Characterizing Liquid Water Distribution in Polymer Electrolyte Fuel Cells Using Operando 2D and 3D X-Ray Imaging
Bibliographic record
Abstract
The potential to reduce the cost of Polymer Electrolyte Fuel Cells (PEFCs) through high power density operation is limited by the attending water management challenges at such operating conditions [1]. Addressing PEFC water management requires detailed understanding of liquid water distribution characteristics of an operating fuel cell, to refine the design of membrane electrode assemblies (MEAs) for improved performance. Recent advances in X-ray operando imaging [2, 3] and custom hardware development [4], have enabled the visualization of liquid water in an operating PEFC, through the analysis of water phase segmented 3-dimensional (3D) images [2, 3] and 2-dimensional (2D) radiographs [5]. 2D image datasets may be acquired in a shorter time than 3D tomographic datasets but only provide grayscale information averaged through the thickness of the sample. 3D image datasets obtained from a reconstruction of many angular radiographs yield a 3D representation of the sample in addition to the grayscale information but take a long collection time. However, time dependent liquid water distribution characteristics such as channel water breakthrough, observable in the 2D image datasets, tend to be elusive to 3D imaging methods. Although efforts have been made to reduce the image acquisition time for 3D X-ray imaging by using Synchrotron sources and improved image processing techniques [2], these efforts tend to compromise image quality and are restricted to a brief temporal snapshot of liquid water distribution. Also, the brightness of the Synchrotron sources necessitates short exposure duration tests due to the likelihood of cell degradation caused by the sample’s exposure to radiation [4]. Given the unique capabilities of 2D and 3D imaging modes, we therefore focus on combining information accessible in both modes to further the understanding of PEFC liquid water distribution. In this work, an improved understanding of liquid water distribution in the PEFC is presented by integrating operando 2D and 3D image datasets acquired under the same cell operating conditions and using laboratory scale X-ray computed tomography (XCT) equipment which was interfaced with fuel cell testing and diagnostic tools. Analysis of 3D image datasets is extended beyond binary information to include the grayscale value (GSV) of liquid water to reveal a change in the through-plane values. These results, when combined with observed 2D liquid water breakthrough characteristics suggest that the change in GSV is indicative of the differences in the intermittency of liquid water presence in the different regions of the GDL; see Figure 1. These results therefore present a more complete interpretation of PEFC operando liquid water distribution, highlighting distinct characteristics from the microporous (MPL) region to the GDL-land interface. Unique findings and insight obtained using this methodology will be discussed. Acknowledgments 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, Western Economic Diversification Canada, and Canada Research Chairs. References Jiao and X. Li, Progress in energy and combustion Science, vol. 37, no. 3, pp. 221–291, 2011. Xu, S. Nagashima, H. P. Nguyen, K. Kishita, F. Marone, F. N. Büchi, and J. Eller, Journal of Power Sources, vol. 490, p. 229492, 2021. Nagai, J. Eller, T. Hatanaka, S. Yamaguchi, S. Kato, A. Kato, F. Marone, H. Xu, and F. N. Büchi, Journal of Power Sources, vol. 435, p. 226809, 2019. T. White, F. P. Orfino, M. El Hannach, O. Luo, M. Dutta, A. P. Young, and E. Kjeang, Journal of The Electrochemical Society, vol. 163, no. 13, pp. F1337–F1343, 2016. R. Banerjee, N. Ge, J. Lee, M. G. George, S. Chevalier, H. Liu, P. Shrestha, D. Muirhead, and A. Bazylak, Journal of The Electrochemical Society, vol. 164, no. 2, p. F154, 2017. 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 | 0.001 |
| 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) | 0.001 | 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".