A Three-Coil Self-Resonant WPT System for Biomedical Applications
Bibliographic record
Abstract
Wireless power transfer (WPT) is a reliable method for powering implantable medical devices. However, achieving high efficiency with a compact receiver (RX) remains a major challenge. Self-resonant wireless power transfer (SRWPT) systems enhance reliability by using parasitic capacitance within planar coils, eliminating external compensation components. This paper presents a fully self-resonant three-coil PCB system that eliminates external capacitors while improving power transfer efficiency for compact RX coils. The system utilizes three specially designed double-layer coil structures: two dedicated to generating high inductance values (L₂ and L₃), and one configured as an auxiliary coil to further enhance impedance. This arrangement achieves inductances up to four times greater than those of a traditional coil with the same footprint. Through a series combination of mutual inductance (Lm) and mutual capacitance (Cm) within the auxiliary coil network, the resonant frequency and system impedance are significantly increased, leading to improved overall efficiency and system robustness. Moreover, the size mismatch between the transmitter (TX) and receiver (RX) coils necessitates precise synchronization of their resonant frequencies. To achieve this, an air-core structure is employed instead of FR4, allowing flexible frequency tuning through coil spacing adjustments while simultaneously reducing power losses. Simulation results demonstrate that the proposed system achieves a power transfer efficiency of 94.6% at a transmission distance of 10 mm.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".