An experimental investigation into filament formation during drop generation by pseudo-plastic viscoelastic biofluid at T-junction generators
Bibliographic record
Abstract
The advancements in droplet microfluidics have made it an eminent technology for high-throughput biomedical applications involving biofluids such as blood, saliva, tear film, and synovial fluid, all characterized by their non-Newtonian/viscoelastic properties. However, most existing models for the design and optimization of droplet microfluidic systems are primarily based on Newtonian fluids. Therefore, studies that systematically explore the dynamics underlying biofluid droplet formation and manipulation are critically important, both fundamentally and practically. This study uses artificial tear films to experimentally investigate the effects of operational parameters such as geometric conditions (width ratio Λ = wdwc and aspect ratio h* = hwc), fluid properties (viscosity ratio η = μdμc), and flow rate ratios (ϕ = QdQc) on filament formation during droplet generation in a T-junction geometry. All the experiments were conducted at a constant capillary number of the continuous phase (Ca = μcQcwchγ). The results indicate that filament growth and rupture characteristics are determined by the predominance of two distinct regions: pre-stretch, governed by the interplay between inertial and capillary forces, and elastocapillarity, regulated by elastic and capillary forces. Specifically, filament rupture is influenced by inertia–capillary effects at low flow rates and viscosity ratios. In contrast, the domination of elastic effects is more pronounced at higher flow rates and viscosity ratios. Additionally, reductions in the width (Λ) and aspect (h*) ratio when Λ≥0.5 and h*≥0.4 increase the elastic effects within the filaments while inertial effects dominate for Λ<0.5 and h*<0.4. The relaxation times (λ) are obtained from the exponential thinning curve, with longer times observed for cases with high elastic effects. Finally, the filament collapses into satellite droplets, and their monodispersity increases with elasticity.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".