Mass Transport Measurements in Porous Transport Layers of a PEM Fuel Cell
Bibliographic record
Abstract
Porous transport layers are an integral part of polymer electrolyte fuel cells (PEMFC). In order to optimize the catalyst layer performance and reduce catalyst consumption, a thorough understanding of mass transport through porous media is necessary. Currently, there is a lack of experimental measurements of effective mass transport properties of porous transport layers. Further, mass transport theories in the literature, such as the binary friction model by Kerkhof [1], have not been extensively validated for porous media. In the present study, mass transport measurements have been performed on the porous media of a PEMFC, namely a GDL and an MPL. The experimental setup described by Pant et al. [2] has been used. The setup uses the diffusion bridge/counter-diffusion technique for the mass transport measurements. The experimental setup has the advantage that it can be used to perform studies for pure diffusion and convection-diffusion mass transport. The setup also facilitates measurement of permeability of porous media, which can then be used in convection-diffusion studies. Preliminary permeability measurements of GDL and MPL from the setup show good agreement with values available in literature. In preliminary experimentation, the conventional diffusivity correlations like Bruggeman equation have been found to overpredict the diffusivities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".