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Record W2285141035 · doi:10.1149/ma2014-02/21/1094

The Effect of Platinum in the Membrane on Fuel Cell Membrane Durability

2014· article· en· W2285141035 on OpenAlexaffabout
Natalia Macauley, Mark Watson, Erik Kjeang, Alireza Sadeghi Alavijeh, Shanna Knights

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneDurabilityPlatinumMaterials scienceProton exchange membrane fuel cellDegradation (telecommunications)Chemical engineeringCathodeChemistryComposite materialCatalysisOrganic chemistryEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Development of highly durable fuel cell membranes is essential to achieve the heavy duty fuel cell bus lifetime targets. The bus duty cycle exposes fuel cell membranes to conditions that can eventually lead to membrane degradation, and limit fuel cell lifetime. Fuel cell membranes can degrade both chemically and mechanically leading to membrane thinning, as well as pinhole and crack formation over time. Chemical degradation occurs primarily due to radical attack of the membrane. It has been observed that during bus operation, platinum particles migrate into the membrane due to catalyst degradation, forming a distinguishable band close to the cathode [1]. The presence of platinum in the membrane (PITM) is known to affect membrane durability. Observations of both negative and positive effects have been reported in the literature [2-5]. The platinum particle size and distribution inside the membrane have been found to be important factors. A low concentration of highly dispersed small particles is believed to result in increased degradation, and on the other hand a Pt band in the membrane with higher Pt concentrations was confirmed to be effective to improve durability [1]. The effect of PITM on membrane degradation is investigated by deliberately generating a platinum band in the membrane and running an in situ Accelerated Membrane Durability Test (AMDT) on fuel cells containing such membranes. The lifetime of reference baseline cells, without intentionally deposited PITM is compared to the lifetime of cells with PITM under AMDT conditions. Our results confirm that the presence of PITM prolongs membrane lifetime, suggesting that PITM is able to decrease membrane chemical degradation, either by decomposing H2O2 (to non-radical products) and/or converting crossover gases to water at the Pt band, thus avoiding radical formation. It was also found that higher PITM concentrations result in a longer membrane lifetime. In addition, membranes with PITM show no thinning, while the baseline membranes show severe membrane thinning after being exposed to the same AMDT conditions. Ex situ mechanical tests on AMDT degraded baseline membranes show significantly reduced mechanical strength of the membranes without PITM. Again, the mechanical strength of the membranes with PITM is on the other hand highly preserved. These findings support the theory that the platinum band mitigates membrane chemical degradation under the conditions tested. However, it is important to understand the precise structure of the particles located in the Pt band. Thorough analysis of these Pt particles will help improve the general understanding of the lifetime enhancing effects of PITM. Therefore a systematic characterization procedure of the end-of-life AMDT membranes is conducted using transmission electron microscopy (TEM). The Pt particle sizes, distribution, distances between particles and Pt crystallinity are reported, and their role in the mitigation mechanism is discussed. Acknowledgments 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. [1] Macauley, N., Ghassemzadeh, L., Lim, C., Watson, M., Kolodziej, J., Lauritzen, M., Holdcroft, S., and Kjeang, E. (2013). ECS Electrochem. Lett, 2(4), F33-F35. [2] Rodgers, M. P., Bonville, L. J., & Slattery, D. K. (2011). ECS Trans., 41(1), 1461-1469. [3] Helmly, S.,Ohnmacht, B., Hiesgenc, R., Gülzowa, E., and Friedrich, K. A. (2013) ECS Trans., 58 (1) 969-990. [4] Rodgers, M. P., Pearman B. P., Bonville L. J., Cullen D. A., Mohajeri N., and Slattery, D. K. (2013). J. Electrochem. Soc., 160(10) F1123-F1128. [5] Gummalla, M., Atrazhev, V. V., Condit, D., Cipollini, N., Madden, T., Kuzminyh, N. Y., Weiss, D., Burlatsky, S. F. (2010). J. Electrochem. Soc. 157, B1542.

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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
Published2014
Admission routes2
Has abstractyes

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