A semi-empirical force balance-based model to capture sessile droplet spread on smooth surfaces: A moving front kinetic Monte Carlo study
Bibliographic record
Abstract
This study reports the development of a semi-empirical force balance-based moving front kinetic Monte Carlo (FB-MFkMC) model to describe droplet spreading on a smooth surface. The proposed model depicts the state-by-state evolution of a sessile droplet in a stochastic manner that captures the molecular-level events taking place in an accurate yet efficient manner. In the developed model, the movement of the droplet triple contact line is depicted using rate expressions that detail the probability that the contact line will locally advance over a set distance at each time point. These rate expressions are derived based on the force balance acting upon the droplet interface, which is captured using analytical inertial and capillary expressions from the literature. This work furthermore derives a new semi-empirical expression to depict the viscous damping force acting on the droplet. The derived viscous force term depends on a fitted parameter c, whose value was observed to vary solely depending on the droplet liquid as captured predominantly by the droplet Ohnesorge number. The proposed FB-MFkMC approach is subsequently validated using data obtained both from conducted experiments and from the literature to support the robustness of the framework. The predictive capabilities of the developed model are further inspected to provide insights on the sessile droplet system behavior.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".