A Data-Driven approach to the Generalization of Free Radical Polymerization Kinetic Models via Automated Flow Chemistry

Abstract

Systematic kinetic screening of chain transfer radical polymerizations was carried out using a continuous flow-based automated synthesis platform tailored for high-throughput screening of polymer reactions. The system features real-time online monitoring of monomer conversion and molecular weight distributions of residual polymers, enabling the generation of consistent, machine-readable kinetic datasets while minimizing user bias and experimental variability. A consistent dataset was obtained for the homopolymerization of butyl acrylate (BA), vinyl acetate (VA) and methyl methacrylate (MMA) mediated by 1-dodecanethiol as the chain transfer agent, across a temperature range of 70 °C to 100 °C. A highly consistent dataset was obatined, allowing the determination of the respective chain transfer constants for each monomer at each temperature. On the test case of vinyl acetate polymerization, a generalized kinetic model for the rate of polymerization in the given parameter space was created via fitting of the individual overall kinetic coefficients for the rate of polymerization, obtained from 1st order kinetic data analysis. Bayesian optimization was then applied to predict which experimental conditions have the best potential to gradually improve the kinetic model, and interactive model improvement is demonstrated. This provides an important stepping stone for the development of self-driving labs that use databases to autonomously pick future experiments to carry out in order to improve its own data basis.