(255ax) Chain-By-Chain Monte Carlo Method for Non-Linear Polymerization

Predicting chain microstructures is an important task for polymer scientists, that is made more interesting and challenging by the polydisperse nature of polymer molecules. In this work, a new method, called â??Chain-by-Chain Monte Carlo Methodâ? (CBC-MC), is presented for simulating chain microstructures one-by-one or chain-by-chain. CBC-MC is a new hybrid method that uses the mean-field background information as concentrations of polymer populations and small molecules from the deterministic solver to provide an environment in which we stochasticallysimulate chains one-by-one with kinetic Monte Carlo. The deterministic solver in this work uses method of moments. The main advantage of CBC-MC is that the use of the deterministic solver allows the elimination of the computational load associated with simulation of the whole ensemble. Method is suited for chemistries, or situations in which chain architecture develops slowly with respect to the background environment, such as controlled reversible-deactivation radical polymerizations. In this work, CBC-MC is applied to a non-linear copolymerization leading to gelation. Effect of a gradient distribution of pendant double bonds along the primary chains on the simulated portion of gel molecules is investigated. Primary chain results are compared with MOM and found to be in perfect agreement. Further investigations are done on primary chain microstructures to better understand multiple phenomena going on in these systems. It has been found that a gradient in PDB distribution along the primary chains can introduce heterogeneities in gel molecules in surface-bound type polymerizations where primary chains within gels are aligned in the same direction but these heterogeneities seem to be disappearing in bulk polymerizations where the chains alignments are random. Results confirm that if applicable, full information regarding the microstructure of chains can be obtained using this method with reduced simulation times and smaller sample sizes.