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Record W2060902217 · doi:10.1063/1.2161820

Parallel arrays of individually addressable single-walled carbon nanotube field-effect transistors

2006· article· en· W2060902217 on OpenAlexaff
Sarah Lastella, Govind Mallick, Raymond Woo, Shashi P. Karna, David A. Rider, Ian Manners, Yung Joon Jung, Chang Y. Ryu, Pulickel M. Ajayan

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceChemical vapor depositionField-effect transistorNanotechnologyTransistorSiliconCarbon nanotube field-effect transistorNanoscopic scaleOptoelectronicsNanotubeElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

High-throughput field-effect transistors (FETs) containing over 300 disentangled, high-purity chemical-vapor-deposition-grown single-walled carbon nanotube (SWNT) channels have been fabricated in a three-step process that creates more than 160 individually addressable devices on a single silicon chip. This scheme gives a 96% device yield with output currents averaging 5.4mA and reaching up to 17mA at a 300mV bias. Entirely semiconducting FETs are easily realized by a high current selective destruction of metallic tubes. The excellent dispersity and nearly-defect-free quality of the SWNT channels make these devices also useful for nanoscale chemical and biological sensor applications.

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)0.000
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 score0.037
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations33
Published2006
Admission routes1
Has abstractyes

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