Low-Profile, Large-Range Compressive Strain Sensing Using Micromanufactured CNT Micropillar Arrays
Bibliographic record
Abstract
Tactile sensors, or sensors that collect measurements through touch, have versatile applications in a wide range of fields including robotic gripping, intelligent manufacturing, and biomedical technology. Hoping to match the ability of human hands to sense physical changes in objects through touch, engineers have experimented with a variety of materials from soft polymers to hard ceramics, but so far, all have fallen short. A grand challenge for developers of "human-like" bionic tactile sensors is to be able to sense a wide range of strains while maintaining the low profile necessary for compact integration. Here, we developed a low-profile tactile sensor (∼300 μm in height) based on patterned, vertically aligned carbon nanotubes (PVACNT) that can repetitively sense compressive strains of up to 75%. Upon compression, reversible changes occur in the points of contact between CNTs, producing measurable changes in electrical admittance. By patterning VACNT pillars with different aspect ratios and pitch sizes, we engineered the range and resolution of strain sensing, suggesting that CNT-based tactile sensors can be integrated according to device specifications.
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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) | 0.000 | 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 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".