Coalescence-induced jumping of nanodroplets on mixed-wettability superhydrophobic surfaces
Bibliographic record
Abstract
Coalescence-induced droplet jumping on superhydrophobic surfaces has been observed at microscale and even nanoscale. The enhancement in jumping velocity of coalescing droplets is crucial for condensation heat transfer enhancement, anti-icing, self-cleaning, and so forth. However, the research on how to acquire a higher jumping velocity is really very limited. In this paper, we use molecular dynamics simulations to study the coalescence-induced jumping of two equally sized nanodroplets on chemically heterogeneous surfaces composed of alternating stripes with different hydrophobicity. We show that the jumping velocity is closely related to the stripe width and wettability contrast, and it can even exceed that on an ideal superhydrophobic surface with 180° contact angle when the striped surfaces are properly designed. We also demonstrate that there is always an optimal stripe width yielding the maximum jumping velocity, whereas its value is independent of the wettability contrast. We reveal that the dominant factor to determine the jumping velocity is the apparent contact angle of equilibrated droplets over heterogeneous surfaces for small stripe widths; it changes to the time of liquid bridges impacting surfaces for moderate stripe widths and to the contact area between equilibrated droplets and relatively hydrophobic stripes for large stripe widths. We believe the present simulations can provide useful guidance to design self-jumping superhydrophobic surfaces.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".