Pore-Scale Modeling of Microporous Layer for Proton Exchange Membrane Fuel Cell: Effective Transport Properties
Bibliographic record
Abstract
A microporous layer (MPL) is a transition layer with a porous material structure, located between the gas diffusion layer (GDL) and catalyst layer (CL) in a proton exchange membrane fuel cell (PEMFC). It not only significantly improves electron transfer and heat conduction in membrane electrode assembly, but also effectively manages liquid water transport to enhance the fuel cell performance. The MPL is usually coated on one side of the GDL. The fragile nature of MPL makes it challenging to characterize the effective transport properties using experimental methods. In this study, a stochastic numerical method is implemented to reconstruct the three-dimensional microstructure of an MPL consisting of carbon particles and PTFE. The reliability of the MPL reconstructed model is validated using experimental data. The relationship between the effective transport properties and the compression strain is obtained using the Pore Scale Model (PSM), while the relationship between the liquid water saturation and capillary pressure is solved by Lattice Boltzmann Method (LBM). The effective transport properties in the MPL are then imported into the two-phase flow fuel cell model. It is found that the effective transport parameters in MPL obtained by PSM and LBM can improve the accuracy of the model calculation. This study provides an effective method to reconstruct the microstructure of MPL that can generate precise MPL transport parameters for utilization in various PEMFC performance prediction models.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".