Integrating phase change materials and spontaneous emulsification: In-situ particle formation at oil–water interfaces
Bibliographic record
Abstract
The properties of interfaces between two immiscible fluids, as engineered by interfacial materials, are crucial in various processes, including printing, coating, and multiphase flow in porous media. Interfacial materials, such as surface-active molecules and particles can be adsorbed from the bulk fluid phases to the interface or formed in-situ at the interface. In this study, we develop a methodology to form soft or stiff silica-based particles in situ at the oil-water interface through coupling of phase change materials and spontaneous emulsification. This process is demonstrated by introducing a heptane micellar solution that spontaneously generates a microemulsion phase when in contact with an aqueous phase. Micron-sized droplets, consisting of an aqueous silicate precursor mixture, undergo a sol–gel reaction, leading to the generation of colloidal-sized particles. SEM micrographs and confocal images of dried samples, taken from the aqueous-heptane interface, reveal the formation of particles post-gelation. The in-situ formation of stiff (or gel-like) particles can be characterized through the dynamics of evaporation, where a significant reduction in the heptane evaporation rate is observed. This particle generation method, utilizing phase change materials and emulsion templates, is spontaneous, energy-efficient, and enables particle formation at the interface at a predetermined time. Our findings offer valuable insights for the fabrication of interfacial materials with tailored properties and controlled timing.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".