Improving the Ethanol Oxidation Activity of Pt-Mn Alloys through the Use of Additives during Deposition
Bibliographic record
Abstract
In this work, sodium citrate (SC) was used as an additive to control the particle size and dispersion of Pt-Mn alloy nanoparticles deposited on a carbon support. SC was chosen, since it was the only additive tested that did not prevent Mn from co-depositing with Pt. The influence of solution pH during deposition and post-deposition heat treatment on the physical and electrochemical properties of the Pt-Mn alloy was examined. It was determined that careful control over pH is required, since above a pH of four, metal deposition was suppressed. Below pH 4, the presence of sodium citrate reduced the particle size and improved the particle dispersion. This also resulted in larger electrochemically-active surface areas and greater activity towards the ethanol oxidation reaction (EOR). Heat treatment of catalysts prepared using the SC additive led to a significant enhancement in EOR activity, eclipsing the highest activity of our best Pt-Mn/C prepared in the absence of SC. XRD studies verified the formation of the Pt-Mn intermetallic phase upon heat treatment. Furthermore, transmission electron microscopy studies revealed that catalysts prepared using the SC additive were more resistant to particle size growth during heat treatment.
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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) | 0.000 | 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".