A plug-and-play modular microcapillary platform for the generation of multicompartmental double emulsions using glass or fluorocarbon capillaries
Bibliographic record
Abstract
Although multiple emulsions have a wide range of applications in biology, medicine, chemistry and cosmetics, the use of microfluidic devices to generate them remains limited to specialist laboratories. This is because of the expertise required to design and operate these technologies. Here we show a plug-and-play microcapillary platform for the generation of multicompartmental double emulsions which only requires a low cost 3D printer for fabrication and syringe pumps for operation. Our microcapillary platform is modular because we fabricate junction boxes from a flexible resin to hold and align any type of standard glass capillary or piece of tubing for droplet formation without the need for capillary alignment. The flexible resin enables total sealing of the capillaries without the need for gaskets or adhesives, and the ability to use any type of capillary or tubing means that surface treatment is not required. We show how our microcapillary platform is able to generate water-in-oil-in-water, oil-in-water-in-oil, and oil-in-oil-in-water multicompartmental double emulsions with between 1 and 10 inner droplets with high accuracy and reproducibility using standard oils (FC40, mineral oil) and inexpensive surfactants (sodium dodecyl sulfate, SDS or 1H,1H,2H,2H-perfluoro-1-octanol, PFO). Additionally, we show the formation of binary multicompartmental double emulsions, where two types of inner phase droplets can be encapsulated in the multicompartmental emulsions. Our results demonstrate how simple and accessible tools can be employed to generate a powerful modular microcapillary platform. We anticipate that the simplicity of fabrication and operation of this platform, coupled with its ability to make a wide variety of different types of emulsions, will be attractive both to microfluidic laboratories and to those without microfluidic expertise who need an enabling tool for multicompartmental double emulsion formation.
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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 | 0.000 |
| 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".