Kinetics and Stoichiometry of Methanol and Ethanol Oxidation in Multi-Anode Proton Exchange Membrane Cells
Bibliographic record
Abstract
The anode catalyst of a direct alcohol fuel cell (DAFC) influences its energy efficiency through both its effect on the cell potential and the reaction stoichiometry (average number of electrons released per fuel molecule; n av ). A method for determining these parameters simultaneously from a polarization curve (current vs. cell potential) is reported and various catalysts have been evaluated in a multi-anode cell. The cell was operated in crossover mode, in which fuel flows though the cathode chamber and diffuses though the membrane to be oxidized at the anode, to provide controlled mass transport conditions. Tafel analysis at low potentials provides kinetic information, while currents at high potentials provide n av values and their potential dependence. The method allows a number of catalysts to be compared under the same conditions, and provides characteristic parameters that could be compared across research groups. It is shown that while PtRu alloy catalysts provide faster kinetics than Pt for both methanol of ethanol oxidation, Pt can provide much higher stoichiometries for ethanol oxidation. The value of this methodology for catalyst screening is demonstrated with mixed Pt/C + PtRu/C anodes which show a pronounced synergistic effect relative to the individual Pt/C and PtRu/C catalysts.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".