Contactless Robotic Micromanipulation in Air Using a Magneto-Acoustic System
Bibliographic record
Abstract
Precise and dexterous handling of micrometerto millimeter-scale objects is a crucial and challenging factor for micromanipulation, especially in the fields of biotechnology, where delicate microcomponents can be easily damaged by contact during handling. Many complex microrobotic techniques, scaling from fully autonomous to teleoperated, have been developed to address the limitations individually. However, a scalable, reliable, and versatile method, which can be applied to a wide range of applications, is not present. This work uniquely combines the advantages of magnetic and acoustic micromanipulation methods to achieve three-dimensional, contactless, and semi-autonomous micromanipulation, with potential for full automation, for use in microassembly applications. Solid and liquid materials, with sizes less than 3 mm (down to 300 μm), are handled in a cylindrical workspace of 30 mm in height and 4 mm in diameter using acoustic levitation, while an externally applied magnetic field controls the orientation of magnetically active components. A maximum vertical positioning root-mean-square error of 1.5% of parts length was observed. This letter presents the concept, design, characterization, and modeling of the new method, along with a demonstration of a typical assembly process.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".