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Record W2740352008 · doi:10.1149/ma2017-02/34/1486

(Invited) 3D Visualization of Membrane Failure

2017· article· en· W2740352008 on OpenAlexaffabout
Erik Kjeang, Yadvinder Singh, Dilip Ramani, Robin White, Sebastian H. Eberhardt, Francesco P. Orfino, Monica Dutta

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneMaterials scienceIonomerElectrolyteDegradation (telecommunications)DurabilityPolymerComposite materialChemical engineeringElectrodeChemistryComputer scienceCopolymer

Abstract

fetched live from OpenAlex

Membrane failure is an important factor for the overall durability of polymer electrolyte fuel cells. Lifetime limiting failure occurs when the membrane loses its ability to reliably separate the reactant gases such that potentially combustive conditions may arise due to mixing of hydrogen and oxygen. While diffusive gas crossover is regularly present at benign rates, critical leak rates require significant convective fluxes that can only occur in the presence of large holes and cracks that span the full thickness of the membrane. Membrane degradation and damage development during fuel cell operation takes place through complex interplay between chemical degradation due to radical attack of the ionomer and mechanical degradation due to hygrothermal variations under mechanically constrained conditions. The combined action of chemical and mechanical degradation, which is difficult to avoid during dynamic fuel cell operation, is known to drastically accelerate the accumulation of membrane damage and be the primary cause of ultimate failure [1]. However, the process through which failure occurs is only partially understood. Membrane failure analysis has historically been performed by 2D imaging techniques such as optical and electron microscopy, which is limited to surface views of the electrodes and cross-sectional snapshots of the internal MEA structure. These techniques are also destructive in nature, demand tedious sample preparation with risk of artifacts, operate under vacuum, and/or are suitable only for electrically conductive samples. Our group recently proposed the use of X-ray computed tomography (XCT) to overcome these limitations and open up a novel non-destructive 3D characterization method for imaging of membranes inside an MEA [2]. This approach leverages recent advances in laboratory-based XCT technology to non-destructively acquire 3D images of membrane damage features and to develop a unique 3D failure analysis framework for fuel cell membranes [3]. This methodology is systematically applied to fuel cells subjected to pure chemical, pure mechanical, and combined chemical and mechanical membrane degradation in order to reveal the different types of membrane failures that may occur during fuel cell operation and assign their presence to specific degradation mechanisms. Novel discoveries made using this technique include distinct identification of I, Y, and X branched cracks (Figure 1), formation of exclusive membrane cracks, interaction of membrane and catalyst layers cracks and delamination sites, and electrode shorts due to excessive chemical membrane degradation. However, regular 3D visualization of membrane failures cannot reveal the root cause of each damage feature. Therefore, our recent efforts have focused on further exploiting the non-destructive nature of XCT scans to perform 4D membrane visualization by means of 3D scans at different points in time [4]. This allows us to track the propagation of membrane damage during the degradation process and also enables back-tracking of failure modes to determine the actual root cause by inspecting same-location scans performed at earlier times. Using this methodology, we have determined under which circumstances catalyst layer cracks may propagate into membrane cracks and vice versa. Overall, 3D visualization of membrane degradation and failure by XCT is shown to be a highly promising method to help understand complex failure modes and degradation mechanisms in fuel cells. 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. Lim, C., Ghassemzadeh, L., Van Hove, F., Lauritzen, M., Kolodziej, J., Wang, G.G., Holdcroft, S. and Kjeang, E., J. Power Sources 257 (2014) 102-110 2. White, R.T., Najm, M., Dutta, M., Orfino, F.P. and Kjeang, E., J. Electrochem. Soc. 163 (2016) F1206-F1208 3. Singh, Y., Orfino, F.P., Dutta, M. and Kjeang, E., J. Power Sources 345 (2017) 1-11 4. White, R.T., Wu, A., Najm, M., Orfino, F.P., Dutta, M. and Kjeang, E., J. Power Sources 350 (2017) 94-102 Figure 1. Representative reconstructed false color tomogram of an MEA, showing planar, cross-sectional, and 3D virtual views of an X-shaped membrane crack and adjacent catalyst layer cracks (M = membrane; ACL = anode catalyst layer; CCL = cathode catalyst layer). 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0970.025

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes2
Has abstractyes

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