Self-Healable Reprocessable Triboelectric Nanogenerators Fabricated with Vitrimeric Poly(hindered Urea) Networks
Bibliographic record
Abstract
In recent years, the advent of highly deformable and healable electronics is exciting and promising for next-generation electronic devices. In particular, self-healable triboelectric nanogenerators (SH-TENGs) serve as promising candidates based on the combination of the triboelectric effect, electrostatic induction, and self-healing action. However, the majority of SH-TENGs have been devised with weak polymeric networks that are healed with reversible supramolecular interactions or disulfides, thus resulting in poor mechanical properties and low resistance to creeping. To address this issue, we demonstrate the integration of mechanically strong and self-healable poly(hindered urea) (PHU) network in the fabrication of effective TENGs. The designed PHU network is flexible but shows greater mechanical property of tensile strength as high as 1.7 MPa at break. The network is capable of self-healing quickly and repeatedly as well as being reprocessable under mild conditions, enabling the recovery of triboelectric performances after the complete healing of the damaged surfaces. Furthermore, the interfacial-polarization-induced enhancement of dielectric constant endows our SH-TENG with the highest triboelectric output performance (169.9 V/cm 2 ) among the reported healable TENGs. This work presents an avenue to the development of mechanical energy-harvesting devices and self-powered sensors with excellent stretchability, high recoverability, and good mechanical strength.
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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".