Electrohydrodynamics: a study of collective \nbehavior and self-organization of an oil-in-oil \nemulsion
Bibliographic record
Abstract
In this thesis, I study the collective behavior and self-organization of immiscible silicone \noil drops in a castor oil medium. Castor oil is a “leaky dielectric” and the silicone \noil drops interact with each other due to electrohydrodynamic forces induced by an \nimposed electric field. The strength and the range of the hydrodynamic interactions \nare modulated by changing amplitude and frequency of the electric field, respectively, \nin a small capacitor. \nThe result of the electrohydrodynamic forces is to induce flows that induce drop \nmotions, deformations and breakup. I study the effect of cell thickness, d, on the size \ndistribution and dynamics of silicone oil drops in presence of an external DC electric \nfield. I also investigate the effect of dimensionality by varying the cell thickness, d, and \nobservation of drop dynamics as well as the observation of an electrohydrodynamically \ndriven convective instability. For the first time, to our knowledge, two-roll structures, \nwith a lateral size that is half the cell thickness, are observed experimentally. Further, \nthis instability is also seen in castor oil medium, in the absence of any liquid-liquid \nand solid-liquid interfaces, indicating the importance of electrokinetic effects. \nNext, I constrain the motion of silicone oil drops in 2D, using dielectrophoretic \ntraps, in order to create a 2D droplet crystal. By driving this crystal with frequencytunable \nelectrohydrodynamic forces, I construct a amplitude-frequency phase diagram \nfor the non-equilibrium order to disorder phase transition of silicone oil drops in castor oil medium. The pure order-to-disorder behaviour is observed for a amplitudefrequency \nregime where no breakup events occur but the hydrodynamic flows are \nstrong enough to deform and partially unpin the droplets from their trap potential. \nFinally, an examination of the underlying flows using tracer particles reveals \nanomalous superdiffusive motion with power law scaling of t3/2. The underlying probability \ndistribution for these anomalous motions is non-Gaussian and has the form \nexp \n(-( x2 )δ/2 \n 4Kγt3/2 \n \nAt short times, it is a simple exponential decay (i.e. δ = 1), \nwhile at longer times the distribution is consistent with δ = 1.4. \nThis system exhibits non-equilibrium self-organization that is frequency- and \namplitude-tunable, that will not only allow more detailed comparisons with detailed \ntheory and simulation in the future. Moreover, it has been demonstrated as a model \nsystem for studying self-organization with tunable hydrodynamic interactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".