Evidence map›Paper›PMID 42066091›Full record

ArticleScience advances2026

Accelerated discovery of highly stable ruthenium-based high-entropy oxides for acidic oxygen evolution.

Yuanhua Tu, Xiangyi Shan, Lunbo Chen, Jiaxi Zhang, Lixia Yang, Furong Cai, Chengzhi Zhong, Pengfei Wang, Han Gao, Jianan Xu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  2. Review
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

14 authors.

Yuanhua TuGuangdong Provincial Key Laboratory of Fuel Cell Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0001-6147-657X
Xiangyi ShanState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.
Lunbo ChenState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.
Jiaxi ZhangGuangdong Provincial Key Laboratory of Fuel Cell Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0001-6177-8690
Lixia YangState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.
Furong CaiState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.
Chengzhi ZhongGuangdong Provincial Key Laboratory of Fuel Cell Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China.
Pengfei WangInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Sichuan, Chengdu 610054, China.
Han GaoState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.ORCID 0009-0008-4794-7425
Jianan XuState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.ORCID 0009-0000-5649-9275
Zhaoyu JinInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Sichuan, Chengdu 610054, China.ORCID 0000-0003-0840-3931
Bin CaiSchool of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China.ORCID 0000-0002-3263-0395
Zhiming CuiGuangdong Provincial Key Laboratory of Fuel Cell Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0002-0305-4181
Min ZhouState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China.ORCID 0000-0003-4073-5370

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing low-cost acidic oxygen evolution reaction (OER) catalysts is essential to scale proton exchange membrane water electrolysis (PEMWE). Ruthenium-based high-entropy oxides (Ru-HEOs) hold promise, yet the vast compositional space renders trial-and-error approaches inefficient. Here, we propose a data-driven, high-throughput strategy that accelerates the discovery of stable acidic OER Ru-HEO catalysts. First, literature mining with a large language model (LLM) identifies non-noble metal combinations with potential synergy with Ru. We then integrate automated powder synthesis and catalyst array fabrication to build a 60-member library. Screening using dual activity-stability metrics pinpoints the optimal composition, (RuNiFeMoCr)

Identifiers

PMID42066091
PMCPMC13134624

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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.