Development of Poly(Hindered Urea) Network for Self-healable Triboelectric Nanogenerators
Bibliographic record
Abstract
In recent years healable electronics have garnered significant attention for their scope in next-generation electronic devices. In particular, triboelectric nanogenerators (TENGs) which are capable of harvesting energies from mechanical motions have been explored as a promising power source for self-powered devices. TENGs possess the benefits of high performance, convenience, eco-friendliness, and low cost and require great strength and functionality to perform. The incorporation of self-healability into the design of TENG systems could improve their lifetime and durability as well as their energy harvesting capabilities. Dynamic covalent chemistries have been utilized extensively for the development of covalent adaptive networks exhibiting self-healability and reprocessability. However, most approaches require the use of external stimuli to establish the reversibility of broken networks. Therefore, the development of a new strategy for the synthesis of robust networks with the balanced properties of void-filling and mechanical strength is required for value-added applications such as flexible electronics. \nHindered urea bond (urea with a bulky substituent, attached to its nitrogen atom) is a promising dynamic covalent chemistry which undergoes dynamic exchange reaction at mild temperature, with no catalysts. The reaction of a bulky amine with isocyanate allows for a simple design of a polyurea self-healing network, further broadening the scope of applications for these materials. \nMy Ph.D. research has aimed to study self-healing mechanisms of hindered urea chemistry and to design and synthesize new materials that can undergo catalyst-free dynamic exchange reactions and thus autonomously repair cracks and starches at lower temperatures as well as exhibit reprocessability. Furthermore, the developed networks were evaluated for TENG performances to better understand the design principles of self-healable TENGs.
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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".