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Record W1996446923 · doi:10.1007/s12274-009-9076-x

The dynamics of the nucleation, growth and termination of single-walled carbon nanotubes from in situ Raman spectroscopy during chemical vapor deposition

2009· article· en· W1996446923 on OpenAlexafffund
Paul Finnie, Andrew Li-Pook-Than, J. Lefebvre

Bibliographic record

VenueNano Research · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council CanadaInstitute for Microstructural SciencesUniversity of Ottawa
FundersCore Research for Evolutional Science and TechnologyNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsRaman spectroscopyNucleationCarbon nanotubeChemical vapor depositionMaterials scienceSpectroscopyAnalytical Chemistry (journal)Saturation (graph theory)Phase (matter)Chemical engineeringNanotechnologyChemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

The dynamics of the chemical vapor deposition (CVD) of single-walled carbon nanotubes (SWNTs) is extracted experimentally using in situ Raman spectroscopy. Nanotubes are grown using a thin film cobalt catalyst and an ethanol precursor in a miniature hot walled reactor with optical access. Raman spectra at room temperature and at the growth temperature are compared for two growth temperatures. The evolution of the G-band, D-band, and radial breathing mode (RBM) is tracked at the growth temperature with time resolution of a few seconds. There are three identifiable phases in the evolution of the Raman signal intensity: an initial exponential increasing phase, a linear growth phase, and a saturation phase. In situ optical spectroscopy thus enables the study of nucleation, steady growth, and deactivation processes to be investigated separately in real time. The evolution curves for all bands (G, D, and RBM), when scaled, collapse onto the same curve, to within experimental uncertainty.

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)0.001
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 score0.026
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations20
Published2009
Admission routes2
Has abstractyes

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