Versatile Electronic Skins for Motion Detection of Joints Enabled by Aligned Few‐Walled Carbon Nanotubes in Flexible Polymer Composites
Bibliographic record
Abstract
Here, novel multifunctional electronic skins (E‐skins) based on aligned few‐walled carbon nanotube (AFWCNT) polymer composites with a piezoresistive functioning mechanism different from the mostly investigated theory of “tunneling current channels” in randomly dispersed CNT polymer composites are demonstrated. The high performances of as‐prepared E‐skins originate from the anisotropic conductivity of AFWCNT array embedded in flexible composite and the distinct variation of “tube‐to‐tube” interfacial resistance responsive to bending or stretching. The polymer/AFWCNT‐based flexion‐sensitive E‐skins exhibit high precision and linearity, together with low power consumption (<10 µW) and good stability (no degradation after 15 000 bending–unbending cycles). Moreover, polymer/AFWCNT composites can also be used for the construction of tensile‐sensitive E‐skins, which exhibit high sensitivity toward tensile force. The polymer/AFWCNT‐based E‐skins show remarkable performances when applied to monitor the motions and postures of body joints (such as fingers), a capability that can find wide applications in wearable human–machine communication interfaces, portable motion detectors, and bionic robots.
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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".