MétaCan
Menu
Back to cohort
Record W2318357697 · doi:10.1149/05002.1887ecst

Nitrogen-doped Graphene as an Active Electrocatalyst for Oxygen Reduction Reaction

2013· article· en· W2318357697 on OpenAlexafffund
Dong Un Lee, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystGrapheneOxygen reduction reactionOxygen reductionOxygenDopingNitrogenMaterials scienceReduction (mathematics)ChemistryInorganic chemistryNanotechnologyElectrodeElectrochemistryMathematicsOptoelectronicsOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Nitrogen-doped graphene (NG) sheets are synthesized by thermal reduction and ammonia treatment of graphene oxide (GO). Scanning electron microscopy has revealed a voile-like morphology of NG sheets, which highly contrasts from the stacked layers observed with GO. X-ray photoelectron spectroscopy has verified successful nitrogen-doping of NG sheets, and Raman spectroscopy has confirmed a higher degree of deformation in NG sheets due to the incorporation of heterogeneous nitrogen atoms. To evaluate NG sheets as a highly active oxygen reduction reaction (ORR) electrocatalyst, half-cell electrochemical testing has been employed using rotating disk electrode (RDE) in an alkaline aqueous electrolyte. NG sheets demonstrate a pseudo four-electron pathway O2 reduction, and a comparable ORR performance to that of a commercial carbon supported platinum (Pt/C) catalyst. This excellent ORR activity of NG sheets is most likely due to the active sites created by nitrogen-doping in the graphene sheets.

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 score0.031
Threshold uncertainty score0.629

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 communication0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueECS TransactionsSame topicFuel Cells and Related MaterialsFrench-language works237,207