How fast do microdroplets generated during liquid-liquid phase separation move in a confined 2D space?
Bibliographic record
Abstract
How liquid transport occurs in confined spaces is relevant to many industrial and lab-scale processes, ranging from enhanced oil recovery to drug delivery systems. In this work, we investigate propelling microdroplets that form from liquid-liquid phase separation in a quasi-2D chamber, focusing on the direction and speed of microdroplets in response to local composition gradients. The confined ternary solution in our experiments comprises a model oil, a good solvent (ethanol) and a poor solvent (water). Depending on the initial solution composition, water-rich or oil-rich microdroplets, form and move spontaneously as the ternary solution mixes with and is displaced by water diffusing from a deep side channel. Microdroplet movement is followed in situ using high-speed bright-field imaging or fluorescence imaging when the solution is doped with a dye. Local ethanol composition gradients are estimated from the variation of fluorescence intensity in the local continuous liquid surrounding the mobile microdroplets. From phase separation of the ternary solution with high oil concentration, mobile oil-rich microdroplets form in a water-rich zone, accompanying the formation of water-rich microdroplets in an oil-rich zone. The fast movement of oil-rich microdroplets induces directional flow transport that mobilizes water-rich microdroplets close to the water-rich zone. The average microdroplet speed increases with the initial oil concentration in the ternary solution. The fastest speed of oil-rich microdroplets observed in our experiments is ~150 $μ$m/s along the surface of a hydrophobic wall. The presence of a sharp ethanol composition gradient is the primary driving force for the fast movement of oil-rich microdroplets in confinement. Our results demonstrate the potential of enhancing liquid transport in confinement through composition gradients arising from phase separation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".