Heterogeneous Through-Plane Porosity Distributions for Treated PEMFC GDLs. II. Effect of MPL Cracks
Bibliographic record
Abstract
In this work, the through-plane porosity distributions of paper and felt gas diffusion layers (GDLs) treated with micro-porous layers (MPLs) are measured using microscale computed tomography. The porosity distributions of the GDL microstructure and cracked MPL coating are analyzed and presented independently. In the MPL core region, the porosity of the cracked MPL is found to steadily decrease towards the catalyst layer side. Through comparison, the extent to which the MPL penetrates the GDL is also observed. From cross-sectional images of the MPL treated GDL, crack locations were found to correspond with adjacent fibre locations, indicating that fibres can influence the presence of cracks in the MPL. We also present the through-plane combined porosity distribution for the GDL and cracked MPL, which can be employed for predictive modelling of liquid water transport in the polymer electrolyte membrane fuel cell (PEMFC).
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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 | 0.001 |
| 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) | 0.001 | 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".