Design of efficient wireless power-transfer system and piezoelectric transducer for sonoporation-based drug delivery implants
Bibliographic record
Abstract
Implantable devices are becoming popular in health and medical applications. In particular, localized and controlled drug release systems have gained clinical relevance in the treatment of many diseases. The power requirement for sonoporation-based systems is comparatively higher than that of other implantable devices. Efficient wireless power delivery and efficient small ultrasound transducer with low aspect ratio (G = length/thickness) is required to obtain high power transfer efficiency for implantable sonoporation based system. To provide power wirelessly to implantable device, resonance-based wireless power delivery system is considered. This system is modeled and optimized for given design constraints. The prototype 4-coil system achieves at least 2 × more efficiency as compared to prior art inductive links operating with comparable size and operating range. With implanted coil of diameter 22 mm and at operating distance of 20 mm, power transfer efficiency of 82% is achieved. The focus of the work is on power delivery in implantable devices. However, the method is general and can be applied to other applications that use wireless power transfer. Sono-Dynamic Therapy (SDT) uses ultrasonic cavitation to enhance the cytotoxicity of chemotherapeutic drugs. SDT requires ultrasound transducer to generate cavities. For implantable application, high electro-mechanical conversion efficiency of transducer is required to achieve high system efficiency and low heat losses in tissues. In the present work, identification of key parameters for transducer selection for implantable sonotherapy systems are given. Effects of ultrasound transducer’s aspect ratio reduction is analyzed and reduction in electro-acoustic conversion efficiency is explained using mode coupling between resonance modes of transducer. Energy harvesting and driver circuit is presented to convert wirelessly received power to drive transducer to generate acoustic waves. This work demonstrates the first prototype of a wirelessly powered sonoporation-based implantable system. Though only two blocks of the prototype are optimized, overall system efficiency is measured as 2.04 % which is close to the theoretical value of 2.24 % of present design. By using an efficient power amplifier (class-E amplifier, efficiency 80%), an overall system efficiency of 22% can be achieved.
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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 | 0.000 |
| Open science | 0.001 | 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".