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Record W2142284888 · doi:10.1109/tnano.2005.851404

Dielectrophoretic Assembly of Carbon Nanofiber Nanoelectromechanical Devices

2005· article· en· W2142284888 on OpenAlexaff
Stéphane Evoy, Michael Riegelman, Nevin Naguib, H. Ye, Papot Jaroenapibal, David E. Luzzi, Yury Gogotsi

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceNanoelectromechanical systemsCantileverFabricationResonatorCarbon nanotubeLithographyOptoelectronicsClampingNanotechnologyMicroelectromechanical systemsPhotolithographyNanolithographyNanofiberCarbon nanofiberBendingComposite materialNanoparticleMechanical engineering

Abstract

fetched live from OpenAlex

We report a technique for the assembly of bottom-up nanomechanical devices. This technique employs the dielectrophoretic manipulation of nanostructures within a multiple layer lithography process. Mechanical resonators were specifically produced by assembling and clamping tubular carbon fibers onto prefabricated pads. Our preliminary results showed that an assembled cantilevered fiber with length L=5 /spl mu/m and width of W=180 nm possessed a resonant frequency of f=1.17 MHz. A shorter L=3-/spl mu/m-long singly clamped resonator of similar width showed a resonance of f=3.12 MHz. This frequency range is in agreement with the low gigapascal bending moduli previously reported for carbon structures showing extensive volume defects. This technology would allow the integration of bottom-up nanostructures with other more established fabrication processes, thus allowing the deployment of engineered nanodevices in integrated systems.

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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on NanotechnologySame topicCarbon Nanotubes in CompositesFrench-language works237,207