Hydrophobicity gradient optimization of fuel cell gas diffusion media for its application in vehicles
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".