Vibration effect on cross-flow and co-flow focusing mechanism for droplet generation
Bibliographic record
Abstract
Microbubbles are widely used in many industries such as water treatment, drug coating, and ultrasonic contrast agents. Cross-flow focusing and co-flow focusing are considered basic mechanisms used for microbubble generation. Typically, to achieve micron-sized droplets requires structure dimensions in the same order of magnitude of the desired droplet sizes. In this paper we report a method of applying an external vibration to a cross-flow and co-flow focusing structure, which allows for smaller droplets to be generated. The junction dimension was 700×400 μm, and the channel width was 800 μm. The two assumed fluids are selected in a way that the Capillary number is high (Ca>10) to make use of necking effect occurred in the downstream. Linear vibration was exerted on the microchannel structure in the direction of central flow. A 2D structure was simulated using finite element software, and the numerical approach was then verified by comparing the experimental data of a typical cross-flow focusing structure taken from our previous study with the corresponding simulation assuming the same parameters. The results show that although the droplet generation regime depends on flow ratio (Qa/Qw) and vibration parameter (ampl×freq), Capillary number also has a significant effect on the regime. Briefly, applying a low-cost linear vibration to the conventional flow focusing structures can be used as an accurate controlling technique for increasing the chance of droplet generation. In fact, vibration motion can change the flow regime and breakup mechanism. It can also change the breakup point at which the droplets are formed.
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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) | 0.001 | 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".