A Scalable and Microfluidic Approach to Water-in-water Microdroplet Generation With a Passive, Membrane-based Cross-flow System
Bibliographic record
Abstract
The generation of water-in-water droplets has recently received great attention for its applicability in biological applications over traditional oil-water droplet systems because of their high biocompatibility. An aqueous two-phase system (ATPS), aqueous mixture of polyethylene glycol (PEG) and dextran (DEX), has an ultra-low interfacial tension which makes monodispersed droplet formation challenging. Recent passive methods in microfluidics with flow-focusing configurations overcome this challenge, but they suffer either from polydispersity, narrow droplet size range, or low throughput. Successful droplet formation in such passive methods occurs in jetting flow regimes with low continuous phase flow rates, Qc<1ŒºLmin .Gravity-driven hydrostatic or highly precise pressure flow control has been used to apply constant, low flow rates that conventional syringe pumps struggle to emulate. Here, a new passive cross-flow configuration is introduced to generate monodispersed ATPS droplets. Our microfluidic device is membrane-integrated with constant flowrate syringe pumps. Additionally, the membrane with three uniform pores enables our device to operate as a parallel system capable of three controlled droplet formations simultaneously, with a wide range of monodispersed droplet diameters from ≈17 to 90 μm (coefficient of variation, CV ≤ 5%) and from ≈90 to 180 μm (CV ≤ 10%).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".