Physico-Chemical and Electrochemical Properties of Platinum−Tin Nanoparticles Synthesized by Pulsed Laser Ablation for Ethanol Oxidation
Bibliographic record
Abstract
Mixed Pt−Sn catalysts were prepared by crossed beam pulsed laser deposition. Five catalyst compositions were investigated, namely, Pt 100 Sn 0, Pt 90 Sn 10, Pt 75 Sn 25, Pt 50 Sn 50, and Pt 30 Sn 70 . The depositions were performed either under vacuum or in the presence of 2 Torr He. The pressure in the deposition chamber has a strong influence on the surface structure and morphology of the catalytic particles, as determined from scanning electron micrographs (SEM) and atomic force microscopy (AFM). For catalysts prepared under He, X-ray diffraction (XRD) patterns show an expansion of the fcc lattice, indicating that Sn atoms are dissolved in it. Up to 13 atom % Sn can be dissolved in the Pt fcc structure. In contrast, less than 3 atom % of Sn can be dissolved in Pt when the catalysts are prepared under vacuum. X-ray photoelectron spectroscopy has revealed that the surface composition of Pt x Sn 100- x catalysts prepared under 2 Torr He closely follows the bulk concentration. Catalysts with the same composition prepared under vacuum exhibit a surface enrichment with Pt atoms. In these catalysts, tin is highly oxidized and the mean [O]/[Sn] surface ratio is 2.35. In contrast, tin in catalysts prepared under 2 Torr He is less oxidized and the mean [O]/[Sn] surface ratio is 1.37. Cyclic voltammogram curves reveal that mixing Sn with Pt lowers the onset oxidation potential of ethanol. This effect is more pronounced for catalysts prepared under 2 Torr He, and E onset = 0.31 V vs RHE is reached for [Pt] bulk = 75 atom %. Similarly, chronoamperometric measurements conducted at +0.5 V vs RHE also demonstrated that Pt 75 Sn 25 catalyst prepared under 2 Torr He is the most active after 1 h of electrolysis. The reasons underlying these differences are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".