Investigation of Gas Diffusion Layer Properties Using X-Ray Microtomography
Bibliographic record
Abstract
An essential part of proton exchange membrane fuel cells (PEMFCs) is the gas diffusion layer (GDL), which provides pathways for by-products to be removed from PEMFCs. One of the main properties of GDLs is porosity. The two widely used experimental methods for finding the porosity of GDLs are mercury intrusion porosimetry (MIP) and method of standard porosimetry (MSP). In addition to these methods, the porosity of GDLs can be calculated based on the high resolution 3D images that are acquired using X-ray microtomography (μXCT) as shown in recent studies (e.g., [7,12]). Despite the general success of using μXCT to measure GDL porosity, different porosity values have been reported for similar GDLs. These variations are due to different assumptions made for determining the surface of the sample, and hence, its external dimensions. In this research, current methods used for calculating porosity of GDLs from μXCT images are discussed, and a new surface identification method based on a rolling ball algorithm is introduced. The main advantage of this new method is that variations in surface topology or roughness are taken into account when calculating porosity. The new method is not only applicable to GDLs, but can be applied to characterize a wide range of highly porous media.
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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 | 0.001 | 0.001 |
| 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".