2022 Volume 71 Issue 9 Pages 471-482
Multivariate analysis was applied to nuclear magnetic resonance (NMR) spectra of methacrylate copolymers. Principal component analysis (PCA) of 13C NMR spectra of linear copolymers of methyl methacrylate (MMA) and tert-butyl methacrylate (TBMA) successfully extracted information on chemical compositions and monomer sequences. Quantitative analysis of the chemical composition and monomer sequence was achieved by partial least-squares (PLS) regression using NMR spectra of the corresponding homopolymers and their blends as a training dataset. PCA was also useful for the extraction of information on chemical composition in branched copolymers prepared by initiator-fragment incorporation radical copolymerization of TBMA and ethylene glycol dimethacrylate with dimethyl 2,2’-azobis(isobutyrate). The chemical compositions and degree of branching were predicted by PLS regression using NMR spectra of the corresponding homopolymers, their blends and branched copolymers as a training dataset. In addition, PCA was found to be a good measure to evaluate the monomer sequence distribution in linear copolymers of MMA and benzyl methacrylate (BnMA) prepared by various polymer reactions. Furthermore, PCA of 1H NMR spectra of linear copolymers of MMA and BnMA was applied to extract information on chemical compositions and monomer sequences. Monomer reactivity ratios were reasonably estimated from a single sample using the diad sequence distributions predicted by PLS regression.
