Droplet formation of biological non-Newtonian fluid in T-junction generators. I. Experimental investigation
Bibliographic record
Abstract
The extension of microfluidics to many bioassay applications requires the ability to work with non-Newtonian fluids. One case in point is the use of microfluidics with blood having different hematocrit levels. This work is the first part of a two-part study and presents the formation dynamics of blood droplets in a T-junction generator under the squeezing regime. In this regime, droplet formation with Newtonian fluids depends on T-junction geometry; however, we found that in the presence of the non-Newtonian fluid such as red blood cells, the formation depends on not only to the channel geometry, but also the flow rate ratio of fluids, and the viscosity of the phases. In addition, we analyzed the impact of the red blood cell concentration on the formation cycle. In this study, we presented the experimental data of the blood droplet evolution through the analysis of videos that are captured by a high-speed camera. During this analysis, we tracked several parameters such as droplet volume, spacing between droplets, droplet generation frequency, flow conditions, and geometrical designs of the T junction. Our analysis revealed that, unlike other non-Newtonian fluids, where the fourth stage exists (stretching stage), the formation cycle consists of only three stages: lag, filling, and necking stages. Because of the detailed analysis of each stage, a mathematical model can be generated to predict the final volume of the blood droplet and can be utilized as a guide in the operation of the microfluidic device for biochemical assay applications; this is the focus of the second part of this study [Phys. Rev. E 105, 025106 (2022)10.1103/PhysRevE.105.025106].
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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 | 0.001 |
| 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".