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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| Vydané v: | Polymer (Guilford) Ročník 343; s. 129388 |
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09.01.2026
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| Abstract | 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.
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•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 |
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| AbstractList | 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 |
| ArticleNumber | 129388 |
| Author | Tamakoshi, Reina Kobayashi, Yuzuki Kawatani, Ryo Yoshida, Ken Nakano, Tamaki Torii, Kohei Mizuguchi, Hitoshi Hirano, Tomohiro |
| Author_xml | – sequence: 1 givenname: Tomohiro orcidid: 0000-0003-2144-6151 surname: Hirano fullname: Hirano, Tomohiro email: hirano@tokushima-u.ac.jp organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 2 givenname: Reina surname: Tamakoshi fullname: Tamakoshi, Reina organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 3 givenname: Yuzuki surname: Kobayashi fullname: Kobayashi, Yuzuki organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 4 givenname: Tamaki surname: Nakano fullname: Nakano, Tamaki organization: Institute for Catalysis (ICAT), Hokkaido University, N21W10, Kita-ku, Sapporo, 001-0021 Japan – sequence: 5 givenname: Ryo surname: Kawatani fullname: Kawatani, Ryo organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 6 givenname: Kohei orcidid: 0009-0009-6526-8060 surname: Torii fullname: Torii, Kohei organization: Research Cluster on Development of Data-driven Prediction System for Polymeric Materials, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 7 givenname: Ken orcidid: 0000-0001-8350-5044 surname: Yoshida fullname: Yoshida, Ken organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan – sequence: 8 givenname: Hitoshi surname: Mizuguchi fullname: Mizuguchi, Hitoshi organization: Department of Applied Chemistry, Tokushima University, 2-1 Minamijosanjima, Tokushima 770-8506, Japan |
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| Cites_doi | 10.1038/pj.2012.110 10.1002/pol.1960.1204414315 10.1038/s41428-018-0027-9 10.1002/macp.1995.021961026 10.1016/j.polymer.2009.05.048 10.1016/j.polymer.2024.127467 10.1021/mz4003112 10.1002/pol.1960.1204514526 10.1295/polymj.2.535 10.1038/pj.2012.61 10.1038/pj.2016.16 10.1002/pol.1965.110030804 10.1002/pol.1962.1206217315 10.1038/s41428-023-00794-5 10.1002/actp.1981.010321203 10.1038/s41428-022-00618-y 10.1016/j.polymer.2022.125207 10.1002/pola.11020 10.1021/ma60014a011 10.1021/ma00060a025 10.1080/10236660008034635 10.1007/BF01507429 10.1002/macp.1982.021830119 10.1021/ma50004a080 |
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| Title | Statistical Prediction of Chemical Composition and Monomer Sequences in Methyl Acrylate–Ethyl Acrylate Copolymers by Multivariate Analysis of NMR Spectra |
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