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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.35\lambda $</tex-math> </inline-formula> at 915 MHz. By exploiting the Huygens’ Box (HB) configuration and tailoring surface currents, our approach compresses the focal spot to a subwavelength full width at half maximum (FWHM)—not only surpassing diffraction-limited beams and Bessel patterns, but also enabling dynamic beamsteering deep into human brain-mimicking media. Operating at an input power of 1 W, this technique ensures reliable power transfer to implants placed 2.5 cm beneath the surface with developing 1.8 V across 9 k<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $</tex-math> </inline-formula>. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".