Stochastic modelling of gradient copolymer chemical composition distribution and sequence length distribution

Stochastic mechanistic models for gradient copolymerisation systems were developed based on a kinetic Monte Carlo algorithm. Due to the discrete nature of the simulations, chains were tracked as binary strings allowing for the storage of the complete sequence of each polymer chain, allowing for an unprecedented level of detail. Models were developed that simulated styrene/4-acetoxystyrene semi-batch gradient copolymer syntheses, and explicit sequence information was determined using simulation results. The chemical composition distribution was mapped for the copolymers, which is capable of providing a visual description of both the size and overall composition distributions of the copolymer and a qualitative description of the chain architecture. This methodology was expanded to track the explicit sequences of each chain that was used to determine the number and weight fraction sequence length distributions. Simulation results show that tail end compositional tapering was never fully achieved. Case studies were conducted to determine the major factors affecting tail end tapering, including increasing the initial batch fraction and varying the 4-acetoxystyrene flow rate. While an increase in initial batch fraction increases 4-acetoxystyrene significantly, head end tapering is lost, while a large increase in flow rate is not capable of fully tapering the tail end.