Evidence map›Paper›PMID 41331249›Full record

ArticleNature communications2025

Tailoring precipitates for enhanced hydrogen trapping in aluminum alloys.

Yucheng Ji, Xiaoqian Fu, Mahdieh Safyari, Chenyang Yao, Fei Shuang, Xuequn Cheng, Xucheng Yin, Xiaogang Li, Masoud Moshtaghi, Chaofang Dong and 1 more

Abstract read
PubMed Full text at PMC Publisher
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 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. Article
  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

11 authors.

Yucheng JiBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Xiaoqian FuBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Mahdieh SafyariMechanics of Materials Lab, Mechanical Engineering Department, School of Energy Systems, LUT University, P.O. Box 20, Lappeenranta, Finland.
Chenyang YaoBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Fei ShuangDepartment of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands.ORCID http://orcid.org/0000-0001-6333-9237
Xuequn ChengBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Xucheng YinDepartment of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China.
Xiaogang LiBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Masoud MoshtaghiMechanics of Materials Lab, Mechanical Engineering Department, School of Energy Systems, LUT University, P.O. Box 20, Lappeenranta, Finland.
Chaofang DongBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China. cfdong@ustb.edu.cn.ORCID http://orcid.org/0000-0002-0430-3135
Poulumi DeyDepartment of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands. p.dey@tudelft.nl.ORCID http://orcid.org/0000-0003-4679-1752

Funding

China Postdoctoral Science Foundation 2024M750172National Natural Science Foundation of China (National Science Foundation of China) 52125102National Natural Science Foundation of China (National Science Foundation of China) U23A20392
6 · The paper itself

Abstract

Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus failing to prevent its accumulation at the critical regions. Experimentally, it is challenging to expeditiously identify and generate phases causing strengthening and acting as strong H traps. Here, we demonstrate a computation-based design strategy to generate precipitates strongly trapping H. Based on the quantum machine learning Al-Sc-Cu potential, the optimal processing parameters of strong H trapping phases are determined, even though they are metastable in nature. Elemental mapping in electron microscope and atom probe tomography confirms the presence of Cu in Al

Identifiers

PMID41331249
PMCPMC12783128

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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.