A MEMS-Based Drug Delivery Device With Integrated Microneedle Array—Design and Simulation
Bibliographic record
Abstract
One of the most effective treatments for type 1 and 2 diabetes is the administration of Insulin. Single needle mechanical insulin pumps are heavy and painful. Microneedle-based MEMS drug delivery devices can be an excellent solution for insulin dosing. The stackable structure provides minimum dimensions and the final product can be in the form of a patch that can be applied to any flat area of human skin. The use of microneedle array provides a safe, painless, and robust injection application. The design of positive volumetric insulin pump is a Multiphysics problem where the volumetric changes of the main pump chamber and the pumped fluid are directly coupled. We use a Multiphysics simulation system to investigate the performance of a MEMS-based insulin micropump with a piezoelectric actuator pumping a viscous Newtonian fluid. The model captures the accumulated out-flow, the netflow, or flow fluctuations based on deflection of piezoelectric diaphragm actuator. Different input voltages and different excitation frequencies cause movement of piezoelectric actuator, which moves the diaphragm disk in positive-negative directions thereby inducing discharge pressures at the microneedle array. In this study, we address various aspects of design and simulation of a MEMS-based piezoelectric insulin micropump including polydimethylsiloxane microvalves and microneedle array. We investigate the micropump performance at human skin interfacial pressure to match minimum to maximum delivery targets/requirements for total range of diabetic patient's expected operating parameters. comsolmultiphysics is used for this study.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".