A Charge-Depletion Study of an Electrostatic Generator With Adjustable Output Voltage
Bibliographic record
Abstract
Micro-scale generators are becoming more popular for harvesting energy to power bio-implantable devices and sensor networks. Most electrostatic generators (ESGs) use constant capacitors as storage or reservoir components in conjunction with a variable capacitor. The main issue with some existing ESG topologies is that these capacitors deplete and discharge over time. This paper studies a typical ESG and derives the charge depletion problem mathematically. Subsequently, a new ESG capable of circumventing this problem is proposed. Closed-form formulas expressing the output voltage and generated power are derived and validated. The proposed ESG harvests 25% of the power that the mechanical energy source generates by actuating the variable capacitor when the maximum-to-minimum capacitance ratio of the variable capacitor is optimized. In the presented case study, the ESG generates 9.75 mW optimally when a variable capacitor with a maximum/minimum capacitance ratio of 39/9.75μF is used for energy harvesting from a 1-Hz knee joint movement of a walking person. The overall volume of the ESG is estimated to be 125 mm3, and the variable capacitor is charged to 5 V at its maximum capacitance. A control mechanism and a self-starting circuit are presented for this ESG architecture, which allows it to generate any desired output voltage. This capability can be used to harvest the maximum available kinetic energy and compensate load variations.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".