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Record W2108518451 · doi:10.1109/memsys.2014.6765609

Ferrofluid-assisted micro rotary motor for minimally invasive endoscopy applications

2014· article· en· W2108518451 on OpenAlexafffund
Babak Assadsangabi, Min Hian Tee, Simon Wu, Kenichi Takahata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFerrofluidRotor (electric)MagnetBearing (navigation)Materials scienceNeodymium magnetMagnetic levitationMagnetic bearingPrismMechanical engineeringComputer scienceOpticsMagnetic fieldPhysicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper reports a micro rotary motor that is enabled with magnetic fluid called ferrofluid 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. The fabricated prototype with the rotor coupled with a 1-mm-sized prism mirror is revealed to provide both step-wise and continuous rotations with revolution rates up to 1875 rpm, verifying the effectiveness of the bearing and motor mechanism. The prototype device is operated to demonstrate its ability of endoscopic imaging in an experimental model.

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 score0.345
Threshold uncertainty score0.484

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 head0.010
GPT teacher head0.220
Teacher spread0.209 · 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
Published2014
Admission routes2
Has abstractyes

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