Measurements of Permeability and Effective in-Plane Gas Diffusivity of Gas Diffusion Media Under Compression
Bibliographic record
Abstract
A novel experimental setup, based on a diffusion bridge, is proposed to accurately and simultaneously measure the in-plane permeability and effective molecular diffusivity of gas diffusion layers used in PEMFC under compression. The permeability is measuredby introducing nitrogen in one channel, forcing the gas through theporous media, and measuring pressure drop at various flow rates. To measure effective diffusivity, nitrogen and oxygen are introduced in two channels separated by a porous media. The flow rate of the gases are controlled by two mass flow controllers. The absolute pressure of nitrogen in the channel is controlled by a back pressure controller. The ratio of convection and diffusion flux is modified by controlling the pressure difference between the gases using a differential pressure controller, connected to the oxygen channel. The oxygen mole fraction in the nitrogen channel is meaured at various differential pressures. A steady state one dimensional Fick - Darcy model is used to estimate the permeability and effective molecular diffusivity based on the experimental data. Toray 090 samples with 0 and 40%PTFE at four compression levels are studied. The permeability and diffusibility (ratio of effective to bulk diffusivity) are found to be between 0.98×10−11 and 0.13×10−11, and, 0.52 and 0.09 respectivelyand are shown to decrease with compression and PTFE.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".