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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".