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Record W4416600108 · doi:10.1149/ma2025-02401982mtgabs

Interactions between Gas Diffusion Layer Structure and Membrane Durability in Fuel Cells

2025· article· W4416600108 on OpenAlexaffabout
Fabusuyi Akindele Aroge, Nitish Kumar, Francesco P. Orfino, Kim Pascal, Esmaeil Navaei Alvar, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDurabilityMicroporous materialMembrane electrode assemblyProton exchange membrane fuel cellMembraneCathodeLayer (electronics)Fuel cells

Abstract

fetched live from OpenAlex

Quality control of membrane electrode assembly (MEA) components is critical for the durability and lifetime of fuel cells. Previous gas diffusion layer (GDL) research has shown that features such as microporous layer (MPL) cracks or substrate pores may contribute to buckling-driven cracks in the membrane 1,2 . Our group recently reported a methodology for controlled implementation of GDL holes to isolate and determine their express impact on membrane durability 3 , which revealed that through-plane catalyst-coated membrane (CCM) cracks are likely to develop at small GDL holes located in regions under high compression, due to maximum stress concentration. The objective of the present work is to evaluate the systematic interactions between the GDL structure and chemo-mechanical membrane durability in fuel cells, considering a broader range of GDL materials with variations in physical properties and surface structure and their compatibility with thin, mechanically reinforced membranes. An in-situ 4D visualization technique by X-ray computed tomography (XCT) was chosen for the study, and the GDL samples were visualized using an XCT-compliant small-scale fuel cell, as was previously shown 4 . The MEAs comprised of GORE-SELECT® mechanically reinforced membrane, Pt/C catalyst layers, and GDLs with selected variations in substrate architecture (hydroentangled, dry-laid (Freudenberg) and wet-laid (AvCarb®) non-woven carbon papers), MPL thickness, and artificial hole presence. Based on previous work 3 , a 0.2 mm 2 artificial through-thickness GDL hole was implemented within the fuel cell active area and the XCT field of view on the cathode GDL. All MEAs, with and without GDL holes, were tested under a custom-developed chemo-mechanical accelerated stress test protocol with an inclination towards mechanical stressors. Periodic identical location in-situ imaging was performed to track the degradation phenomena. The XCT images indicated significant uniform membrane thinning and minor GDL impingement in the defect-free baseline wet-laid GDL MEA. However, when tested with GDL holes, severe membrane buckling resulted in a substantial crack network formation in the CCM beneath the channels. The outcomes for the dry-laid and wet-laid substrates were similar in terms of test lifetimes, although certain differences were observed in the localized failure mechanisms. The extent of non-uniform global membrane thinning and fiber impingement increased drastically for the case of thin MPL. It is worth noting that exacerbated membrane thinning may be attributed to the membrane not being chemically stabilized. Furthermore, the relatively high surface roughness of the thin MPL enhanced the crack network formation in the catalyst layer, significantly reducing the membrane lifetime. Therefore, this study demonstrates that MPL roughness diminishes the lifespan of the MEA by promoting the creation of impingement sites and buckling, which leads to catalyst layer cracks and membrane thinning, culminating in membrane failure. A smooth MPL structure that is capable of distributing applied stress is required in order to ensure high membrane durability. Keywords: fuel cell, membrane durability, X-ray computed tomography, gas diffusion layer defect, quality control Acknowledgement This research was supported by the Natural Sciences and Engineering Research Council of Canada, Mitacs, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Pacific Economic Diversification Canada, and Ballard Power Systems. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. References: S. Prass, S. Hasanpour, P. K. Sow, A. B. Phillion, and W. Mérida, J. Power Sources , 319 , 82–89 (2016). D. Ramani et al., J. Power Sources , 512 , 230446 (2021). Y. Chen et al., ECS Meet. Abstr. , MA2023 - 02 , 1780–1780 (2023). Y. Chen et al., J. Power Sources , 520 , 230674 (2022).

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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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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
Published2025
Admission routes2
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→