Entropy-driven design of multifunctional electrocatalysts: Advances and perspectives in high-entropy materials
Bibliographic record
Abstract
High-entropy materials (HEMs) have attracted extensive attention in the field of electrocatalysis due to their high performance enabled by their multi-component, tunable structural characteristics and excellent stability. HEMs are usually composed of five or more metal elements, and have core advantages such as high configurational entropy, lattice distortion and multi-element synergistic effect, which provide new possibilities for composition regulation and performance optimization of catalysts. Especially at the nanoscale, HEMs show a larger specific surface area, abundant active sites and higher catalytic reaction efficiency, further expanding their application potential in electrochemical reactions. This paper systematically reviews the classification, structure construction and regulation strategies of HEMs, and focuses on their research progress in critical electrocatalytic reactions including water splitting (HER, OER), hydrogen oxidation (HOR), oxygen reduction (ORR), carbon dioxide reduction (CO 2 RR), nitrate reduction (NO 3 – RR) and electrooxidation of organics (EOO). In addition, the preparation methods of HEMs, the structure-performance relationship and the entropy regulation mechanism in the catalytic process are analyzed. Finally, this paper proposes the key challenges currently faced by HEMs in electrocatalytic applications and looks forward to their future development direction, providing a theoretical basis and design ideas for building a new generation of efficient and sustainable electrocatalysts. This paper provides a systematic and comprehensive review of high-entropy materials (HEMs), covering their classification, structural regulation, and advancements in electrocatalytic applications, emphasizing preparation techniques, structure-function relationships, and entropy engineering mechanisms, outlining future challenges and directions. • Systematic review of the high-entropy materials’ latest applications in electrocatalysis. • Focus on the structural design and innovation of high-entropy materials in electrocatalysis. • Explore the synthesis strategies of high-entropy materials. • Insight into the development direction of high-entropy materials in the industrialization of electrocatalysis.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".