Evidence map›Paper›PMID 42619391›Full record

ReviewSmall (Weinheim an der Bergstrasse, Germany)2026

From Experimental Optimization to AI Empowerment: Advanced Strategies of Oxygen Evolution Reaction for Large-Scale PEMWE.

Zihe Liu, Junyao Xu, Zhiyi Peng, Shiyan Wang, Longlu Wang

Abstract readReview
In one paragraph

Review in Small (Weinheim an der Bergstrasse, Germany), 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

5 authors.

Zihe LiuCollege of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, People's Republic of China.
Junyao XuCollege of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, People's Republic of China.
Zhiyi PengCollege of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, People's Republic of China.
Shiyan WangCollege of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, People's Republic of China.ORCID https://orcid.org/0000-0002-3742-4072
Longlu WangCollege of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications (NJUPT), Nanjing, People's Republic of China.ORCID https://orcid.org/0000-0003-3823-6660

Funding

Natural Science Foundation of China 22479079Natural Science Foundation of Jiangsu Province BK20201381Science Foundation of Nanjing University of Posts and Telecommunications NY219144 NY221046
6 · The paper itself

Abstract

Proton exchange membrane water electrolysis (PEMWE) has emerged as a core technology for green hydrogen production owing to its fast dynamic response and efficient compatibility with intermittent renewable energy sources. It is now transitioning from megawatt- to gigawatt-scale applications, yet still faces many practical challenges. The oxygen evolution reaction at the anode is a key breakthrough point for enhancing the overall performance of PEMWE. In recent years, significant progress has been made in this field via traditional experimental optimization, while artificial intelligence has also become a major driver of technological paradigm shifts. This paper first systematically reviews the core challenges of PEMWE's large-scale application. Then, it delves into the optimization strategies for the anode reaction of PEMWE from three dimensions, establishing a strategy system that covers traditional experimental optimization to artificial intelligence (AI) assisted driving. Finally, future development directions toward scaled-up application are prospected. This paper aims to bridge traditional experimental optimization methods and AI-enabled approaches, accelerating the large-scale application of PEMWE and promoting the high-quality development of the green hydrogen industry.

Indexed as

artificial intelligenceexperimental optimizationoxygen evolution reactionPEMWE

Identifiers

PMID42619391
PMCPMC13580320

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.