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A kinetic Monte Carlo model of polyurethane polymerization which explicitly tracks the polymer sequences is developed and shared. This model is benchmarked against theoretical and experimental polyurethane data and used to investigate the effect on oligomer distributions of unequal reactivity of the first and second isocyanate to react. The reverse reactions using thermodynamic consistency are then added to the framework, and analogous to the addition polymerization concept of ceiling temperature, equilibrium chain length distributions at various temperatures are calculated. For a mixture of three monomers AA, BB, and CC, where BB and CC do not react with one another, are present in stoichiometric proportions, and have different enthalpies of reaction with AA, an odd‐even effect emerges. Odd length chains are more likely than even length chains for temperatures at which BB and CC have significantly different equilibrium conversions. The concept of ceiling temperature that is typically cited for addition polymers is extended here to provide a measure of conditions under which depolymerization for recycling is favored. [ABSTRACT FROM AUTHOR]

Kinetic Monte Carlo (KMC) has been widely used in the simulation of polymeric reactions. The power of KMC is highlighted by its ability to keep track of the length and sequence of every radical or polymer chain, while it is computationally more expensive than deterministic kinetic models. This paper introduces an acceleration method that significantly reduces the computational cost of KMC simulations, while keeping the same features as the full kinetic Monte Carlo simulations. Case studies are used to demonstrate the general applicability of this method to free radical copolymerization. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4013-4021, 2017 [ABSTRACT FROM AUTHOR]

It is challenging to track explicit copolymer sequences and related measures using continuum kinetic models. We developed a generalized framework based on kinetic Monte Carlo (KMC) to model free radical polymerization of any number of monomers to obtain the explicit sequence and its distribution. The KMC framework was applied to copolymerization of butyl acrylate/methyl methacrylate (BA/MMA) and methyl acrylate/methyl methacrylate (MA/MMA), and the simulation output was compared with experimental results. Sequence characteristics that were not accessible via experiments were then predicted. Explicit monomer sequence information was used to construct operating diagrams to design a representative copolymer recipe with a specific sequence target as a function of molecular weight and monomer conversion. [ABSTRACT FROM AUTHOR]

Stochastic mechanistic models for gradient copolymerisation systems were developed based on a kinetic Monte Carlo algorithm. Due to the discrete nature of the simulations, chains were tracked as binary strings allowing for the storage of the complete sequence of each polymer chain, allowing for an unprecedented level of detail. Models were developed that simulated styrene/4-acetoxystyrene semi-batch gradient copolymer syntheses, and explicit sequence information was determined using simulation results. The chemical composition distribution was mapped for the copolymers, which is capable of providing a visual description of both the size and overall composition distributions of the copolymer and a qualitative description of the chain architecture. This methodology was expanded to track the explicit sequences of each chain that was used to determine the number and weight fraction sequence length distributions. Simulation results show that tail end compositional tapering was never fully achieved. Case studies were conducted to determine the major factors affecting tail end tapering, including increasing the initial batch fraction and varying the 4-acetoxystyrene flow rate. While an increase in initial batch fraction increases 4-acetoxystyrene significantly, head end tapering is lost, while a large increase in flow rate is not capable of fully tapering the tail end. [ABSTRACT FROM AUTHOR]

A multiscale modeling approach was developed to capture concentration variations in the fluid in two dimensions for catalytic flow reactors. The methodology couples continuum descriptions of the fluid phase and kinetic Monte Carlo simulations of the catalyst domain. A number of catalytic domains, placed as patches along the length of the reactor, were solved using kinetic Monte Carlo (kMC) and linked with a finite difference (FD) solver for the fluid phase. Patch dynamics concepts, such as lifting, restriction and interpolation, were employed to provide the complete set of boundary conditions to the continuum solver. A simple kinetic mechanism involving adsorption, desorption and a single-step surface reaction was used to validate the approach by comparing the solution obtained using the multiscale scheme with a model solved using a wholly implicit solution. Solutions from a mean-field model and the multiscale scheme for a system in which surface diffusion was low were then contrasted. [ABSTRACT FROM AUTHOR]

The diversity of the potential arrangements of multiple monomers along the length of polymer chains and their impact on polymer properties spark interest in the design of polymer sequence characteristics for particular applications. Kinetic Monte Carlo (KMC) is a technique that can track the explicit arrangement of monomers in the polymer chains, yet it is difficult to integrate with conventional gradient-based optimization algorithms that are typically invoked to design polymer properties. In this work, we applied and compared derivative-free optimization algorithms to incorporate KMC simulations and find synthesis conditions for achieving property targets and minimizing reaction time, advancing our ability to carry out the design of polymer microstructures and control polymerization processes. [ABSTRACT FROM AUTHOR]

The modeling of the molecular weight distribution of polymers is critical in polymerization modeling. Moment-based models and kinetic Monte Carlo are two methods used to calculate the number and weight average molecular weight during simulation of polymerization. While these two methods are commonly considered to yield equivalent results in the molecular weight distribution, we show that there can be significant differences between these two methods when copolymerization of multiple monomers is modeled. In particular, clear differences in results exist for low molecular weight polymers where the molar mass of one monomer is two or more times greater than the others. Detailed analysis is provided for the cause of the difference, and we demonstrate the importance of tracking the exact number of each type of monomer in every polymer chain when calculating the moments of molecular weight. [ABSTRACT FROM AUTHOR]

Abstract: Conventional continuum mechanistic models for polymer degradation typically involve thousands of coupled differential-algebraic equations, requiring an efficient solver to solve the complex set of stiff model equations. This can be overcome by formulating the problem in terms of a stochastic simulation procedure, requiring only iterative operations to solve the model. The present work describes the detailed mechanistic modeling of pyrolysis of poly(styrene peroxide) (PSP) using kinetic Monte Carlo (KMC) simulation to predict the product yields and gain a better understanding of the product evolution pathways. The traditionally accepted mechanism of PSP pyrolysis proposed by Mayo and Miller, which involves the key reaction steps of peroxide bond fission, alkoxy radical recombination and disproportionation, and end chain β-scission, was initially tested using the KMC model to predict the peroxide concentration profile and the product yields. This model was only qualitatively able to predict the major products, benzaldehyde and formaldehyde, while the formation of minor products like α-hydroxy acetophenone, phenyl glycol, and phenyl glyoxal was not captured at all. Hence, a new mechanism that also incorporated hydrogen abstraction and β-scission was proposed and implemented in KMC. The final model tracked 949 reactions of 83 species. The rate coefficients for all the reaction steps were based on the existing literature reports, and hence no parameter estimation was done to fit the model against the experimental data. The revised model was quantitatively able to predict all the products of PSP pyrolysis, which was attributed to the stabilization of the alkoxy radicals by hydrogen abstraction, and the subsequent generation of additional alkoxy radicals by β-scission. KMC allowed the dominant pathways for the formation of minor products and dimers to be identified explicitly. Finally, the implications of this study in understanding the effect of trace peroxide bonds on poly(styrene) pyrolysis are outlined. [Copyright &y& Elsevier]