Quality Implications of Foreign Metallic Particles in the Membrane Electrode Assembly of a Fuel Cell
Bibliographic record
Abstract
Hydrogen-based polymer electrolyte membrane fuel cells (PEMFCs) are advantageous in terms of high efficiency, zero emissions, and low-temperature operation in both stationary and transportation sectors. Recently, efforts to reduce the cost by developing novel materials used in the membrane electrode assembly (MEA) and its design are underway. Besides the materials, the other factor that could contribute to cost reduction is the high volume production of MEA components with a high yield 1 . Additionally, during MEA assembly, certain abnormal process conditions or external contaminants may negatively affect the quality of the MEA and its compatibility with scalable high-volume manufacturing. The durability of the perfluorosulfonic acid (PFSA) ion exchange membrane is a major concern for the fuel cell industry, especially under Fenton’s cations such as Fe 2+ 2–5 . Stainless steel instruments used during the production of catalyst coated membranes (CCMs) may be considered as a possible source contributor of contamination. Micron sized metallic particles of SS316L (or Fe) could attach themselves on to the membrane during fabrication; thus, potentially cause MEA structural damage by perforation, rupture, or electrode delamination. Additionally to structural damage, there is also a possibility that Fe 2+ cations leaching from metal corrosion would reduce the membrane proton conductivity, cause backbone chain scission, etc 2,6 . Whereas the presence of cations has been studied extensively in ex-situ Fenton’s reagent testing of ion-exchanged membranes, controlling, and understanding the role of such cations in the in-situ fuel cell environment is much more challenging. In such an instance, membrane thinning and degradation are anticipated due to the presence of cations which act as a catalyst for radical generation during fuel cell operation 6 ; therefore, it is essential to understand the effect of such metallic particles on the membrane and by extension, improving the fundamental understanding of the effects of various irregular features and foreign contaminants is desirable 7,8 . The present work focuses on understanding the effect of unintended solid metallic particles in the MEA during and after the conditioning phase of a fuel cell. Stainless steel 316L (SS316L) and pure iron (Fe) particles are located conveniently at the interface of the cathode catalyst layer (CCL) and membrane (M) inside an MEA using a robust and controlled method. During the conditioning phase, the MEA is subjected to a sequential order of air starves, cyclic voltammetry, and constant current hold procedures. After each conditioning procedure, cell imaging is performed using the X-ray computed tomography (XCT) method, which has been proven to provide relevant information without damaging the integrity of the fuel cell 9,10 . Figure 1(a) &(c) shows the position of SS316L and Fe 50µm particles, respectively located at the CCL/M interface before conditioning, as originally planned. Initializing the air starve cycles, the MEAs containing particles indicates that the SS316L-50µm remains intact, as seen in Figure 1(c), and the Fe-50µm tends to dissolve and leave behind a void directly exposing the membrane as shown in Figure 1(d). The dissolution of Fe particles also leaves behind an ion concentration of more than 50 ppm in the active area. It is believed that Fe 2+ leaches from SS316L corrosion as well; however, the process is delayed by the native oxide layer formation on its surface and the concentration of Fe 2+ is lower. Therefore, it is important to know the response of such particles when conditioning an MEA so that better quality control processes can be identified prior to fuel cell assembly. Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, Ballard Power Systems, and W.L. Gore & Associates. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. References G. Bender, W. Felt, and M. Ulsh, J. Power Sources , 253 , 224–229 (2014) http://dx.doi.org/10.1016/j.jpowsour.2013.12.045. J. G. Goodwin, K. Hongsirikarn, S. Greenway, and S. Creager, J. Power Sources , 195 , 7213–7220 (2010) http://dx.doi.org/10.1016/j.jpowsour.2010.05.005. J. Qi et al., J. Power Sources , 286 , 18–24 (2015) http://dx.doi.org/10.1016/j.jpowsour.2015.03.142. A. Tavassoli et al., J. Power Sources , 322 , 17–25 (2016) http://dx.doi.org/10.1016/j.jpowsour.2016.05.016. N. Kumar et al., Int. J. Energy Res. , 44 , 6804–6818 (2020). S. Kundu, L. C. Simon, and M. W. Fowler, Polym. Degrad. Stab. , 93 , 214–224 (2008). S. Komini Babu et al., J. Electrochem. Soc. , 168 , 024501 (2021). A. Phillips, M. Ulsh, K. C. Neyerlin, J. Porter, and G. Bender, Int. J. Hydrogen Energy , 43 , 6390–6399 (2018) https://doi.org/10.1016/j.ijhydene.2018.02.050. D. Ramani et al., Int. J. Hydrogen Energy , 45 , 10089–10103 (2020) https://doi.org/10.1016/j.ijhydene.2020.02.013. Y. Singh et al., J. Power Sources , 412 , 224–237 (2019). Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".