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Polymers 2016, 8(6), 240; doi:10.3390/polym8060240

New Statistical Models for Copolymerization

1
Chair of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
2
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, 07743 Jena, Germany
3
Jena Center for Soft Matter (JCMS), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cavallotti
Received: 9 May 2016 / Revised: 7 June 2016 / Accepted: 15 June 2016 / Published: 22 June 2016
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Abstract

For many years, copolymerization has been studied using mathematical and statistical models. Here, we present new Markov chain models for copolymerization kinetics: the Bernoulli and Geometric models. They model copolymer synthesis as a random process and are based on a basic reaction scheme. In contrast to previous Markov chain approaches to copolymerization, both models take variable chain lengths and time-dependent monomer probabilities into account and allow for computing sequence likelihoods and copolymer fingerprints. Fingerprints can be computed from copolymer mass spectra, potentially allowing us to estimate the model parameters from measured fingerprints. We compare both models against Monte Carlo simulations. We find that computing the models is fast and memory efficient. View Full-Text
Keywords: copolymer kinetics; copolymer fingerprint; Markov model copolymer kinetics; copolymer fingerprint; Markov model
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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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MDPI and ACS Style

Engler, M.S.; Scheubert, K.; Schubert, U.S.; Böcker, S. New Statistical Models for Copolymerization. Polymers 2016, 8, 240.

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