Three Dimensional, High Resolution MPL Generation for Thermal and Mass Transport Modeling of PEM Fuel Cells
Bibliographic record
Abstract
The microporous layer (MPL) of the polymer electrolyte membrane (PEM) fuel cell has been shown to significantly improve the performance of the fuel cell1. However, there remains a lack of conclusive evidence for the transport processes through the MPL. The nano-scale pore sizes limit the probing capabilities of experimental and visualization studies2,3. Based on information from previous characterization studies done at a relatively low resolution, a stochastically generated MPL model has been built that closely represents the structural properties of the MPL coating of SGL 25BC, which is a commercially available gas diffusion layer (GDL) used in automotive applications. This model is the first step for designing MPLs and studying their thermal and mass transport properties. In this work, the MPLs have been generated with a particle diameter of 60 nm and a filling radius of 12 nm, which statistically represents the MPL of SGL 25BC2. Additional MPL structures have also been generated with varying particle diameters, filling radii, and particle overlaps to better understand the structural properties impact on thermal and mass transport. The filling radius represents the addition of a binding material to the MPL, and polytetrafluoroethylene (PTFE) is commonly used in this manufacturing process. PTFE is uniformly distributed throughout the MPL structure, as shown by George et al.4 and assumed by El Hannach et al.5. The MPL structure being generated treats the carbon particles and the PTFE as separate materials. This allows for accurate simulation of heat transfer through the structure, which is dependent on the thermal properties of the solid. Figure 1 shows a 1.5 µm x 1.5 µm x 1.5 µm domain of the MPL which has been generated at a voxel resolution of 5 nm/voxel. Higher resolution structures (at 1 nm/voxel) have also been generated. However, the computational cost of the higher resolution is not justified by the improvement in the results. The resulting structures have been validated by calculating the diffusivity of oxygen through the pores. The diffusivity calculations have been done using the pore network modeling technique, implemented through our open source pore network model, OpenPNM (www.openpnm.org). The high resolution of the structure allows for studying the particle to particle contact region, which is extremely critical in heat transfer studies. This model has been used to study the effect of particle size, binder ratio, and structure on the heat and mass transport through the MPL. This work will provide valuable data for improved performance modeling of the fuel cell towards the goal of reducing the experimental cost and the iteration time of designing new MPLs for advanced heat and mass transport management. Figure 1: 1.5 µm side length domain of MPL generated using the scheme mentioned in this work. References 1. Z. Qi and A. Kaufman, J. Power Sources, 109, 38 – 46 (2002). 2. S. J. Botelho and A. Bazylak, J. Power Sources, 280, 173–181 (2015). 3. E. A. Wargo, V. P. Schulz, A. Çeçen, S. R. Kalidindi, and E. C. Kumbur, Electrochimica Acta, 87, 201–212 (2013). 4. M. George, R. Banerjee, J. Wang, and A. Bazylak, Electrochem. Commun. (Submitted). 5. M. El Hannach, R. Singh, N. Djilali, and E. Kjeang, J. Power Sources, 282, 58–64 (2015). Figure 1
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".