Screen-Printed Resistive Tactile Sensor for Monitoring Tissue Interaction Forces on a Surgical Magnetic Microgripper
Bibliographic record
Abstract
With the recent development of novel miniaturized magnetically controlled microgripper surgical tools (of diameter 4 mm) for robot-assisted minimally invasive endoscopic intraventricular surgery, the surgeon loses feedback from direct physical contact with the tissue. In this case, surgeons will have to rely on tactile haptic feedback technologies to retain their ability to limit tissue trauma and its associated complications during operations. Current tactile sensors for haptic feedback cannot be integrated to the novel tools primarily due to size limitations and low force range requirements of these highly dextrous surgical operations. This study introduces the design and fabrication of a novel 9 mm 2, ultra-thin and flexible resistive tactile sensor whose operation is based on variation of resistivity due to changes in contact area and piezoresistive (PZT) effect of the sensor’s materials and sub-components. Structural optimization was performed on the sub-components of the sensor design, including microstructures, interdigitated electrodes, and conductive materials in order to improve minimum detection force while maintaining low hysteresis and unwanted sensor actuation. To achieve a low-cost design suitable for disposable tools, multiple layers of the sensor sub-component were screen-printed to produce thin flexible films. Multi-walled carbon nanotubes and thermoplastic polyurethane composites were fabricated, optimized, and processed into suitable inks to produce conductive films to be assembled with printed interdigitated electrodes and microstructures. The assembled sensor’s electromechanical performance indicated three distinct linear sensitivity modes within the sensing range of 0.04–1.3 N. Results also indicated repeatable and low-time responses while maintaining the flexibility and robustness of the overall sensor. This novel ultra-thin screen-printed tactile sensor of 110 μm thickness is comparable to more expensive tactile sensors in terms of performance and can be mounted onto the magnetically controlled micro-scale surgical tools to increase the safety and quality of endoscopic intraventricular surgeries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".