High Performance Triboelectric Nanogenerator by Hot Embossing on Self‐Assembled Micro‐Particles
Bibliographic record
Abstract
Triboelectric nanogenerator (TENG) is a novel technology for energy harvesting and active sensing which shows a great potential toward extracting the ambient kinetic energy. The surface morphology of triboelectric layers is one of the most important elements for enhancing the output of TENG devices. Current surface modification methods mostly require complicated, long term, size limited, and costly processes. This paper presents a cost effective, facile, repeatable, and fast method for large scale surface modification of triboelectric layers which significantly enhance the performance of TENG devices. This method simply utilizes the hot‐embossing of polymers on self‐assembled micro‐particles to create semi‐ordered micro‐sized structures on the surfaces. The modified surfaces increase the output performance of the TENG such as open circuit voltage and short circuit current for more than four times. As the presence of water degrades the output of TENG, this technique is very beneficial to remarkably improve the hydrophobicity of the contact layers. The proposed method can be applied to a variety of polymers with an area scalable fabrication toward the use of TENG in real world industrial applications.
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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".