Coalescence-induced jumping of micro-droplets on heterogeneous superhydrophobic surfaces
Bibliographic record
Abstract
The phenomenon of droplets coalescence-induced self-propelled jumping on homogeneous and heterogeneous superhydrophobic surfaces was numerically modeled using the volume of fluid method coupled with a dynamic contact angle model. The heterogeneity of the surface was directly modeled as a series of micro-patterned pillars. To resolve the influence of air around a droplet and between the pillars, extensive simulations were performed for different droplet sizes on a textured surface. Parallel computations with the OpenMP algorithm were used to accelerate computation speed to meet the convergence criteria. The composition of the air-solid surface underneath the droplet facilitated capturing the transition from a no-slip/no-penetration to a partial-slip with penetration as the contact line at triple point started moving to the air pockets. The wettability effect from the nanoscopic roughness and the coating was included in the model by using the intrinsic contact angle obtained from a previously published study. As the coalescence started, the radial velocity of the coalescing liquid bridge was partially reverted to the upward direction due to the counter-action of the surface. However, we found that the velocity varied with the size of the droplets. A part of the droplet kinetic energy was dissipated as the merged droplet started penetrating into the cavities. This was due to a different area in contact between the liquid and solid and, consequently, a higher viscous dissipation rate in the system. We showed that the effect of surface roughness is strongly significant when the size of the micro-droplet is comparable with the size of the roughness features. In addition, the relevance of droplet size to surface roughness (critical relative roughness) was numerically quantified. We also found that regardless of the viscous cutoff radius, as the relative roughness approached the value of 44, the direct inclusion of surface topography was crucial in the modeling of the droplet-surface interaction. Finally, we validated our model against existing experimental data in the literature, verifying the effect of relative roughness on the jumping velocity of a merged droplet.
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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 | 0.000 | 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".