Active Release of Microobjects Using a MEMS Microgripper to Overcome Adhesion Forces
Bibliographic record
Abstract
Due to force scaling laws, large adhesion forces at the microscale make rapid accurate release of microobjects a long-standing challenge in pick-place micromanipulation. This paper presents a new microelectromechanical systems (MEMS) microgripper integrated with a plunging mechanism to impact the microobject for it to gain sufficient momentum to overcome adhesion forces. The performance was experimentally quantified through the manipulation of 7.5-10.9-mum borosilicate glass spheres in an ambient environment under an optical microscope. Experimental results demonstrate that this microgripper, for the first time, achieves a 100% successful release rate (based on 200 trials) and a release accuracy of 0.70 plusmn0.46 mum. Experiments with conductive and nonconductive substrates also confirmed that the release process is not substrate dependent. Theoretical analyses were conducted to understand the release principle. Based on this paper, further scaling down the end structure of this microgripper will possibly provide an effective solution to the manipulation of submicrometer-sized objects.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".