Structurally ordered high‐entropy intermetallic nanoparticles with enhanced C–C bond cleavage for ethanol oxidation
Bibliographic record
Abstract
Abstract Efficient ethanol oxidation reaction (EOR) is challenging due to the multiple reaction steps required to accomplish full oxidation to CO2 in fuel cells. High‐entropy materials with the adjustable composition and unique chemical structure provide a large configurational space for designing high‐performance electrocatalysts. Herein, a new class of structurally ordered PtRhFeNiCu high‐entropy intermetallics (HEIs) is developed as electrocatalyst, which exhibits excellent electrocatalytic activity and CO tolerance for EOR compared to high‐entropy alloys (HEAs) comprising of same elements. When the HEIs are used as anode catalysts to be assembled into a high‐temperature polybenzimidazole‐based direct ethanol fuel cell, the HEIs achieve a high power density of 47.50 mW/cm2, which is 2.97 times of Pt/C (16.0 mW/cm2). Online gas chromatography measurements show that the developed HEIs have a stronger C–C bond‐breaking ability than corresponding HEAs and Pt/C catalysts, which is further verified by density functional theory (DFT) calculations. Moreover, DFT results indicate that HEIs possess higher stability and electrochemical activity for EOR than HEAs. These results demonstrate that the HEIs could provide a new platform to develop high‐performance electrocatalysts for broader applications.
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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".