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Record W2037136969 · doi:10.1109/biorob.2010.5627785

Microscale hydrogel-based computer-triggered polymorphic microrobots for operations in the vascular network

2010· article· en· W2037136969 on OpenAlexaff
Seyed Nasrollah Tabatabaei, Jacinthe Lapointe, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMicroscale chemistryMagnetic fieldMagnetic nanoparticlesScannerPropulsionVascular networkNanotechnologyMaterials scienceComputer scienceNanoparticleBiomedical engineeringPhysicsEngineeringArtificial intelligenceAerospace engineeringAnatomyMathematics

Abstract

fetched live from OpenAlex

A new type of microrobots for medical interventions in the vascular network is presented. In its simplest form, each microscale robots consists of magnetic nanoparticles (MNP) embedded in a thermo-sensitive hydrogel, also known as PNIPA. The nanoparticles are not only used for propulsion/steering, and MRI-based tracking inside the body due to local magnetic field inhomogeneity, but also for triggering a change of volume of the microrobots. The latter is possible since the MNP can introduce heat in the hydrogel-based microrobots when placed in an AC magnetic field. In this paper, spherical PNIPA-MNP were synthesized and propelled by the magnetic gradient field inside a clinical MRI scanner before being submitted when at a targeted location to a special apparatus capable of generating an AC magnetic field of 4 kA.m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> at 160 kHz. Temperature elevations and change in the overall volume of the microrobots were recorded.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.457
Threshold uncertainty score0.442

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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