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Record W2994588981 · doi:10.1088/1361-665x/ab36a0

Ferrofluid-enabled micro rotary-linear actuator for endoscopic three-dimensional imaging and spectroscopy

2019· article· en· W2994588981 on OpenAlexafffund
Babak Assadsangabi, Sayed Mohammad Hashem Jayhooni, Michael Short, Haishan Zeng, Kenichi Takahata

Bibliographic record

VenueSmart Materials and Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsMaterials scienceMicroactuatorFerrofluidActuatorSpectroscopyOpticsFluidicsBiomedical engineeringComputer scienceMagnetic fieldEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Abstract This work develops the first tubular microactuator functionalized by ferrofluid that enables both rotational and axial motions in a simultaneous or selective manner for endoscopic imaging and spectroscopy catheter applications. A layer of ferrofluid attracted on the magnetic rotor/slider lifts it off the inner walls of the catheter tube, offering a near friction-less electromagnetic revolution of the rotor and/or its sliding motion along the catheter’s axis controlled by a fluidic pressure. A device prototype coupled with a prism mirror is microfabricated and evaluated to verify the effectiveness of the device design for 3-dimesnional (3D) scanning of a probing laser beam. Measurements show a superior revolving stability along its axis compared to the preceding design without axial motion function. An application of angle-resolved endoscopic Raman spectroscopy is experimentally demonstrated through an ex-vivo test using mouse tissue. The study suggests a promising potential of the microactuator for 3D endoscopic applications with Raman spectroscopy and likely with other modalities including ultrasound and optical coherence tomography.

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 score0.086
Threshold uncertainty score0.605

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 head0.221
Teacher spread0.215 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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