In-Situ Capabilities of Lab-Based X-Ray Microscopy for Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
X-ray Computed Tomography has proven to be a crucial method to properly characterize and understand a wide variety of materials applications. Due to the non-destructive 3D imaging capabilities, this imaging methodology provides unique insight into material properties with comparatively minimal sample processing. One limitation, however, has been resolution capabilities. Previously, X-ray tomography imaging has utilized the principle of geometric magnification to obtain resolution. This has several limitations, the main impact being the realistic resolution achievable for a given sample size. Recently, the application of using a photon-converting scintillator and objective lens magnification has enabled much higher resolution imaging [1]. A unique ability of this system architecture is to enable non-destructive, multi-length scale visualization for relatively large sample sizes; with an imaging field of view range from tens of millimeters down to tens of micrometers, and resolution capabilities reaching 500 nm in instruments such as the ZEISS Xradia Versa. This architecture provides capabilities perfectly suited for complex device visualization, such as polymer electrolyte fuel cell (PEFC) systems, as well as the ability to image in-situ. By combining 3-dimensional visualization through repeated identical location tomography scans at various temporal stages, powerful in-situ investigations of dynamic material properties can be obtained. This methodology is often termed as 4DCT (4-dimensional computed tomography) and is particularly well suited to study various dynamic and evolutionary processes in PEFCs due to their complex multi-layered and entangled system. Detailed images of the membrane electrode assembly (MEA) can be periodically obtained while the cell is still assembled in its operational housing as well as while it is producing current, to observe transient liquid water pathways [2, 3]. Extraction of quantitative information regarding material/geometrical properties, such as thickness, porosity, saturation, and local deformation, as well as temporal changes to these properties and morphology during degradation processes is facilitated by advanced image processing and visualization methodologies [3,4,5]. In this presentation, custom tools, workflows, and analysis methods are showcased that allow for insight into the lifetime changes of cathode catalyst layer morphology, water saturation, and crack propagation. It has been found through ageing that morphological interaction between different layers can have considerable impact on degradation mechanisms [5,6,7]. We present an overview of the 4DCT approach applied to various fuel cell degradation studies as well as GDL water distribution during in situ imaging. These visualization methods uncover unique evidence around the strongly interactive nature of material degradation within a fuel cell that has previously been unobserved. Figure 1: Overview of lab-based in-situ imaging methodology within the Zeiss Xradia Versa system. A model of the X-ray microscope is shown with an ‘opened’ in-situ fuel cell device. Resulting segmented liquid water and cathode catalyst layer before and after ageing are shown with GDL, ionomer membrane and anode catalyst layer removed for visualization. Acknowledgements The authors would like to acknowledge the support from Monica Dutta and Ballard Power Systems in providing samples and discussion of the presented work, as well as all members of the Fuel Cell Research Lab for their kind support. References [1] ZEISS Xradia Versa Product Information. https://www.zeiss.com/microscopy/us/products/x-ray-microscopy/zeiss-xradia-610-and-620-versa.html , accessed 24 April, 2020. [2] White, et al., J. Power Sources. 350 (2017) 94–102. [3] White, et al., Scientific Reports. 9 (2019) 1843. [4] White, et al., J. Electrochem. Soc. 166 (2019) F914-F925. [5] Ramani, et al., Int. J. Hydrogen Energ. 45 (2020) 10089-10103. [6] Singh, et al., J. Power Sources. 412 (2019) 224–237. [7] Singh, et al., J. Power Sources. 345 (2017) 1–11. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".