Drop Rebound after Impact: The Role of the Receding Contact Angle
Bibliographic record
Abstract
Data from the literature suggest that the rebound of a drop from a surface can be achieved when the wettability is low, i.e., when contact angles, measured at the triple line (solid-liquid-air), are high. However, no clear criterion exists to predict when a drop will rebound from a surface and which is the key wetting parameter to govern drop rebound (e.g., the "equilibrium" contact angle, θeq, the advancing and the receding contact angles, θA and θR, respectively, the contact angle hysteresis, Δθ, or any combination of these parameters). To clarify the conditions for drop rebound, we conducted experimental tests on different dry solid surfaces with variable wettability, from hydrophobic to superhydrophobic surfaces, with advancing contact angles 108° < θA < 169° and receding contact angles 89° < θR < 161°. It was found that the receding contact angle is the key wetting parameter that influences drop rebound, along with surface hydrophobicity: for the investigated impact conditions (drop diameter 2.4 < D0 < 2.6 mm, impact speed 0.8 < V < 4.1 m/s, Weber number 25 < We < 585), rebound was observed only on surfaces with receding contact angles higher than 100°. Also, the drop rebound time decreased by increasing the receding contact angle. It was also shown that in general care must be taken when using statically defined wetting parameters (such as advancing and receding contact angles) to predict the dynamic behavior of a liquid on a solid surface because the dynamics of the phenomenon may affect surface wetting close to the impact point (e.g., as a result of the transition from the Cassie-Baxter to Wenzel state in the case of the so-called superhydrophobic surfaces) and thus affect the drop rebound.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".