Role of the Secondary Metal in Ordered and Disordered Pt–M Intermetallic Nanoparticles: An Example of Pt<sub>3</sub>Sn Nanocubes for the Electrocatalytic Methanol Oxidation
Bibliographic record
Abstract
When comparing alloy catalysts with different degrees of ordering, it is important to maintain surface facets to understand the effect of different arrangements of surface atoms. This is even more important when both metals are involved in the reaction steps, which is the case of Pt 3 Sn for the methanol oxidation reaction (MOR). We have prepared 95 and 60% ordered Pt 3 Sn nanocubes with {100} facets for the MOR. We show that the Sn atoms in the 60% ordered Pt 3 Sn nanocubes can be electrochemically oxidized to Sn 4+, whereas the Sn atoms in the 95% ordered Pt 3 Sn nanocubes are more resistant to oxidation. The Sn 4+ in the disordered catalysts makes them more active than the ordered catalysts. At low overpotentials, the electrochemically formed Sn 4+ in the 60% ordered Pt 3 Sn nanocubes bind OH, oxidizing the CO intermediate adsorbed on Pt more efficiently. At high overpotentials, Sn 4+ prevents the passivation of the Pt sites due to adsorption of OH. These effects lead to a 5.6 times higher activity of the 60% ordered nanocubes compared to the 95% ordered nanocubes. These results illustrate the importance in catalyst design of controlling the environment and especially the atoms neighboring Pt for intermetallic Pt–M electrocatalysts.
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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) | 0.000 | 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 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".