Screen-Printed Capacitive Tactile Sensor for Monitoring Tool–Tissue Interactions and Grasping Performances of a Surgical Magnetic Microgripper
Bibliographic record
Abstract
With miniaturization and wireless actuation for a class of magnetic microgrippers for robot-assisted minimally invasive endoscopic intraventricular surgery, surgeons are unable to acquire tactile sensory information on tissues and organs during tool–tissue manipulation and grasping tasks. To minimize the risks of tissue trauma and improve surgical performance, surgeons require haptic feedback technologies to be integrated onto microscale surgical tools for tactile information. However, current sensors cannot be equipped onto the interior jaw of the microgripper due to low-pressure range and small-scale criteria for RMIS implementation for pediatric neurosurgery. This study proposes a 24 mm 2, ultrathin, and flexible capacitive tactile sensor for the interior jaws of a disposable surgical magnetically-controlled microgripper to potentially monitor and regulate tool–tissue manipulation pressures/forces in real time to improve grasping performances and quality of surgical procedures. To lower fabrication costs, multiple layers of the capacitive sensor were screen-printed and assembled to produce a 100 μm thick sensor. To enhance the range and sensitivity, four different morphologies were developed for the dielectric layer and integrated into the sensor design. The dielectric layers were fabricated by optimizing and processing thermoplastic polyurethane (TPU) into a suitable ink adequate for screen printing large surfaces and microstructures. The final optimized capacitive tactile sensor with a grid-like microstructured dielectric design’s electromechanical performance was modeled as a bilinear response with two sensitivity modes for a sensing range of 0.42–54.2 kPa (0.01–1.30 N applied on 24 mm 2 of gripper jaw). The results also indicated performance comparable to more expensive tactile sensors with a hysteresis of 8.8% and a repeatable response to applied cycling loadings with a maximum response signal decay of 1.85%. This study highlights that simple screen printing method can be used as a low-cost alternative to fabricate high-performance tactile sensors to be integrated to the interior jaw of the microgripper designed for disposable endoscopic intraventricular surgeries.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".