Evidence map›Paper›PMID 40664630›Full record

ArticleNature communications2025

Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning.

Jing Sun, Pengzhu Lin, Lin Zeng, Zixiao Guo, Yuting Jiang, Cailin Xiao, Qinping Jian, Jiayou Ren, Lyuming Pan, Xiaosa Xu and 3 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. Review
  2. 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

13 authors.

Jing Sun *Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Pengzhu Lin *Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Lin ZengDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0002-0510-1754
Zixiao GuoDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Yuting JiangDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Cailin XiaoDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.
Qinping JianDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Jiayou RenDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Lyuming PanDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0002-6192-8385
Xiaosa XuDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Zheng LiDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China.
Lei WeiDepartment of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.
Tianshou ZhaoDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Kowloon, China. zhaots@sustech.edu.cn.ORCID http://orcid.org/0000-0003-4825-2381

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rational design of gas diffusion layers (GDL) is an example of a long-standing pursuit to increase the power density and reduce the cost of proton exchange membrane fuel cells (PEMFC). However, current state-of-the-art GDLs are designed by trial-and-error, which is a time-consuming endeavor. Here, we propose a closed-loop workflow of Bayesian machine learning approach to guide the design of GDL structures. With artificial neural network accelerating the calculation of anisotropic transport properties of reconstructed GDLs, Bayesian optimization algorithm identifies optimal structures in only 40 steps, maximizing the PEMFC's limiting current density. Results suggest that the optimal porous GDL structure consists of highly orientated fibers with moderate diameters, which is successfully fabricated with a controlled electrospinning technique. The PEMFC demonstrates a high power density of 2.17 W cm

Identifiers

PMID40664630
PMCPMC12263830

What OpenQuestion holds

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Registered trials

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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.