Ethanol Electro-Oxidation on Carbon-Supported PtRuCu/C Catalyst in a Proton Exchange Membrane Electrolysis Cell
Bibliographic record
Abstract
The electrochemical oxidation of ethanol in cells with proton exchange membrane (PEM) electrolytes is important to develop energy technologies based on bioethanol. Direct ethanol fuel cells (DEFC) have been considered attractive power sources with high potential for use in vehicles and electronic devices, despite their low efficiencies, which need to be increased. On the other hand, ethanol electrolysis cells (EEC) can be used for hydrogen production.The importance of direct ethanol fuel cell technology for a sustainable energy future has resulted in comprehensive studies of the electrochemical oxidation of ethanol and the development of many different anode catalysts.1 Preparing catalysts with high efficiency for the ethanol oxidation reaction has been a major challenge. Although Pt has high activity for ethanol oxidation in acidic media, it is easily poisoned by adsorbed intermediates like COad and CHx, thus high overpotentials are required. PtRu alloy has become important in studies of the oxidation of ethanol, because it has high activity at low potentials. However, product analysis has shown that the main product from the oxidation of ethanol at PtRu is acetic acid and only a small CO2 yield is produced.2 Since increasing the production of CO2 is the major point to enhance the efficiency of DEFCs, it is necessary and crucial to develop and modify PtRu/C. Many researchers have introduced a third transition metal (ternary catalyst) to improve the activity and performance of PtRu/C at high potentials and also to increase the production of CO2.3,4 The purpose of this work is to explore the effect of incorporating Cu into PtRu/C catalysts on the production of CO2. A proton exchange membrane electrolysis cell was used to compare the performance of both PtRu/C and PtRuCu/C catalysts and measure the CO2 yield from the oxidation of ethanol. Interestingly, adding Cu enhanced the production of CO2, and the performance. Acknowledgments This work was supported by the Nature Science and Engineering Research Council of Canada and Memorial University References (1) Akhairi, M. A. F.; Kamarudin, S. K. Catalysts in Direct Ethanol Fuel Cell (DEFC): An Overview. Int. J. Hydrogen Energy 2016, 41, 4214–4228. (2) Altarawneh, R. M.; Pickup, P. G. Product Distributions and Efficiencies for Ethanol Oxidation in a Proton Exchange Membrane Electrolysis Cell. J. Electrochem. Soc. 2017, 164, F861–F865. (3) Xue, S.; Deng, W.; Yang, F.; Yang, J.; Amiinu, I. S.; He, D.; Tang, H.; Mu, S. Hexapod PtRuCu Nanocrystalline Alloy for Highly Efficient and Stable Methanol Oxidation. ACS Catal. 2018, 8, 7578–7584. (4) Barroso, J.; Pierna, A. R.; Blanco, T. C.; Ruiz, N. Trimetallic Amorphous Catalyst with Low Amount of Platinum: Comparative Study for Ethanol, Bioethanol and CO Electrooxidation. Int. J. Hydrogen Energy 2014, 39, 3984–3990.
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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 | 0.001 | 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".