Self‐Powered Underwater Pressing and Position Sensing and Autonomous Object Grasping with a Porous Thermoplastic Polyurethane Film Sensor
Bibliographic record
Abstract
Abstract Most flexible ionic tactile sensors can hardly be used in deep sea due to their poor antiswelling and anticompression properties under high hydrostatic pressure. To achieve pressure and position sensing under high hydrostatic pressure, a self‐powered underwater tactile sensor made of a porous thermoplastic polyurethane (TPU) film is presented in this paper. The sensor works by generating an electric current due to the different moving velocities of ions in the porous film under pressing. Experimental results show that the magnitude of the generated current signal increases with the applied pressure, the contacting area, and ion concentration of the solution. The direction and magnitude of the current signal depend on the pressing position of the film. The signal magnitude decreased with the closer to the center of the film. The maximum pressure sensitivity and positioning resolution are 0.62 kPa −1 and 1.31 mm respectively. Response time (0.19 s), 0.67 s recovery time, and 50–600 kPa pressure detection range are achieved. In addition, the signal magnitude is decreased only by 15.53% when the sensor is placed underwater at a simulated depth of 100 m. Proof of concept demonstration of underwater autonomously grasping objects of different weights with this sensor is successfully achieved.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".