A simple guideline for designing droplet microfluidic chips to achieve an improved single (bio)particle encapsulation rate using a stratified flow-assisted particle ordering method
Bibliographic record
Abstract
Encapsulation of a single (bio)particle into individual droplets (referred to as single encapsulation) presents tremendous potential for precise biological and chemical reactions at the single (bio)particle level. Previously demonstrated successful strategies often rely on the use of high flow rates, gel, or viscoelastic materials for initial cell ordering prior to encapsulation into droplets, which could potentially challenge the system's operation. We propose to enhance the single encapsulation rate by using a stratified flow structure to focus and pre-order the (bio)particles before encapsulation. The stratified flow structure is formed using two simple aqueous Newtonian fluids with a viscosity contrast, which together serve as the dispersed phase. The single encapsulation rate is influenced by many parameters, including fluid viscosity contrast, geometric conditions, flow conditions and flow rate ratios, and dimensionless numbers such as the capillary number. This study focuses on investigating the influences of these parameters on the focused stream of the stratified flow, which is key for single encapsulation. The results allow the proposal of a simple guideline that can be adopted to design droplet microfluidic chips with an improved single encapsulation rate demanded by a wide range of applications. The guideline was validated by performing the single encapsulation of mouse embryonic stem cells suspended in a gelatin-methacryloyl solution in individual droplets of phosphate buffer saline, achieving a single encapsulation efficiency of up to 70%.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".