Enhanced displacement of phase separating liquid mixtures in 2D confined spaces
Bibliographic record
Abstract
Displacing liquid in a confined space is important for technological processes, ranging from porous membrane separation to CO$_{2}$ sequestration. The liquid to be displaced usually consists of multiple components with different solubilities in the displacing liquid. Phase separation and chemical composition gradients in the liquids can influence the displacement rate. In this work, we investigate the effects of liquid composition on the displacement process of ternary liquid mixtures in a quasi-2D microchannel where liquid-liquid phase separation occurs concurrently. We focused on model ternary mixtures containing 1-octanol, ethanol, and water. These mixtures are displaced with water or with ethanol aqueous solution. The spatial distribution of subphases arising from phase separation and the displacement rates of the solution are impacted by the initial ternary solution composition. The boundary between the solution and displacing liquid changes from a defined interface to a diffusive interface as the initial 1-octanol composition in the solution is reduced. The displacement rate also varies non-linearly with the initial 1-octanol composition. The slowest displacement rate arises in the intermediate 1-octanol concentration, where a stable three-zone configuration forms at the boundary. At very low 1-octanol concentration, the displacement rate is fast, associated with droplet formation and motion driven by the chemical concentration gradients formed during phase separation. The excessive energy provided from phase separation may contribute to the enhanced displacement at intermediate to high 1-octanol concentrations, but not at the low 1-octanol concentration with enhancement from induced flow in confinement. The knowledge gained from this study highlights the importance of manipulating phase separation to enhance mass transport in confinement for a wide range of separation processes.
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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 | 0.001 |
| 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".