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Record W2319820982 · doi:10.1021/jp108864y

Nitrogen Doping Effects on Carbon Nanotubes and the Origin of the Enhanced Electrocatalytic Activity of Supported Pt for Proton-Exchange Membrane Fuel Cells

2011· article· en· W2319820982 on OpenAlexaff
Yougui Chen, Jiajun Wang, Hao Liu, Mohammad Norouzi Banis, Ruying Li, Xueliang Sun, Tsun‐Kong Sham, Siyu Ye, Shanna Knights

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBallard Power Systems (Canada)Western University
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceProton exchange membrane fuel cellX-ray photoelectron spectroscopyCatalysisRaman spectroscopyChemical engineeringXANESChemical vapor depositionCarbon fibersGraphiteElectrocatalystElectrochemistryNanotechnologyInorganic chemistrySpectroscopyChemistryComposite numberComposite materialOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Carbon nanotubes (CNTs) and nitrogen-doped carbon nanotubes (CNx) were synthesized by the floating catalyst chemical vapor deposition (FCCVD) method. Pt nanoparticles were deposited onto the two catalyst supports with the ethylene glycol reduction method. Different from CNTs that contain straight hollow tubes, CNx contain a bamboolike structure with kinks along the tubes and more surface defects. X-ray photoelectron spectroscopy (XPS) confirms the nitrogen atoms in the graphite matrix and reveals the chemical natures of the doped nitrogen atoms. Carbon K-edge near-edge X-ray absorption fine structure (NEXAFS) and Raman characterizations reveal higher defectiveness in CNx than CNTs. The unique structure and surface property of CNx leads to a better dispersion of Pt nanoparticles on CNx than on CNTs, as revealed by TEM images. Pt supported on CNx (Pt/CNx) exhibited a higher electrochemical surface area (ECSA) and higher catalytic activity toward oxygen reduction reaction (ORR), in comparison to Pt supported on CNTs (Pt/CNTs). Better performance of Pt/CNx than Pt/CNTs has been confirmed by single-cell fuel cell tests.

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 score0.002
Threshold uncertainty score0.386

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 science0.0010.000
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations241
Published2011
Admission routes1
Has abstractyes

Explore more

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