MEMS actuators for biomedical applications: a review
Bibliographic record
Abstract
Abstract Micro-electromechanical-system (MEMS) based actuators, which transduce certain domains of energy into mechanical movements in the microscopic scale, are increasingly contributing to the areas of biomedical engineering and healthcare applications. They are enabling new functionalities in biomedical devices through their unique miniaturized features. An effective selection of a particular actuator, among a wide range of actuator types available in the MEMS field, needs to be made through the assessment of many factors involved in both the actuator itself and the target application. This paper presents an overview of the state-of-the-art MEMS actuators that have been developed for biomedical applications. The actuation methods, working principle, and imperative features of these actuators are discussed along with their specific applications. An emphasis of this review is placed on temperature-responsive, electromagnetic, piezoelectric, and fluid-driven actuators towards various application areas including lab-on-a-chip, drug delivery systems, cardiac devices and surgical tools. It also highlights the key issues of MEMS actuators in light of biomedical applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.006 | 0.003 |
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".