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Record W3114762970 · doi:10.1149/ma2020-02352257mtgabs

Exploring Component Interactions during Mechanical Degradation of Fuel Cell Membranes with 4D in Situ Visualization

2020· article· en· W3114762970 on OpenAlexaffabout
Yadvinder Singh, Robin White, Dilip Ramani, Marina Najm, Alex Boswell, Francesco P. Orfino, Monica Dutta, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneMaterials scienceIonomerElectrolyteMembrane electrode assemblyDelamination (geology)Proton exchange membrane fuel cellComposite materialPolymerElectrodeChemistry

Abstract

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The ionomer membrane facilitates critical functions in a polymer electrolyte membrane fuel cell. Dynamic operational duty cycles can gradually introduce various damage features within the membrane, which could lead to overall performance failure of the fuel cell. Membrane durability is affected by chemical, mechanical, and thermal stressors that often act synergistically to degrade the ionomer membrane during fuel cell operation [1]. However, to understand the effect of each individual stressor, a useful approach is to isolate and study these degradation mechanisms separately. For instance, pure mechanical membrane degradation is typically affected in situ by actively cycling the membrane hydration levels under electrochemically inert conditions. These conditions generate cyclic mechanical stresses within the constrained membrane, leading to fatigue-induced fracture and electrode delamination under prolonged exposures [2]. When the cracks penetrate completely through the membrane thickness and spread across a substantial planar area, internal gas crossover is accelerated through convective transport, and ultimate fuel cell failure could result [3]. Furthermore, the mechanical membrane damage is strongly influenced by the microstructure of the adjoining membrane electrode assembly (MEA) components and cell design. Failure analysis investigations, using both 2D and 3D imaging, have consistently found significant proportion of membrane cracks to be co-located with the electrode cracks, which is typically attributed to the local stress concentration effects that could favour membrane crack initiation at these sites [2,4–6]. Recently, X-ray computed tomography (XCT) based 4D in situ visualization approach adopting a custom X-ray transparent fuel cell assembly [7] provided further insights into such interaction effects by demonstrating the temporal development of new membrane cracks underneath pre-existing electrode features [8]. Given the dominant role of electrode cracks in membrane fracture, the present work was undertaken to explore low crack density electrodes as a mitigation strategy against mechanical membrane degradation. This was achieved by subjecting two separate MEAs with different initial cathode catalyst layer (CCL) crack densities, i.e., high and low crack designs, to accelerated stress test (AST) involving humidity cycling for generating pure mechanical degradation. Periodic XCT-based in situ visualization was performed to elucidate differences in the magnitude and evolutionary processes of the membrane/MEA degradation between the two examined cases. Failure mechanisms responsible for mechanical membrane fracture were investigated in relation to the role of neighbouring components ― both electrodes and gas diffusion layers (GDLs). Membrane damage was found to be significantly curtailed through minimization of ab initio crack density in the CCL. While through-thickness membrane cracks developed within a similar timeframe in both high and low cathode crack density MEAs, the coverage area of fatigue-induced membrane fracture in the latter case was nearly six times lower after 2000 AST cycles, thereby establishing the adopted treatment of electrode morphology as an effective mitigation strategy in slowing the mechanical membrane degradation rate. Hydration-dehydration cycles, however, still altered this initial morphology by introducing electrode cracks at early stages of the AST. These electrode cracks, as an intermediate step, exclusively governed the subsequent initiation and propagation of membrane cracks. Two distinct membrane failure mechanisms were identified, each characterized by the presence and absence, respectively, of the MEA’s permanent buckling deformation into void spaces of the GDL (Figure 1). The buckling phenomenon was found to be strongly influenced by the non-uniform GDL microstructure and had a significant contribution towards the eventual frequency and coverage of membrane fracture. This finding suggests that critical component interactions during membrane failure extend beyond the immediately adjacent electrode layers. Additional comparative in situ visualizations of the fuel cell assembly held at varying hydration states showed that the size and geometry of the membrane fracture features that had developed in the low cathode crack MEA varied substantially between dry and wet environments. Acknowledgements 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. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. References [1] Rodgers, et al., Chem. Rev. 112 (2012) 6075–6103. [2] Sadeghi Alavijeh, et al., J. Electrochem. Soc. 162 (2015) F1461–F1469. [3] Lai, et al., J. Fuel Cell Sci. Technol. 6 (2009) 21002. [4] Singh, et al., J. Electrochem. Soc. 164 (2017) F1331–F1341. [5] Singh, et al., J. Power Sources. 345 (2017) 1–11. [6] Huang, et al., J. Polym. Sci. Part B 44 (2006) 2346–2357. [7] White, et al., J. Power Sources. 350 (2017) 94–102. [8] Singh, et al., J. Power Sources. 412 (2019) 224–237. Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.034
GPT teacher head0.221
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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