Dynamic and Reprocessable Fluorinated Poly(hindered urea) Network Materials Containing Ionic Liquids to Enhance Triboelectric Performance
Bibliographic record
Abstract
Triboelectric nanogenerators (TENGs), a newly developed energy harvesting device that converts surrounding environmental mechanical stimuli into electricity, have been significantly explored as an ideal long-term power source for electrical devices. Despite recent advances, the development of advanced TENG devices with sufficient outputs to sustainably power electronic devices and rapid self-healability under mild conditions to improve their lifetime and function is highly demanded. Here, we report a robust self-healable and reprocessable TENG fabricated with a covalent adaptive network based on mechanically strong fluorinated poly(hindered urea) (F-PHU) integrated with ionic liquid as an efficient dielectric material to improve its triboelectric efficiency and self-healing capability simultaneously. The synthesis and integration of a well-defined reactive copolymer having both pendant fluorinated and t -butylamino bulky groups are the key to fabricate robust F-PHU networks containing fluorinated dangling chains that can interact with ionic liquids to induce ionic polarization, which raises the dielectric constant and thus increases triboelectric performance. They also are cross-linked with dynamic bulky urea linkages for rapid self-healability and high reprocessability through their reversible exchange reactions at moderate temperatures. The developed ionic F-PHU materials exhibit a high TENG output performance (power density of 173.0 mW/m 2 ) as well as high TENG output recovery upon repairing their surface damages. This work demonstrates that such a synergistic design of triboelectric ionic F-PHU materials could have great potential for applications requiring high-performance and long-lasting energy harvesting.
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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".