Statistical Prediction of Chemical Composition and Monomer Sequences in Methyl Acrylate–Ethyl Acrylate Copolymers by Multivariate Analysis of NMR Spectra

Quantitative characterization of copolymer microstructures, including chemical composition and monomer sequences, is essential because these primary structures strongly influence material properties. In this study, we applied multivariate analysis to NMR spectra of methyl acrylate (MA)–ethyl acrylat...

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Bibliographic Details
Published in:Polymer (Guilford) Vol. 343; p. 129388
Main Authors: Hirano, Tomohiro, Tamakoshi, Reina, Kobayashi, Yuzuki, Nakano, Tamaki, Kawatani, Ryo, Torii, Kohei, Yoshida, Ken, Mizuguchi, Hitoshi
Format: Journal Article
Language:English
Published: Elsevier Ltd 09.01.2026
Subjects:
Monomer sequence
Multivariate analysis
NMR
Multivariate analysis
Monomer sequence
NMR
ISSN:0032-3861
Online Access:Get full text
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Summary:Quantitative characterization of copolymer microstructures, including chemical composition and monomer sequences, is essential because these primary structures strongly influence material properties. In this study, we applied multivariate analysis to NMR spectra of methyl acrylate (MA)–ethyl acrylate (EA) copolymers to predict their chemical composition and monomer sequences. By applying partial least squares (PLS) regression, the MA composition was successfully predicted from 13C NMR spectra. 1H NMR spectra also provided satisfactory predictions, but 13C NMR spectra yielded more accurate results, likely because the chemical shifts of the 13C signals of the individual monomeric units were more distinct, despite spectral overlap creating an apparently featureless line shape. Monomer sequences, particularly the hetero-diad sequence, were difficult to predict using 1H NMR spectra, whereas the use of 13C NMR spectra markedly improved the prediction accuracy owing to their wider chemical shift range. This level of primary structural detail in acrylate copolymers cannot be achieved using conventional NMR analysis that relies on peak assignment and integration. These findings demonstrate that NMR spectroscopy combined with statistical multivariate analysis offers a powerful approach for determining the primary structures of acrylate copolymers. [Display omitted] •Multivariate analysis of 1H and 13C NMR spectra of MA–EA copolymers was performed•PLS regression enabled prediction of chemical composition and monomer sequences•MA composition was predicted from both spectra despite severe signal overlaps•Monomer sequences were accurately predicted at diad level using 13C NMR spectra•Predictions from 1H NMR spectra were less reliable due to a narrower chemical shift range
ISSN:0032-3861
DOI:10.1016/j.polymer.2025.129388