Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores.

ABSTRACT: Designing single-species selective membranes for high-precision separations requires a fundamental understanding of the molecular interactions governing solute transport. Here, we comprehensively assess molecular-level features that influence the separation of 18 different anions by nanoporous cellulose acetate membranes. Our analysis identifies the limitations of bulk solvation characteristics to explain ion transport, highlighted by the poor correlation between hydration energy and the measured permselectivity (R² = 0.37). Entropy-enthalpy compensation, spanning 40 kilojoules per mole, leads to a free-energy barrier (∆G^‡) variation of only ~8 kilojoules per mole across all anions. We apply machine learning to elucidate descriptors for energetic barrier

SUBMITTER: Ritt CL 

PROVIDER: S-EPMC8759746 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature