(Invited) Examining Catalyst Layer Design Strategies for Improved Fuel Cell Performance and Durability
Bibliographic record
Abstract
Introduction The commercialization of fuel cell products for a variety of applications including forklifts, buses, cars and other heavy duty transport is well underway. Recent studies suggest that over the next 10 years, the total cost of ownership of fuel cell vehicles will become more favourable than both battery electric vehicles and internal combustion engine commercial vehicles1,2. This is driven, in part, by a forecasted almost 50% decrease in the cost of fuel cell systems. While this achievement is mainly due to product volume increases and related economies of scale, further improvements of fuel cell technology can be achieved through next generation catalyst layer designs. Design approaches related to the individual component characteristics, optimization of the component interactions to obtain enhanced morphology and microstructure, as well as processing techniques are currently being developed. Strategies to lower catalyst loadings and move to higher current operation and power densities include incorporating high activity catalysts and improving proton conductivity and oxygen transport by integrating advanced ionomers. Advanced cathode catalysts3 such as Pt alloy (de-alloy), core-shell and shape controlled nanocrystals show more promise in achieving higher mass activity than conventional carbon supported platinum catalyst, although durability or technology maturity challenges remain. Alternatively, while performance gains through ohmic improvements can be achieved by utilizing high proton conductivity ionomers4, additional opportunities such as incorporating novel ionomer materials5, with increased oxygen permeability to allow for reduction of the oxygen thin film transport resistance are of interest. In addition to maximizing the catalyst and ionomer attributes, catalyst-ionomer interaction understanding at the molecular scale and its influence on catalyst layer morphology and transport properties is crucial. Earlier studies of ionomer thin films6, molecular dynamics modeling7 as well as catalyst layer characterization by XPS8, indicate that the ionomer morphology within confined thin films, as well as the resulting film properties, are not only dependent on the surface properties of the substrate / catalyst material and water content, but can be altered in situ as those parameters change. Therefore, the catalyst-ionomer interaction and morphology must be better understood and controlled. Although it can be optimized through design choices, processing parameters during catalyst layer fabrication can also be effective. For example, Figure 1a) shows changes to the cathode catalyst layer pore size distribution with four different processes, incorporating changes to the ink preparation, coating and drying parameters, while keeping the same catalyst layer composition and loading. The optimized process resulted in improved catalyst and ionomer dispersion, lower ionic resistivity and improved performance. Moreover, Figure 1b) shows a clear relationship of improved performance with larger catalyst layer pore size. In addition to microstructural characteristics, macroscopic non-uniformities of the catalyst layer, such as cracks and roughness variations, can also affect the performance and durability of the MEA and are an area of focus for quality control tools9. Recent four dimensional X-ray computed tomography studies have shown how pre-existing cracks within the catalyst layer grow with accelerated stress testing and can be detrimental to both the voltage and membrane durability of the MEA10-11. All in all, a multiscale research approach to gain understanding focussed on nanoscale, microscale and macroscale features of the catalyst layer for next generation designs is recommended and will be the focus of the presentation. Acknowledgement The authors would like to acknowledge Natural Sciences and Engineering Research Council of Canada (NSERC), Mitacs, Automotive Partnership Canada (APC), and the US Department of Energy (DOE) for funding various aspects of this work. References Deloitte and Ballard. (2020). Fueling the Future of Mobility: Hydrogen and fuel cell solutions for transportation. Volume 1 [White paper]. Deloitte China. https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/finance/deloitte-cn-fueling-the-future-of-mobility-en-200101.pdf Hydrogen Council. (2020). Path to hydrogen competitiveness: A cost perspective. https://hydrogencouncil.com/wp-content/uploads/2020/01/Path-to-Hydrogen-Competitiveness_Full-Study-1.pdf Banham, D. and Ye, S., ACS Energy Letters 2(3): 629-638 (2017) Young, A.P. et al. 230th ECS Meeting in Honolulu, Hawaii (October 2-7, 2016) Rolfi et al. J. Power Sources, 396 (2018) 95 - 101 Kusoglu, A. and Weber, A. Z. Chem. Rev. 2017, 117, 3, 987–1104 Borges, D. D. et al. J. Phys. Chem. C 2015, 119, 2, 1201–1216 Artyushkova, K. et al. J. Power Sources 284 (2015) 631-641 Philips, A. et al. Int. J. Hydrog. Energy 2018, 43, 12, 6390-6399 White, R. T. et al. J. Power Sources 350 (2017) 94-102 Singh, Y. et al. J. Power Sources 412(2019) 224-237 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".