Anisotropic Fluorinated‐Elastomer‐Blended Micro‐Dominoes for Wearable Triboelectric Nanogenerators
Bibliographic record
Abstract
Abstract Triboelectric nanogenerators (TENGs) have emerged as promising portable and sustainable energy sources in daily life, harvesting energy from human motion, water, and wind. However, they still face limitations in aspects such as contact area, deformability, wettability, and manufacturing method. Here, a wearable TENG incorporating an anisotropic domino structure based on a fluorinated elastomer blend is presented. Because of its thin, elongated structure with broad sides, the TENG achieves substantially larger contact areas and high bendability. Introducing a fluorinated elastomer into the polydimethylsiloxane matrix via a simple blending process not only enhances the triboelectric performance but also reduces surface energy and improves the stretchability of elastomers. The anisotropic arrangement of dominoes, in synergy with the fluorinated elastomer, mimics the surface physicochemical properties of natural rice leaves, resulting in anisotropic superhydrophobic wetting behavior with a self‐cleaning effect and controlled directional water flow for efficient water energy harvesting. Therefore, the TENG functions as an energy‐harvesting leaf that captures energy from wind and water droplets, as well as a wearable energy‐harvesting wristband that generates power from human motions such as touching, shaking, and hand washing.
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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".