2026-04-30T03:49:20Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:2279 2026-01-26T22:01:21Z openaire_data community-mcarchive

Bonnet, Nicephore Marzari, Nicola Bonnet, Nicephore Marzari, Nicola 2025-05-26 A first-principles approach for calculating ion separation in solution through 2D membranes is proposed. Ionic energy profiles across the membrane are obtained first, where solvation effects are explicitly simulated by machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive effects, and a mean-field treatment of the electrochemical double layer charging is used. Entropic contributions are assessed analytically and through a thermodynamic integration scheme. Ionic separations are then inferred through a microkinetic model of the filtration process, accounting for steady-state charge separation effects across the membrane. The approach is applied to Li+, Na+, K+ sieving through a crown-ether functionalized graphene membrane, with a case study of the mechanisms for a highly selective and efficient extraction of lithium from aqueous solutions. This record contains the MD trajectories used to generate the energy and free energy profiles of Fig. 4. application/zip text/markdown https://doi.org/10.24435/materialscloud:mg-wh oai:materialscloud.org:2279 mcid:2025.85 eng Materials Cloud https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:zx-x4 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode 2D membrane Ion sieving Machine learning Molecular dynamics Ion sieving in 2D membranes from first principles info:eu-repo/semantics/other