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Record W2150966311 · doi:10.1149/1.2266157

Fe-C-N Oxygen Reduction Catalysts Prepared by Combinatorial Sputter Deposition

2006· article· en· W2150966311 on OpenAlexafffund
E. Bradley Easton, Arman Bonakdarpour, J. R. Dahn

Bibliographic record

VenueElectrochemical and Solid-State Letters · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityU.S. Department of Energy
KeywordsCatalysisMaterials scienceOxygen reductionSputteringAnnealing (glass)Sputter depositionDeposition (geology)Chemical engineeringOxygenNitrogenThin filmOxygen reduction reactionCombinatorial synthesisNanotechnologyMetallurgyCombinatorial chemistryChemistryOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Thin-film libraries of libraries (; ) have been prepared by combinatorial sputter deposition. The libraries were subsequently annealed at 700, 800, and to induce structural and compositional changes. Respectable catalytic activity was achieved with libraries having and that were annealed at . This is explained in terms of nitrogen content and the degree of graphitization of the annealed catalysts.

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 score0.018
Threshold uncertainty score0.966

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 head0.207
Teacher spread0.204 · 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

Citations58
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueElectrochemical and Solid-State LettersSame topicCatalytic Processes in Materials ScienceFrench-language works237,207