Influence of Microstructure Topography on the Oblique Impact Dynamics of Drops on Superhydrophobic Surfaces
Bibliographic record
Abstract
This report investigates the influence of microstructure topography on the restitution coefficient, maximum spreading diameter, and contact time of oblique drop impacts on superhydrophobic surfaces. The five surfaces tested allow for comparison of open- versus closed-cell structures, feature size and spacing, and hierarchical versus nanoscale-only surface structures. By decoupling the restitution coefficient into a normal (ε n ) and tangential component (ε t ), it is demonstrated that both ε n and ε t are largely independent of the microstructure topography. Instead, the restitution coefficient is governed almost exclusively by the normal Weber number. Next, a new model is presented that relates the maximum spreading diameter to an adhesion coefficient that characterizes the overall adhesive properties of the superhydrophobic microstructure during drop rebounding. Through this analysis, we discovered that surface geometries with greater microstructure roughness (i.e., overall surface area) promote a higher maximum spreading diameter than flatter geometries. Furthermore, the contact time of drop impacts on flat surfaces is positively correlated with the impact velocity due to penetration of the liquid into the porous nanostructure. However, this trend reverses for oblique impacts due to the presence of stretched rebounding behavior. Finally, substrates patterned with sparse pillar microstructures can exhibit pancake bouncing behavior, resulting in extremely low contact times. This unique bouncing mechanism also significantly influences the restitution coefficient and spreading diameter of oblique impacts.
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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 | 0.000 |
| 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".