Microfluidic generation of aqueous two-phase system (ATPS) droplets by controlled pulsating inlet pressures
Bibliographic record
Abstract
We present a technique that generates droplets using ultralow interfacial tension aqueous two-phase systems (ATPS). Our method combines a classical microfluidic flow focusing geometry with precisely controlled pulsating inlet pressure, to form monodisperse ATPS droplets. The dextran (DEX) disperse phase enters through the central inlet with variable on-off pressure cycles controlled by a pneumatic solenoid valve. The continuous phase polyethylene glycol (PEG) solution enters the flow focusing junction through the cross channels at a fixed flow rate. The on-off cycles of the applied pressure, combined with the fixed flow rate cross flow, make it possible for the ATPS jet to break up into droplets. We observe different droplet formation regimes with changes in the applied pressure magnitude and timing, and the continuous phase flow rate. We also develop a scaling model to predict the size of the generated droplets, and the experimental results show a good quantitative agreement with our scaling model. Additionally, we demonstrate the potential for scaling-up of the droplet production rate, with a simultaneous two-droplet generating geometry. We anticipate that this simple and precise approach to making ATPS droplets will find utility in biological applications where the all-biocompatibility of ATPS is desirable.
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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".