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Record W2530096311 · doi:10.1039/c6lc00981f

Programmable assembly of heterogeneous microparts by an untethered mobile capillary microgripper

2016· article· en· W2530096311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNational Science Foundation
KeywordsCapillary actionComputer scienceEngineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

At the sub-millimeter scale, capillary forces enable robust and reversible adhesion between biological organisms and varied substrates. Current human-engineered mobile untethered micromanipulation systems rely on forces which scale poorly or utilize gripper-part designs that promote manipulation. Capillary forces, alternatively, are dependent upon the surface chemistry (which is scale independent) and contact perimeter, which conforms to the part surface. We report a mobile capillary microgripper that is able to pick and place parts of various materials and geometries, and is thus ideal for microassembly tasks that cannot be accomplished by large tethered manipulators. We achieve the programmable assembly of sub-millimeter parts in an enclosed three-dimensional aqueous environment by creating a capillary bridge between the targeted part and a synthetic, untethered, mobile body. The parts include both hydrophilic and hydrophobic components: hydrogel, kapton, human hair, and biological tissue. The 200 μm untethered system can be controlled with five-degrees-of-freedom and advances progress towards autonomous desktop manufacturing for tissue engineering, complex micromachines, microfluidic devices, and meta-materials.

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.

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 score0.070
Threshold uncertainty score0.532

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 head0.235
Teacher spread0.227 · 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