Active pneumatic control of centrifugal microfluidic flows for lab-on-a-chip applications
Bibliographic record
Abstract
This paper reports a novel method of controlling liquid motion on a centrifugal microfluidic platform based on the integration of a regulated pressure pump and a programmable electromechanical valving system. We demonstrate accurate control over the displacement of liquids within the system by pressurizing simultaneously multiple ports of the microfluidic device while the platform is rotating at high speed. Compared to classical centrifugal microfluidic platforms where liquids are solely driven by centrifugal and capillary forces, the method presented herein adds a new degree of freedom for fluidic manipulation, which represents a paradigm change in centrifugal microfluidics. We first demonstrate how various core microfluidic functions such as valving, switching, and reverse pumping (i.e., against the centrifugal field) can be easily achieved by programming the pressures applied at dedicated access ports of the microfluidic device. We then show, for the first time, that the combination of centrifugal force and active pneumatic pumping offers the possibility of mixing fluids rapidly (~0.1 s) and efficiently based on the creation of air bubbles at the bottom of a microfluidic reservoir. Finally, the suitability of the developed platform for performing complex bioanalytical assays in an automated fashion is demonstrated in a DNA harvesting experiment where recovery rates of about 70% were systematically achieved. The proposed concept offers the interesting prospect to decouple basic microfluidic functions from specific material properties, channel dimensions and fabrication tolerances, surface treatments, or on-chip active components, thus promoting integration of complex assays on simple and low-cost microfluidic cartridges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".