Stability of PtZn Nanoparticles Supported on Carbon in Acidic Electrochemical Environments
Bibliographic record
Abstract
The stability of Pt and PtZn alloy nanoparticles supported on glassy carbon toward metal dissolution during oxygen reduction reaction (ORR) is detailed. The Pt/Zn nanoparticles were created using sequential electrodeposition steps. The electrochemically created particles are approximately 1:1 Pt:Zn, the as-created particles having a Pt rich surface as determined by Auger electron spectroscopy. The Pt particles were found to be spherical, while the Pt/Zn particles were oddly shaped. These Pt and PtZn particles were subjected to durability tests at various potentials in oxygen saturated H 2 SO 4 . The surface composition of the PtZn particles was analyzed after fixed times at 0.75, 1.0, and 1.25 V/RHE. The Zn surface content was unchanged over 14 h at 0.75 V, substantially increased when conditioned at 1.0 V, and the Zn was absent from the surface after 2.5 h at 1.25 V. The Pt and PtZn nanoparticles were further analyzed using tapping mode AFM which found that the PtZn particles were more strongly adhered to the GC substrate as compared to similarly prepared Pt nanoparticles. Heating at 120 °C for 2 h caused the Pt nanoparticles to grow by aggregation, while the PtZn particles became more spherical. These measurements suggest that the PtZn alloy is stable at potentials less than 1.0 V/RHE during ORR, and that the PtZn alloy is more strongly adhered to the carbon surface than Pt.
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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 | 0.001 |
| 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 | 0.001 | 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".