Relative Diffusivity of Partially Saturated GDLs
Bibliographic record
Abstract
Gas diffusivity in GDLs of PEM fuel cells is significantly affected by water content, as water blocks pores and reduces gas flow. This effect increases at higher current densities due to more water production. However, few studies in the literature have experimentally measured gas diffusivity in partially saturated GDLs, primarily due to the challenges of controlling GDL saturation during measurement. Existing studies reported an exponential decay in diffusivity as saturation increases. However, the rate of diffusivity reduction differs between studies. One study suggests that the higher reduction in diffusivity observed in other papers is due to the GDL stacking to increase resistance to gas flow which causes the formation of a thin film of water at the interface between the stacked GDLs during the saturation process, obstructing the diffusion pathway and leading to inaccurate measurements. In this study, we have developed a new ex-situ method using the symmetrical modified Loschmidt cell to measure gas diffusivity in partially saturated GDLs. This method ensures no water film between the layers, providing more accurate diffusivity measurements. The technique has been applied to two commercially available GDL samples (Toray and AvCarb) under different saturation levels (defined as water volume over the total pore volume of the GDL). Our results indicate a significant decline in relative gas diffusivity as the saturation level increases. Moreover, all GDLs show very low relative diffusivity at saturation levels above 0.3, explaining the deterioration in fuel cell performance under flooding conditions.
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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 | 0.000 | 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".