Periodic Tracking of Operando Liquid Water Distributions in PEFCs Subjected to Voltage Cycling Using Micro X-Ray Computed Tomography
Bibliographic record
Abstract
Low temperature polymer electrolyte fuel cell (LT-PEFC) durability is a key aspect for successful commercialization of this technology. Corrosion of carbonaceous fuel cell components was identified as a major degradation mechanism [1] reducing fuel cell lifetime. Carbon corrosion, which takes place at high local electrochemical potentials in the presence of water, leads to a loss of electrochemical surface area due to detachment and agglomeration of the Pt nanoparticles deposited on the carbon support material as well as loss of electrical pathways. The corrosion induced structural collapse of the cathode catalyst layer (CCL) causes a more tortuous pore space as well as blockage for reactant gas pathways to the catalyst surface and, hence, increased mass transport overpotential. Additionally, the CCL can be made more hydrophilic by addition of oxide surface groups [2]. This can potentially induce changes in gas diffusivity as the CCL becomes more prone to liquid water flooding. Understanding and quantifying the role of liquid water in the various degradation mechanisms is therefore essential for developing novel fuel cell materials and mitigation strategies. In this work, operando lab-based micro X-ray computed tomography (µ-XCT) was applied, for the first time, to track the liquid water distribution in the MEA throughout the lifetime of a fuel cell. This novel methodology is enabled by the non-invasive and non-destructive nature of lab-based XCT [3], allowing multiple identical-location scans at different points in time without interfering with the fuel cell operation or the degradation process. The technique is demonstrated by monitoring the changes in the liquid water distribution within the gas diffusion and catalyst layers of a LT-PEFC subjected to a voltage cycling accelerated stress test designed to induce carbon corrosion. Changes in water accumulation within the porous transport layers as a result of carbon support corrosion are correlated to changes in CCL morphology (thickness and crack size) and CCL composition (Pt/ionomer/carbon content). Representative three dimensional tomography datasets, recorded at beginning-of-life, are shown in Figure 1. The imaging results are further supplemented by electrochemical characterization such as polarization curves, electrochemical surface area (ECSA) and electrochemical impedance spectroscopy (EIS). Highlights of the presentation include the impact of catalyst layer collapse and thinning on liquid water retention behavior of the CCL as well as the effect of increased heat generation on GDL water saturation. Acknowledgments: Funding for this research was provided 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 grant. References: [1] R. Borup, J. Meyers, B. Pivovar, Y.S. Kim, R. Mukundan, N. Garland, D. Myers, M. Wilson, F. Garzon, D. Wood, P. Zelenay, K. More, K. Stroh, T. Zawodzinski, J. Boncella, J.E. McGrath, M. Inaba, K. Miyatake, M. Hori, K. Ota, Z. Ogumi, S. Miyata, A. Nishikata, Z. Siroma, Y. Uchimoto, K. Yasuda, K.I. Kimijima, N. Iwashita, Chem. Rev.,107, 3904 (2007). [2] K. H. Kangasniemi, D. A. Condit, and T. D. Jarvi, J. Electrochem. Soc., 151, E125 (2004). [3] R. T. White, M. Najm, M. Dutta, F. P. Orfino, and E. Kjeang, J. Electrochem. Soc., 163 F1206-F1208, (2016). Figure 1. (a) Operando liquid water distribution (blue) and gas diffusion layer structure (light gray) at the cathode. (b) Water (blue), flow field (light gray) and cathode catalyst layer grayscale values in false color including cracks (dark gray). Both images represent a 3D rendering of the segmented phases in perspective projection at BOL (field of view 2.7x2.9 mm2; 750 mA cm-2; 23°C). 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".