Solvent Effects on the Formation of Surface Nanodroplets by Solvent Exchange
Bibliographic record
Abstract
Solvent exchange is a simple process to form oil nanodroplets at solid-liquid interfaces with well-defined location and morphology. In this process, a good solvent of the oil is displaced by a poor solvent, leading to the nucleation and growth of oil droplets from a transient oversaturation at the mixing front. Our recent work has shown that the final volume of the droplets is related to the flow conditions. In this work, we investigate the effects of the type and the composition of solvents on the droplet formation under the same flow conditions. Water nanodroplets were produced by ethanol/cyclohexane (solution A) and cyclohexane (solution B) on a hydrophilic substrate. We found that the droplet size increases first and then decreases with an increase of the initial ethanol concentration in solution A. This is attributed to the phase separation of ethanol-cyclohexane-water; in particular, the composition of solution A on the phase boundary above the Ouzo region. The same reason also contributes to the lower efficiency in droplet formation for a longer alkane. The important implication from this work is that the maximal droplet volume is limited by the phase separation of the solvents used in the solvent exchange.
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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 | 0.000 |
| 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 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".