Wireless power transfer system rigid to tissue characteristics using metamaterial inspired geometry for biomedical implant applications
Bibliographic record
Abstract
Conventional resonant inductive coupling wireless power transfer (WPT) systems encounter performance degradation while energizing biomedical implants. This degradation results from the dielectric and conductive characteristics of the tissue, which cause increased radiation and conduction losses, respectively. Moreover, the proximity of a resonator to the high permittivity tissue causes a change in its operating frequency if misalignment occurs. In this report, we propose a metamaterial inspired geometry with near-zero permeability property to overcome these mentioned problems. This metamaterial inspired geometry is stacked split ring resonator metamaterial fed by a driving inductive loop and acts as a WPT transmitter for an in-tissue implanted WPT receiver. The presented demonstrations have confirmed that the proposed metamaterial inspired WPT system outperforms the conventional one. Also, the resonance frequency of the proposed metamaterial inspired TX is negligibly affected by the tissue characteristics, which is of great interest from the design and operation prospects. Furthermore, the proposed WPT system can be used with more than twice the input power of the conventional one while complying with the safety regulations of electromagnetic waves exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".