Highly Conductive Cellulose Strain Sensor with Excellent Negative Resistance Variation and Joule Heating Property
Bibliographic record
Abstract
The rational design of a wearable strain sensor with heating property has attracted great interest. In this study, a flexible conductivity hierarchical cellulose strain sensor (MX@Ag@CY) with heating property was fabricated via in situ formation of silver nanoparticles (Ags) on cotton yarn (CY) and subsequent dip-coating with MXene (MX). Ags coupled with MX coating endowed the cotton yarn with a high conductivity, where the resistance of the optimized composite MX@Ag 0.47 @CY was about 22 Ω/cm. Compared with the previously reported strain sensors, the woven MX@Ag 0.47 @CY fabric strain sensor showed a distinctive negative resistance variation, wherein it showed an enhanced conductivity with the increased strain owing to its unique architecture. The woven MX@Ag 0.47 @CY fabric strain sensor exhibited a repeatable response and displayed long-term stability in the strain range of 0–55%. In addition, the strain sensor demonstrated great detectability on large-scale human movements when directly attached to the elbow, wrist, or knee. Furthermore, when MX@Ag 0.47 @CY served as a heater (at an applied DC voltage of 6 V), it presented high heating temperature (92.4 °C), homogeneous temperature distribution, low operation voltage (1–6 V), and excellent thermal stability even under strain.
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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".