Power Optimization of TENGs via Load Capacitance Sizing
Bibliographic record
Abstract
A power optimization strategy is described for triboelectric energy harvesting systems by optimizing the load capacitor size within a full wave rectifier (FWR). In AC harvesters such as triboelectric nanogenerators (TENGs) with an FWR, the average power delivered to the rectifier increases in each cycle, ultimately reaching a steady state determined by system parameters. Through cycle-level analysis of input voltage, current, and rectifier turn-on time during mechanical motion, an optimal load capacitance is identified that maximizes power delivery while minimizing transient time to reach this maximum power. This approach achieves peak power delivery via capacitor sizing alone, eliminating the need for additional circuitry. Experimental results using a vertical contact-separation triboelectric nanogenerator with internal capacitance varying from 24pF to 96pF demonstrate that the optimal rectifier capacitance of 390pF achieves maximum power delivery (900nW at 1.7Hz and 2.7μW at 5Hz) within the second cycle, while suboptimal capacitances either fail to reach peak power or delay it to the seventh cycle or later. Sensitivity analysis reveals that the method exhibits high robustness, with capacitances within ±30% of the optimal value can still maintain ≥ 90% of peak power, providing flexibility when implementing or selecting the capacitor size.
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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 | 0.000 | 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 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".