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/m2) 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".