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%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".