Evidence map›Paper›PMID 40014395›Full record

ArticleACS nano2025

Ion Sieving in Two-Dimensional Membranes from First Principles.

Nicéphore Bonnet, Nicola Marzari

Abstract read
In one paragraph

Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nicéphore BonnetTheory and Simulation of Materials (THEOS), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.ORCID 0009-0005-3610-3321
Nicola MarzariTheory and Simulation of Materials (THEOS), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.ORCID 0000-0002-9764-0199

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive effects, and a mean-field treatment of the charging of the electrochemical double layer is used. Entropic contributions are assessed analytically and validated against thermodynamic integration. 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

Indexed as

2D membraneselectrochemical double layerfirst-principles calculationsion sievingmachine learningmicrokinetic modelmultiscale modeling

Identifiers

PMID40014395
PMCPMC11912574

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.