Modeling study of an air‐breathing micro direct methanol fuel cell with an extended anode catalyst region
Bibliographic record
Abstract
A three-dimensional model was developed for an air-breathing micro direct methanol fuel cell (μDMFC) with an extended anode catalyst region on the cell channels. The model was evaluated against experimental studies for a μDMFC under several anode distribution conditions, and the results showed a close agreement. The model was employed to study if catalysts coated on the fluid-flow channel walls could enhance the power generation performance. Further, the effects of the anode catalyst loading of channel walls on the overall cell and individual electrode performances were examined. The modeling results indicated that the fuel cell with anodes both on the proton-exchange membrane and on channel walls did not show superior performance to the fuel cell with anode catalysts only on the membrane since the overall power generation was mainly limited by the kinetics of the methanol electrode reaction but not the methanol transfer. The modeling results also demonstrated that increasing the anode catalyst loading on channel walls decreased the cathode potential due to an increase in the ohmic loss for the fuel cell with anode catalysts both on the membrane and on channel walls. Reducing channel dimensions decreased the ionic resistance and increased the methanol concentration at the anode and the methanol crossover flux to the cathode.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".