Wireless power transfer system for deep-implanted biomedical devices
Bibliographic record
Abstract
In this paper, a dual-band implantable rectenna is proposed for recharging and operating biomedical implantable devices at 0.915 and 2.45 GHz. The rectenna system consists of a compact dual-band antenna based on a meandered-resonator as well as efficient dual-band rectifier circuit. Both components (antenna and rectifier) are integrated inside a capsule device to simulate and experimentally validate the rectenna. The antenna occupies lower volume ([Formula: see text] [Formula: see text]), where compactness is achieved using meandered geometry and a slotted ground plane. It maintains quasi-omnidirectional radiation patterns and peak realized gains of -22.1 dBi (915 MHz) and -19.6 dBi (2.45 GHz); thus, its capability is enhanced to harvest the ambient energy from multiple directions. Moreover, a dual-band rectifier is designed using a dual-branch matching network (an L-matching network and open-circuited stub in each branch) with a radio frequency (RF) to direct current (DC) conversion efficiency of 79.9% for the input power of 1 dBm (lower band: 0.915 GHz) and 72.8% for the input power of 3 dBm (upper band: 2.45 GHz). To validate the concept of the rectenna, the implantable antenna and rectifier are fabricated and attached together inside a capsule device, with the measured results verifying the simulated responses. The proposed rectenna efficiently rectifies two RF signals and effectively superimposes on a single load, thus, providing a distinct advantage compared to single-band rectennas. To the best of the authors' knowledge, this is the first-ever implantable rectenna to perform dual-band RF signal rectification.
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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) | 0.003 | 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".