Microfluidic control of droplet formation from stable emulsions formed by aqueous two-phase systems
Bibliographic record
Abstract
Aqueous two-phase systems (ATPSs) form from the thermodynamic separation of two dissolved incompatible solutes, such as two polymers, a polymer and a salt, and a polymer and a surfactant. At most supercritical concentrations, ATPS emulsions can be formed by vigorous mixing. These emulsions typically settle into distinct layers in minutes to hours. However, it is also possible to choose ATPS compositions with extremely long settling times that resemble stable emulsions. Here, we generated stable emulsions from a polyethylene glycol (PEG)-dextran ATPS by selecting ATPS compositions at the extreme ends of the tie lines connecting the binodal curve delineating phase-separating compositions. Droplets of PEG in a continuous dextran phase did not coalesce appreciably over the course of several days when stored in a conical tube or syringe. However, upon exposure to laminar flow conditions in a microfluidic channel, droplets were observed to coalesce. Through microscopic characterization of droplet volume, an increase in droplet size and decrease in overall droplet number was observed as a function of channel distance, suggesting a progressive droplet merging phenomenon. This novel approach to control droplet size by encouraging coalescence of stable emulsions under laminar flow in a microfluidic channel enables the production of droplets ranging from fL to several pL, which may enable various future biotechnology applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".