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Record W2066527415 · doi:10.1149/1.3655433

Electrocatalytic Activity of Non-Stoichiometric Perovskites toward Oxygen Reduction Reaction in Alkaline Electrolytes

2011· article· en· W2066527415 on OpenAlexaff
Xiao‐Zi Yuan, Xiaoxia Li, Wei Qu, Douglas G. Ivey, Haijiang Wang

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of AlbertaBC Innovation CouncilNational Research Council Canada
Fundersnot available
KeywordsStoichiometryElectrolyteCatalysisPerovskite (structure)Electron transferTransmission electron microscopyRotating disk electrodeInorganic chemistryAdsorptionMaterials scienceElectrocatalystElectrochemistryChemistryElectrodePhysical chemistryCrystallographyNanotechnologyOrganic chemistryCyclic voltammetry

Abstract

fetched live from OpenAlex

Perovskite LaxCa0.4MnO3 (x = 0.4, 0.5, 0.6) powder was prepared through a sol-gel method and characterized by X-ray diffraction (XRD), a gas adsorption technique (BET) and transmission electron microscopy (TEM). The electrocatalytic properties of LaxCa0.4MnO3/C composites towards the oxygen reduction reaction (ORR) were studied using rotating ring-disk electrode (RRDE) techniques and Koutecky-Levich theory in both 1 M and 6 M KOH electrolytes. The results show better ORR activities for non-stoichiometric perovskites than that for stoichiometric La0.6Ca0.4MnO3. The overall electron transfer numbers for these LaxCa0.4MnO3 composites are in the range of 3.2-3.7, and with decreasing values of x the electron transfer number increases and accordingly H2O2 production decreases. These results suggest that the existence of a Mn reduction/oxidation pair in Ca-doped nonstoichiometric perovskites could activate the ORR reaction sites, resulting in improved catalytic activity.

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)0.0010.000

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 head0.017
GPT teacher head0.228
Teacher spread0.210 · 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".

Quick stats

Citations19
Published2011
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207