MétaCan
Menu
Back to cohort
Record W2096571351 · doi:10.1149/1.2363947

Thermal Evolution of the Structure and Activity of Magnetron-Sputtered TM–C–N (TM=Fe, Co) Oxygen Reduction Catalysts

2006· article· en· W2096571351 on OpenAlexafffund
E. Bradley Easton, Ruizhi Yang, Arman Bonakdarpour, J. R. Dahn

Bibliographic record

VenueElectrochemical and Solid-State Letters · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityU.S. Department of Energy
KeywordsMaterials scienceAmorphous solidAnnealing (glass)Sputter depositionThin filmCatalysisScanning electron microscopeOxygenDiffractionChemical engineeringHomogeneousAnalytical Chemistry (journal)SputteringCrystallographyMetallurgyNanotechnologyComposite materialChemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Thin-film libraries of (; ; ) have been prepared by combinatorial sputter deposition. The libraries were subsequently annealed at to induce structural and compositional changes. Using grazing-incidence X-ray diffraction and scanning electron microscopy, structural changes were followed as a function of annealing temperature. At temperatures above , the previously homogeneous and amorphous thin films became a heterogeneous mixture of (partially) graphitized nitrogen-containing carbon and either or . The onset of this transformation is accompanied by a rapid decrease in N content and occurs as a function of both transition metal content and temperature. Catalytic activity for oxygen reduction is at its maximum partway through this transformation.

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)0.000
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 score0.012
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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

Citations51
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueElectrochemical and Solid-State LettersSame topicElectrocatalysts for Energy ConversionFrench-language works237,207