Droplet formation of biological non-Newtonian fluid in T-junction generators. II. Model for final droplet volume prediction
Bibliographic record
Abstract
This work represents the second part of a two-part series on the dynamics of droplet formation in a T-junction generator under the squeezing regime when using solutions of red blood cells as the dispersed phase. Solutions containing red blood cells are non-Newtonian; however, these solutions do not behave in the same way as other non-Newtonian fluids currently described in the literature. Hence, available models do not capture nor predict important features useful for the design of T-junction microfluidic systems, including droplet volume. The formation of a red blood cell-containing droplet consists of three stages: a lag stage, a filling stage, and a necking stage, with the lag stage only observed in narrow dispersed phase channel setups. Unlike other shear-thinning fluids, thread elongation into the main channel at the end of the necking stage is not observed for red blood cell solutions. In this work, a model that predicts the final droplet volume of a red blood cell containing droplets in T-junction generators is presented. The model combines a detailed analysis of the geometrical shape of the droplet during the formation process, with force and Laplace pressure balances to obtain the penetration depth (b_{fill}^{*}) and the critical neck thickness (2r_{pinch}^{*}) of the droplet. The performance of the model was validated by comparing the operational parameters (droplet volume, the spacing between the droplet, and the generation frequency) with the experimental data across a range of the dimensionless parameters (flow rate ratios, continuous phase viscosities, and channel geometries).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".