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 CO 2 . Herein, a ternary CoPtAu nanoparticle catalyst system is reported in which Co and Pt form an intermetallic L1 0 ‐structure and Au segregates on the surface to alloy with Pt. The L1 0 ‐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/mg Pt , 1.49 A/mg Pt , and 11.97 A/mg Pt , respectively in 0.1 m HClO 4 . The L1 0 ‐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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".