Evolution of Membrane Degradation in Fuel Cells Observed By X-Ray Computed Tomography
Bibliographic record
Abstract
Perfluorosulfonic acid (PFSA) ionomer membranes are subjected to simultaneous chemical and mechanical degradation under fuel cell operation. Open circuit voltage (OCV) operation is typically employed as an in-situ accelerated stress test (AST) to expedite the chemical degradation which plays a key role in membrane thinning. The in-situ mechanical degradation is typically produced using wet/dry humidity cycles leading to micro crack initiation/propagation in the membrane [1]. Characterization of morphological changes in the membrane electrode assembly (MEA) is helpful in understanding the degradation mechanisms at play during fuel cell operation. 2D visualization techniques, such as optical and electron microscopy [2], have typically been used to determine the structural features of the MEA. However, these techniques are destructive and 2D in nature, and demand elaborate sample preparation. The X-ray computed tomography (XCT) technique overcomes these limitations by integrating several 2D images acquired at diverse incident angles into a virtual 3D image. Furthermore, modern XCT systems achieve sub µm resolution which is adequate for imaging membrane cracks and other damage features. In this work, the XCT technique is employed to investigate the structural evolution of membrane degradation over time in the presence of combined chemical and mechanical stressors. Additionally, the observed trends are correlated with various material and diagnostic properties to develop a better understanding of the degradation patterns. Partially degraded MEAs extracted after different numbers of operating cycles when subjected to cyclic open circuit voltage (COCV) AST protocol are visualized using the XCT technique and the evolution of membrane degradation is studied by mapping the 3D structural/morphological changes over time. Crack distribution and morphology are examined from various perspectives by studying the 2D planar and cross-sectional views of the 3D reconstructed images. No through-thickness membrane crack is detected up to 60% of the MEA lifetime which suggests that sizeable crack development occurs mainly during the 60-85% period of the MEA lifetime. A detailed survey exhibits eight cracks at end of life (EOL) and five cracks at 85% degradation across a survey area of 0.44 mm2. All identified membrane cracks are observed to have a single distinct fragment of X or I-shape on the surface of the membrane. The X-cracks in their initial stage of development observed after 85% degradation are likely to grow along their width, while I-cracks tend to be slender, as shown in Figure 1. In addition, about 31% of cracks after 85% degradation and 50% of cracks at EOL are exclusive membrane cracks without any connectivity with the catalyst layers. The membrane crack width is almost uniform along its length after 85% degradation but has greater variation along the length at EOL resulting in an increased maximum crack width to crack length ratio likely caused by in-plane stresses [3]. The rapid decrease in the fracture strain of the membrane during the AST suggests increasing brittleness, which represents the most pronounced change in the mechanical properties [4]. This illustrates that the membrane encounters reduced mechanical strength and leads to more branching of cracks with increasing AST cycles. The combined effect of mechanical and chemical degradation is likely to have created favorable conditions for crack initiation and propagation inside the mechanically weakened membrane. The work summarized here is a unique attempt to study the evolution of membrane degradation with a 3D perspective. These new findings demonstrate that adoption of XCT technology can provide a distinct advantage in understanding the pattern of membrane degradation, thereby enabling the capture of critical failure modes that may be invoked at different stages of fuel cell operation. Acknowledgement This research was funded by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Ballard Power Systems through an Automotive Partnership Canada (APC) grant. References [1]R.M.H. Khorasany, A.S Alavijeh, E. Kjeang, G.G. Wang, R.K.N.D. Rajapakse. J. Power Sources 274 (2015) 1208-1216 [2]K.S Alcantara, D.C. Martınez-Casillas, K.B. Zheng, Q. Zhu, M. Abdellah, D. Haase, T. Pullerits, O. Solorza-Feria, S.E. Canton. Int. J. Hydrogen Energy, 39 (10) 5358–5370. [3]Y. Singh, O. Luo, F.P. Orfino, M. Dutta, E. Kjeang, 3D Analysis of PEM Fuel Cell Membrane Cracks using X-Ray Computed Tomography, in: 228th Meet. Electrochem. Soc., Phoenix, 2015. [4] C. Lim, L. Ghassemzadeh, F. Van Hove, M. Lauritzen, J. Kolodziej, G.G. Wang, S. Holdcroft, E. Kjeang. J. Power Sources. 257 (2014) 102. 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".