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Record W1981537885 · doi:10.1149/1.3619794

Redox Stability of Sm0.95Ce0.05Fe1-xCrxO3-δ Perovskite Materials

2011· article· en· W1981537885 on OpenAlexafffund
Syed M. Bukhari, Javier B. Giorgi

Bibliographic record

VenueJournal of The Electrochemical Society · 2011
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePerovskite (structure)X-ray photoelectron spectroscopyConductivityOxygenElectrical resistivity and conductivityRedoxAnodeDopingAdsorptionChemical engineeringAnalytical Chemistry (journal)MetallurgyChemistryElectrodeOptoelectronicsPhysical chemistryEnvironmental chemistryElectrical engineering

Abstract

fetched live from OpenAlex

This paper examines the relative REDOX stability and conductivity of new Cr-doped perovskite materials (Sm0.95Ce0.05Fe1-xCrxO3-δ, x = 0–0.10) for potential use as anode materials and sensors for reducing gases at low temperatures. These perovskite materials were characterized by XRD, XPS and SEM. A reduction stability test revealed that these perovskites are stable at temperatures below 800°C under reducing conditions. The introduction of Cr in the lattice was found to be in the form of Cr+3 and Cr+6. As a function of Cr content, the ratio of Cr+3/Cr+6 was found to increase. The reduction treatment tended to decrease the concentration of lattice oxygen but the surface adsorbed oxygen was found to be higher in reduced samples as compared to fresh samples. This observation suggested that these materials have a tendency to recapture oxygen when exposed to air after the reduction treatment. The reduction treatment improved the electrical conductivity due to the formation of nanoparticles. The x = 0.03 perovskite has highest electrical conductivity under both air and reducing atmospheres and shows the greatest promise for use in sensor applications for reducing gases at low temperatures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.036
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.249
Teacher spread0.229 · 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 teacher head, not a consensus.

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".

Quick stats

Citations7
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207