An Electronically Perceptive Bioinspired Soft Wet-Adhesion Actuator with Carbon Nanotube-Based Strain Sensors
Bibliographic record
Abstract
The development of bioinspired switchable adhesive systems has promising solutions in various industrial/medical applications. Switchable and perceptive adhesion regardless of the shape or surface shape of the object is still challenging in dry and aquatic surroundings. We developed an electronic sensory soft adhesive device that recapitulates the attaching, mechanosensory, and decision-making capabilities of a soft adhesion actuator. The soft adhesion actuator of an artificial octopus sucker may precisely control its robust attachment against surfaces with various topologies in wet environments as well as a rapid detachment upon deflation. Carbon nanotube-based strain sensors are three-dimensionally coated onto the irregular surface of the artificial octopus sucker to mimic nerve-like functions of an octopus and identify objects via patterns of strain distribution. An integration with machine learning complements decision-making capabilities to predict the weight and center of gravity for samples with diverse shapes, sizes, and mechanical properties, and this function may be useful in turbid water or fragile environments, where it is difficult to utilize vision.
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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".