On the Oblique Impact Dynamics of Drops on Superhydrophobic Surfaces. Part I: Sliding Length and Maximum Spreading Diameter
Bibliographic record
Abstract
Oblique water drop impacts were performed on a superhydrophobic surface at normal Weber numbers in the range of 3 < We n < 80 and at angles of incidence in the range of 0 < AOI < 60°. While holding We n constant, we varied the AOI to investigate how the oblique nature of the impact affects the sliding length and spreading diameter of impacting drops. Our sliding length measurements indicate that drops impacting at We n < 10 retain essentially full mobility on the surface, whereas the sliding of higher- We n impacts is inhibited by drag forces. We attribute this trend to increased penetration into air-trapping surface features occurring in higher- We n impacts, which results in more adhesion between the liquid and solid. Regarding the spreading of drops on SHP surfaces, the dimensionless maximum spread diameter ( D * max ) increases not only with We n but also with the angle of incidence such that more oblique drop impacts stretch to a wider maximum diameter. We attribute this behavior to adhesion forces, which act to stretch the drop as it slides tangentially across the surface in oblique impacts. On the basis of this theory, we derived a model predicting D * max for any We n and AOI. The model’s predictions are highly accurate, successfully predicting D * max for our entire experimental space. Finally, by placing the camera above the sample, we observed that oblique drop impacts spread into an elliptical shape, and we present a model predicting the maximum spread area.
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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 | 0.001 |
| 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 | 0.001 |
| 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".