Stochastic Modeling of Polymer Microstructure From Residence Time Distribution

Abstract

Stochastic modeling constitutes a powerful technique to obtain complete distributions of microstructural properties of polymer materials and may help to synthesize polymers with well-defined microstructure, as in the case of controlled radical polymerizations (CRP). However, these techniques have been often applied to describe polymerizations performed in batch or idealized continuous plug flow conditions. The present manuscript describes a Monte Carlo technique that can be used to calculate microstructural properties in polymerization systems performed continuously with arbitrary residence time distributions (RTD), under regime of complete micromixing segregation. The technique approximates the continuous system by a set of batch reactors, operated independently, whose volumes and batch times can be related to the discretized version of the RTD. The technique is applied successfully to describe a styrene-based CRP reaction carried out in an ideal continuous stirred tank reactor and a tubular non-ideal tubular reactor.

Modeling the reaction event history and microstructure of individual macrospecies in postpolymerization modification

“…This matrix-based solution strategy is related to the one used in previous studies on the detailed simulation of individual (linear) chains as formed during polymerization. [45][46][47][48][49][50][51] With extra matrix elements it additionally tracks the complete reaction event history of every polyolefin based macrospecies, for example, the number of H abstractions it underwent. It can be understood that this matrix needs to be sufficiently large to be kinetically representative and therefore requires a high computer memory.…”

Deterministic Approaches for Simulation of Nitroxide-Mediated Radical Polymerization

“…Recent reviews summarize relevant works using MC methods in polymer science [21,22]. In NMP, these methods have been used to predict copolymer SLD [10,11,23,24], chain functionality and the full MWD [25], kinetics of branching [26], and bivariate MWD-CCD in continuous processes with arbitrary residence time distributions [27] or to track the exact position of functional comonomers in the copolymer chains [28]. Besides, the model-based design of copolymer synthesis by NMP using MC models has also been performed [11,28,29].…”

Modeling of sequence length distribution for olefin copolymerization with vanadium‐based catalyst

“…At present, SLD modeling methods can be divided into two classes, namely probabilistic and deterministic methods. Probabilistic methods, which consider the relative probabilities of different comonomers inserting into the polymer chain, include direct probabilistic methods, triad sequence distribution approaches and kinetic Monte Carlo methods . Although these methods are easy to implement, reaction history information is sometimes ignored, which may result in inaccurate predictions.…”