Highly Conductive Cellulose Strain Sensor with Excellent Negative Resistance Variation and Joule Heating Property
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it