Altering Emulsion Stability with Heterogeneous Surface Wettability
Bibliographic record
Abstract
Emulsions-liquid droplets dispersed in another immiscible liquid-are widely used in a broad spectrum of applications, including food, personal care, agrochemical, and pharmaceutical products. Emulsions are also commonly present in natural crude oil, hampering the production and quality of petroleum fuels. The stability of emulsions plays a crucial role in their applications, but controlling the stability without external driving forces has been proven to be difficult. Here we show how heterogeneous surface wettability can alter the stability and dynamics of oil-in-water emulsions, generated by a co-flow microfluidic device. We designed a useful methodology that can modify a micro-capillary of desired heterogeneous wettability (e.g., alternating hydrophilic and hydrophobic regions) without changing the hydraulic diameter. We subsequently investigated the effects of flow rates and heterogeneous wettability on the emulsion morphology and motion. The experimental data revealed a universal critical timescale of advective emulsions, above which the microfluidic emulsions remain stable and intact, whereas below they become adhesive or inverse. A simple theoretical model based on a force balance can be used to explain this critical transition of emulsion dynamics, depending on the droplet size and the Capillary number-the ratio of viscous to surface effects. These results give insight into how to control the stability and dynamics of emulsions in microfluidics with flow velocity and different wettability.
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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 | 0.001 |
| 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) | 0.000 | 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".