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Record W2601452937 · doi:10.1002/smll.201603978

Rotating‐Electric‐Field‐Induced Carbon‐Nanotube‐Based Nanomotor in Water: A Molecular Dynamics Study

2017· article· en· W2601452937 on OpenAlexaff
Md. Mushfiqur Rahman, M. M. Chowdhury, Md. Kawsar Alam

Bibliographic record

VenueSmall · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotubeElectric fieldPolarizabilityDipoleRotation (mathematics)Materials scienceMolecular dynamicsChemical physicsOrientation (vector space)Field (mathematics)Rotational speedDielectrophoresisNanotubeWater modelNanotechnologyMechanicsClassical mechanicsPhysicsComputational chemistryMoleculeChemistryGeometry

Abstract

fetched live from OpenAlex

Using molecular dynamics simulations, it is shown that a carbon nanotube (CNT) suspended in water and subjected to a rotating electric field of proper magnitude and angular speed can be rotated with the aid of water dipole orientations. Based on this principle, a rotational nanomotor structure is designed and the system is simulated in water. Use of the fast responsiveness of electric‐field‐induced CNT orientation in water is employed and its operation at ultrahigh‐speed (over 1011 r.p.m.) is shown. To explain the basic mechanism, the behavior of the rotational actuation, originated from the water dipole orientation, is also analyzed . The proposed nanomotor is capable of rotating an attached load (such as CNT) at a precise angle as well as nanogear‐based complex structures. The findings suggest a potential way of using the electric‐field‐induced CNT rotation in polarizable fluids as a novel tool to operate nanodevices and 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.251
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations44
Published2017
Admission routes1
Has abstractyes

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