Electrodeposited high-entropy alloys as electrocatalysts in water electrolysis for hydrogen production: a review on impacts of composition and synthesis parameters
Bibliographic record
Abstract
Abstract High-entropy alloys are described as materials that have equiatomic and multi-element compositions. Their unique atomic structure may provide alternative electrocatalysts for water electrolysis over traditional and expensive noble metal-based catalysts, delivering superior catalytic activity and stability. Among various high-entropy alloys synthesis methods, electrodeposition stands out as a versatile and cost-effective approach due to its mild conditions and precise control over composition and deposition properties. This review focuses on noble metalfree high-entropy alloys prepared by electrodeposition, with applications in water electrolysis. The impacts of alloying elements and electrodeposition parameters on the morphology, composition, and electrochemical performance of the resulting coatings for hydrogen evolution reaction and oxygen evolution reaction are also examined. The roles of key alloying elements are discussed, including their individual contributions during the electrodeposition process, interactions within the bath, and effects on the final coating. The review also discusses critical deposition parameters such as bath chemistry, pH value, current density, temperature, and bath agitation, and their influences on properties and electrochemical activity of electrodeposited coatings. Finally, future research directions and recommendations in several key areas are outlined to address important knowledge gaps for further advancing the optimization and application of electrode-posited high-entropy alloys as effective electrocatalysts for water electrolysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".