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Record W2773214808 · doi:10.1166/jnn.2018.15316

Comparative Study of Various Types of Metal-Free N and S Co-Doped Porous Graphene for High Performance Oxygen Reduction Reaction in Alkaline Solution

2017· article· en· W2773214808 on OpenAlexafffund
Sedigheh Sadegh Hassani, Mohammad Reza Ganjali, Leila Samiee, Alimorad Rashidi, Saeideh Tasharrofi, Amir Yadegari, Fatemeh Shoghi, Richard Martel

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceGrapheneX-ray photoelectron spectroscopyFourier transform infrared spectroscopyRaman spectroscopyHeteroatomElectrochemistryInorganic chemistryPyrolysisSulfurScanning electron microscopeNanosheetChemical engineeringNanotechnologyElectrodeChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Heteroatom doping into carbon structures is an effective approach to enhance the electrochemical performance of carbon materials. In the work presented here, the electrocatalysts including: nitrogen and co-doped nitrogen and sulfur on porous graphene (PG) were synthesized by different precursors. The physico-chemical properties of the prepared samples were determined using X-ray Diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), N2 sorption-desorption, Transmission electron microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM) and X-ray photoelectron spectroscopy (XPS). The prepared samples were further applied for oxygen reduction reaction (ORR) and the effects of pyrolysis temperature, precursor type and dose, on the prepared samples structure and their electrochemical performances were investigated. The results revealed that synergistic effect of nitrogen and sulfur co-doped on the graphene structure leads to improvement in catalytic activity and current. Furthermore, S and N co-doped graphene prepared using sulfur trioxide pyridine complex exhibited excellent methanol tolerance and long-term stability.

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 score0.022
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

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