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Record W4231895103 · doi:10.1149/ma2015-01/27/1622

Sulfur Tolerance of La<sub>0.3</sub>M<sub>0.7</sub>Fe<sub>0.7</sub>Cr<sub>0.3</sub>O<sub>3</sub> <sub>-δ</sub> (M= Sr, Ca) Solid Oxide Fuel Cell Anodes

2015· article· en· W4231895103 on OpenAlexaffabout
Paul Kwesi Addo, Beatriz Molero-Sánchez, Aligül Büyükaksoy, Scott Paulson, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsYttria-stabilized zirconiaMaterials scienceAnodeElectrolyteCermetSolid oxide fuel cellOxideElectrochemistryCathodeInorganic chemistryPerovskite (structure)CatalysisChemical engineeringElectrodeCubic zirconiaChemistryMetallurgyCeramicOrganic chemistry

Abstract

fetched live from OpenAlex

Solid oxide fuel cells (SOFCs) are highly efficient electrochemical devices that also demonstrate excellent fuel flexibility, functioning on fuels such as H2, CO, methane. Traditionally, SOFCs are based on a Ni-YSZ (yttria stabilized zirconia) cermet anode, a YSZ electrolyte, and a lanthanum strontium manganite (LSM) cathode. Although Ni-YSZ cermets are excellent SOFC anodes, largely because of their excellent catalytic activity towards fuel oxidation, work in our group and by others has shown that Ni is susceptible to poisoning in low levels (1-100 ppm) of H2S exposure at SOFC operating temperatures (700-1000 oC) [1-4]. It has been suggested that H2S inhibits the H2 oxidation reaction (HOR) rates because it readily dissociates to form a surface adsorbed Ni-S layer (Sads) on catalytic sites normally involved in H2 dissociation and subsequent oxidation [4], thereby decreasing the performance of the SOFC. As a result, extensive research has been carried out to develop sulfur tolerant SOFC anode materials based on Ni-free conducting metal oxides, such as perovskites. Previous and current studies in our group employing La0.3M0.7Fe0.7Cr0.3O3-δ (M= Sr, Ca) perovskite oxides as both the anode and cathode electrode material, screen printed onto both sides of a YSZ electrolyte with a Gd0.9Ce0.1O2-δ (GDC) buffer layer separating the electrodes and the YSZ, showed very good electrochemical performance in both H2 and CO/CO2 atmospheres in the absence of H2S [5-6]. Therefore, in this work, extensive electrochemical studies, focusing on the performance of La0.3M0.7Fe0.7Cr0.3O3-δ (M= Sr, Ca) in a symmetrical SOFC configuration and operating on H2 and/or CO/CO2 fuels containing varying concentrations of H2S (5- 30 ppm), were carried out at 500 - 800 oC. Also, the effect of the dc bias on the sulfur tolerance of these materials has been examined. Our preliminary results have shown that La0.3Sr0.7Fe0.7Cr0.3O3-δ (LSFCr) electrodes, operated as an anode in humidified H2 plus up to 9 ppm H2S (balance in H2) at 800 oC showed no H2S poisoning. The polarization resistance (Rp) observed for the cell in 30% humidified H2 was 1.20 Ω.cm2 at 800 oC and, with the addition of 9 ppm H2S, there was no observed change in Rp. However, in dry CO/CO2, the addition of H2S showed a ca. 3% increase in Rp, but without any further degradation after longer term exposures to H2S. Importantly, when the H2S was removed from this gas mixture, the performance recovered fully. Acknowledgements – We are very grateful to the SOFC Canada NSERC Strategic Research Network, as well as Carbon Management Canada, for the support of this work. References Y. Matsuzaki and I. Yasuda, Solid State Ionics 132, 261 (2000). Z. Cheng, S. Zha and M. Liu, J. Power Sources, 172,688 (2007). L. Debeeleeck et al, Phys.Chem.Chem.Phys., 16, 9383(2014). J. B. Hansen, Electrochem. Solid-State Lett., 11, B178 (2008). M. Chen, S. Paulson, V. Thangadurai and V. Birss, J. Power Sources, 236, 68 (2013). P. Addo, B. Molero-Sánchez, M. Chen, S. Paulson and V. Birss, 11th European SOFC and SOE Forum, Luzerne, Switzerland, 2014.

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)0.000
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 score0.002

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)0.0000.000

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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
Published2015
Admission routes2
Has abstractyes

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