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

Ritt, Cody L.; Liu, Mingjie; Pham, Tuan Anh; Epsztein, Razi; Kulik, Heather J.; Elimelech, Menachem

doi:10.1126/sciadv.abl5771

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

Journal Article · 14 January 2022 · Science Advances

DOI: https://doi.org/10.1126/sciadv.abl5771 · OSTI ID:1880952

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Yale Univ., New Haven, CT (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Technion-Israel Institute of Technology, Haifa (Israel)

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 (ΔG‡) variation of only ~8 kilojoules per mole across all anions. We apply machine learning to elucidate descriptors for energetic barriers from a set of 126 collected features. Notably, electrostatic features account for 75% of the overall features used to describe ΔG‡, despite the relatively uncharged state of cellulose acetate. Our work presents an approach for studying ion transport across nanoporous membranes that could enable the design of ion-selective membranes.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Energy Frontier Research Centers (EFRC) (United States). Center for Enhanced Nanofluidic Transport (CENT)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC52-07NA27344; SC0019112

OSTI ID:: 1880952

Report Number(s):: LLNL-JRNL-837586; 1057480

Journal Information:: Science Advances, Journal Name: Science Advances Journal Issue: 2 Vol. 8; ISSN 2375-2548

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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