Rapid and Highly Controlled Generation of Monodisperse Multiple Emulsions via a One-Step Hybrid Microfluidic Device
Bibliographic record
Abstract
Multiple Emulsions (MEs) contain a drop laden with many micro-droplets. A single-step microfluidic-based synthesis process of MEs is presented to provide a rapid and controlled generation of monodisperse MEs. The design relies on the interaction of three immiscible fluids with each other in subsequent droplet formation steps to generate monodisperse ME constructs. The design is within a microchannel consists of two compartments of cross-junction and T-junction. The high shear stress at the cross-junction creates a stagnation point that splits the first immiscible phase to four jet streams each of which are sprayed to micrometer droplets surrounded by the second phase. The resulted structure is then supported by the third phase at the T-junction to generate and transport MEs. The ME formation within microfluidics is numerically simulated and the effects of several key parameters on properties of MEs are investigated. The dimensionless modeling of ME formation enables to change only one parameter at the time and analyze the sensitivity of the system to each parameter. The results demonstrate the capability of highly controlled and high-throughput MEs formation in a one-step synthesis process. The consecutive MEs are monodisperse in size which open avenues for the generation of controlled MEs for different applications.
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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".