Effect of Size on the Electrochemical Stability of Pt Nanoparticles Deposited on Gold Substrate
Bibliographic record
Abstract
Pulsed laser deposition was used to prepare Pt nanoparticles of various sizes ranging from 1.8 to 6.0 nm. These nanoparticles were deposited on highly oriented pyrolytic graphite (HOPG) and gold substrates. For Pt deposited on HOPG and Au, the size of the nanoparticles was established by scanning tunneling microscopy (STM) and calculated from the electrochemically active surface area (EASA) obtained through measurements of the hydrogen underpotential deposited charges, Q Hupd . The diameters determined from these two sets of measurements agreed with each other to within 60%. X-ray photoelectron spectroscopy (XPS) was used to assess the size of the nanoparticles before and after an electrochemical treatment that involved potential cycling in 0.5 M H 2 SO 4 . The upper potential limit was progressively increased from 1.15 to 1.40 V in steps of 0.05 V, and the EASA was continuously monitored. The EASA decreased with cycle number and with increasing upper potential limit to 1.35 V vs RHE. This effect was found to be larger for the smaller Pt nanoparticles (50% decrease for ϕ = 1.8 nm) than for the larger ones (20% decrease for ϕ = 3.0 nm). It was found by XPS that the diameter of the smaller Pt nanoparticles increased from ϕ = 1.8 nm to ϕ = 6.5 nm as a result of the electrochemical treatment, whereas the diameter of the larger nanoparticles (ϕ = 3.0 nm) remained constant. In this potential range, the corrosion of the gold substrate is minimal, and this observation can be explained by an increase of the equilibrium soluble Pt concentration with decreasing Pt nanoparticle diameter.
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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 | 0.002 |
| 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 | 0.001 | 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".