Ion sieving in 2D membranes from first principles

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Bonnet, Nicephore</dc:creator>
  <dc:creator>Marzari, Nicola</dc:creator>
  <dc:date>2025-05-26</dc:date>
  <dc: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.</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2025.85</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:mg-wh</dc:identifier>
  <dc:identifier>mcid:2025.85</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2279</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>2D membrane</dc:subject>
  <dc:subject>Ion sieving</dc:subject>
  <dc:subject>Machine learning</dc:subject>
  <dc:subject>Molecular dynamics</dc:subject>
  <dc:title>Ion sieving in 2D membranes from first principles</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>