Evidence map›Paper›PMID 41093876›Full record

ArticleNature communications2025

Collectivity effect in cluster catalysis under operational conditions.

Jia-Lan Chen, Xue-Chun Jiang, Li Feng, Jinze Zhu, Jian-Wen Zhao, Jin-Xun Liu, Wei-Xue Li

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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  3. The role of the NNature communications · 2026
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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

7 authors.

Jia-Lan ChenState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.ORCID http://orcid.org/0009-0007-0192-5995
Xue-Chun JiangState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.ORCID http://orcid.org/0009-0000-7017-0353
Li FengState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
Jinze ZhuState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
Jian-Wen ZhaoState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
Jin-Xun LiuState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. jxliu86@ustc.edu.cn.ORCID http://orcid.org/0000-0002-7499-4197
Wei-Xue LiState Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China. wxli70@ustc.edu.cn.ORCID http://orcid.org/0000-0002-5043-3088

Funding

National Natural Science Foundation of China (National Science Foundation of China) 22172150, 22222306, 22221003, and 22432004
6 · The paper itself

Abstract

Subnanometer metal clusters are promising catalysts but are limited by structural heterogeneity and dynamics under operational conditions. Herein, we employ artificial intelligence-enhanced multiscale modeling, integrated with statistical analysis, to exhaustively explore the catalytic sites of cluster catalysts under reaction conditions. We discover that numerous sites across varying sizes, compositions, isomers, and locations collectively contribute to overall activity due to their high intrinsic activity and abundance. The collectivity of active sites, despite their distinct local environments, configurations and reaction mechanisms, arises from their high intrinsic activity and considerable population. Data-driven machine learning reveals that this collectivity is governed by the balance between local atomic coordination and the adsorption energy. Using CO oxidation on Cu/CeO

Identifiers

PMID41093876
PMCPMC12528435

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.