An electrohydrodynamic technique for rapid mixing in stationary droplets on digital microfluidic platforms
Bibliographic record
Abstract
This paper presents an electrohydrodynamic technique for rapid mixing of droplets in open and closed digital microfluidic (DMF) platforms. Mixing is performed by applying a high frequency AC voltage to the coplanar or parallel electrodes, inducing circulation zones inside the droplet which results in rapid mixing of the content. The advantages of the proposed method in comparison to conventional mixing methods that operate based on transporting the droplet back and forth and side to side include 1) a shorter mixing time (as fast as 0.25 s), 2) the use of a fewer number of electrodes, reducing the size of the chip, and 3) the stationary nature of the technique which reduces the chance of cross-contamination and surface biofouling. Mixing using the proposed method is performed to create a uniform mixture after merging a water droplet with another droplet containing either particles or dye. The results show that increasing the frequency, and or the amplitude of the applied voltage, enhances the mixing process. However, actuation with a very high frequency and voltage may result in shedding pico-liter satellite droplets. Therefore, for each frequency there is an effective range of the amplitude which provides rapid mixing and avoids shedding satellite droplets. Also, the increase in the gap height between the two plates (for the closed DMF platforms) significantly enhances the mixing efficiency due to the lower viscous effects. Effects of the addition of salts and DNA to the samples were also studied. The electrothermal effect decreased for these cases, which was solved by increasing the frequency of the applied voltage. To assure the high frequency actuation does not increase the sample temperature excessively, the temperature change was monitored using a thermal imaging camera and it was found that the increase in temperature is negligible.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".