Predictive Model of Supercooled Water Droplet Pinning/Repulsion Impacting a Superhydrophobic Surface: The Role of the Gas–Liquid Interface Temperature
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Bibliographic record
Abstract
Dynamical analysis of an impacting liquid drop on superhydrophobic surfaces is mostly carried out by evaluating the droplet contact time and maximum spreading diameter. In this study, we present a general transient model of the droplet spreading diameter developed from the previously defined mass-spring model for bouncing drops. The effect of viscosity was also considered in the model by definition of a dash-pot term extracted from experiments on various viscous liquid droplets on a superhydrophobic surface. Furthermore, the resultant shear force of the stagnation air flow was also considered with the help of the classical Homann flow approach. It was clearly shown that the proposed model predicts the maximum spreading diameter and droplet contact time very well. On the other hand, where stagnation air flow is present in contradiction to the theoretical model, the droplet contact time was reduced as a function of both droplet Weber numbers and incoming air velocities. Indeed, the reduction in the droplet contact time (e.g., 35% at a droplet Weber number of up to 140) was justified by the presence of a formed thin air layer underneath the impacting drop on the superhydrophobic surface (i.e., full slip condition). Finally, the droplet wetting model was also further developed to account for low temperature through the incorporation of classical nucleation theory. Homogeneous ice nucleation was integrated into the model through the concept of the reduction of the supercooled water drop surface tension as a function of the gas-liquid interface temperature, which was directly correlated with the Nusselt number of incoming air flow. It was shown that the experimental results was qualitatively predicted by the proposed model under all supercooling conditions (i.e., from -10 to -30 °C).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it