Polymer chain microstructure, including characteristics such as molecular weight and branch length, can impact the end-use properties of the polymer. The assumptions contained in deterministic models prevent examination of the structure of individual polymer chains, so removal of these assumptions is necessary to gain insight into molecular-level mechanisms that determine chain microstructure. The work presented here uses a combination of stochastic and deterministic models to examine two significant mechanistic issues in free radical polymerization. The zero-one assumption concerning the number of radicals is often made for miniemulsion polymerization using oil-soluble initiators because of accelerated termination due to radical confinement. Although most of the initiator is present inside the particles, opposing viewpoints exist as to whether the locus of radical generation is the particle phase or the aqueous phase. A well-mixed kinetic Monte Carlo (KMC) model is used to simulate the molecular weight distribution and the results are compared to estimated molecular weights for several chain-stopping events, with the finding that the dominant nucleation mechanism varies with reaction temperature and particle size. Intramolecular chain transfer to polymer, or backbiting, is often assumed to produce only short-chain branches. Using a lattice KMC model, a cumulative distribution function (CDF) is obtained for branch lengths produced by backbiting. Implementation of the CDF in both a rate-equation model and the well-mixed KMC model shows that, for the butyl acrylate solution polymerization system used for comparison, backbiting is responsible for most of the branches, including long-chain branches, even though overlap of the polymer coils in the solution is predicted, a condition which would normally be expected to lead to significant intermolecular chain transfer to polymer. The well-mixed KMC model provides a more thorough analysis of chain microstructure while the rate-equation model is more computationally efficient.

No description available.