A Simple and Effective Physical Ball‐Milling Strategy to Prepare Super‐Tough and Stretchable PVA@MXene@PPy Hydrogel for Flexible Capacitive Electronics
Bibliographic record
Abstract
Abstract Biomimetic flexible electronics for E‐skin have received increasing attention, due to their ability to sense various movements. However, the development of smart skin‐mimic material remains a challenge. Here, a simple and effective approach is reported to fabricate super‐tough, stretchable, and self‐healing conductive hydrogel consisting of polyvinyl alcohol (PVA), Ti 3 C 2 T x MXene nanosheets, and polypyrrole (PPy) (PMP hydrogel). The MXene nanosheets and Fe 3+ serve as multifunctional cross‐linkers and effective stress transfer centers, to facilitate a considerable high conductivity, super toughness, and ultra‐high stretchability (elongation up to 4300%) for the PMP hydrogel with. The hydrogels also exhibit rapid self‐healing and repeatable self‐adhesive capacity because of the presence of dynamic borate ester bond. The flexible capacitive strain sensor made by PMP hydrogel shows a relatively broad range of strain sensing (up to 400%), with a self‐healing feature. The sensor can precisely monitor various human physiological signals, including joint movements, facial expressions, and pulse waves. The PMP hydrogel‐based supercapacitor is demonstrated with a high capacitance retention of ≈92.83% and a coulombic efficiency of ≈100%.
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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".