Efficient Oxidation of Ethanol at Ru@Pt Core-Shell Catalysts in a Proton Exchange Membrane Electrolysis Cell
Bibliographic record
Abstract
Efficient electrochemical oxidation of ethanol in fuel cells and electrolysis cells is important for generating power and hydrogen, respectively, from renewable resources. PtRu alloys are most widely employed as catalysts because they provide high activities at low potentials. However, they produce acetic acid as the main product from ethanol, which results in low faradaic and overall efficiencies. In contrast, Pt provides high selectivity for the complete oxidation of ethanol to CO2, but low activities. Ru@Pt core–shell nanoparticles can improve efficiency by delivering higher activity than Pt and enhanced formation of CO2relative to PtRu. Here, Ru@Pt catalysts have been prepared by depositing Pt onto a commercial carbon-supported Ru catalyst. The influence of the amount of Pt deposited has been investigated in H2SO4(aq) at ambient temperature and in a proton exchange membrane cell at 80 °C. Activities for ethanol oxidation were intermediate between those for commercial Pt and PtRu catalysts, providing higher currents than Pt at low potentials, and higher currents than PtRu at high potentials. Faradaic yields of CO2(38%–48%) were greatly increased relative to the PtRu alloy catalyst (11%). This will optimize the efficiency of ethanol oxidation in PEM electrolysis and fuel cells.
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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 | 0.001 |
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".