Visualization of Water Distribution in Fuel Cell Microporous and Catalyst Layers with 3D Nanoscale X-Ray Imaging
Bibliographic record
Abstract
As an efficient green energy technology, polymer electrolyte membrane fuel cells (PEMFCs) are rapidly growing in a wide range of applications. Development of high power-density PEMFCs is under investigation to further reduce the overall cost and footprint. However, in such operational conditions, excessive water production could restrict the transport of reactants due to flooding, and subsequently degrade the performance of the PEMFC. This makes the water management a major challenge and necessitates a good understanding of water distribution and transport within the porous layers of the PEMFC [1, 2]. 3D X-ray computed tomography (XCT) is a strong tool for visualizing the water distribution within porous media. Micro-scale XCT has shown great promise in understanding and visualizing the water behavior within the gas diffusion layer (GDL) substrate region possessing ~10-100 µm pores [3–6]. This resolution, however, is not sufficient to capture water distribution in microporous and catalyst layers (MPL and CL), which are predominantly composed of nano-scale pores. This work aims to explore a method of water capture and visualization within the MPL and CL of a PEMFC using a lab-based nano-resolution XCT (NXCT) with a spatial resolution as high as 50 nm. Benefitting from the non-invasive NXCT imaging technique, the same spot of a dry/wet material has been studied in Zernike phase contrast mode using a custom-built enclosed fixture with high X-ray throughput to maintain a constant humidity (100% RH) over the tomographic acquisition period. Water domains have been identified after alignment of wet and dry image data sets followed by subtraction of dry from wet data set. Figure 1a shows a 2D projection of the MPL on AvCarb GDL after vacuum-assisted water infiltration. A gold microsphere is fixed on the sample within a thin layer of epoxy to allow for accurate identification of the region of interest. Figure 1b shows a selected segmented 2D projection of wet MPL illustrating how liquid water clusters are distributed within the pores and the 3D volume rendering of structural features is displayed in Figure 1c. Using the above-described method, 18% and ~7% volume fraction of liquid water has been identified within the wetted MPL and CL, respectively. The methodology discussed here is a step toward deep understanding of water transport phenomena in CL and MPL materials dictating the strategies for materials design to prevent flooding under high current densities. Moreover, the set-up and methodology can be further optimized to visualize water domains under controlled temperature and relative humidity, to simulate the real operating conditions. Figure 1. XCT imaging of a wet MPL: (a) A radiograph of MPL on AvCarb GDL after vacuum-assisted water infiltration; (b) a selected 2D projection of MPL showing the segmented solid (green), liquid water (blue), and unoccupied pores (black) domains; and (c) 3D volume rendering of a subdomain in the MPL showing the distribution of liquid water within the pores of the structure (voxel size 32 nm in b and c). Acknowledgement Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Ballard Power Systems, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Canada Research Chairs. References [1] Q. Liu, F. Lan, J. Chen, C. Zeng, and J. Wang, J. Power Sources, 517, 2021, 230723, 2022. [2] A. Bazylak, Int. J. Hydrogen Energy, 34, 9, 3845–3857, 2009. [3] R. T. White et al., Sci. Rep., 9, 1, 1–12, 2019. [4] M. Andisheh-Tadbir, F. P. Orfino, and E. Kjeang, J. Power Sources, 310, 61–69, 2016. [5] J. Eller, T. Rosén, F. Marone, M. Stampanoni, A. Wokaun, and F. N. Büchi, J. Electrochem. Soc., 158, 8, B963–B970, 2011. [6] P. Shrestha, C. Lee, K. F. Fahy, M. Balakrishnan, N. Ge, and A. Bazylak, J. Electrochem. Soc., 167, 5, 054516, 2020. 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".