(Invited) Pt/Metal Oxide/Ti and Pt/Metal Oxide/Carbon Composite Films for Ethanol Oxidation
Bibliographic record
Abstract
Metal oxides such as RuO2 and SnO2 can promote the oxidation of organic fuels at Pt nanoparticles in a number of ways. These include changing the d-band level of the Pt via electron transfer (electronic effect) and providing surface –OH functionality for the oxidation of adsorbed CO (bifunctional effect) [1]. Carbon black coated with a mixture of Ru and Sn oxides has been shown to increase the activity of Pt for ethanol oxidation without a significant loss of selectivity for its complete oxidation to CO2 [2]. Here we report on the use of various mixed metal oxide composites prepared by thermal decomposition of metal acetylacetonate complexes (M(acac)n). Oxide layers were deposited onto Ti foil and high surface area carbon electrodes, and drop coated with preformed Pt nanoparticles. Cyclic voltammetry in H2SO4(aq) and polarization experiments in a proton exchange membrane cell were used to study the co-catalytic effects of the oxide layers. The use of acetylacetonate precursors provides a versatile method for screening libraries of oxide supported catalysts, as well as the production and screening of electrodes for fuel cells. Initially, decomposition of Ru(acac)3 and Sn(acac)2 and their mixtures on Ti foil electrodes was investigated. These experiments reproduced the effects previously observed with oxide layers deposited on glassy carbon from KRuO4 and SnCl4 [3]. Deposits formed from both complexes increased the activity for ethanol oxidation of Pt nanoparticles drop coated onto their surfaces, while use of mixtures produced a strong synergistic effect. Deposits formed by thermal decomposition of a variety of other acac complexes, including Ga(acac)3, Zr(acac)4, and In(acac)3, also increased the activity of Pt nanoparticles for ethanol oxidation. Mixed Ru+Sn oxides were also produced by thermal decomposition of Ru(acac)3 and Sn(acac)2 on carbon black, in order to better characterize the oxide deposits and produce catalysts that could be used in fuel cells. X-ray diffraction and energy dispersive X-ray spectrometry confirmed the presence of a mixed oxide. Preformed Pt nanoparticles were adsorbed onto the oxide coated carbon, supported on carbon fiber paper, and the resulting electrodes were evaluated for ethanol oxidation in a proton exchange membrane cell at 80 °C. 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, 2017, 4, 39-44 [2] D.D. James, R.B. Moghaddam, B. Chen, P.G. Pickup, Journal of the Electrochemical Society, 2018, 165, F215-F219 [3] R. B. Moghaddam and P. G. Pickup, Electrochim. Acta, 2012, 65, 210– 215
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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 | 0.001 |
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".