Modeling Methanol Crossover by Diffusion and Electro‐Osmosis in a Flowing Electrolyte Direct Methanol Fuel Cell
Bibliographic record
Abstract
Abstract A CFD model is created to analyze methanol transport in a flowing electrolyte direct methanol fuel cell (FE‐DMFC) by solving the 3D advection‐diffusion equation, with consideration of electro‐osmosis. The average methanol flux at the anode and cathode surfaces is simulated and compared to equivalent direct methanol fuel cells. Methanol crossover is defined as methanol flux at the cathode surface, and the results reveal that methanol crossover can be drastically reduced by the flowing electrolyte. The performance of the FE‐DMFC at peak power current density is evaluated, and diffusion is shown to be the dominant contribution, although electro‐osmosis increases with current density. The power consumption of the electrolyte pump is shown to be negligible compared to the cell power output. This indicates that thin electrolyte channels with high flow rates could further improve the efficiency.
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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 | 0.001 |
| 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 | 0.000 |
| 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".