Stochastic modelling of gradient copolymer chemical composition distribution and sequence length distribution

Cho, Andrew S.; Broadbelt, Linda J.

doi:10.1080/08927020903513035

Cited by 15 publications

(10 citation statements)

References 44 publications

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“…At %A ¼ %B ¼ 50% (Figure 2(e)), a second mode in the SLD begins to manifest itself, becoming more distinct with decreasing %A in the cases where %A < %B (Figure 2(f)-(i)). These results are consistent with those of Cho and Broadbelt for kinetic Monte Carlo modeling of the SLD in gradient copolymers, [12] which are copolymers in which the monomer ratio (e.g., the ratio of A to B) changes gradually and unidirectionally as a function of copolymer molar mass [5,18] or where the local compositional gradient is the first derivative of composition with respect to degree of polymerization. As N increases, this second distribution both becomes narrower (less disperse) and extends to increasingly longer segment lengths.…”

Section: Monte Carlo Simulation In Random Copolymers 251supporting

confidence: 88%

Monte Carlo Simulation of the Sequence Length and Junction Point Distributions in Random Copolymers Obeying Bernoullian Statistics

Dong

Striegel

2012

International Journal of Polymer Analysis and Characterization

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The sequence length and junction point distributions (SLD and JPD, respectively) in random copolymers influence processing and end-use properties such as thermal transitions, chain conformation, and elastic properties. There are currently no experimental methods to determine SLD. As such, information on this type of distribution can be accessed, at present, only through models and simulations. Here, we present the results of Monte Carlo simulations of the SLD and the JPD of generic AB random copolymers with Bernoullian distributions of interest and with individually varying chain length (degree of polymerization) and monomer ratio. Results from the simulations show, among other things, the development of bimodality in the SLD of a particular monomer as a function of increasing percent of that monomer in the copolymer and the development of JPD as a function of varying monomer ratios, and that, in select cases, the JPD based on an even number of junction points is much larger than the corresponding distribution based on an odd number of junction points. The results presented should help guide the design and optimization of ''interactive'' macromolecular separation methods capable of determining the SLD and JPD of random copolymers.

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Section: Monte Carlo Simulation In Random Copolymers 251supporting

confidence: 88%

Monte Carlo Simulation of the Sequence Length and Junction Point Distributions in Random Copolymers Obeying Bernoullian Statistics

Dong

Striegel

2012

International Journal of Polymer Analysis and Characterization

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“…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].…”

Section: Deterministic Methods For the Simulation Of Nmpmentioning

confidence: 99%

Deterministic Approaches for Simulation of Nitroxide-Mediated Radical Polymerization

Asteasuain

2018

International Journal of Polymer Science

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Since its development in the last decades, controlled radical polymerization (CRP) has become a very promising option for the synthesis of polymers with controlled structure. The design and production of tailor-made materials can be significantly improved by developing models capable of predicting the polymer properties from the operating conditions. Nitroxide-mediated polymerization (NMP) was the first of the three main variants of CRP to be discovered. Although it has lost preference over the years against other CRP alternatives, NMP is still an attractive synthesis method because of its simple experimental implementation and environmental friendliness. This review focuses on deterministic methods employed in mathematical models of NMP. It presents an overview of the different techniques that have been reported for modelling NMP processes in homogeneous and heterogeneous media, covering from the prediction of average properties to the latest techniques for modelling univariate and multivariate distributions of polymer properties. Finally, an outlook of model-based design studies of NMP processes is given.

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“…Efficient accounting algorithms78 are necessary for this purpose, given the large amount of data processed. Copolymerization systems studied by the GSSA include statistical copolymerization with terminal and penultimate termination models,79 multiblock copolymerization,80 and gradient copolymerization 81–85. The bivariate distribution of copolymer composition and molecular weight can be obtained by combining GSSA with simultaneous property accounting algorithms by means of a two‐dimensional fixed pivot technique 86.…”

Section: Computer Simulation Approaches For Polymerization In Bulkmentioning

confidence: 99%

Progress in Computer Simulation of Bulk, Confined, and Surface‐initiated Polymerizations

Bain

Turgman‐Cohen

Genzer

2012

Macro Theory & Simulations

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In this article we provide a brief summary of computational techniques applied to investigate polymerization reactions in general, with a focus on systems under confinement and initiated from surfaces. We concentrate on two major classes of techniques, i.e., stochastic methods and molecular modeling. We describe the major principles of the two classes of methodologies and point out their strengths and weaknesses. We review a variety of studies from the literature and conclude with an outlook of these two classes of computer simulation approaches as they are applied to “grafting from” polymerizations.

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Stochastic modelling of gradient copolymer chemical composition distribution and sequence length distribution

Cited by 15 publications

References 44 publications

Monte Carlo Simulation of the Sequence Length and Junction Point Distributions in Random Copolymers Obeying Bernoullian Statistics

Monte Carlo Simulation of the Sequence Length and Junction Point Distributions in Random Copolymers Obeying Bernoullian Statistics

Deterministic Approaches for Simulation of Nitroxide-Mediated Radical Polymerization

Progress in Computer Simulation of Bulk, Confined, and Surface‐initiated Polymerizations

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