Coalescence‐induced jumping of microdroplets on superhydrophobic surfaces—A numerical study
Bibliographic record
Abstract
Abstract We develop a numerical framework for simulating the coalescence and jumping of microdroplets on superhydrophobic surfaces. The framework combines the volume of fluid (VOF) method with models for advancing and receding contact angles on a number of superhydrophobic surfaces. We demonstrate the temporal and spatial convergence of the framework and show agreement between our numerical results and other experimental studies. The capillary‐inertial scaling is investigated together with the existence of a cut‐off behaviour frequently observed in the lower size‐range of that regime. We investigate findings in some of the previous studies that the cut‐off behaviour can be attributed to viscosity effects and dissipation due to interaction with surface microstructures. We exemplify specific features related to the jumping process and the corresponding energy budget analysis when microdroplets coalesce and jump. We have tested droplets of a radius as small as 0.5 μm that are still jumping but recorded a decrease in the jumping velocity and the degree of energy conversion compared to the jumping of larger droplets. We argue and prove that strong capillary forces originating from the high curvature oscillations dissipate the energy of the system significantly faster in the case of microdroplets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".