Evaluation of Porous Media Gas Diffusion Models for PEMFC Applications
Bibliographic record
Abstract
Abstract. Polymer electrolyte membrane fuel cells (PEMFCs) are considered as zero emission power sources for transportation and stationary power purposes. The membrane electrode assembly (MEA) is the core of PEMFC and is composed of a gas diffusion layer (GDL), catalyst layer (CL) and proton exchange membrane (PEM). GDL is a carbon-based, fibrous porous medium that simultaneously provides a path for heat, mass and electron transport, as well as providing a mechanically robust support for the CL. The gas diffusion in the GDL can be estimated by Fick’s law where the effective diffusion coefficient of gaseous species is used. There are many models in the literature based on correlations defining the effective diffusion coefficients through GDLs. Some of these models were originally derived to estimate the transport properties of a porous media composed of spherical particles, e.g. Bruggeman approximation and effective medium approximation. Inherently, such models tend to result to more inaccurate outcomes compared to the models which assume the GDL structure as cylindrical carbon fibers, i.e. the diffusion model based on percolation theory. The percolation theory model considers GDL as a medium composed of freely overlapping fibers oriented in different directions. However, there are several models available in the literature with less simplifying assumptions in GDL structure. The pore network model (PNM) reconstruct the porous media using topology and size information extracted from high resolution tomographic patterns. Also CFD based models can even investigate the actual GDL structure and reconstruct the 3D stochastic porous medium microstructure. These models combine pore-scale model with CFD approaches, e.g. lattice Boltzmann method (LBM) or direct numerical simulation (DNS). This study compares the available models for dry gas diffusion in GDL with experimental data acquired from symmetrical modified Loschmidt cell (SMLC). The SMLC is employed to measure the effective diffusion coefficient of oxygen passing through GDL samples, i.e. SGL SIGRACET 24BA, 24 BC, 25BA, 25BC and TGP-H-060. The SMLC result for effective diffusion coefficient in TGP-H-060 has less than 2% difference with the available data in the literature for this type of GDL. In order to evaluate the accuracy of effective medium models and percolation theory model, the experimental data for the above-mentioned GDLs is compared with the predictions of these models. The porosity of GDL samples are in the valid range of diffusion models. The diffusion models based on the effective medium approximation have the greater difference with SMLC data in compare with the percolation theory model. The model’s predictions are the worst for the GDLs with microporous layer (MPL), i.e. SIGRACET 24 BC and 25BC. Since the MPL imposes an extra resistance to gas diffusion which is not considered in any GDL diffusion models. The least error in model’s outcome is 30% which associates to the effective diffusion coefficient predicted by percolation theory model for SIGRACET 25BA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".