A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials.

Xin Zhong, Xiong Liang, Huixia Zhang

Abstract read

In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Xin ZhongSchool of Physics and Mechanical and Electronical Engineering, Longyan University, Longyan 364012, China.

Xiong LiangSchool of Physics and Mechanical and Electronical Engineering, Longyan University, Longyan 364012, China.

Huixia ZhangCollege of Life Sciences, Longyan University, Longyan 364000, China.

Funding

Education Department of Fujian Province JAT241138 and JAT241139Fujian Province Science and Technology Association 2025J01313087, 2025J011710Longyan University LB2025003 and LB2025009

6 · The paper itself

Abstract

Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field's evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010-2026 and retained 730 valid records from 1581 initial publications after screening. VOSviewer, CiteSpace, and R were used to analyze publication growth, collaboration networks, journal sources, and thematic evolution. Results show that annual output remained generally below 20 before 2019, then increased rapidly and reached approximately 280 publications in 2025, indicating accelerated integration of machine learning with adsorption simulation, material screening, and performance evaluation. The source distribution broadened from a limited set of chemistry and engineering journals to diverse venues, with recent high publication weights in

Indexed as

carbon dioxide capturegas adsorptionmachine learningmetal–organic frameworksnanoporous materials

Identifiers

PMID42506516

DOI10.3390/nano16140883

PMCPMC13415077

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.