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Record W2230334892 · doi:10.12783/jmc.v1i2.58

Synthesis and characterization of ultralong single-walled carbon nanotubes

2013· article· en· W2230334892 on OpenAlexvenueno aff
Jianing An, Lianxi Zheng

Bibliographic record

VenueJournal of Medical Cases · 2013
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon nanotubeNanoelectronicsRaman spectroscopyChemical vapor depositionNanotechnologyMaterials scienceField-effect transistorOptoelectronicsNanotubeCharacterization (materials science)TransistorVoltageOptics

Abstract

fetched live from OpenAlex

Ultarlong aligned single-walled carbon nanotubes (SWNTs) are very useful for high performance nanoelectronics because they have uniform properties along tube axis. In this study, ultralong and well-aligned SWNT arrays were synthesized via an ultralow gas flow chemical vapor deposition (CVD) system using ethanol as the carbon source. Raman spectroscopy was employed to comprehensively characterize the as-grown SWNTs. The intensity ratio of D band and G band (I D /I G ) of the SWNTs was extremely low, indicating the nanotubes are of high quality. The frequencies of RBM and G band acquired along one isolated tube axis maintained constant over a large length scale, which reveals that the SWNT is structural and chiral uniform. Field-effect transistors (FETs) were fabricated with such ultralong single nanotube as conduction channel, exhibiting excellent performances. Our results show possibility of large scale fabricating SWNT-based electronic devices and perspective of creating integrated circuits on individual SWNTs.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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