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Dielectrophoretically trapping semiconductive carbon nanotube networks

2008· article· en· W1976794733 on OpenAlexaff
Robert Cicoria, Yu Sun

Bibliographic record

VenueNanotechnology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceCarbon nanotubeDielectrophoresisNanotechnologyChemical vapor depositionTrappingCoatingField-effect transistorCarbon nanotube field-effect transistorTransistorVoltageElectrical engineeringMicrofluidics

Abstract

fetched live from OpenAlex

The construction of carbon nanotube field effect transistors (CNT-FETs) can be accomplished by techniques such as spin coating, dielectrophoresis (DEP), and chemical vapor deposition, among which DEP has advantages in terms of the ease of manufacturability and the potential of scalability. In this study, we improved on DEP trapping of CNTs by demonstrating that CNT-FETs of CNT networks with highly semiconductive characteristics are formed with proper tuning of DEP parameters. By investigating the factors that are significant in forming these networks, we rationalize guidelines for effectively fabricating highly semiconductive CNT-FETs with mixtures of metallic and semiconductive multi-walled carbon nanotubes.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.217
Teacher spread0.206 · 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.

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

Citations15
Published2008
Admission routes1
Has abstractyes

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