Method for Analyzing 2D X-Ray Transmission Images for Operando Liquid Water Distribution in a Polymer Electrolyte Fuel Cell
Bibliographic record
Abstract
There is a potential to increase the zero-emission polymer electrolyte fuel cell (PEFC) efficiency through high power density operation. However, water management issues become significant under these conditions, necessitating improved water management strategies [1]. As a precursor, understanding of liquid water distribution is instrumental to developing optimal water management strategies. Various techniques including neutron imaging, electron microscopy, and X-ray imaging have been used to study liquid water transport in fuel cells. Of these, the X-ray computed tomography (XCT) method has provided unprecedented insights gained through in-operando visualization, yielding 3-dimensional (3D) information [2, 3]. The 3D grayscale data set is obtained by first acquiring multiple projections of the sample at different angles which are then reconstructed to yield a 3D representation. The 3D representation may then be processed to segment features such as liquid water and other features of interest [3, 4]. However, the acquisition of such 3D datasets typically takes several hours on a lab-scale XCT instrument [5]. This may sometimes result in a dataset that is difficult to interpret if the imaged sample evolves significantly during the acquisition time. Furthermore, phenomena of interest, such as liquid water distribution may sometimes be challenging to capture with 3D datasets. In this work, we therefore explore the use of transmission radiograph imaging to further understand the distribution of liquid water in an operating fuel cell. The method developed involves the analysis of in-operando transmission images within the framework of the X-ray attenuation laws to provide qualitative, as well as quantitative saturation and liquid water distribution information. Sequential images of a miniaturized operating fuel cell were acquired at 0- and 90-degree angles to the fuel cell plane within a laboratory XCT equipment, while cell operational conditions were controlled by an external fuel cell test station. This approach trades off 3D information for short time scans afforded by 2D acquisition procedure, enabling the identification of liquid water droplet breakthrough dynamics at the cathode gas diffusion layer, as shown in Figure 1. The observed liquid water breakthrough yields new findings on the nature of liquid water distribution in the flow channels, often elusive to 3D approaches. Methods and analysis developed may therefore be used to augment information derived from 3D visualization methods. 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, Canada Research Chairs. References Jiao K and Li X 2011 Progress in energy and combustion Science 37 221–291. Nagai Y, Eller J, Hatanaka T, Yamaguchi S, Kato S, Kato A, Marone F, Xu H and B¨uchi F N 2019 Journal of Power Sources 435 226809. Eller J, Roth J, Marone F, Stampanoni M and B¨uchi F N 2016 Journal of The Electrochemical Society 164 F115. White R T, Eberhardt S H, Singh Y, Haddow T, Dutta M, Orfino F P and Kjeang E 2019 Scientific reports 9 1–12. Withers P J, Bouman C, Carmignato S, Cnudde V, Grimaldi D, Hagen C K, Maire E, Manley M, Du Plessis A and Stock S R 2021 Nature Reviews Methods Primers 1 1–21. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".