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Record W2018456117 · doi:10.1109/tnano.2013.2279262

The Mutual Interactions of Carbon Nanotubes During Dielectrophoresis

2013· article· en· W2018456117 on OpenAlexaff
Ali Kashefian Naieni, Alireza Nojeh

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2013
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotubeDielectrophoresisMaterials scienceElectric fieldNanotechnologyElectrodeDeposition (geology)Chemical physicsPulmonary surfactantNanomaterialsChemical engineeringChemistryPhysicsMicrofluidics

Abstract

fetched live from OpenAlex

Dielectrophoresis (DEP) has been widely used for the deposition of various types of nanomaterials including carbon nanotubes (CNTs). Here, we report the results of experiments that show that the interactions between deposited and suspended nanotubes during the deposition process can considerably affect the dynamics and the final results of the deposition. Semiperiodic stripes of nanotubes bridging two electrodes are formed from solutions containing no surfactant. The periodicity of the patterns depends on the geometry of the electrodes. Finite-element method simulations are used to explain the mechanisms underlying the observed experimental outcomes. The pattern formation is shown to be related to the mutual effects of CNTs on each other. The reason lies in the changes in the electric field as a result of deposition of CNTs. These changes directly alter the DEP force field and, therefore, the way the CNTs are guided. The extent of effectiveness of the electrothermal force, which turns out to be substantial for some solutions, is also investigated, and it is shown that although in some situations the heat generated by the current passing through the nanotubes considerably increases this force, the DEP force remains dominant when a surfactant-free solution is used.

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 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 score0.034
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.186
Teacher spread0.181 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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