Nicephore Bonnet^1*, Nicola Marzari^1*

1 Theory and Simulation of Materials (THEOS), Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Corresponding authors emails: nicephore.bonnet@epfl.ch, nicola.marzari@epfl.ch

DOI10.24435/materialscloud:mg-wh [version v1]

Publication date: May 26, 2025

How to cite this record

Nicephore Bonnet, Nicola Marzari, Ion sieving in 2D membranes from first principles, Materials Cloud Archive 2025.85 (2025), https://doi.org/10.24435/materialscloud:mg-wh

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
data.zip MD5md5:0356d3d9e3e7ccdf6b3e273016a533ba	261.2 MiB	MD trajectories, energies and forces used to generate Fig. 4. See README.txt for detailed description.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

2D membrane Ion sieving Machine learning Molecular dynamics