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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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 10 11 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.733

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 head0.013
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

Quick stats

Citations44
Published2017
Admission routes1
Has abstractyes

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