Impact of Micro-Porous Layer Properties on Fuel Cell Performance
Bibliographic record
Abstract
Fuel cell engines, in general, and polymer electrolyte fuel cells, in particular, are potential substitutes for internal combustion engines in vehicles. Considering the current stage of fuel cell performance, the technology is gaining competiveness and is not far from being mature. Decades of research and development in this field have led to the existence of high power density stacks with acceptable durability. The power generation inside the fuel cell is facilitated by a membrane electrode assembly (MEA) which contains a catalyst coated membrane (CCM) and two gas diffusion layers (GDLs) exposed to hydrogen and oxygen on opposite sides. One of the major performance enhancing additions to the MEA architecture is the micro-porous layer (MPL), which is a thin layer of carbon nano-particles mixed with hydrophobic agent (e.g., PTFE) coated on the macro-porous GDL substrate. While the main role of the GDL is to provide reactant transport from the flow channels to the reaction sites in the catalyst layer, the underlying motivation of adding the thin MPL layer is to create a smooth transition between the macro-pores in the substrate and the nano-pores of the adjacent catalyst layer. It has been shown experimentally that the presence of the MPL can enhance the fuel cell performance under typical operating conditions; however, the fundamental understanding of its direct and indirect performance benefits is still an active field of research. Reliable, intrinsic MPL properties are difficult to measure experimentally, provided that the thin, delicate MPL material is not available as a discrete object and therefore requires a physical support upon which it is coated. Most commonly to date, MPL properties have been extracted from properties measured on full GDLs using established methods. However, due to the coupling and overlap of the macro-porous and micro-porous parts, such estimates are relatively crude. Numerical modeling of the structure and properties can therefore be useful to improve the fundamental understanding of the MPL material. More importantly, linking MPL structure and properties to fuel cell performance will ultimately close the design cycle for development of new materials and MEA concepts with enhanced performance. The present work focuses on examining the sensitivity of the fuel cell performance on MPL properties. To achieve this goal, a steady state three-dimensional two-phase flow numerical model is developed to simulate the fuel cell performance. The results of the simulations demonstrate a weak dependence of fuel cell performance on the electrical and thermal conductivity of the MPL within the considered range of analysis, i.e., between 0.05 to 0.25 W m-1 K-1 for the thermal conductivity and between 500 to 2500 S m-1 for the electrical conductivity. The effective diffusivity of the MPL, on the other hand, causes substantial changes in the fuel cell performance. Notably, the nano-scale pores of the MPL are sufficiently small for the mass transport to be dominated by Knudsen diffusion, which is intimately linked to the internal morphology of the material and also depends on the nature of the diffusing species. It is observed that when the effective diffusivity is decreased, the cell voltage drops considerably, as depicted in Figure 1. This shows the significance of MPL effective diffusivity and its impact on fuel cell performance. The effect is particularly critical at high current densities, which is a relevant operating range for automotive fuel cell technologies. MPL materials with sufficiently large pore sizes and porosity that support an effective diffusivity of at least 0.20 are recommended to avoid performance trade-offs. Acknowledgments: The research was supported by Mercedes-Benz Canada, Fuel Cell Division and the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".