ArticleChemistry of materials : a publication of the American Chemical Society2026
Ion Distribution and Cation Exchange at Mica-Electrolyte Interfaces Probed with Deep Potential Molecular Dynamics.
Article in Chemistry of materials : a publication of the American Chemical Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate description of the ion distribution and dynamics at mineral-aqueous solution interfaces is crucial to understanding a variety of technological and environmental phenomena. To obtain better insight, many studies have combined experimental measurements with classical molecular dynamics simulations, yet their interpretation remains limited by the empirical nature of the classical force fields. Here, we investigate the Stern layer structure and cation exchange mechanism at muscovite mica-electrolyte interfaces using nanosecond time scale molecular dynamics simulations based on deep neural network interatomic potentials trained on density functional theory (DFT) data. Focusing on mica with exposed surface K
Identifiers
What OpenQuestion holds
Registered trials
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.