High‐entropy alloy stabilized and activated Pt clusters for highly efficient electrocatalysis
Bibliographic record
Abstract
Abstract Although Pt and other noble metals are the state‐of‐the‐art catalysts for various energy conversion applications, their low reserve, high cost, and instability limit their large‐scale utilization. Herein, we report a hybrid catalysts design featuring noble metal clusters (e.g., Pt) uniformly dispersed and stabilized on high‐entropy alloy nanoparticles (HEA, e.g., FeCoNiCu), denoted as HEA@Pt, which is prepared via ultra‐fast shock synthesis (∼300 ms) for HEA alloying combined with Pt galvanic replacement for surface anchoring. In our design, the HEA core critically ensures high dispersity, stability, and tunability of the surface Pt clusters through high entropy stabilization and core‐shell interactions. As an example in the hydrogen evolution reaction, HEA@Pt achieved a significant mass activity of 235 A/gPt, which is 9.4, 3.6, and 1.9‐times higher compared to that of homogeneous FeCoNiCuPt (HEA‐Pt), Pt, and commercial Pt/C, respectively. We also demonstrated noble Ir stabilized on FeCoNiCrMn nanoparticles (HEA‐5@Ir), achieving excellent anodic oxygen evolution performance and highly efficient overall water splitting when combined with the cathodic HEA@Pt. Therefore, our work developed a general catalysts design strategies by using high entropy nanoparticles for effective dispersion, stabilization, and modulation of surface active sites, achieving a harmonious combination of high activity, stability, and low cost.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".