(Invited) Pt/Metal Oxide/Ti and Pt/Metal Oxide/Carbon Composite Films for Ethanol Oxidation
Bibliographic record
Abstract
Metal oxides prepared by thermal decomposition of metal acetylacetonate (acac) complexes have a significant support effect on platinum catalyzed oxidation of ethanol. Ru and Sn oxides and mixed Ru-Sn oxides deposited onto Ti foils and high surface area carbon support materials were drop coated with preformed Pt nanoparticles. Cyclic voltammetry in H2SO4(aq) and polarization experiments in proton exchange membrane cells have demonstrated great versatility for screening the co-catalytic effects of oxide layers and scale-up to fuel cell testing. A strong synergistic effect has been observed between Ru and Sn oxide in both environments, with a 2:1 ratio of Ru(acac)3 to Sn(acac)2 ratio in the oxide precursor solution producing the most active catalyst. Electrodes prepared by using Pb(acac)2, In(acac)3 and MoO2(acac)2 also provided substantially enhanced activities for ethanol oxidation.
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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) | 0.002 | 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".