Bibliographic record
Abstract
Ultrasonic transcutaneous energy transmission (UTET) is a promising new technology for delivering electrical power to active biomedical implants. A key piece of technology required to make UTET devices viable is a “dry” acoustic coupling that can be worn daily for long periods of time by a patient. The dry coupling must provide a low-reflectance, low-loss, transmission pathway for acoustic energy. For other ultrasound applications such as diagnostic imaging, a coupling gel is typically used to acoustically interface a transducer with the patient. However, in a more permanent application like UTET coupling gel is likely dry out or wash away over time and so a solid-state solution is needed. The solid coupling must be comfortable, must not lead to skin irritation or other skin problems and must provide good acoustic coupling without requiring large and uncomfortable contact force with the skin. We report on our investigations into the use of soft silicones for this application. Silicones are highly biocompatible, and at less than 2 MHz they exhibit low acoustic losses. Soft silicones have a “sticky” quality to them that provides good acoustic coupling to skin (<20% loss) with very low contact force (<0.5 N). We present the results of studies into the relationship between contact force and acoustic reflectance for soft silicone couplings in a dry ex vivo porcine skin model. Silicone can be made with a wide array of mechanical properties. As a result, the acoustic impedance of the coupling material can be matched to skin, reducing losses due to reflection. We present two methods used to impedance match to skin; one by increasing the material’s speed of sound, the other by increasing its density. Material property effects on the UTET link efficiency are simulated using a KLM model and the simulation is compared to empirical measurements of the efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".