Modeling Diffusivity in Catalyst Layer of a PEMFC Based on a Unit Cell Approach
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFC) convert the reaction energy of hydrogen and air to electricity without harmful emissions. PEMFC rely on a membrane-electrode assembly (MEA) constructed from multiple layers of micro/nano porous materials and associated interfaces. The MEA includes a polymer electrolyte membrane (PEM) that conducts protons, a composite nano-structured catalyst layer (CL), and a fibrous gas diffusion layer (GDL) that distributes reactant gases and collects current. The production cost and limited durability of the platinum catalyst layer is a significant challenge for the commercialization of hydrogen fuel cells. The greater the activity of the Pt nanoparticles, the lower the required Pt loading on the carbon support, and the lower the production costs for the catalyst layer. Hydrogen and oxygen reactants diffuse to the catalyst Pt particles. Existing models for the diffusivity of CL are either not accurate or computationally demanding, making them difficult to use for CL optimization. In this study, the CL is represented by unit cells based on porosimetry and analysis of SEM images of CL structure. The mass diffusion problem is analytically solved for the unit cell to calculate effective diffusivity of CL. Unlike other simple models which use porosity as the only input to calculate diffusivity the proposed model considers the pore size distribution as well as structure of CL, which improves the accuracy. The model presented in this study shows results within acceptable accuracy respect to published experimental data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".