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Record W2518484239 · doi:10.1149/ma2016-02/40/3041

Performance Enhancement of La<sub>0.3</sub>Sr<sub>0.7</sub>Fe<sub>0.7</sub>Cr<sub>0.3</sub>O<sub>3 </sub>(LSFCr) Electrodes in CO<sub>2</sub>/CO Atmosphere

2016· article· en· W2518484239 on OpenAlexaff
Paul Kwesi Addo, Suresh Mulmi, Beatriz Molero-Sánchez, Parastoo Keyvanfar, Venkataraman Thangadurai, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerovskite (structure)Thermogravimetric analysisMaterials scienceOxideCatalysisElectrodeAnalytical Chemistry (journal)Phase (matter)Atmospheric temperature rangeDiffractionThermal stabilityFuel cellsInorganic chemistryChemistryChemical engineeringCrystallographyPhysical chemistryMetallurgyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Mixed conducting La0.3Sr0.7Fe0.7Cr0.3O3-δ (LSFCr) perovskite electrodes have been shown in our prior work to exhibit very good catalytic activity as both a CO2/CO fuel electrode and as an air electrode [1,2], making them ideal for use in symmetrical, reversible, solid oxide fuel cells (RSOFCs). On the fuel side, LSFCr has shown a higher activity for CO2 reduction than for CO oxidation [1,2] and has also displayed good stability over ca. 100 hours of operation. However, it was of interest to better understand the chemical stability of LSFCr in CO2 and CO2/CO environments, and also to determine if the performance could be improved still further with the addition of co-catalysts [3]. Based on the X-ray diffraction (XRD) analysis of LSFCr powder, exposed to either CO2 or CO2/CO, and at temperatures between 25 and 800 oC, for 24 hrs, it was seen that LSFCr remains a single phase perovskite, with no secondary phases, such as SrCO3, detected. Similarly, and contrary to what has been reported for many other perovskite oxides [4], thermogravimetric analysis (TGA) of the powder in a 60% CO2/N2 environment showed no mass increase over a similar temperature range, again ruling out the formation of SrCO3, demonstrating excellent stability. In terms of performance enhancement, it was of interest to determine the effect of adding Ni and/or GDC co-catalysts to the LSFCr matrix on the electrochemical performance of LSFCr, similar to what has been done for LSCM perovskites [5]. Therefore, three types of symmetrical half cells, using LSFCr, 5 wt.% Ni+LSFCr, and 40 wt. % GDC+LSFCr electrodes, all prepared by mechanical mixing of the precursor powders and screen-printing onto a 300 µm thick YSZ electrolyte coated with a GDC buffer layer, were studied in 70% CO2:30% CO at temperatures ranging between 650 and 800 oC. Preliminary data obtained from electrochemical impedance measurements showed that the total polarization resistance of these cells at 800 oC were 1.54 Ω cm2, 1.10 Ω cm2, and 0.90 Ω cm2 respectively, showing that the addition of either of these co-catalysts decreases the polarization resistance. From the Nyquist plots, it is seen that the addition of GDC decreases the resistance of both the high and low frequency arcs, while the addition of Ni influences mainly the low frequency arc. Based on the literature [6-8], GDC likely improves oxide ion conduction in the LSFCr-GDC electrode and also exhibits some catalytic activity for CO2/CO reduction/oxidation, while the addition of Ni to LSFCr helps to catalyze the surface processes during CO2/CO reduction/oxidation. References [1] P. K. Addo, B. Molero-Sanchez, M. Chen, S. Paulson and V. Birss, Fuel Cells, 15, 689 (2015). [2] B. Molero-Sanchez, P. Addo, S. Paulson, and V. Birss, Faraday Discussion, 182, 159 (2015). [3] F. Bidrawn, G. Kim, G. Corre, J. T. S. Irvine, J. M. Vohs, R. J. Gorte, Electrochem. Solid-State Lett., 11, B167 (2008). [4] E. Bucher, A. Egger, G. B. Caraman, and W. Sitte,J. Electrochem. Soc., 155 (11), B1224 (2008). [5] X. Yue, J. T. S. Irvine, J. Electrochem. Soc., 159, F442 (2012). [6] J. Liu, W.Weppner, Ionics, 5, 115 (1999). [7] R. D. Green, C.-C. Liu, S. B. Adler, Solid State Ionics, 179, 647 (2008). [8] C. Y. Cheng, G. H. Kelsall, L. Kleiminger, J. Appl. Electrochem. 43, 1131 (2013).

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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 score0.004

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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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Citations2
Published2016
Admission routes1
Has abstractyes

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