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UNIFORM- AND HIGH-YIELD CARBON NANOTUBES WITH MODULATED NITROGEN CONCENTRATION FOR PROMISING NANOSCALE ENERGETIC MATERIALS

2010· article· en· W4229644717 on OpenAlexaff
Hao Liu, Yong Zhang, Ruying Li, Hakima Abou‐Rachid, Louis‐Simon Lussier, Xueliang Sun

Bibliographic record

VenueInternational Journal of Energetic Materials and Chemical Propulsion · 2010
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsDefence Research and Development CanadaWestern University
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceCarbon fibersNanoscopic scaleNanotechnologyNitrogenChemical engineeringX-ray photoelectron spectroscopyChemical vapor depositionTransmission electron microscopyChemistryComposite materialOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

It is well known that pure polynitrogen systems are metastable. Recently, a theoretical study showed that when a polymeric nitrogen chain is encapsulated in a carbon nanotube, it will be stable at ambient pressure and room temperature, which makes carbon nanotubes now promising as nanoscale energetic materials. Here, we report a systematic study of multiwalled carbon nanotubes with different nitrogen-doping amounts produced by aerosol-assisted chemical vapor deposition, in which growth temperature, hydrogen flow rate, and aerosol amount have been varied. The morphological and compositional changes of nitrogen-doped carbon nanotubes were characterized by means of scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy. The detailed investigation of nitrogen-doped carbon nanotubes will provide a route to obtain evidence of the above theoretical prediction, and will have potential applications in nanoscale energetic materials.

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 score0.011
Threshold uncertainty score0.682

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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