Hydrophobicity gradient optimization of fuel cell gas diffusion media for its application in vehicles
Why this work is in the frame
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Bibliographic record
Abstract
During Fuel Cell Vehicle (FCV) operation, the liquid water in gas diffusion media (GDM) prevents the reaction gas from reaching the reaction zone and lead to output power fluctuation and reduce the lifespan of FCV. In the present research, hydrophobicity gradient settings of micro-porous layer (MPL) and gas diffusion layer (GDL) are optimized to improve the water removal ability of GDM. Computational fluid dynamics (CFD) model is constructed for numerical simulations to analyze the fuel cell power output and the water content in the GDM with different hydrophobicity gradients. Experiments with different hydrophobicity gradients, which are specifically prepared with corresponding concentrations of polytetrafluoroethylene (PTFE) solutions, are conducted for validation of simulation results. It is shown that the positive hydrophobicity gradient of MPL and GDL provides a better capacity for water removal and oxygen transport. The contact angles of MPL and GDL are further optimized as 147.9°-138.6° by genetic algorithm integrated with the CFD simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it