Evidence map›Paper›PMID 40593630›Full record

ArticleNature communications2025

Edge-activated graphene nanopores for thermally robust hydrogen membrane separations.

Chi Cheng, Lohyun Kim, Aaron H Persad, Chun Man Chow, Rohit Karnik

Abstract read
In one paragraph

Article in Nature communications, 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

5 authors.

Chi Cheng *Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. chi.cheng2@unsw.edu.au.ORCID http://orcid.org/0000-0001-8596-1801
Lohyun Kim *Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0001-7964-704X
Aaron H PersadDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0001-5479-9215
Chun Man ChowDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0002-4746-6129
Rohit KarnikDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. karnik@mit.edu.ORCID http://orcid.org/0000-0003-0588-9286

Funding

Kwanjeong Educational Foundation (KEF) 19Am0145
6 · The paper itself

Abstract

Temperature-dependent, selective molecular diffusion through porous materials is crucial for membrane separations and is typically modeled as an Arrhenius-type activated process. Although this dependence can be described phenomenologically by an activation energy, tracing its molecular origins is often difficult, hindering robust membrane design for practical applications. Here, we investigate gas transport across monolayer nanoporous graphene membranes and observe significant, reversible, temperature-robust, and gas species-selective activated transport, with increased selectivity at rising temperatures, unlike many conventional membranes. Combined experiment and modelling trace this behavior to graphene nanopore edge functional groups, whose thermal fluctuations modulate effective pore size. This activated transport remains stable with aging over 1 year and shows selectivity exceeding 70 for hydrogen/hydrocarbon mixture separation at 220 °C, representative of dehydrogenation reactor temperatures. Our results demonstrate the thermal and long-term robustness of nanoporous graphene membranes, suggesting potential for precise engineering of nanopore surface chemistries in membranes for challenging molecular separations.

Identifiers

PMID40593630
PMCPMC12219706

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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.