Oscillating dispersed-phase co-flow microfluidic droplet generation: Multi-droplet size effect
Bibliographic record
Abstract
Controllable generation of microdroplets at desired sizes and throughputs is important in many applications. Many biological assays require size-optimized droplets for effective encapsulation of analytes and reagents. To perform size optimization, different-size droplets must be generated from identical sources of samples to prevent potential cross-sample variations or other sources of error. In this paper, we introduce a novel alteration of the co-flow droplet generation technique to achieve multi-size generation of monodispersed droplets. Using a custom-made mechanism, we oscillate the disperse-phase (d-phase) flow nozzle perpendicular to the continuous phase (c-phase) flow in a co-flow channel. Oscillation of the d-phase nozzle introduces an additional lateral drag force to the growing droplets while exposing them to various levels of axial drag owing to the parabolic velocity distribution of the c-phase flow. Superimposing both effects results in simultaneous and repeatable generation of monodispersed droplets with different sizes. The effect of nozzle oscillation frequency (f = 0–15 Hz) on droplet generation at different d-phase (Qd = 0.05, 0.10, and 0.50 ml/min) and c-phase (Qc = 2, 5, and 10 ml/min) flow rates was studied. A wide range of monodispersed droplets (4nl–4 μl) were generated using this method. Droplet sizes were directly proportional to the We number and inversely proportional to the Ca number and oscillation frequency. Our technique is promising for applications such as aqueous two-phase systems, where due to inherently low interfacial tension, the d-phase flow forms a long stable jet which can be broken into droplets using the additional oscillatory drag in our device.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".