MétaCan
Menu
Back to cohort
Record W2102338182 · doi:10.1149/1.2435670

Co–C–N Oxygen Reduction Catalysts Prepared by Combinatorial Magnetron Sputter Deposition

2007· article· en· W2102338182 on OpenAlexaff
Ruizhi Yang, Arman Bonakdarpour, E. Bradley Easton, Patricia Stoffyn-Egli, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2007
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCatalysisSputter depositionAmorphous solidNitrogenSputteringDeposition (geology)OxygenChemistryPartial pressureCarbon fibersThin filmChemical engineeringCavity magnetronInorganic chemistryMaterials scienceNanotechnologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Thin-film libraries of (, ) were prepared by combinatorial magnetron sputter deposition in an gas mixture followed by subsequent heat-treatment at 700, 800, or in atmosphere. By increasing the nitrogen partial pressure during sputtering, the nitrogen content was increased significantly in the as-sputtered libraries and more nitrogen remained in the libraries after heat-treatment. The catalytic activities of the libraries towards the oxygen reduction reaction (ORR) were studied using the rotating ring-disk electrode (RRDE) technique. libraries heat-treated at with and showed good catalytic activities towards ORR in solution at room temperature. The heating temperature that induces the onset of catalytic activity coincides with the temperature at which both substantial nitrogen release from the originally amorphous films and the formation of graphitic carbon and β-Co occurs. This temperature varies significantly with both the Co and N content in the films. The production of and the corrosion stability of the libraries are also discussed.

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 score0.033
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.216
Teacher spread0.213 · 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

Citations68
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicElectrocatalysts for Energy ConversionFrench-language works237,207