Dispersion of particles in a sessile droplet evaporating on a heated substrate
Bibliographic record
Abstract
ABSTRACT: A coupled volume-of-fluid (VOF) and discrete element model (DEM) is developed and used to study the dispersion of particles in an evaporating pinned sessile droplet on a heated substrate. Fully resolved simulations of evaporating droplets are performed to study the effects of substrate temperature and the Marangoni stresses to study the fluid flow and temperature distribution within the droplet. The fluid flow inside the evaporating droplets is used to predict the behavior of particles, studying the effect of relative particle density and the aforementioned effects on the particle dispersion within the droplet. This study shows that the presence of Marangoni stresses significantly affects the flow and temperature distribution inside the droplet, which, in turn, influences the dispersion of particles in the droplet. The fluid velocity induced by the Marangoni stresses is nearly two orders of magnitude larger than the velocity generated by capillary flow as a result of evaporation, promoting a strong convective mixing within the droplet, while working to equilibrate the temperature distribution at the interface. In the absence of Marangoni stresses, the dispersion of particles is governed by the competing effects of adsorption by the downward-moving interface as a result of evaporation, and particle sedimentation under the influence of gravity. However, both these effects become less dominant in the presence of a flow induced by the Marangoni stresses, causing the particles to initially move toward the apex of the droplet along the interface and, subsequently, toward a stagnation point on the interface.
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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 | 0.001 | 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".