Methanol and ethanol oxidation on carbon-supported platinum-based nanoparticles using a proton exchange membrane electrolysis cell
Bibliographic record
Abstract
Direct alcohol fuel cells (DAFC) have been extensively studied as energy conversion devices. The most extensively studied alcohol is methanol, which has a higher anodic oxidation rate than other alcohols because it does not need to dissociate a C-C bond in order to oxidize completely to CO₂. However, recent research has focused on ethanol, which has several advantages over methanol, such as its high energy density, low crossover rate, and low toxicity. Ethanol would be a promising fuel if it could completely oxidize to CO₂ to generate 12 electrons. Our research aimed to develop high-activity catalysts that are more effective and give higher CO₂ faradaic yields compared to the most effective and widely known PtRu catalyst. Electrolysis cells are used instead of fuel cells to evaluate our prepared anodic catalysts to avoid chemical reactions between ethanol and oxygen. In this study, we have prepared Pt-based core-shell nanoparticles with PtRu and Ru cores and Pt at the surface in different amounts (PtRu@Pt and Ru@Pt). These catalysts were prepared using the polyol method without adding stabilizers that would block the surface and decrease alcohol oxidation activity. We investigated the effect of Pt thickness on Ru and PtRu cores by conducting cyclic voltammetry (CV) at ambient temperature and in a proton exchange membrane electrolysis cell (PEMEC) at 80°C. The results indicate that methanol and ethanol oxidation selectivity to form CO₂ increased with increasing Pt shell thickness. As compared to PtRu, PtRu@Pt₁.₇ had a higher selectivity for complete ethanol oxidation, while Ru@Pt₀.₆ had a higher activity for methanol oxidation. In many studies, Rh has been shown to increase the selectivity of Pt for oxidizing ethanol to CO₂. The Rh@Pt core-shell catalysts were prepared in an alkaline medium using ethanol as the reducing agent. Electrochemical results indicate that Rh@Pt with half a monolayer of Pt would be a highly effective catalyst for direct ethanol fuel cells. Furthermore, PtRh nanoparticles were investigated for methanol and ethanol oxidation reactions. The catalysts were synthesized using formic acid as a reducing agent. According to the PEMEC study, Pt₃.₀Rh demonstrated the best ethanol oxidation selectivity relative to Pt and PtRu at most applied potentials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".