Ethanol and Methanol Electrolysis at Core-Shell Ru@Pt and PtRu@Pt Catalysts with Different Pt Shell-Thickness
Bibliographic record
Abstract
One of the attractive methods used to prepare hydrogen gas is the electrolysis of alcohols such as methanol and ethanol in a proton exchange membrane (PEM) cell. Platinum is the most common catalyst used to provide high selectivity for C-C bond cleavage for ethanol, leading to an increase in the amount of hydrogen gas produced. However, the cost of the Pt and poisoning of the surface by the adsorbed CO intermediate limit its use. Combining Pt with other metals such as ruthenium in the form of core-shell nanoparticles enhances its ability for alcohol oxidation [1,2]. Therefore, more hydrogen gas is evolved in a PEM electrolysis cell, especially at lower applied potentials. In addition, these modified catalysts can be used in the anodes of direct alcohol fuel cells. Here, we aimed to prepare Pt-based core-shell nanoparticles in which commercial catalysts PtRu and Ru were used as the core, and different amounts of Pt were deposited on their surface as a shell (PtRu@Pt and Ru@Pt). These catalysts were prepared using the polyol method without adding any stabilizer that would block their surface and affected their alcohol oxidation activity. The formation of these core-shell nanoparticle catalysts was confirmed by using thermogravimetric analysis, transmission electron microscopy, scanning electron microscopy with an energy dispersive X-ray analyzer, X-ray diffraction and X-ray photoelectron spectroscopy. Moreover, the effect of the Pt thickness on methanol and ethanol electrochemical oxidation was studied using cyclic voltammetry at room temperature and a PEM electrolysis cell at 80 °C. Results show that methanol and ethanol oxidation activity increased greatly as the thickness of the Pt shell was increased, although the half potential was shifted to a higher potential. Moreover, the addition of the Pt to the shell increased the limiting current, indicating that the selectivity toward CO2 formation increased and that more hydrogen was being produced. Finally, Ru as a core showed a lower improvement for methanol oxidation than PtRu. However, Ru@Pt catalysts with more than one Pt monolayers gave higher ethanol oxidation performances than the PtRu@Pt catalysts. Acknowledgments This work was supported by the Natural Science and Engineering Research Council of Canada and Memorial University. References: T. M. Brueckner, E. Wheeler, B. Chen, E. N. El Sawy and P. G. Pickup, J. Electrochem. Soc., 166, F942 (2019). E. El Sawy, T. M. Brueckner and P. G. Pickup, J. Electrochem. Soc., 167, 106507 (2020).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".