima University 2-1 Minamijosanjima Tokushima770-8506 Japan

Acrylonitrile (AN) was radically copolymerized with styrene (ST) or α-methylstyrene (MS) to prepare bipolymers with different compositions. All three comonomers were used to prepare terpolymers with different compositions. The three kinds of copolymers were mixed to prepare polymer blends. Statistical analysis of data derived from nuclear magnetic resonance (NMR) spectra of the polymer blends was conducted to determine the blending parameters, such as the chemical composition and mole fraction of the component copolymers. Partial least-squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) regression were used as the exploratory techniques. The use of 1H NMR spectral data as explanatory variables showed poor prediction, probably because of low resolution due to the narrow spectral width. Accordingly, 13C NMR spectra, which have wider spectral widths, were used as the explanatory variables, resulting in the successful prediction of the blending parameters of the terblends containing a terpolymer as a component. It should be noted that it was not necessary to obtain the NMR spectra of all the polymer blends as the explanatory variables, i.e., linear combinations of the NMR spectra of the component copolymers were sufficient. © 2024, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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