Unstructured Pore Network Modeling with Heterogeneous PEMFC GDL Porosity Distributions
Bibliographic record
Abstract
This is the first investigation of the liquid water saturation profile dependence on empirically determined heterogeneous polymer electrolyte membrane fuel cell (PEMFC) gas diffusion layer (GDL) porosity distributions. An unstructured, two-dimensional pore network model using an invasion percolation algorithm is presented. Random fiber placements are based on the heterogeneous porosity distributions of six commercially available GDL materials recently obtained through X-ray-computed tomography visualizations. The pore space is characterized with a Voronoi diagram, and simulations are performed with a single inlet liquid water cluster. Saturation profiles are also computed for GDLs with uniform, sinusoidal, and square-wave porosity distributions. Liquid water tends to accumulate in regions of high porosity due to the associated lower capillary pressures. The results of this work suggest that GDLs tailored to have smooth porosity distributions have fewer pockets of high saturation levels within the bulk of the material. Finally, a study on theoretical surface modifications demonstrates that low porosity surface treatments at the catalyst layer ∣ GDL interface result in greatly reduced overall saturation levels of the material.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".