Effect of Penetrant Volume on Diffusion in Dense Crosslinked Networks
ORAL
Abstract
Passive membranes have been used for separations because they are less energy intensive compared to thermal separations. There remains a challenge to separate molecules with similar molecular weight and intramolecular interactions. Tight flexible polymer networks are proposed to improve separation efficiency because the sub-nm mesh sizes can discriminate differences in geometries of two molecules. Nevertheless, the mechanisms for how tight networks regulate transport for different molecules are not fully understood. We seek to provide a molecular-level understanding of tight network-based selectivity to design materials for separation applications with Molecular Dynamics simulation with the standard bead-spring model for semi-flexible polymers. The model is parametrized for poly-n-butyl-acrylate (PnBA) networks synthesized by experimental collaborators to model dilute spherical penetrant diffusion in the networks. We studied the effect of crosslink on the alpha relaxation time and glass transition temperatures (Tg) for networks, and the results agree with experiments and theory. Diffusion coefficients for spherical penetrants with various diameters were calculated and we observed a stronger exponential dependence of diffusion coefficient and the ratio of system temperature to Tg which is also found in experiment and theory. The results show the importance of penetrant volume in determining its diffusivity and the success of our simple model to capture key physics of molecule transport in dense crosslinked networks at temperatures close to Tg. We also extend the model to study penetrant diffusion in vitrimers, polymer networks with associative dynamic covalent bonds. We aim to understand how the bond exchange mechanism impacts molecular transport.
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Presenters
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Tsai-Wei Lin
University of Illinois at Urbana-Champaign
Authors
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Tsai-Wei Lin
University of Illinois at Urbana-Champaign
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Charles E Sing
University of Illinois at Urbana-Champaign