Gold–Mediated Enhancement of Methanol Oxidation Activity and Selectivity on Pt–Au@Ni Electrocatalysts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The methanol electrooxidation reaction (MOR) on mixed-metal electrocatalysts has drawn tremendous interest as a means to improve the catalytic efficiency and product selectivity. Herein, bifunctional Pt–Au@Ni catalysts formed through galvanic exchange are shown to exhibit high catalytic activity toward MOR. Voltammetric responses of MOR on Pt–Au@Ni and Pt@Ni catalysts exhibit characteristics of Ni-catalyzed MOR at high overpotentials and Pt-catalyzed MOR at low overpotentials but with a significantly higher current density on a per-surface Pt atom basis compared to a Pt electrode. In situ surface-enhanced infrared absorption spectroscopy (SEIRAS) measurements reveal that Pt–Au@Ni promotes selective conversion of methanol to formate on Pt sites with greatly suppressed CO formation while increasing the formate to carbonate product ratio on Ni sites. Favorable effects of composition engineering on the electronic structures of the materials, which are assessed by using XPS, are evoked to rationalize the greatly enhanced intrinsic activity of Pt sites in the Pt–Au@Ni catalyst. The enhancement is linked to alteration of the electronic structure of Pt by addition of Au and Ni leading to improved metal–reactant/intermediate interaction energies, in accordance with the Sabatier principle.
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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) | 0.002 | 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".