Investigating the Structure of the Bi-Layered Gas Diffusion Layer Using X-Ray Computed Tomography
Bibliographic record
Abstract
One of the most important objectives of the GDL in a PEM fuel cell is the transport of reactant gases from the gas flow channels to the reaction sites at the catalyst layer. Most state-of-the-art GDLs are composed of a carbon fiber paper coated with a microporous layer composed of carbon nano-particles. This bi-layered GDL structure has been proven to provide significant improvement to the performance of PEM fuel cells. In order to improve our understanding of reactant transport through these GDL materials, it is important for us to characterize the structure of these materials. In this study, we use X-ray Computed Tomography (X-CT) to study the structure of the bi-layered GDL at the microscale. This work presents a unique segmentation routine developed in-house to identify the distinct components of the bi-layer GDL, isolating the carbon fiber, the microporous layer and the void regions as individual phases. Two commercially available GDL samples, SGL 35BA and SGL 35BC are segmented with this novel algorithm to obtain unique porosity profiles. The MPL is identified separately in SGL 35BC along with the fibrous substrate region. It is observed that in the case of this sample, there is no region where only the MPL is present. The entire thickness of the MPL region is within the substrate region with fibers present throughout the MPL region. The substrate region is 300 μm thick while the MPL is present up to 200 μm from one side.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".