Correlation for the Effective Gas Diffusion Coefficient in Carbon Paper Diffusion Media
Bibliographic record
Abstract
The understanding of mass transport limitations in polymer electrolyte membrane (PEM) fuel cells is crucial in the research and progress of this technology. The structure of the components, specifically the as diffusion layer (GDL), of PEM fuel cells, is complex. Thus, for the purpose of simulating mass transport in the GDL, the effect of the structure on the diffusion coefficient is taken into account by introducing an effective diffusion coefficient. The effective diffusion coefficient of a gas is lower than its corresponding bulk diffusion coefficient due to the presence of a solid matrix in the porous materials. Currently, the Bruggeman approximation is the most widely used correlation for estimating the effective diffusion coefficient in the GDL. Other semiempirical models are also available. However, these correlations overestimate the effective diffusion coefficient due to the assumptions on which they are based. In this study, correlations for the through-plane and in-plane diffusibility in the GDL are developed based on a three-dimensional (3D) simulation of gas diffusion in the GDL. The 3D structure of the TORAY carbon paper with no binding material is reconstructed using stochastic models and used as the modeling domain. The numerical results are shown to have a good agreement with experimental data of diffusibility in both directions. Correlations for two different porosity ranges are given.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".