Extractable content of functional acrylic resins produced by radical copolymerization: A comparison of experiment and stochastic simulation
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Authors
Nasresfahani, Amin
Idowu, Loretta A.
Hutchinson, Robin A.
Date
2019-12-15
Type
journal article
Language
Keyword
Copolymer composition distribution , Kinetic Monte Carlo simulation , Radical copolymerization , Starved-feed semi-batch
Alternative Title
Abstract
A comprehensive stochastic simulator that considers all probable secondary reactions essential for the description of high-temperature radical copolymerization of acrylate/methacrylate is implemented based on previously established accelerating techniques. The comparisons between the predictions and six experimental datasets are detailed for semi-batch copolymerization of 2-hydroxyethyl acrylate (HEA) with butyl methacrylate (BMA) under starved-feed condition. Macroscopic properties – free monomer and molar mass (MW) average profiles, final polymer molecular mass distributions, and the variation of acrylate composition with time– are reasonably well predicted over the range of initiator and comonomer levels studied. The simulation output also predicts the weight fraction and MW averages of the polymer chains that contain no HEA functionality, results that are compared to the experimental extractables obtained after crosslinking the copolymer resin. The general trends are well-captured, indicating that the model can be utilized in the future to optimize recipe and operating conditions to minimize the production of the non-functional material.
Description
The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.cej.2019.122087 ©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Citation
Nasresfahani, A., Idowu, L. A., & Hutchinson, R. A. (2019). Extractable content of functional acrylic resins produced by radical copolymerization: A comparison of experiment and stochastic simulation. Chemical Engineering Journal, 378, 122087. doi:10.1016/j.cej.2019.122087
Publisher
Elsevier BV