A versatile multilayer liquid–liquid encapsulation technique
Bibliographic record
Abstract
HYPOTHESIS: Generating multi-layer cargo using conventional methods is challenging. We hypothesize that incorporating a Y-junction compound droplet generator to encase a target core inside a second liquid can circumvent the kinetic energy dependence of the impact-driven liquid-liquid encapsulation technique, enabling minimally restrictive multi-layer encapsulation. EXPERIMENTS: Stable wrapping is obtained by impinging a compound droplet (generated using Y-junction) on an interfacial layer of another shell-forming liquid floating on a host liquid bath, leading to double-layered encapsulation. The underlying dynamics of the liquid-liquid interfaces are captured using high-speed imaging. To demonstrate the versatility of the technique, we used various liquids as interfacial layers, including magnetoresponsive oil-based ferrofluids. Moreover, we extended the technique to triple-layered encapsulation by overlaying a second interfacial layer atop the first floating interfacial layer. FINDINGS: The encapsulating layer(s) effectively protects the water-soluble inner core (ethylene glycol) inside the water bath. A non-dimensional experimental regime is established for successful encapsulation in terms of the impact kinetic energy, interfacial layer thickness, and the viscosity ratio of the interfacial layer and the outer core liquid. Using selective fluorescent tagging, we confirm the presence of individual shell layers wrapped around the core, which presents a promising pathway to visualize the internal morphology of final encapsulated droplets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".