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Record W2291036053 · doi:10.1109/memsys.2016.7421583

A steerable smart catheter tip realized by flexible hydrogel actuator

2016· article· en· W2291036053 on OpenAlexaff
Madeshwaran Selvaraj, Kenichi Takahata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceActuatorBimorphLayer (electronics)Resistive touchscreenBendingPolyimideOptoelectronicsCatheterBiomedical engineeringComposite materialNanotechnologyComputer sciencePiezoelectricitySurgery

Abstract

fetched live from OpenAlex

This paper reports the first smart catheter tip realized by integrating a thermoresponsive hydrogel on a flexible strip of microfabricated heater, targeting at applications in minimally invasive vascular treatments and other surgical procedures. A bimorph-like active tip structure is fabricated by integrating a 500-μm-thick layer of poly(N-isopropylacrylamide) hydrogel on top of the 20-mm-long region of a 3.5-mm-wide flexible polyimide strip that embeds a micropatterned resistive heater. The activation of the heater stimulates the hydrogel layer to shrink and bend the flexible tip, providing the catheter with controlled steering ability for a wide range of navigation angles. Infrared imaging shows uniform heating over the entire hydrogel region within a ~2-°C variation. The fabricated prototypes are assembled on commercial catheter tubes and operated to demonstrate bending angles of up to 130°, which is significantly larger compared with other active catheters reported. Dynamic characterization of the device also shows promising results towards the target application.

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 communication0.0000.000
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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Same topicHydrogels: synthesis, properties, applicationsFrench-language works237,207