Modification of Carbon Black by Thermal Decomposition of Lead Acetylacetonate to Improve Activities for Ethanol Oxidation at Supported Pt Catalysts
Bibliographic record
Abstract
Direct ethanol fuel cells (DEFC) are a type of promising energy-conversion device with high efficiency. The investigation of anode catalysts for DEFC is extremely important in improving energy efficiency. Improvement of the catalytic activity and long-term stability of Pt catalysts supported on some metal oxides has been reported for ethanol oxidation in acidic conditions. This synergistic effect mainly arises from the bifunctional effect and charge transfer between metal oxides and Pt catalysts.[1] In our study, thermal decomposition of Pb(acac)2 (acac = acetylecetonate) was used to rapidly prepare lead oxide supports on titanium foil and high surface area carbon electrode materials for electrocatalysis. On titanium electrodes, a thin layer of lead oxide was first prepared, followed by drop coating with a certain amount of a Pt nanoparticle solution. For high surface area carbon electrodes, Pt nanoparticles were adsorbed onto a carbon black-lead oxide composite, followed by painting a catalyst ink onto carbon fibre paper. The catalysts were characterized by X-ray diffraction and energy dispersive X-ray spectrometry. The electrochemical performance of the catalysts for ethanol oxidation was studied through cyclic voltammetry, chronoamperometry and in a proton exchange membrane electrolysis cell. Durability was studied through investigating the composition change before and after performing cyclic voltammetry. The lead oxide support was found to have a significant influence on onset potential, current density and product distribution for ethanol oxidation. Acknowledgments: This work was supported by the Natural Sciences and Engineering, Research Council of Canada and Memorial University. [1] G.M. Alvarenga, H.M. Villullas, Current Opinion in Electrochemistry, 4, 39, (2017).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".