Bouncing of cloud-sized microdroplets on superhydrophobic surfaces
Bibliographic record
Abstract
The control of microdroplet impact on superhydrophobic surfaces (SHSs) is becoming imperative owing to its effect on several industrial applications, such as corrosion protection, self-cleaning, ice resisting, and de-icing. While most of the experimental studies on the impact dynamics of droplets are based on macrodroplets, it is unclear how the obtained results can be applied to microdroplet impact on SHSs. In this work, a comprehensive experimental analysis ranging from millimeter- to micrometer-sized droplets using a novel drop on demand microdispensing system is performed. Several SHSs were synthesized to control droplet impact by enforcing bouncing on the surface during the impingement process. The current analysis focuses on experimentally capturing and analyzing the impact behavior of cloud-sized microdroplets and macrodroplets (D0 = 10 μm–2500 μm) upon SHS impact, with hysteresis, under controlled environmental conditions. Different droplet impact parameters, such as droplet contact time, maximum spreading diameter, and restitution coefficient, were experimentally obtained. Interestingly, this investigation highlighted a contrast in the behavior of microdroplets and macrodroplets upon impact on rough SHSs. It was found that critical parameters controlling droplet dynamics, such as the maximum spreading diameter and coefficient of restitution, cannot be described by current models in the literature. A preliminary theoretical model based on energy balance and accounting for the substrate hysteresis is proposed to explain some of these findings. Finally, the effect of SHS roughness on the bouncing of cloud-sized microdroplets (D0 = 10 μm–100 μm) was examined in the context of synthesizing SHSs.
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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 | 0.001 |
| 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".