Bibliographic record
Abstract
High entropy alloys have garnered significant research interest in the field of electrochemistry in recent years due to their unique catalytic properties. These materials, characterized by a multi-principal element composition, have demonstrated superior catalytic performance and enhanced stability compared to traditional catalysts. However, the inherent complexity of high entropy alloys poses significant challenges, particularly in two key areas. First, this complexity makes studying and fully understanding the material's properties and behavior difficult. Second, the synthesis of nanoscale high entropy alloys is often complex, costly, and differs substantially from their bulk counterparts. This review begins by discussing the core principles that govern the unique characteristics of high entropy alloys. The central portion of the review focuses on the latest methodologies for synthesizing nanoscale high entropy alloys and briefly tabulates the catalytic performance of these materials. In the concluding section, we examine the recent studies on the formation mechanisms of high entropy alloy nanoparticles, with a particular focus on wet-chemistry synthesis methods conducted under mild conditions. We hope this review will help researchers better understand high entropy alloys and high entropy alloy synthesis methods for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".