Acoustic Bubble and Magnetic Actuation-Based Microrobot for Enhanced Multiphase Drug Delivery Efficiency
Bibliographic record
Abstract
This paper proposes an acoustic bubble and magnetic actuation-based microrobot for enhancing multiphase drug delivery efficiency. The proposed device can encapsulate multiphase drugs, including liquids, using the two bubbles embedded within the microtube. Additionally, using the magnetic actuation of the loaded magnetic liquid metal, it can deliver drugs to target cells. This study visualized the flow patterns generated by the oscillating bubble within the tube to validate the drug release principle. In addition, to investigate the effect of the oscillation properties of the inner bubble on drug release, the oscillation amplitude of the inner bubble was measured under various experimental variables using a high-speed camera. Subsequently, we designed a microrobot capable of encapsulating bubbles, drugs, and magnetic liquid metal and fabricated it using microfabrication technology based on ultra-precision 3D printing. As a proof of concept, we demonstrated the transport and drug release of the microrobot encapsulating the drug in a Y-shaped channel simulating a blood vessel. The proposed device is anticipated to enhance the efficiency of drug therapy by minimizing drug side effects, reducing drug administration frequency, and improving the stability of the drug within the body. This paper is expected to be applicable not only to targeted drug delivery but also to various biomedical fields, such as minimally invasive surgery and cell manipulation, by effectively delivering multiphase drugs using the simple structure of a microrobot.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".