Developing a fast and tunable micro-mixer using induced vortices around a conductive flexible link
Bibliographic record
Abstract
This paper presents a numerical study of a micro-mixer based on the continuous deformation of a conducting flexible link. The induced vortices around the link enhance the mixing process. This micro-mixer consists of one straight microchannel and one conductive flexible link. One end of the link is fixed on the upper wall of the channel and the other end can move freely due to the fluid-solid interactions. Since this link is conductive, vortices form around the link (once the electric field is applied). Applying a time-varying DC electric field causes variation in the applied forces to the link; thus, the link will swipe the channel and acts as a micro-stirrer to enhance mixing results. The presented results show that there is a direct relationship between mixing efficiency and the length of the link, as well as the amplitude of time-varying DC electric field. The effects of Young’s modulus, the average of applied electric field, and link position are also studied. Link with lower Young’s modulus swipes larger area inside the channel and enhances the mixing efficiency. By increasing the length of the conductive link, large vortices will be induced around it and mixing efficiency enhances. Our numerical results show that average mixing efficiency of link with a length of L = 0.625 W = 156.25 μm is about 90%. The proposed micro-mixer is simple to be fabricated and mixes the fluid streams in a short period of time with high efficiency. Such micro-mixers can be used in various microfluidics, biomedical, or chemical 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 | 0.000 | 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 | 0.000 | 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".