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Record W2038173552 · doi:10.1088/0960-1317/18/6/065009

Planar frictional micro-conveyors with two degrees of freedom

2008· article· en· W2038173552 on OpenAlexaff
Byron Shay, Ted Hubbard, Marek Kujath

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPlanarFabricationRotational speedMechanical engineeringActuatorEngineeringMaterials scienceElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

The design, fabrication and operation of planar frictional micro-conveyors with two degrees of freedom (2-DOF) were investigated. The frictional micro-conveyors consisted of two parts: a driving unit and a mobile plate. The driving unit was comprised of thermal actuators which were attached to inverted feet. The plate was in constant frictional contact with the feet surfaces and by properly sequencing the motion of the feet, the plate moved with stepwise advances. Two different 2-DOF designs were constructed: an X –θ conveyor capable of linear and rotational yaw motions and an X – Y conveyor capable of planar translation. Both types of micro-conveyors were fabricated using 10 µm thick silicon-on-insulator technology. The X –θ driving unit's size was 580 × 960 µm 2 and it moved a 700 × 380 µm 2 plate. The X – Y driving unit's size was 720 × 720 µm 2 and it moved a 530 × 530 µm 2 plate. The X –θ conveyor was capable of moving the plate at a linear speed of up to 33 µm s −1 and a rotational speed of up to 7° s −1 . The X – Y conveyor was capable of translating the plate at a speed of 20 µm s −1 along either axis. The conveyors were able to transport loads in excess of 850 µg.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.007
GPT teacher head0.174
Teacher spread0.167 · 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 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

Citations7
Published2008
Admission routes1
Has abstractyes

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