Achieving High‐Performance Triboelectric Nanogenerator by DC Pump Strategy
Bibliographic record
Abstract
Abstract Triboelectric nanogenerator (TENG) has been demonstrated as a promising solution for powering widely distributed electronics in the new era of Internet of Things (IoTs), however, high‐performance TENG always relies on tribo‐materials with high triboelectric property. Herein, a simple self‐charge excitation technique that charge injection is directly realized by self‐generated electrostatic breakdown charge instead of voltage‐multiplying circuit is proposed to break through the limitation of triboelectrification. By using the designed electrostatic breakdown charge excitation TENG (EBE‐TENG), the output performance of poor tribo‐material based TENG such as polyethylene (PE) can be enhanced by 5.7 times, which even approximates that of high tribo‐material based TENG such as fluorinated ethylene propylene (FEP). Moreover, electrostatic breakdown charge excitation technique also exhibits a universal applicable ability for other different triboelectric materials. This work not only greatly simplifies the charge excitation system, but also broadens the availability of materials for achieving high‐performance TENG.
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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".