Ternary CoPtAu Nanoparticles as a General Catalyst for Highly Efficient Electro‐oxidation of Liquid Fuels
Bibliographic record
Abstract
Abstract Efficient electro‐oxidation of formic acid, methanol, and ethanol is challenging owing to the multiple chemical reaction steps required to accomplish full oxidation to CO2. Herein, a ternary CoPtAu nanoparticle catalyst system is reported in which Co and Pt form an intermetallic L10‐structure and Au segregates on the surface to alloy with Pt. The L10‐structure stabilizes Co and significantly enhances the catalysis of the PtAu surface towards electro‐oxidation of ethanol, methanol, and formic acid, with mass activities of 1.55 A/mgPt, 1.49 A/mgPt, and 11.97 A/mgPt, respectively in 0.1 m HClO4. The L10‐CoPtAu catalyst is also stable, with negligible degradation in mass activities and no obvious Co/Pt/Au composition changes after 10 000 potential cycles. The in situ surface‐enhanced infrared absorption spectroscopy study indicates that the ternary catalyst activates the C−C bond more efficiently for ethanol oxidation.
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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 | 0.002 | 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".