An Improved Synchronous Charge Extraction (SCE) Rectifier for Energy-harvesting from Capacitive Power Sources
Bibliographic record
Abstract
This work presents a new Synchronous Charge Extraction (SCE) rectifier circuit that overcomes the parasitic capacitance present in capacitive power harvesting sources such as near-field Capacitive coupling and vibration based Piezoelectric and Acoustic transducers. This capacitance restricts the harvested charge flow towards the output load during every input voltage cycle thereby curtailing the output voltage and harvested power in typical full-wave rectifiers and voltage multiplier circuits. The proposed design uses BJT-based electronic breakers to connect and disconnect an inductor in sync with the harvested input voltage effectively reducing the charge lag and harvested power throughput due to the power harvester’s internal capacitance. The SCE rectifier’s switching mechanism with the inductor also boosts the input voltage further, which can charge an energy-storing output capacitor of 100µF to voltages significantly larger than the harvested input voltage. The use of BJTs also eliminates cold-start issues where circuit may have insufficient voltage to drive its own switches. Testing was conducted alongside other passive and switching rectifiers to charge a 100 µF energy—storing output capacitor. Measurements show the proposed design having a 164% improvement over a full bridge rectifier, a 36% improvement over a passive voltage-doubler, and a 47% improvement over a reference design using bias-flip topology.
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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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".