New generation of spinning systems for robust active mixing on microfluidic CDs: oil/water emulsion as an evaluation test
Bibliographic record
Abstract
Microfluidic CDs (or Lab-on-Disc) continue to emerge in various applications of real life sciences, including biomedical and pharmaceutical fields. However, microfluidic CDs with advanced and efficient unit operation tools, such as pumping, valving, and mixing, need to be implemented to achieve the required applications in these fields. In this work, a novel generation of a spinning system to perform robust active mixing is developed for microfluidic CDs. The developed system is equipped with a dual-motor and dual-CD configuration to perform magnetically driven active mixing. The results show that the developed spinning system can provide a wide range of mixing frequencies independent of the spinning speed of the microfluidic CD. To evaluate the performance of this system under extreme conditions, an emulsion process of oil and water was conducted. Although the oil produced high drag force on the mixing magnet, the emulsion process successfully reached a steady state of mixing within a few seconds (approximately 3.5 s), and the mixture became homogeneous at 75 seconds. To demonstrate one of the potential applications of the proposed developed spinning setup, microparticles were successfully extracted from water to oil using water/oil emulsion on the microfluidic CD. In conclusion, mixing can be performed without influencing the integrated microfluidic components such as valves or pumps. This improvement can widen the range of applicability of microfluidic CDs in multi-step and complex processes where mixing is essential.
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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) | 0.000 | 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".