Pt Islands on 3 D Nut‐like PtAg Nanocrystals for Efficient Formic Acid Oxidation Electrocatalysis
Bibliographic record
Abstract
Abstract Precise control of structures offers a great opportunity to efficiently tune the catalytic performance of nanomaterials, enhacing both their activity and durability. Herein, we achieve a new class of Pt islands on 3 D nut‐like PtAg nanocrystals by exploiting the lower electronegativity of Ag in conjunction with the galvanic replacement of catalytically active Pt to Ag. Such nanostructures coated with Pt nanoparticles, exhibiting exposed facets, and active surface composition enhance formic acid oxidation electrocatalysis with optimized PtAg1 nut‐like catalysts and achieved a factor of 4.0 and 2.4 in mass and specific activities (1728.3 mA mg−1 and 3.31 mA cm−2) relative to those of the commercial Pt/C (431.2 mA mg−1 and 1.41 mA cm−2), respectively. Moreover, such 3 D PtAg1 nut‐like catalysts also display great enhancement in durability with less decay for at last 500 cycles, showing a great potential to serve as promising catalysts for fuel cells and other applications. Our work provides a fundamental insight on the effect of the morphology toward liquid fuel electrooxidation, which may pave a new way for the fabrication of highly efficient electrocatalysts for fuel cells.
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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".