Optimization of Synthesis Parameters Employed during Pt Nanoparticle Formation by in situ Reduction
Bibliographic record
Abstract
This work is focused on the optimization of the synthesis conditions of a Pt sol phase containing suspended metallic Pt nanoparticles with the primary goal being to produce thin (ca. 1 monolayer) Pt films having the highest possible electroactive surface area per gram. This is gauged here by the surface roughness factor, determined from the magnitude of the Pt electrochemical response in sulfuric acid solution. Two Pt(IV) chloride compounds (H 2 PtCl 6, Na 2 PtCl 6 ) are shown to be the best Pt precursors, producing stable Pt nanoparticles with an average particle diameter of 1−3 nm. Sodium ethoxide and formic acid are found to be excellent reducing agents of the PtCl 6 2- anion, although formaldehyde results in a lower yield of Pt nanoparticles. A ratio of sodium ethoxide to H 2 PtCl 6 of 2:1 and a 72 °C reflux in ethanol between 30 min and 5 h resulted in the highest Pt roughness factor (ca. 8). Transmission electron microscopy analysis has verified that all of the reducing agents produce Pt particles of a similar size and that the higher roughness factors are the result of a higher yield of Pt nanoparticles. The effect of time of storage of Pt sols formed using sodium ethoxide showed that only a minor aging effect is observed over long periods of time, likely minimized by the stabilization offered by PtCl 3 (C 2 H 4 ) -, a species formed as a byproduct during the synthesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".