We have successfully prepared an elastomeric material exhibiting excellent temperature-controlled self-healing characteristics.

While Kinetic Monte Carlo (KMC) techniques provide a powerful means to model polymer microstructure, the associated computational cost has been a barrier to their widespread adoption. The case of radical semibatch polymerization under starved-feed policy is a particularly challenging application: at the initial stage, a large simulation volume is required to accurately represent the low concentration of radicals generated at the start of the reaction, while the reactant feed dictates the further increase of the simulation volume with time. A combination of approaches is implemented in a stepwise fashion to greatly accelerate the KMC representation of this system. First, a correction factor is developed to maintain a constant simulation volume in order to improve the efficiency of the solution, followed by scaling of the reaction rates to preserve accuracy at low control volumes and further reduce computational effort. A novel strategy for storing the explicit chain sequences and parallel analysis of the stochastic data is also implemented, with the computational time required to accurately represent a semibatch radical copolymerization test case reduced from 50 to less than 2 min. The accelerated stochastic approach provides a foundation for future optimization of feeding strategies to minimize the fraction of nonfunctionalized chains formed during the production of low molar mass copolymers.

A comprehensive stochastic simulator that considers all probable secondary reactions essential for the description of high-temperature radical copolymerization of acrylate/methacrylate is implemented based on previously established accelerating techniques. The comparisons between the predictions and six experimental datasets are detailed for semi-batch copolymerization of 2-hydroxyethyl acrylate (HEA) with n-butyl methacrylate (BMA) under starved-feed condition. Macroscopic properties -free monomer and molar mass (MW) average profiles, final polymer molecular mass distributions, and the variation of acrylate composition with time-are reasonably well predicted over the range of initiator and comonomer levels studied. The simulation output also predicts the weight fraction and MW averages of the polymer chains that contain no HEA functionality, results that are compared to the experimental extractables obtained after crosslinking the copolymer resin. The general trends are well-captured, indicating that the model can be utilized in the future to optimize recipe and operating conditions to minimize the production of the non-functional material.

Olefin block copolymers (OBCs) are new class of thermoplastic elastomers having low glass transition temperature soft blocks and highly crystalline hard blocks synthesized by reversible chain shuttling between two catalysts with considerably different comonomer responses through a chain transfer agent. Theoretical representation of kinetics and microstructure evolution in chain shuttling polymerization (CSP) is of vital importance especially since the existing characterization tools have severe limitations in retrieving the blocky nature of OBCs. In this work, we correspondingly develop an effective model to represent CSP in practical conditions and compare our predictions to the existing experimental data. We illuminate kinetics and microstructure development in both OBC chains and individual blocks. We specifically clarify the effect of varying the reversibility of transfer reactions through tuning chain shuttling agent and hydrogen concentrations on OBC chains and blocks in terms of their corresponding molecular weight and chemical composition distribution and draw guidelines for achieving OBCs with desired properties.

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