Oscillating dispersed-phase co-flow microfluidic droplet generation: jet length reduction effect
Bibliographic record
Abstract
Microdroplet generation methods are assessed by two important criteria of droplet throughput and size dispersity. The widely-used co-flow droplet generation technique is bottlenecked with droplet polydispersity at high throughputs due to transition to an unstable jetting regime at high dispersed-phase (d-phase) flow rates. In this paper, we introduce a novel technique to oscillate the d-phase nozzle inside the continuous phase (c-phase) channel to suppress the jetting effect. The effect of the nozzle oscillation frequency (0-15 Hz) on the jet length was studied at different d-phase (Qd = 1.8, 2.4 and 3.0 ml min-1) and c-phase (Qc = 6, 12 and 18 ml min-1) flow rates and d-phase viscosities (1, 2.5, and 6 mPa s). The jet length was directly proportional to the d-phase flow rate and inversely proportional to the oscillation frequency. Oscillation-induced jet length reduction was more significant at high jet velocities, but a less steep jet length reduction was always observed at oscillation frequencies higher than 10 Hz. A maximum jet length reduction of 70.8% was obtained at the highest d-phase and lowest c-phase flow rates. Increasing the viscosity of the d-phase resulted in diminishing the effect of oscillation on jet length reduction. Moreover, we observed that nozzle oscillation could disintegrate the long jet into droplets of various sizes that were mostly smaller than the stationary-mode droplets. We hypothesize that oscillating the dispersion nozzle at lower flow rates, without the jetting effect, can simultaneously generate multi-size monodisperse droplets. This active technique can also be implemented into aqueous two-phase systems (ATPSs) in which droplet generation is a difficult task.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".