Evidence map›Paper›PMID 40559311›Full record

ReviewNanomaterials (Basel, Switzerland)2025

Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization.

Xiongwei Liang, Shaopeng Yu, Bo Meng, Yongfu Ju, Shuai Wang, Yingning Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Photocorrosion, Protection, and Predictive Design in Solar Water Splitting.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  2. Review
  3. Strategies for Photoelectrochemical Splitting of Water.International journal of molecular sciences · 2026
    Review
  4. Review
  5. 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

6 authors.

Xiongwei LiangCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.ORCID 0009-0006-4497-4407
Shaopeng YuCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.
Bo MengCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.
Yongfu JuCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.
Shuai WangCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.
Yingning WangCold Region Wetland Ecology and Environment Research Key Laboratory of Heilongjiang Province, Harbin University, Harbin 150086, China.

Funding

Heilongjiang Provincial Natural Science Foundation of China LH2022B019
6 · The paper itself

Abstract

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. We first delineate the fundamental physicochemical criteria for efficient photoanodes, including suitable band alignment, visible-light absorption, charge carrier mobility, and electrochemical stability. Conventional strategies such as nanostructuring, elemental doping, and surface/interface engineering are critically evaluated. We then discuss the integration of ML techniques-ranging from high-throughput density functional theory (DFT)-based screening to experimental data-driven modeling-for accelerating the identification of promising oxides (e.g., BiVO

Indexed as

machine learningmaterials informaticsmetal oxide semiconductorsnanostructured photoanodesphotoelectrochemical water splittingsolar fuels

Identifiers

PMID40559311
PMCPMC12196338

What OpenQuestion holds

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LicenceCC BY
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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.