A Hybrid Generator with Electromagnetic Transduction for Improving the Power Density of Triboelectric Nanogenerators and Scavenging Wind Energy
Bibliographic record
Abstract
Abstract Due to the low output power of triboelectric nanogenerators (TENGs) in harvesting wind energy, this work proposes a hybridization scheme with electromagnetic generators (EMGs) to improve the power density of TENGs. Then, a novel configuration is designed and experiments of impedance matching and output power are conducted to compare the power density of triboelectric nanogenerators with/without electromagnetic generators under a wind speed of 5.5 m s−1. According to the experimental results, the power density of the hybrid generator is 29.8 times higher than that of the triboelectric nanogenerator without electromagnetic generators (TENG‐WEMGs). To further demonstrate the output performance of the hybrid generator, experiments of charging capacitors and powering electronics are implemented at the same wind speed. Based on the experimental results, a capacitor of 2.2 mF is charged to 25.7 V within 20 s, 170 LEDs are lit, and the Bluetooth tracker is driven to transmit signals in real time. In addition, this work investigates the influences of different resistant loads of EMG on the average power of TENGs. This work can be of great significance to further develop self‐powered sensors.
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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".