Highly Viscoelastic, Stretchable, Conductive, and Self-Healing Strain Sensors based on Cellulose Nanofibers-Reinforced Polyacrylic Acid Hydrogel
Bibliographic record
Abstract
Abstract Conductive and self-healing hydrogels are among the emerging materials that mimic the human skin and are important due to their probable prospects in soft robots and wearable electronics. However, the mechanical properties of the hydrogel matrix limit their applications. In this study, we developed a physicochemically dual cross-linked chemically modified-cellulose nanofibers-carbon nanotubes/polyacrylic acid (TOCNF-CNTs/PAA) hydrogel. The TOCNFs acted both as a nanofiller and dispersant to increase the mechanical strength of the PAA matrix and break the agglomerates of the CNTs. The final self-healing and conductive TOCNF-CNTs/PAA-0.7 (mass ratio of CNTs to AA) hydrogel with a uniform texture exhibited highly intrinsic stretchability (breaking elongation to ca. 850%), enhanced tensile properties (ca. 59kPa), ideal conductivity (ca. 2.88S·m− 1) and pressure sensitivity. Besides, the composite hydrogels achieved up to approximately 98.36% and 99.99% self-healing efficiency for mechanical and electrical properties, respectively, without any external stimuli. Therefore, the as-designed multi-functional self-healing hydrogels, combined with stretching, sensitivity, and repeatability, possess the ability to monitor human activity and develop multifunctional, advanced, and commercial products such as wearable strain sensors, health monitors, and smart 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) | 0.000 | 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".