A fast and switchable microfluidic mixer based on ultrasound-induced vaporization of perfluorocarbon
Bibliographic record
Abstract
Mixing two fluids together within a microfluidic device still remains a challenging operation today. In order to achieve this goal, a number of effective micromixers have been developed over the years based on the use of either passive or active systems. Typically, passive mixers require no external energy, are more robust, and are easy to manufacture albeit they are poorly flexible. Active mixers, on the other hand, rely on external disturbance and are thus more difficult to use but are proven to have greater efficacy. Here, we report a particularly effective, remotely induced and switchable microfluidic mixer, which relies on the concomitant use of ultrasound and a perfluorocarbon (PFC) phase, with the latter benefiting from its immiscibility with most fluids and its low boiling point. More specifically, our approach is based on localized vaporization of a PFC phase at the focal zone of a transducer leading to efficient mixing of two adjacent fluids. The results show that mixing occurs ~100 ms following the delivery of the acoustic pulse, while a laminar flow is re-established on roughly the same time scale. Overall, this method is simple and effective, does not require tailored channel geometries, is compatible with both hydrophilic and hydrophobic microfluidic systems, and is applicable to a wide range of Reynolds numbers (10(-4) < Re < 2.10(0)), and the PFC phase can be easily separated from the mixed phase at the end of the run.
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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 | 0.000 | 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".