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Record W2519257889 · doi:10.1149/ma2016-02/38/2842

Modeling of Chemical Membrane Degradation Mitigation and Performance Tradeoffs in Ceria-Supported Fuel Cells

2016· article· en· W2519257889 on OpenAlexaffabout
Ka Hung Wong, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneRedoxDegradation (telecommunications)ElectrolyteChemical engineeringIonomerCeriumChemistryRadicalOpen-circuit voltageMaterials scienceElectrodeInorganic chemistryVoltagePolymerOrganic chemistryElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Ceria supported membranes have been proposed to mitigate chemical membrane degradation in polymer electrolyte fuel cells. It was confirmed that ceria as a radical scavenger protects the membrane under open circuit voltage (OCV) or ex-situ Fenton’s durability conditions [1-7]. However, its effectiveness has not been examined for cell voltages below OCV, which are necessary conditions for field operation of fuel cells. On the other hand, ceria membrane additive can be considered as a cation contamination since it is dissolved in the membrane [8]. Performance tradeoffs have been observed experimentally with the use of ceria membrane additive, and the tradeoffs are more significant at lower cell voltages [9]. Therefore, a comprehensive investigation on chemical membrane mitigation and performance tradeoffs in ceria-supported fuel cells is required. In the present work, a transient in-situ chemical degradation model for simulating the transport and reaction of cerium redox couples in the membrane electrode assembly (MEA) is developed and integrated with the chemical membrane degradation models in which the effects of iron redox couples on membrane degradation and detailed ionomer degradation processes are included [10-11].The developed model is then applied to investigate the mitigation effectiveness of cerium redox couples and the fundamental mechanisms for the performance tradeoffs under different cell voltage conditions. The modeling results reveal that abundant Ce(III) ions are available in the membrane to quench hydroxyl radicals at high cell voltages. Since the hydroxyl radical is the dominant reactive species to attack the membrane ionomer, the modeling results demonstrate the primary mechanism for the significant mitigation observed in ceria supported MEAs at OCV conditions. However, this type of mitigation is found to be suppressed at low cell voltages, where electromigration drives Ce(III) ions into the cathode catalyst layer and reduces the available Ce(III) ion in the membrane. Ce(III) ion is the dominant species for hydroxyl radical quenching, and inadequate amount of Ce(III) ion in the membrane leads to a ten-fold reduction in the mitigation effectiveness at cell voltages below 0.7 V. The simulated Ce(III) ion migration to the cathode catalyst layer is found to be responsible for the performance losses observed in ceria supported MEAs. The modeling results reveal that proton starvation can occur in the cathode catalyst layer due to the local Ce(III) ion accumulation. Without the adequate supply of protons in the ionomer of the cathode catalyst layer, proton conductivity and oxygen reduction kinetics are reduced. Significant performance tradeoffs in the form of combined ohmic and kinetic voltage losses are therefore evident and shown to increase with current density. Overall membrane durability and fuel cell performance management is shown to be possible at high cell voltages with the use of ceria membrane additive, where cerium migration and the associated performance loss are insignificant. Unfortunately, at low cell voltages additional steps must be taken to address proton starvation in the cathode catalyst layer and inadequate amount of Ce(III) ion in the membrane in order to achieve a durable membrane without compromising fuel cell performance. Acknowledgement: This research was supported by Ballard Power Systems and the Natural Sciences and Engineering Research Council of Canada through an Automotive Partnership Canada (APC) grant. The authors wish to thank their colleagues at SFU FCReL and Ballard for providing valuable comments and advices. References: [1] Coms et al., ECS Trans. 16 (2) (2008) 1735–1747 [2] Xiao et al., J. Power Sources 195 (16) (2010) 5305 – 5311 [3] Wang et al., J. Membrane Sci. 421422 (0) (2012) 201 – 210 [4] Pearman et al., J. Power Sources 225 (0) (2013) 75 – 83 [5] Pearman et al., Polym. Degard. Stabil. 98 (9) (2013) 1766 – 1772 [6] Wang et al., Electrochimica Acta 109 (0) (2013) 775 – 780 [7] Lim et al., ECS Electrochem. Lett. 4 (4) (2015) F29–F31 [8] Hayes et al., J. Electrochem. Soc. 149(12) (2002) C623–C630 [9] Cheng et al., J. Electrochem. Soc., 160(1) (2013) F27–F33 [10] Wong et al., J. Electrochem. Soc., 161(9) (2014) F823–F832 [11] Wong et al., ChemSusChem, 8(6) (2015) 1072–1082

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.188
Teacher spread0.178 · 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 designSimulation or modeling
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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