Subwavelength-Scale Focused Wireless Powering of Implantable Medical Devices by Superoscillations
Bibliographic record
Abstract
This article presents a wireless power delivery method for implantable neurostimulation devices, leveraging superoscillatory (SO) waveforms within a parallel-plate cavity to achieve a previously unmatched spatial precision of$0.35\lambda $at 915 MHz. By exploiting the Huygens’ Box (HB) configuration and tailoring surface currents, our approach compresses the focal spot to a subwavelength full width at half maximum (FWHM)—not only surpassing diffraction-limited beams and Bessel patterns, but also enabling dynamic beamsteering deep into human brain-mimicking media. Operating at an input power of 1 W, this technique ensures reliable power transfer to implants placed 2.5 cm beneath the surface with developing 1.8 V across 9 k$\Omega $. Integrated with a miniature cm-scale rectenna, the system effectively converts the focused RF field into dc power, offering a pathway to support multiple battery-free implantable devices simultaneously and maintaining steady energy delivery over a prescribed region of interest (ROI). By combining SO-based beamforming, cavity-backed antenna arrays, and efficient rectification, our solution addresses the longstanding challenge of powering through beamsteering cm-scale deep-brain implants. The result is a platform that can be adapted to various anatomical targets, ultimately broadening the scope of safe, noninvasive, and scalable neurostimulation therapies.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".