Microporous Layer Design for High Performing Open-Cathode Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFCs) are a key sustainable energy source which can cater for energy demands ranging from miniature power requirements to portable, automotive and various stationary power applications. PEMFCs deliver electricity by means of undergoing electrochemical reactions and produce water and heat as by-products. At high current densities, flooding and overheating can cause local hot spots leading to degradation and failure of the membrane electrode assembly (MEA) during long term operations [1]. Hence, hygrothermal management is normally ensured by means of liquid cooling of the bipolar plates. However, this traditional design incurs extra cost of balance-of-plant (BOP) components and enhances the overall size of the system. Using ambient air as oxidant as well as coolant with an ‘open-cathode’ design can drastically reduce the system size and weight, however the performance of such a system is limited by overheating and drying of the MEA. The objective of the present work is to develop a suitable microporous layer (MPL) design for efficient cell performance of an open-cathode PEMFC system operating at hot ambient air conditions. A pre-validated comprehensive, 3D computational fuel cell model [2] is used to study the mass, momentum and heat balance inside the system along with water transport and current distribution in various components of the system. The gas diffusion layer (GDL) comprised of a bi-layer macroporous substrate and MPL structure is one of the key components of the overall system design and plays a significant role in water transport inside the cells [3]. Whereas in conventional liquid-cooled systems, the presence of a hydrophobic MPL can facilitate wicking of liquid water out of the MEA to prevent oversaturation and flooding [4], ‘open-cathode’ fuel cells are more likely to experience drying than flooding and may therefore benefit from alternate MPL designs. As shown in Figure 1, thinner MPLs (30 μm) with higher porosity (60%) are found to result in elevated open-cathode cell performance as a result of optimum water retention and O2 diffusion across the MPL. The increment in performance is found to be broadly visible at higher current densities, where mass transport effects are more dominant. The MPL effective diffusivity which is a function of its porosity along with the MPL thickness are also found to be important for the overall thermal and water balance of the MEA. The optimum RH and temperature levels achieved in the MEA are further found to negate the overheating and drying effects obtained in earlier system designs. Acknowledgments This work was supported by the funding provided by Simon Fraser University and Indian Oil R&D Centre under the SFU-IOCL joint PhD program in clean energy. References [1] S. Shahsavari, A. Desouza, M. Bahrami, and E. Kjeang, Int. J. Hydrogen Energy, vol. 37, no. 23, pp. 18261–18271, 2012. [2] I01B-1486, A. Sagar, S. Chugh, A. Sharma, and E. Kjeang, in 236th ECS Meeting (October 13-17, 2019), 2019. [3] K. Kang and H. Ju, J. Power Sources, vol. 194, no. 2, pp. 763–773, 2009. [4] U. Pasaogullari and C. Y. Wang, Electrochim. Acta, vol. 49, no. 25, pp. 4359–4369, 2004. 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".