Parameter Estimation and Kinetic Monte Carlo Simulation of Styrene and n-Butyl Acrylate Copolymerization through ATRP

Abstract

Controlled polymerization causes the final product to have predictable properties, allowing it to have a more diverse application, in turn, increasing its added value. In the present work, a dynamic Monte Carlo (MC) model is used to describe the copolymerization of styrene and n-butyl acrylate through atom transfer radical polymerization (ATRP). A further investigation was performed to estimate the values of kinetic constants with no explicit value reported in the literature using particle swarm optimization (PSO). The model had excellent performance when compared with the experimental data from the literature. The model also provided access to the molecular weight and chemical composition distributions and the polymer microstructures. The results show that the propagation and termination constants' difference affected the polymer structure directly and significantly impacted the distribution curves' broadening. The developed model might be used in the future for monitoring and controlling the average molecular weights and the molecular weight distributions in the chemical process industry.

Active Learning as a Tool for Optimizing “Plug‐n‐Play” Electrochemical Atom Transfer Radical Polymerization

“…[20,21] The use of simulation models to study polymerization reactions has also developed over the last decade. [22][23][24] When coupled to external electric field control, the complexity of the reaction is intensified. From an operational point of view, this has been addressed through the development of simplified electrochemical atom transfer radical polymerization (seATRP) [25] which employs sacrificial electrodes to enable 3-electrode (potential controlled) or 2-electrode (current controlled) configurations in undivided electrochemical cells.…”

Precision of Architecture-Controlled Bottlebrush Polymer Synthesis: A Monte Carlo Analysis

“…In the recent years, there has been a growing interest in better understanding the polymer microstructure using stochastic or kinetic Monte Carlo modeling-based approaches. − There have been several models that provide the chain length distribution for given set of parameters related to a polymerization process, whether it may be for modeling (co)­polymerization kinetics, surface-initiated polymerizations (SIP), , or radical-based polymerization processes. − Though these simulations provide a better understanding of the effect of synthesis conditions on the polymer microstructure, they provide no perspective when it comes to complex polymers like architecture-controlled bottlebrush polymers.…”

Kinetic Monte Carlo Tool for Kinetic Modeling of Linear Step‐Growth Polymerization: Insight into Recycling of Polyurethanes

“…[35] First developed by Gillespie in the context of well-mixed gas phase chemical reactions, kMC has since been applied to systems ranging from vacancy diffusion to thin film growth to free radical polymerization. [35][36][37][38][39][40][41][42][43][44] A fundamental assumption of kMC is that the system is well-mixed, which is most likely to be violated by polymer entanglement at high molecular weight. [36,41] That is, in the limit of very high conversion, the process may no longer be kinetically limited and therefore the predictions of the kinetic model may need to be coupled with a model of physical phenomena like diffusion to be accurate.…”

“…[35][36][37][38][39][40][41][42][43][44] A fundamental assumption of kMC is that the system is well-mixed, which is most likely to be violated by polymer entanglement at high molecular weight. [36,41] That is, in the limit of very high conversion, the process may no longer be kinetically limited and therefore the predictions of the kinetic model may need to be coupled with a model of physical phenomena like diffusion to be accurate. Nonetheless, kMC has been demonstrated to accurately predict polymerization and depolymerization results.…”

“…Nonetheless, kMC has been demonstrated to accurately predict polymerization and depolymerization results. [41,45,46] Here, we develop a robust, tunable kinetic Monte Carlo framework that tracks the explicit sequences, i.e., not just the average sequence lengths, of the polymer chains during polymerization, enabling straightforward access to sequences, for arbitrary combinations of monomers and reaction conditions. With this approach, we may specify unequal reactivity of different monomers, time variable feed composition, additional side reactions, or reverse reactions.…”