Water-in-Water Droplets by Passive Microfluidic Flow Focusing
Bibliographic record
Abstract
We present a simple microfluidic system that generates water-in-water, aqueous two phase system (ATPS) droplets, by passive flow focusing. ATPS droplet formation is achieved by applying weak hydrostatic pressures, with liquid-filled pipette tips as fluid columns at the inlets, to introduce low speed flows to the flow focusing junction. To control the size of the droplets, we systematically vary the interfacial tension and viscosity of the ATPS fluids and adjust the fluid column height at the fluid inlets. The size of the droplets scales with a power law of the ratio of viscous stresses in the two ATPS phases. Overall, we find a drop size coefficient of variation (CV; i.e., polydispersity) of about 10%. We also find that when drops form very close to the flow focusing junction, the drops have a CV of less than 1%. Our droplet generation method is easily scalable: we demonstrate a parallel system that generates droplets simultaneously and improves the droplet production rate by up to one order of magnitude. Finally, we show the potential application of our system for encapsulating cells in water-in-water emulsions by encapsulating microparticles and cells. To the best of our knowledge, our microfluidic technique is the first that forms low interfacial tension ATPS droplets without applying external perturbations. We anticipate that this simple approach will find utility in drug and cell delivery applications because of the all-biocompatible nature of the water-in-water ATPS environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".