Evidence map›Paper›PMID 40900004›Full record

ArticleACS applied materials & interfaces2025

Large-scale Investigations of Water Diffusion in Metal-Organic Frameworks with One-Dimensional Channels.

I-Ting Sung, Li-Chiang Lin

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 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.

I-Ting SungDepartment of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106319, Taiwan.ORCID 0009-0007-3654-9627
Li-Chiang LinDepartment of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106319, Taiwan.ORCID 0000-0002-2821-9501

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metal-organic frameworks (MOFs), known for their highly versatile nature, show considerable promise as adsorbents and membranes for water-related applications such as water harvesting and water filtration. One of the key factors that may influence their efficiency is the diffusion of water within MOFs. However, the behaviors and mechanisms of water diffusion in MOFs remain relatively underexplored. To this end, this study utilizes molecular dynamics (MD) simulations to comprehensively analyze water diffusion in hundreds of distinct MOFs. Herien, MOFs that exhibit rapid water diffusion under saturated conditions are identified. Moreover, structure-property relationships and underlying atomistic mechanisms are explored, showing that water mobility depends on a subtle interplay between pore topology and host-guest interaction. For instance, MOFs with similarly small pores can exhibit markedly different water diffusivities. In hydrophobic MOFs, weak framework-water interactions allow ultrafast single-file diffusion, but mobility plunges when the pores narrow further. In hydrophilic MOFs, strong framework-water interactions immobilize a layer of water on the pore walls; these fixed water molecules can interestingly shield strong adsorption sites, enhancing transport in wider pores yet impeding diffusion in narrower ones. Finally, to address the high computational cost associated with computing diffusivity, this work further evaluates the feasibility of using surrogate descriptors for rapid diffusivity estimation. Overall, this work offers molecular-level key insights into water transport phenomena.

Indexed as

large-scale computationsmetal−organic frameworksmolecular simulationsstructure−property relationshipswater diffusion

Identifiers

PMID40900004
PMCPMC12447398

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Registered trials

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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.