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Membrane Integrity in the Presence of Foreign Particles in the Membrane Electrode Assembly

2025· article· en· W4416600771 on OpenAlexafffundabout
Nitish Kumar, Amin Bahrami, Yixuan Chen, Ethan Allan Brown, Francesco P. Orfino, Monica Dutta, Michael Lauritzen, Erin Setzler, Alexander Agapov, Erik Kjeang

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersWestern Economic Diversification CanadaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationW. L. Gore and AssociatesBallard Power Systems
KeywordsProton exchange membrane fuel cellMembraneDurabilityParticle (ecology)Membrane electrode assemblyElectrodeCathodeCatalysis

Abstract

fetched live from OpenAlex

Cost-effective, large-scale production of reliable proton exchange membrane fuel cells (PEMFCs) is crucial to meet growing green energy demands. High-throughput production of PEMFCs relies on heavy machinery, which may inadvertently introduce foreign particles into the membrane electrode assembly (MEA) and affect PEMFC durability and performance 1–3 . A wide variety of particles could conceivably be introduced into the MEA and may or may not be detected. Thus, understanding the potential impacts of these particles is essential. The objective of the present work is to determine the impacts of foreign particle dimensions, shape, hardness, and chemical composition on membrane integrity and durability in PEMFCs. To accommodate a suitably wide range of particles for testing, the study focuses on assessing the nature and extent of membrane damage upon fuel cell assembly, conditioning, and initial operation as well as the prospects of mitigation. Composite material (CM) spheres with diameters of 5, 15, 60, and 100 µm along with 500 µm spherical glass beads, 50 µm slightly oxidized iron (Fe) particles, and stainless steel 316L particles of 50 and 500 µm were selected for this research. The particles were strategically placed between a mechanically reinforced GORE-SELECT® membrane (MemA) and the cathode catalyst layer. An MEA was fabricated using the particle-laden catalyst coated membrane (CCM) and assembled within a small-scale fixture (SSF) fuel cell. Non-invasive 3D characterization through X-ray computed tomography 4 imaging revealed that CM particles completely dissolved when measuring up to 15 µm in diameter, creating pinhole-like voids in the CCM. Additionally, a slightly oxidized Fe particle of approximately 55±5 µm also showed complete dissolution, demonstrating that the dissolution of particles was mainly determined by their chemical composition and, to a lesser extent, their size. Larger particles, specifically those measuring 100 µm or more, caused damage in the MEA structure, including deformation of the gas diffusion layer (GDL) and a torus-shaped void in the catalyst layer. Early fabrication damage during the decal transfer process was commonly noted, especially as the surface features of the particles became increasingly random. Additionally, as the size and hardness of the particles increased, especially the SS316L 500 µm particles, significant permanent membrane rupture, GDL damage, and cavities in the MEA were observed. Figure 1 illustrates the types of observed MEA damage associated with particle sizes. As the particle size increased, the prevalence of some damage types also rose, highlighting the importance of mitigating damage from larger particles. Performance analysis indicated that cyclic voltammetry and polarization curve data could effectively identify early cell failures caused by damage incurred by the particles. Different shapes and surface morphologies of SS316L particles were further tested with a thinner chemically and mechanically reinforced GORE-SELECT® membrane (MemB) for mitigation purposes, as they caused the maximum damage to the MemA MEA. It was found that MemB could accommodate all four selected particles, despite varying degrees of surface randomness, without any membrane ruptures after the decal transfer phase. However, significant permanent damage to other MEA components remained evident. Keywords: Quality control, cost reduction, X-ray computed tomography, foreign particles 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 J. Chen, H. Liu, Y. A. Huang, and Z. Yin, J. Manuf. Process. , 23 , 175–182 (2016). M. Bahrami et al., J. Electrochem. Soc., 170 , 114527 (2023). N. Kumar et al., J. Electrochem. Soc. , 171 , 074513 (2024) Y. Singh, F. P. Orfino, M. Dutta, and E. Kjeang, J. Electrochem. Soc. , 164 , F1331–F1341 (2017). Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 head0.238
Teacher spread0.229 · 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 routes3
Has abstractyes

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