Characterization of PEMFC Gas Diffusion Layer Porosity
Bibliographic record
Abstract
The porosity, thickness, skeletal and bulk density of a wide range of gas diffusion layers were measured using a buoyancy method. In this method, thin porous samples were weighed both dry and submerged in a wetting fluid, allowing their solid volume to be determined by application of Archimedes principle. The results showed that GDL porosity decreased as the amount of hydrophobic polymer additive was increased. In general, the observed decrease in porosity was in agreement with the theoretical pore volume reduction calculated for a given PTFE loading. A simple mass-based analysis was performed to estimate the amount of PTFE in a given sample, which revealed that materials generally do not possess the PTFE loading they are reported to have, a fact that was confirmed by thermogravimetric analysis. When the measured porosity values were plotted against these improved estimates of PTFE loading, the agreement with the theoretically expected porosity trend was excellent. Comparisons were also made to gas pycnometry. Finally, measurements were made on samples cut from various locations on a given sheet. It was found that although thickness and areal mass varied between locations, the porosity remained relatively constant.
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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 | 0.000 |
| 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".