The effect of confinement on the electrohydrodynamic behavior of droplets in a microfluidic oil-in-oil emulsion
Bibliographic record
Abstract
A two-fluid emulsion (silicone oil drops in the “leaky dielectric”, castor oil) with electrohydrodynamically driven flows can serve as a model system for tunable studies of hydrodynamic interactions [Varshney et al., Sci. Rep., 2012, 2, 738]. Flows on multiple length- and time-scales have been observed but the underlying mechanism for these chaotic, multi-scale flows is not understood. In this work, we conducted experiments varying the thickness of the test cell to examine the role of substrate interactions on size distribution, mean square displacement and velocity of the drops as a function of the electric field strength. We find that the electric capillary number, CaE, at the threshold of drop breakup is of order unity for cell thicknesses of 100 μm or thicker, but much larger for thinner cells. Above this threshold, there is a clear transition to super-diffusive droplet motions. In addition, we observe that there is a convective instability prior to the onset of chaotic flows, with the lengthscale associated with the convection rolls increasing linearly with an increase in the cell thickness. The fact that the convective instability appears to occur in the leaky dielectric castor oil regardless of whether the second component is liquid drops, solid particles, or dissolved dye has implications on the underlying mechanism for the unsteady flows.
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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) | 0.000 | 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".