PtRu Alloy Nanoparticles. 2. Chemical and Electrochemical Surface Characterization for Methanol Oxidation
Bibliographic record
Abstract
Platinum–ruthenium (PtRu) nanoparticles (NPs) were evaporatively deposited in a 1:1 mass ratio onto carbon paper, using three different orders of deposition: Pt deposited onto Ru, Ru deposited onto Pt, and both Pt and Ru deposited simultaneously. The three samples were further annealed at 650 °C for 1.5 h. A sample of Pt NPs on carbon paper was also prepared as a reference. All the deposits and the reference (a total of seven samples) were characterized by X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), time-of-flight secondary ion mass spectrometry (TOF-SIMS), and electrochemical techniques, in order to investigate the relationship between their catalytic surface chemical properties and their electrocatalytic activities during the methanol oxidation reaction. The simultaneous deposition of Pt and Ru demonstrated higher electrocatalytic activity, as well as excellent chronoamperometric stability, compared to either sequential deposition. This can be attributed to the synergistic effects between Pt and Ru species at the surface. Annealing at 650 °C led to a reduction of the electrocatalytic oxidation peaks. This appears to be due to the deposition of surface hydrocarbons at high temperature, thereby blocking active catalysis sites on the NP surface, as well as to the decomposition of metal oxides, which occurs above 350 °C.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".