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 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".