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Polymers 2017, 9(3), 101;

Exploring the Limits of the Geometric Copolymerization Model

Life Sciences Group, Centrum Wiskunde & Informatica, Science Park 123, 1089XG Amsterdam, The Netherlands
Chair of Bioinformatics, Friedrich Schiller University, Ernst-Abbe-Platz 2, 07743 Jena, Germany
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany
Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
Author to whom correspondence should be addressed.
Academic Editor: Sébastien Perrier
Received: 4 February 2017 / Revised: 7 March 2017 / Accepted: 8 March 2017 / Published: 13 March 2017
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The geometric copolymerization model is a recently introduced statistical Markov chain model. Here, we investigate its practicality. First, several approaches to identify the optimal model parameters from observed copolymer fingerprints are evaluated using Monte Carlo simulated data. Directly optimizing the parameters is robust against noise but has impractically long running times. A compromise between robustness and running time is found by exploiting the relationship between monomer concentrations calculated by ordinary differential equations and the geometric model. Second, we investigate the applicability of the model to copolymerizations beyond living polymerization and show that the model is useful for copolymerizations involving termination and depropagation reactions. View Full-Text
Keywords: copolymer kinetics; copolymer fingerprint; Markov model; Monte Carlo simulations copolymer kinetics; copolymer fingerprint; Markov model; Monte Carlo simulations

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Engler, M.S.; Scheubert, K.; Schubert, U.S.; Böcker, S. Exploring the Limits of the Geometric Copolymerization Model. Polymers 2017, 9, 101.

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