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.

Copolymer fingerprint, Copolymer kinetics, Markov model, Monte Carlo simulations
dx.doi.org/10.3390/polym9030101
Polymers

Engler, M.S, Scheubert, K, Schubert, U.S, & Böcker, S. (2017). Exploring the limits of the geometric copolymerization model. Polymers, 9(3). doi:10.3390/polym9030101