Regulation of droplet size and flow regime by geometrical confinement in a microfluidic flow-focusing device
Bibliographic record
Abstract
We have developed a coupled level set and volume of fluid-based computational fluid dynamics model to analyze the droplet formation mechanism in a square flow-focusing microchannel. We demonstrate a flexible manipulation of droplet formation and flow regime based on the modified flow-focusing microchannel with a constricted orifice. Furthermore, we have systematically studied the influence of geometrical confinement, flow rate, and interfacial tension on the droplet formation regime, length, volume, velocity, and shape. Three different flow regimes, namely squeezing, dripping, and jetting, are observed, and the flow regime maps are formulated based on the Reynolds and capillary numbers. After an extensive numerical investigation, we described the boundaries between the different regimes. Droplet shape is also quantified based on the deformation index value. Plug-shaped droplets are observed in the squeezing regime, and near spherical droplets are found in the dripping and jetting regimes. Our study provides insights into the transition of a regime under various geometrical confinement and fluid properties. The results reveal that the modified flow-focusing microchannel can substantially enhance dripping while decreasing the squeezing regime, which is of paramount importance from the standpoint of producing high throughput stable and monodisperse microdroplets. Eventually, this work emphasizes the importance of geometrical confinement, fluid properties, and flow conditions on the droplet formation process in a flow-focusing microchannel that can effectively provide helpful guidelines on the design and operations of such droplet-based microfluidic systems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".