Coupling and maintaining link efficiency ultrasonics in transcutaneous energy transfer systems
Bibliographic record
Abstract
We present on the development of a 12.8 mm diameter ultrasonic transcutaneous energy transfer system for powering implanted hearing devices. The system was based on two custom 8mm diameter PMN-PT 1-3 composite transducers operating at 1.25MHz and featured three significant innovations. First, to ensure long-term biocompatibility and reliability of the implanted portion of the link and to ensure robust attachment to bone, the implanted transducer was designed into a 12.8 mm diameter, 3.5 mm thick hermetically sealed titanium package in an easy-to-implant form factor. The transducer was designed to deliver sound efficiently through the casing walls using a mass-spring acoustic matching technique. Second, for the external unit a “dry” acoustic coupling system based on a silicone pressure sensitive adhesive was designed that combined efficient acoustic coupling, robust adhesion to skin and that was comfortable to wear. The external unit was aligned with the internal unit using a magnetic alignment system. Third, we developed and demonstrated an efficient Class E transmit amplifier and receive electronics that incorporated compensation for changes in acoustic separation between the transducer to maintain robust, high efficiency power transmission. The system achieved 33% DC-to-DC electrical conversion efficiency through 5mm of water and 19% DC-to-DC efficiency through 5mm of porcine tissue.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".