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Record W2088790855 · doi:10.1021/jp060099h

Mechanistic Analysis of the Oxygen Reduction Reaction at (La,Sr)MnO<sub>3</sub> Cathodes in Solid Oxide Fuel Cells

2006· article· en· W2088790855 on OpenAlexaff
Anne C. Co, Viola Birss

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2006
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCathodeOxideElectron transferChemistryOxygenSolid oxide fuel cellCyclic voltammetryDissociation (chemistry)AdsorptionMass transferVoltammetryElectrodeReaction rateRate-determining stepReaction mechanismAnalytical Chemistry (journal)Inorganic chemistryElectrolyteElectrochemistryPhysical chemistryCatalysisChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The primary aim of this work was to establish the mechanism of the oxygen reduction reaction (ORR) at (La(0.8)Sr(0.2))0.98MnO3 (LSM)-based cathodes in solid oxide fuel cells. Rate equations, based on the Butler-Volmer equation and employing either Langmuir or Temkin adsorption conditions for reactant and intermediate species, were derived, yielding predicted reaction orders and transfer coefficients. Experimental data were collected using half-cell cyclic voltammetry in a variable pO2 atmosphere (0.03 to 1 atm) at 600 to 900 degrees C, using both dense and porous LSM-based cathodes, employed to establish the impact of the accessibility of the active site on cathode activity. The rate of the ORR at dense LSM has been found to be limited by the dissociation of O(2ads)- at low currents and by the first electron-transfer step, reducing O(2ads) to O(2ads)-, at high currents. However, at porous LSM cathodes, the reaction mechanism is more difficult to deduce because the electrode morphology impacts significantly on the measured kinetic and mechanistic parameters, giving anomalous transfer coefficients of <0.5.

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 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 score0.025
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.245
Teacher spread0.238 · 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.

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".

Quick stats

Citations31
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207