Nanonewton Force Sensing and Control in Microrobotic Cell Manipulation
Why this work is in the frame
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Bibliographic record
Abstract
Cellular force sensing and control techniques are capable of enhancing the dexterity and reliability of microrobotic cell manipulation systems. In this paper we present two experimental techniques for nanonewton force sensing and control in microrobotic cell manipulation. A vision-based cellular force sensing approach, including a microfabricated elastic cell holding device and a sub-pixel visual tracking algorithm, was developed for resolving forces down to 3.7 nN during microrobotic mouse embryo injection. The technique also experimentally demonstrated that the measured mechanical difference could be useful for in situ differentiation of healthy mouse embryos from those with compromised developmental competence without requiring a separate mechanical characterization process. Centered upon force-controlled microrobotic cell manipulation, this paper also presents nanonewton force-controlled micrograsping of interstitial cells using a microelectromechanical systems (MEMS)-based microgripper with integrated two-axis force feedback. On-chip force sensors are used for detecting contact between the microgripper and cells to be manipulated (resolution: 38.5 nN at 15Hz) and sensing gripping forces (resolution: 19.9 nN at 15Hz) during force-controlled grasping. The experimental results demonstrate that the microgripper and the control system are capable of rapid contact detection and reliable force-controlled micrograsping to accommodate variations in size and stiffness of cells with a high degree of reproducibility.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it