Wireless implantable chip with integrated nitinol-based pump for radio-controlled local drug delivery
Bibliographic record
Abstract
We demonstrate an active, implantable drug delivery device embedded with a microfluidic pump that is driven by a radio-controlled actuator for temporal drug delivery. The polyimide-packaged 10 × 10 × 2 mm(3) chip contains a micromachined pump chamber and check valves of Parylene C to force the release of the drug from a 76 μL reservoir by wirelessly activating the actuator using external radio-frequency (RF) electromagnetic fields. The rectangular-shaped spiral-coil actuator based on nitinol, a biocompatible shape-memory alloy, is developed to perform cantilever-like actuation for pumping operation. The nitinol-coil actuator itself forms a passive 185 MHz resonant circuit that serves as a self-heat source activated via RF power transfer to enable frequency-selective actuation and pumping. Experimental wireless operation of fabricated prototypes shows successful release of test agents from the devices placed in liquid and excited by radiating tuned RF fields with an output power of 1.1 W. These tests reveal a single release volume of 219 nL, suggesting a device's capacity of ~350 individual ejections of drug from its reservoir. The thermal behavior of the activated device is also reported in detail. This proof-of-concept prototype validates the effectiveness of wireless RF pumping for fully controlled, long-lasting drug delivery, a key step towards enabling patient-tailored, targeted local drug delivery through highly miniaturized implants.
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 | 0.001 | 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".