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Record W2345046720 · doi:10.1109/jmems.2016.2551222

Catheter-Based Microrotary Motor Enabled by Ferrofluid for Microendoscope Applications

2016· article· en· W2345046720 on OpenAlexafffund
Babak Assadsangabi, Min Hian Tee, Simon Wu, Kenichi Takahata

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsFerrofluidRotor (electric)Materials scienceLevitationNeodymium magnetMagnetPrismBearing (navigation)Substrate (aquarium)CatheterMechanical engineeringOpticsComputer scienceMagnetic fieldPhysicsEngineeringSurgeryGeology

Abstract

fetched live from OpenAlex

This paper reports the first microrotary motor enabled with a novel ferrofluid-based levitation mechanism used as an extremely simple miniaturized bearing material for microendoscopy applications. The ferrofluid bearing is magnetically sustained on the permanent magnet rotor that is levitated by the bearing layer inside a tubular substrate, an endoscope catheter. The levitated rotor is electromagnetically driven by two photo-defined meander-type coils formed around the outer walls of the catheter that enables 90°-step angular actuation of the rotor. Two types of micromotors with 1.6-mm and 500-μm-sized rotors are designed, fabricated, and tested. The fabricated prototypes of the motors are successfully operated to rotate the prism mirrors integrated with the motors, with revolution rates as high as 1875 and 1500 r/min for the 1.6-mm and 500-μm rotor types, respectively. Thermal behaviors of the devices are also characterized and reported. The experimental results demonstrate the effectiveness of the motor design and indicate high potential for side-viewing microendoscopic applications.

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 score0.003

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 science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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