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Record W2149436458 · doi:10.1109/nano.2008.86

Field Emission Properties of Carbon Nanotube Thin Films Grown on Different Substrate Materials

2008· article· en· W2149436458 on OpenAlexafffund
Niraj Sinha, Yu Sun, John T. W. Yeow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsField electron emissionMaterials scienceCarbon nanotubeQuartzSubstrate (aquarium)Insulator (electricity)Field emission microscopyThin filmEvaporationCarbon fibersComposite materialOptoelectronicsNanotechnologyElectronDiffractionOptics

Abstract

fetched live from OpenAlex

The electron field emission characteristics from multiwalled carbon nanotube (MWNT) films grown on two different substrate materials have been studied in this paper. The MWNT films were grown on a flat conductor surface (stainless steel) and a flat insulator surface (quartz). The field emission experiments were carried out under a diode configuration. It was found that the film on the quartz surface exhibits better emission capability, while the film on the stainless steel surface shows better emission stability. Energy-dispersive X-ray spectroscopy analysis revealed that carbon atoms evaporated and deposited on the surface of anode during field emission, which is in agreement to studies previously reported in literature. It is estimated that the degradation was due to local heating of tips of CNTs, leading to evaporation and deposition of carbon atoms during the field emission process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2008
Admission routes2
Has abstractyes

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