Stochastic modeling of microstructure of homopolymers and copolymers in batch reactor




Polymers; Modeling; Stochastic; Distributions; Probability.


Find the microstructure of the product generated in a reaction of polymerization is desirable from a material science standpoint, due to the association between the microstructure and the physical properties. For the science of this fact, this paper aims to use stochastic modeling to obtain the microstructure and key information from a set of polymer chains generated during a reaction. From this data, the present article contributes to the minimization of experimental expenses, besides the saving of time, since no experiments are necessary to discover the characteristics of the polymer obtained under certain reaction conditions. This information cannot be found by other usual methodologies for modeling chemical reactions, such as the deterministic form. Also, from a given desired structure, the initial concentration and temperature conditions for forming that product can be obtained. This study was conducted based on Monte Carlo stochastic methods, by which we seek to replicate the randomness present in chemical reactions. The algorithm created in C ++ language determines the variation of the number of molecules of each species with time, besides the chemical composition, the sequence of mere and size of the generated chains. This approach applies to straight-chain homopolymerizations and copolymerizations. In this paper, we studied the polymerization in styrene batch reactors to form polystyrene, in addition to the copolymerization of styrene with alpha-methyl styrene. These simulations were characterized by forming chains with small blocks of monomers.


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How to Cite

CARVALHO, M. D.; BELIIDO, J. D. A.; SIQUEIRA, A. M. de O.; CAMPOS, J. C. C. Stochastic modeling of microstructure of homopolymers and copolymers in batch reactor. Research, Society and Development, [S. l.], v. 9, n. 2, p. e04921930, 2020. DOI: 10.33448/rsd-v9i2.1930. Disponível em: Acesso em: 14 apr. 2024.