Study on catalyst‐support interactions in high‐entropy catalysts toward electrochemical water splitting reactions
Bibliographic record
Abstract
Abstract Electrochemical water splitting represents an advanced and sustainable approach for hydrogen production, fundamentally involving the hydrogen evolution reaction and the oxygen evolution reaction. The choice of electrocatalysts has a significant influence on the efficacy of these reactions. As global demand for clean hydrogen energy escalates, high‐entropy catalysts, particularly those derived from high‐entropy alloys (HEAs), have emerged as viable and cost‐effective alternatives to conventional noble metal catalysts. This review examines recent advancements in the development of both noble and non‐noble metal‐based HEAs, with a focus on their applications in electrochemical water splitting. It emphasizes strategies to enhance extrinsic activity, notably through the exploitation of strong metal‐support interactions and the engineering of porous surface architectures. We pay particular attention to the design principles that optimize both the intrinsic catalytic properties and extrinsic structural features, ultimately enhancing the overall electrocatalytic performance. By offering the latest insights into the rational design of HEAs, this review aims to propel their integration into next‐generation water‐splitting technologies, addressing both efficiency and sustainability in hydrogen production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".