Liquid–liquid encapsulation with a constrained interfacial layer
Bibliographic record
Abstract
HYPOTHESIS: Liquid-liquid encapsulation enables rapid wrapping of liquid core droplets using liquid interfacial layer(s) floating on a host liquid bath, but is conventionally limited to shell-forming liquids lighter than the host bath and suffers from uncontrolled lateral spreading of the interfacial layer. We hypothesize that introducing a simple hydrophobic loop at the air-host liquid interface will (i) anchor denser interfacial liquids by interfacial pinning, circumventing the density constraint, and (ii) confine lateral spread, allowing thicker films to form from the same volume of the interfacial layer and improving process control. EXPERIMENTS: Encapsulation experiments were performed using two hydrophobic loops placed one at a time at the surface of deionized water or surfactant-laden aqueous baths. Shell-forming liquids included silicone oils and dibutyl phthalate. Top and side view imaging characterized lens formation with and without the loop. Core droplets were released from controlled heights to vary the impact Weber number, and dynamics were captured using high-speed imaging. A regime map was constructed for three cases: no loop and two loops with different dimensions. Minimum and maximum effective volumes were identified for each loop. FINDINGS: The hydrophobic loop enabled stable confinement of denser interfacial liquids and produced visibly thicker lenses. The trapping-to-penetration transition boundary shifted upward with confinement, more strongly for smaller loops. Each loop exhibited a finite operable volume window. The thicker confined layers also reduced air entrapment by prolonging the residence time of the core droplet. Despite differences in geometry, the two loops showed similar transition Weber numbers at overflow, suggesting geometric similarity at the overflow limit. Overall, the loop enhances shell-liquid utilization, reduces air inclusion, and extends the versatility of liquid-liquid encapsulation.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".