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Record W2901238354 · doi:10.1149/ma2018-02/44/1469

Influence of Key Parameters of Pulsed Oxidation Technique on the Mitigation of Carbon Monoxide Poisoning in a Polymer Electrolyte Fuel Cell

2018· article· en· W2901238354 on OpenAlexaff
Paran Jyoti Sarma, C. Gardner, Sachin Chugh, Alok Sharma, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCarbon monoxideElectrolyteHydrogenDirect-ethanol fuel cellChemical engineeringHydrogen fuelMaterials scienceAnodePolymerPROXCatalysisProton exchange membrane fuel cellChemistryElectrodeComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen generated by reforming various hydrocarbons contains impurities such as Carbon monoxide (CO) in small quantities (10-50 ppm) [1]. CO concentrations of this level rapidly poison the platinum and platinum alloy catalysts used in polymer electrolyte fuel cells (PEFC)s even at elevated temperatures. Hydrogen produced by reforming can be treated to be used as a fuel for stationary and automotive fuel cell applications. However, the cost of obtaining pure hydrogen (99.999% purity) for these applications becomes very high. Several mitigation strategies have been proposed and developed to make this fuel compatible with PEFC applications. This includes bleeding air [2,3] into the fuel stream to oxidize the CO and the use of pulsed oxidation to periodically remove CO from the surface of the catalyst. [4, 5]. Although air bleeding can remove CO, it has some disadvantages. Air bleeding into a fuel cell stack can cause overheating at the anode if the air is not controlled and mixed properly and can result in the formation of hydrogen peroxide which can lead to membrane degradation. In comparison with the study and development of air bleeding, pulsed oxidation studies involve use of pulses of current through an external source to oxidise CO in the feed. Previous studies involved only half cell work or room temperature studies which don’t demonstrate practical applications of this technique. In this work, we present results of a study which investigates the use of pulsed oxidation technique to mitigate the effect of CO on the performance of a PEFC. Experiments were carried out on a 5 cm2 active area single cell fuel cell Measurements were done at 80 ºC using membrane electrode assemblies with a Pt-Ru/C anode catalyst and a Pt/C cathode catalyst. 500 ppm CO mixed with pure hydrogen was fed to the anode and air was fed to the cathode. To conduct the experiments, the cell potential was monitored when the fuel source was switched from pure hydrogen to 500 ppm CO doped hydrogen. Current pulses of varying magnitude were initiated through a potentiostat when the cell voltage had fallen to a predetermined level (threshold voltage) to oxidize CO adsorbed on the Pt active sites. In this paper, results will be presented to demonstrate the effect of pulse width, threshold voltage and pulse current on the overall efficiency of the pulsed oxidation process. It was observed that an optimized pulsed oxidation technique allows us to effectively run a PEFC at steady state conditions at a given current density even in the presence of a high concentration of CO. Acknowledgement: This work is supported by the funding provided by IndianOil R&D and Simon Fraser University under the SFU-IOCL joint PhD program in clean energy. [1] Hydrogen production from liquid hydrocarbon fuels for PEMFC application Y. Chen, H. Xu, Y. Wang, X. Jin and G. Xiong, Fuel Process. Technol. 87, 971 (2006) [2] L. Sung, B. Hwang, K. Hsueh, F. Tsau, J. Power Sources, 195, 1630 (2010) [3] W.A. Adams, J. Blair, K.R. Bullock, C.L. Gardner, J. Power Sources, 145, 55 (2005) [4] L.P.L. Carrette, K.A. Friedrich, M. Huber and U. Stimming, Phys. Chem. Chem. Phys., 3, 320 (2001) [5] C.G. Farrell, C.L. Gardner and M. Ternan, J. Power Sources, 117, 282 (2007)

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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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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
Published2018
Admission routes1
Has abstractyes

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