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Record W2525349685 · doi:10.1149/ma2016-02/38/2501

Nanoscale Imaging of Voltage Cycled Cathode Catalyst Layers in Polymer Electrolyte Fuel Cells

2016· article· en· W2525349685 on OpenAlexaffabout
Senthil Velan Venkatesan, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOverpotentialElectrolyteCatalysisMaterials scienceCathodeChemical engineeringIonomerElectrodeComposite materialNanotechnologyChemistryPolymerElectrochemistryOrganic chemistry

Abstract

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Widespread commercialization of polymer electrolyte fuel cells (PEFCs) requires improvement in the performance and durability of its key components. Catalyst layer degradation is a major factor contributing to the performance loss [1]. The relatively slower oxygen reduction reaction (ORR) kinetics and mass transport in the cathode contribute to a significant overpotential and thus requires noble metal catalysts to be used. The cathode catalyst layer (CCL) must maintain the heterogeneous catalyst structure for the species transport to/from the reaction sites which facilitates durable operation and low Pt loading. During operation, the heterogeneous CCL structures may experience localized non-uniform potential loads, fuel starvation, development of hot spots, and potential cycling that eventually leads to carbon corrosion, Pt agglomeration, ionomer redistribution, Pt oxidation, and Pt isolation in nano holes [2]. The affected structure may result in reduced catalytic activity, reduced Pt utilization and fuel utilization, inaccessible active sites, inefficient water management, increased electronic resistance, and increased mass transport resistance [3]. Hence, spatially resolved imaging of the catalyst layer and its constituents is required to unravel the effects of elemental and structural degradation. The present study aims to investigate the effects of high voltage excursions, which are typically encountered during start up/shut down conditions, on the heterogeneous nanoscale structure of the cathode catalyst layer. Membrane electrode assemblies (MEAs) with CCL comprising of a commercial 50:50 wt.% Pt/C catalyst and Nafion ionomer were subjected to a voltage cycling accelerated stress test (AST) with upper and lower potential limits of 1.3 and 0.6 V, respectively. The AST was operated until 4,700 cycles. Further details on the MEAs and AST can be found elsewhere [4]. The potential cycling is intended to induce CCL degradation, similar to that observed during field operation of fuel cells. The low potential operation leads to a higher current to be drawn from the fuel cell and causes temperature rise and flooding of the CCL. High voltage operation on the other hand triggers Pt oxidation and dissolution, carbon corrosion, and ionomer dehydration. Beginning-of-life (BOL) and end-of-test (EOT) MEAs extracted from in-situ AST were analyzed using transmission electron microscopy (TEM). The samples were embedded in epoxy resin and sliced to thin films (~70-90 nm) using ultramicrotome to reveal the cross-section and collected on copper grids for imaging. The nanoscale distribution of solid/void phases and the elemental maps were recorded at high resolution using a Tecnai Osiris TEM instrument from FEI. Elemental mapping of the CCLs was carried out by selectively capturing the generated X-rays with characteristic energy due to electron interaction with the sample. The X-ray energy distinguishes different elements present in the sample. The energy dispersive X-ray (EDX) analysis offers the sophistication of imaging the ionomer phase through the fluorine elemental map and carbon and platinum through their respective elemental maps. The BOL catalyst layer exhibiting uniformly distributed fluorine is presented in Figure 1(A). In contrast, the fluorine map of the EOT CCL (Figure 1(B)) reveals regions of increased concentration and closely compacted ionomer. The carbon and platinum elemental maps (not shown) at EOT reveal vacant carbon and aggregated platinum regions as a result of voltage cycling. The solid/void morphology of CCLs seen from the dark field images also revealed an increased solid content at EOT. Images indicate that the catalyst layer experienced compaction which will affect the porosity and triple-phase regions promoting ORR. The obtained results are in good qualitative agreement with the lower resolution structure visualized by X-ray computed-tomography and the present images confirm the trends observed in the 2-D virtual slices of ionomer dominated and Pt/C dominated catalyst layer regions [5]. Overall, the observed effects of compositional, structural, and morphological degradation of the CCL during voltage cycling contributes to the fundamental understanding of its complex degradation process and can aid development of catalyst layers with improved performance and extended durability. ACKNOWLEDGMENTS Research funding provided by Automotive Partnership Canada (APC), Natural Sciences and Engineering Research Council of Canada (NSERC) and Ballard Power Systems is gratefully acknowledged. We also thank Ballard Power Systems for experimental support. This work made use of the 4D LABS shared facilities supported by the Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, and Simon Fraser University. REFERENCES [1] M. K. Debe, Nature, 486 43 (2012). [2] T. Saida et al., Angew. Chem., 124 10457 (2012). [3] R. Borup et al., Chem. Rev., 107 3904 (2007). [4] A. P. Hitchcock et al., J. Power Sources, 266 66 (2014) [5] A. Pokhrel et al., Meeting Abstracts. No. 37. 1357, The Electrochemical Society (2015). 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.192
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
Published2016
Admission routes2
Has abstractyes

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