A Skin-Inspired Stretchable, Self-Healing and Electro-Conductive Hydrogel with a Synergistic Triple Network for Wearable Strain Sensors Applied in Human-Motion Detection
Bibliographic record
Abstract
Hydrogel-based strain sensors inspired by nature have attracted tremendous attention for their promising applications in advanced wearable electronics. Nevertheless, achieving a skin-like stretchable conductive hydrogel with synergistic characteristics, such as ideal stretchability, excellent sensing performance and high self-healing efficiency, remains challenging. Herein, a highly stretchable, self-healing and electro-conductive hydrogel with a hierarchically triple-network structure was developed through a facile two-step preparation process. Firstly, 2, 2, 6, 6-tetrametylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofibrils were homogeneously dispersed into polyacrylic acid hydrogel, with the presence of ferric ions as an ionic crosslinker to synthesize TEMPO-oxidized cellulose nanofibrils/polyacrylic acid hydrogel via a one-pot free radical polymerization. A polypyrrole conductive network was then incorporated into the synthetic hydrogel matrix as the third-level gel network by polymerizing pyrrole monomers. The hierarchical 3D network was mutually interlocked through hydrogen bonds, ionic coordination interactions and physical entanglements of polymer chains to achieve the target composite hydrogels with a homogeneous texture, enhanced mechanical stretchability (elongation at break of ~890%), high viscoelasticity (maximum storage modulus of ~27.1 kPa), intrinsic self-healing ability (electrical and mechanical healing efficiencies of ~99.4% and 98.3%) and ideal electro-conductibility (~3.9 S m−1). The strain sensor assembled by the hybrid hydrogel, with a desired gauge factor of ~7.3, exhibits a sensitive, fast and stable current response for monitoring small/large-scale human movements in real-time, demonstrating promising applications in damage-free wearable electronics.
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.
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".