Tuning MPL Intrusion to Increase Oxygen Transport in Dry and Partially Saturated Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers
Bibliographic record
Abstract
Pore network modelling was utilized to simulate oxygen transport behavior and water saturation within stochastically generated gas diffusion layers (GDLs) at various liquid water inlet coverages of the GDL/catalyst layer interface. This study demonstrates that when the GDL is invaded by liquid water through cracks in the microporous layer (MPL), a deeply intruded MPL leads to lower breakthrough saturation for all liquid water inlet coverages ranging from 7% to 80%. Furthermore, a deeply intruded MPL shifts the peak liquid water saturation away from the catalyst layer, reduces the in-plane coverage of water, and reduces the tortuosity of the liquid water pathways. Under dry conditions, the oxygen diffusion coefficient was found to linearly decrease as a function of the MPL intrusion depth into the GDL. Under partially saturated conditions, increasing MPL intrusion depth has a beneficial effect on oxygen transport. The beneficial impact of increasing MPL intrusion depth on oxygen transport increases at higher inlet coverages.
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".