Determination of Permeability of the Gas Diffusion Layer of Proton Exchange Membrane Fuel Cells (PEMFCs)
Bibliographic record
Abstract
One vital component in the Proton Exchange Membrane Fuel Cells (PEMFCs) is the Gas Diffusion Layer (GDL). This layer is a pathway for the reactants to reach the reaction site and for the by-products to be removed. Due to this application, the permeability of this layer has been investigated experimentally and numerically [1]. This layer has a highly porous structure, and hence identifying a geometry that resembles best the GDL for modeling flow through this layer is very important. In this study, the 3D image of a GDL sample has been obtained with high resolution imaging, X-ray microtomography, and is used as a model to simulate flow through this layer. This layer typically treated with micro-porous layer (MPL). In this paper, MPL was separated and permeability through the MPL is investigated separately using an image-processing method developed in-house. The results indicate that MPL reduces the permeability of GDL considerably. This work facilitates the study of the effect of MPL on the permeability of the GDL, which is impossible to be determined in experimental approaches [2]. [1] J. Pharoah, "On the permeability of gas diffusion media used in PEM fuel cells," J. Power Sources, vol. 144, pp. 77-82, 2005. [2] J. Ihonen, M. Mikkola and G. Lindbergh, "Flooding of gas diffusion backing in PEFCs physical and electrochemical characterization," J. Electrochem. Soc., vol. 151, pp. A1152-A1161, 2004.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".