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Record W1667228739 · doi:10.1109/coase.2015.7294329

Dipole field controlled micro- and nanomanipulation

2015· article· en· W1667228739 on OpenAlexaff
Maxime Latulippe, Sylvain Martel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsWorkspaceDesign for manufacturabilityTorqueComputer scienceMagnetic fieldDistortion (music)DipoleMechanical engineeringAcousticsPhysicsMaterials scienceEngineeringRobotArtificial intelligence

Abstract

fetched live from OpenAlex

A novel non-contact micro- and nanomanipulation method is proposed. This method, Dipole Field Manipulation (DFM), exploits the distortion of a uniform magnetic field induced around ferromagnetic cores. While this distortion can generate strong magnetic gradients to induce pulling forces on a manipulated object, the direction of the field can be used to induce magnetic torques. This paper presents a first insight on DFM capabilities and control possibilities. We show that by varying the orientation and strength of the uniform field, DFM has the potential to achieve effective manipulation in up to six degrees of freedom. Using conventional electromagnetic coils as the uniform field source and a mobile core, gradients can reach 1-2 T/m or more in the whole manipulation workspace. Such coils provide fast directional force and torque variation capabilities, and allow an easy access to the workspace for feedback visualization and core positioning. A first concept for a 6-DOF DFM system is proposed. Results of a preliminary experiment where a particle was moved around a 0.5×1 mm rectangular path demonstrate the feasibility of the method to induce sufficient forces in any direction.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.303

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 head0.017
GPT teacher head0.242
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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