Kim, W.K.; Chudoba, R.; Milster, S.; Roa, R.; Kanduc, M.; Dzubiella, J.: Tuning the selective permeability of polydisperse polymer networks. Soft Matter 16 (2020), p. 8144-8154
10.1039/d0sm01083a
Open Access Version
Abstract:
We study the permeability and selectivity (‘permselectivity’) of model membranes made of polydisperse polymer networks for molecular penetrant transport, using coarse-grained, implicit-solvent computer simulations. In our work, permeability P is determined on the linear-response level using the solution–diffusion model, P = KDin, i.e., by calculating the equilibrium penetrant partition ratio K and penetrant diffusivity Din inside the membrane. We vary two key parameters, namely the network–network interaction, which controls the degree of swelling and collapse of the network, and the network–penetrant interaction, which tunes the selective penetrant uptake and microscopic energy landscape for diffusive transport. We find that the partitioning K covers four orders of magnitude and is a non-monotonic function of the parameters, well interpreted by a second-order virial expansion of the free energy of transferring one penetrant from a reservoir into the membrane. Moreover, we find that the penetrant diffusivity Din in the polydisperse networks, in contrast to highly ordered membrane structures, exhibits relatively simple exponential decays. We propose a semi-empirical scaling law for the penetrant diffusion that describes the simulation data for a wide range of densities and interaction parameters. The resulting permeability P turns out to follow the qualitative behavior (including maximization and minimization) of partitioning. However, partitioning and diffusion are typically anti-correlated, yielding large quantitative cancellations, controlled and fine-tuned by the network density and interactions, as rationalized by our scaling laws. We finally demonstrate that even small changes of network–penetrant interactions, e.g., by half a kBT, modify the permselectivity by almost one order of magnitude.