Recent progress in conductive self‐healing hydrogels for flexible sensors
Bibliographic record
Abstract
Abstract Flexible sensors have great potential in the application of wearable and implantable devices, and conductive hydrogels have been widely used in wearable sensing devices due to their biomimetic structure, biocompatibility, adjustable transparency and stimuli‐responsive electrical properties. Conventional conductive hydrogels are prone to be damaged in their application process and lack of long‐term reliability. Inspired by natural organisms such as mussels, introduction of self‐healing capabilities has been regarded as a promising approach to extend the service life of hydrogel sensing devices. This work reviews the synthesis methods and the associated self‐healing mechanisms of representative conductive self‐healing hydrogels. The sensing principles and structural design of conductive hydrogel sensors are also reviewed and their applications for in vivo/in vitro signal monitoring are introduced. The remaining challenges and perspectives in this field are discussed in order to direct the future research on the integration of self‐healing capabilities, good sensing properties and excellent mechanical performances into flexible sensors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".