Multivariate statistical analyses of 1H NMR data for binary and ternary copolymer blends to determine the chemical compositions and blending fractions of the components

Statistical 1H nuclear magnetic resonance (NMR) analyses were conducted with ternary copolymer blends. Two out of the three monomers, acrylonitrile, styrene, and α-methylstyrene, were subjected to radical copolymerization to synthesize three kinds of copolymers that were mixed to prepare binary and ternary copolymer blends. The 1H NMR spectral matrix for the copolymers and their blends (explanatory variables) was combined with the blending parameter matrix (objective variables). Cross-validation with the least absolute shrinkage and selection operator regression confirmed that excellent regression models were constructed with a dataset composed of data for eight copolymers and forty-five binary blends; these were used to predict the blending parameters for the binary blends, such as the chemical compositions and mole fractions of the component copolymers. Accordingly, the models were then used to predict the blending parameters for the ternary blends, which resulted in successful and highly accurate predictions. Other regularized regression models, such as Ridge regression and Elastic Net, were also examined.

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Natureʼs AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41428-023-00794-5