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Record W1436633982 · doi:10.1149/ma2015-02/37/1353

Evidence for Micro-Porous Layer Degradation Under Accelerated stress test Conditions 

2015· article· en· W1436633982 on OpenAlexaffabout
Mehdi Andisheh-Tadbir, Monica Dutta, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBallard Power Systems (Canada)Simon Fraser University
Fundersnot available
KeywordsMaterials scienceDegradation (telecommunications)Layer (electronics)CorrosionCathodePorosityMembrane electrode assemblyCarbon fibersChemical engineeringComposite materialProton exchange membrane fuel cellElectrodeChemistryAnodeFuel cells

Abstract

fetched live from OpenAlex

Durability is important for high-volume fuel cell production and commercialization. Different fuel cell components are prone to various types of degradation. The ionomer inside the membrane and catalyst layer undergoes degradation chemically and/or mechanically and Pt may dissolve and coalesce resulting in a loss of active surface area. The carbon in the catalyst support and gas diffusion layer (GDL), meanwhile, may degrade through corrosion and/or erosion [13]. GDL degradation is mainly characterized by the changes in its properties. The micro-porous layer (MPL), which is the most recently added component of modern seven-layer membrane electrode assemblies (MEAs), has a soft and delicate structure comprising of carbon nanoparticles and PTFE that may also be susceptible to degradation. Although MPL is key for high fuel cell performance, particularly at high current densities where water management is a concern, there is no investigation in the literature to date that accounts for its possible degradation behavior during fuel cell operation. The methods used previously to study degradation of the macro-porous GDL substrate could also be applied in general to evaluate MPL degradation, albeit a higher resolution may be required. As an example, erosion of PTFE from the MPL would lead to changes in hydrophobicity, and any structural changes due to electrochemical degradation in the form of carbon corrosion would likely alter the surface characteristics and morphology [1,2], as shown previously for the GDL substrate. In this work, the cathode MPL of a highly corroded MEA is analyzed using nano-scale X-ray computed tomography (NXCT). Samples for this study are obtained from a cathode corrosion accelerated stress test, in which the MEA is subjected to voltage cycling at high temperature and relative humidity. The goal is to compare the structure and properties of the MPL at the beginning of life (BOL) and degraded states. A ZEISS Xradia 810 Ultra NXCT scanner is used to visualize and reconstruct the 3D structure of the MPL with 50 nm resolution. Cylindrical samples of 350 µm diameter are punched from the MEAs and trimmed under microscope in preparation for NXCT scanning. Presented in Fig. 1 are the obtained 3D structures for the BOL and degraded MPLs. The degraded sample is observed to contain smaller agglomerates than the BOL material, which could be caused by material loss from carbon corrosion; additionally, the structure appears more compact with smaller pores which suggests that the structure is collapsed. Table 1 lists the calculated porosity and average pore size obtained using an in-house MATLAB code [3,4]. The porosity is constant for the two samples; however, the average pore size is decreased for the degraded sample. The thickness of the BOL and degraded MPLs are examined using cross-sectional scanning electron microscopy (SEM), revealing a 30% thickness reduction for the degraded sample. The reduction in average pore size along with the reduction in thickness evidences that the MPL experienced degradation through carbon corrosion, resulting in structural collapse. Acknowledgments 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. References [1]W. Schmittinger, A. Vahidi, Journal of Power Sources 180 (2008) 1. [2]R.L. Borup, J.R. Davey, F.H. Garzon, D.L. Wood, M. a. Inbody, Journal of Power Sources 163 (2006) 76. [3]M. El Hannach, R. Singh, N. Djilali, E. Kjeang, Journal of Power Sources 282 (2015) 58. [4]A. Nanjundappa, A.S. Alavijeh, M. El Hannach, D. Harvey, E. Kjeang, Electrochimica Acta 110 (2013) 349. 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 score0.004
Threshold uncertainty score0.014

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.097
GPT teacher head0.298
Teacher spread0.200 · 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
Published2015
Admission routes2
Has abstractyes

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