Drop impact and wettability: From hydrophilic to superhydrophobic surfaces
Bibliographic record
Abstract
Experiments to understand the effect of surface wettability on impact characteristics of water drops onto solid dry surfaces were conducted. Various surfaces were used to cover a wide range of contact angles (advancing contact angle from 48° to 166°, and contact angle hysteresis from 5° to 56°). Several different impact conditions were analyzed (12 impact velocities on 9 different surfaces, among which 2 were superhydrophobic). Results from impact tests with millimetric drops show that two different regimes can be identified: a moderate Weber number regime (30 < We < 200), in which wettability affects both drop maximum spreading and spreading characteristic time; and a high Weber number regime (We > 200), in which wettability effect is secondary, because capillary forces are overcome by inertial effects. In particular, results show the role of advancing contact angle and contact angle hysteresis as fundamental wetting parameters to allow understanding of different phases of drop spreading and beginning of recoiling. It is also shown that drop spreading on hydrophilic and superhydrophobic surfaces occurs with different time scales. Finally, if the surface is superhydrophobic, eventual impalement, i.e., transition from Cassie to Wenzel wetting state, which might occur in the vicinity of the drop impact area, does not influence drop maximum spreading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".