Experimental and computational study of microfluidic flow‐focusing generation of gelatin methacrylate hydrogel droplets
Bibliographic record
Abstract
ABSTRACT This work presents the experimental and computational study of droplet generation for hydrogel prepolymer solution in oil using a flow‐focusing device. Effects of different parameters on hydrogel droplet generation and droplet sizes in a flow‐focusing device were investigated experimentally and computationally. First, three dimensional (3D) computational simulations were conducted to describe the physics of droplet formation in each regime and mechanism of three different regimes: squeezing, dripping, and jetting regime of hydrogel were investigated. Subsequently, the effects of viscosity, inertia force, and surface tension force on droplet generation, and droplet size were studied through these experiments. The experiments were carried out using different concentration of gelatin methacrylate (GelMA) hydrogel (5 wt % and 8 wt %) as the dispersed phase and two different continuous phase liquids (light mineral oil and hexadecane) with various concentrations of surfactant (0 wt %, 3 wt %, and 20 wt %). All experimental data was summarized by capillary number of dispersed phases and the continuous phases to characterize the different regimes of droplet generation and to predict the transition of dripping to a jetting regime for GelMA solution in flow‐focusing devices. It is shown that the transition of dripping to a jetting regime for GelMA happens at lower capillary numbers compared to aqueous solutions. Moreover, by increasing the viscous force of continuous phase or decreasing the interfacial force, the size of GelMA droplets was decreased. By controlling these parameters, the droplet sizes can be controlled between 30 μm and 200 μm, which are very suitable for cell encapsulation. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43701.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".