Programmable assembly of heterogeneous microparts by an untethered mobile capillary microgripper
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".