Pore Space Characterization of Compressed PEMFC GDLs Using 3D Micro-Computed Tomography
Bibliographic record
Abstract
In this work, micro-computed tomography was employed to characterize the effect of rib and channel compression on the through-plane porosity distributions of polymer electrolyte membrane fuel cell (PEMFC) gas diffusion layers (GDLs). Two GDLs with micro-porous layers (MPLs): a paper based GDL (GDL A), and a felt GDL (GDL B) were compressed at 1.2 MPa in an ex situ flow field apparatus with 1mm × 1mm channels. Porosity distributions of compressed GDLs were compared with those of uncompressed GDLs, and the microstructural differences caused during the manufacturing of paper, felt, and cloth GDLs are discussed. The results of this study will aid modellers in generating realistic stochastic GDL pore structures for multiphase flow simulations.
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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".