Composition Regulation by Flow Copolymerization of Methyl Methacrylate and Glycidyl Methacrylate with Free Radical Method

Abstract

It is important to match the feeding ratio of comonomers to the composition ratio in the resulting copolymers as closely as possible in industrial production, where the goal is often to produce more a homogeneous composition in copolymers. In this study, a flow copolymerization system with a conventionally initiated free radical method, together with randomly selected polymerization conditions is investigated. It is succeeded in achieving a closer match between the composition ratio and feeding ratio than previously reported in the copolymerization of styrene with methyl methacrylate and of glycidyl methacrylate with methyl methacrylate, which will widen the range of applications, by precisely controlling the mixing and heating in a flow polymerization apparatus. This is confirmed by the fact that the estimated values of reactivity ratios, r 1 and r 2 , which are used in the reaction kinetics of copolymerization, are close to 1.

Bayesian optimization of radical polymerization reactions in a flow synthesis system

“…This apparatus was employed in our previous studies and has been demonstrated to be capable of reproducible and precise synthesis with little variation in polymer properties, meaning that it is suitable for generating data amenable to machine learning applications. 59,60 Various processing parameters (the proportion of St in the raw materials, the proportion of MMA, the amount of initiator, the solvent-to-monomer ratio (SM), the flow rate (which in turn, determined the reaction time) and the reaction temperature) were involved in the synthesis of individual copolymer specimens. During these trials, Bottle-1 contained the St, initiator and solvent while Bottle-2 held a mixture of MMA, initiator and solvent.…”

“…In our previous work, we developed a flow copolymerization system based on a microflow mixer and explored the relationship between process conditions and reactivity ratios in styrene -methyl methacrylate (St-MMA) and glycidyl methacrylate -MMA copolymers. 59 This prior study also incorporated the prediction of physical properties when using unlearned molecules as monomers (that is, molecular extrapolation prediction) based on the use of density functional theory calculations to model MMA-based copolymers. 60 The present research employed BO to identify the optimal conditions for synthesizing St-MMA copolymers with targeted compositions.…”

Bayesian optimization of radical polymerization reactions in a flow synthesis system

“…This apparatus was employed in our previous studies and has been demonstrated to be capable of reproducible and precise synthesis with little variation in polymer properties, meaning that it is suitable for generating data amenable to machine learning applications. 59,60 Various processing parameters (the proportion of St in the raw materials, the proportion of MMA, the amount of initiator, the solvent-to-monomer ratio (SM), the flow rate (which in turn, https://doi.org/10.26434/chemrxiv-2024-9n229 ORCID: https://orcid.org/0000-0002-4601-2432 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4.0 determined the reaction time) and the reaction temperature) were involved in the synthesis of individual copolymer specimens.…”

“…In our previous work, we developed a flow copolymerization system based on a microflow mixer and explored the relationship between process conditions and reactivity ratios in styrene -methyl methacrylate (St-MMA) and glycidyl methacrylate -MMA copolymers. 59 This prior study also incorporated the prediction of physical properties when using unlearned molecules as https://doi.org/10.26434/chemrxiv-2024-9n229 ORCID: https://orcid.org/0000-0002-4601-2432 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4.0 monomers (that is, molecular extrapolation prediction) based on the use of density functional theory calculations to model MMA-based copolymers.…”

Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers

“…We have constructed a flow copolymerization system using a microflow mixer and are investigating copolymerization with more equal composition ratios for MMA–St and MMA–GMA copolymers. 28 In the present work, we synthesized binary copolymers of MMA and GMA/St/4-acetoxystyrene (PACS)/tetrahydrofurfuryl methacrylate (THFMA)/cyclohexyl methacrylate (CHMA) via a free-radical method using the same flow copolymerization system used in our previous work. To more efficiently develop new materials, we used ML predictions to explore the extrapolation region for monomers that would provide desirable polymer properties.…”