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

Abstract

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.