Self-sorting of bidisperse particles in evaporating sessile droplets
Bibliographic record
Abstract
This study investigates the dispersion and self-sorting dynamics of bidisperse particles, i.e. , a mixture of two distinct particle sizes, during the evaporation of ethanol droplets on a heated substrate, focusing on the influence of surface wettability, Marangoni stresses, and relative particle density. To this end, numerical simulations are carried out using a two-stage numerical approach: the first stage simulates the gas-liquid flow along with the heat and vapor distribution, while the second stage models the particle behavior using Lagrangian particle tracking. The results reveal that for an ethanol droplet evaporating with a constant contact angle in the absence of thermocapillary Marangoni stresses, the flow induced by the receding motion of the contact line supersedes the capillary flow, moving the fluid from the contact line to the apex of the droplet. This flow moves the particles from the bulk of the droplet to the apex of the droplet and suppresses size-based self-sorting of the particles. However, in the presence of Marangoni stresses, a flow along the interface near the apex of the droplet promotes the self-sorting of particles based on their size, whereby smaller particles concentrate near the droplet apex and larger particles form an outer shell around them.
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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) | 0.000 | 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 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".