Wetting Dynamics of Nanoparticle Dispersions: From Fully Spreading to Non-sticking and the Deposition of Nanoparticle-Laden Surface Droplets.
Bibliographic record
Abstract
Controlled transport of liquid droplets on solid surfaces is critical in many practical applications, such as self-cleaning surfaces, coating, drug delivery, and agriculture. Non-adhesive liquid drops levitate on solid surfaces; therefore, they are highly mobile and directed toward desired locations by external stimuli. Although research on liquid-repellent surfaces has proliferated, the existing methods are still limited to creating surface roughness or coating the liquid droplets. Here, we create non-contact aqueous drops on hydrophilic surfaces in an oleic environment and use them to deposit submicrometer droplets encapsulating nanoparticles on solid surfaces. A glass surface is buried under an oil phase that contains a high concentration of Span 80 surfactants, and a drop of silica nanoparticle dispersion is released on the solid surface. We study the effect of surfactant concentration in oil and nanoparticle concentration in water on wetting dynamics and report a plethora of droplet spreading regimes from fully wetting to non-wetting. We find a threshold Span 80 concentration above which surfactant assemblies are formed on the solid and prevent the direct contact of the drop with the surface. At the same time, water-in-oil emulsions are generated at the drop-oil interface. The drop moves and leaves a trace of emulsions with encapsulated nanoparticles on the solid. We demonstrate the possibility of local surface coating with hydrophilic nanoparticles in a hydrophobic medium. The developed methodology in this study is a generic approach facilitating the droplet patterning in numerous applications, from pharmaceutical polymetric carriers to the formulation of cosmetics, insecticides, and biomedical diagnoses.
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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) | 0.000 | 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) | 0.001 | 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".