Clarifying the role of Ru in methanol oxidation at Ru<sub>core</sub>@Pt<sub>shell</sub> nanoparticles
Bibliographic record
Abstract
The catalytic activity of Rucore@Ptshell nanoparticles (NPs) towards CO oxidation, a strongly adsorbed intermediate that compromises the performance of direct methanol fuel cells, is known to be significantly better than at Pt alone. However, a systematic study aimed at understanding the beneficial effect of Ru on Pt during the methanol oxidation reaction (MOR) has not been carried out as yet. Here, Rucore@Ptshell NPs, having a controlled Ptshell coverage of zero to two monolayers and two different Rucore sizes (2 and 3 nm), were synthesized using the simple polyol method to determine the precise role and impact of Ru on the MOR in 0.5 M H2SO4 + 1 M methanol at RT and 60 °C. Because the structure of our Rucore@Ptshell NPs is known with such certainty, we were able to show here that the rate of methanol adsorption/dehydrogenation can be accelerated either by compression of the Ptshell (by making the Rucore larger) when it is less than one monolayer in thickness, or by decreasing the electronic effect of the Rucore on the Ptshell (achieved by adding a second Pt layer to the Ptshell). At low overpotentials, decreasing the Ptshell thickness also helps in increasing the rate of the MOR by enhancing the rate of oxidation of adsorbed CO. Finally, it is shown that the bi-functional effect of Ru on the Ptshell plays only a minor role in the catalysis of the MOR, especially at large particles where CO surface diffusion is facilitated.
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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".