Statistical determination of chemical composition and monomer sequence distribution of poly(methyl methacrylate-co-tert-butyl methacrylate)s by multivariate analysis of 13C NMR spectra

Abstract

To evaluate chemical compositions and heterogeneity of comonomer sequences in methyl methacrylate (MMA)-tert-butyl methacrylate (TBMA) copolymers, multivariate analysis was applied to the 13C nuclear magnetic resonance (NMR) spectra of the carbonyl, backbone quaternary and α-methyl carbons of the copolymers. A better linear relationship was found between the first principal component score and the chemical composition in copolymers than was found between the results from spectra of the carbonyl and backbone quaternary carbons. The chemical compositions of 16 copolymers were successfully predicted by partial least squares regression (PLSR). The second principal component was found to reflect the fraction of MMA-TBMA hetero dyad sequence. Dyad sequence distributions of copolymers that were obtained at higher conversions were successfully determined by PLSR with those of copolymers obtained at low conversions as a training set. Multivariate analysis using properly prepared samples provided us with quantitative information of chemical compositions and comonomer sequence distributions, without assignment of the 13C NMR resonance peaks. Multivariate analysis was applied to the 13C nuclear magnetic resonance spectra of homopolymers, homopolymer blends and copolymers of methyl methacrylate (MMA) and tert-butyl methacrylate (TBMA). The first (PC1) and second (PC2) principal components reflected chemical composition of the copolymers and fraction of the MMA-TBMA dyad sequence (fMT), respectively. Dyad and triad sequence distributions of copolymers that were obtained at higher conversions were successfully determined by partial least squares regression with those of copolymers obtained at low conversions as a training set.