Experimental study of droplet shedding on laser-patterned substrates
Bibliographic record
Abstract
An experimental study was performed to investigate the dynamics of droplet shedding under the effect of various shear flow speeds on a laser micromachined surface with superhydrophobic properties. To account for the effect of liquid properties on droplet shedding, four different liquids were used in these sets of experiments, namely, distilled water, ethylene glycol, propylene glycol, and glycerol. The wetting length of the liquid droplets was measured based on the air shear speed, and three different regimes were observed based on the critical Weber and Ohnesorge numbers. In the first regime, where the Weber and Ohnesorge numbers are low, droplets deform with slight movement or rotation without detachment from the surface. Under the second regime, where the Weber number is relatively high and the Ohnesorge number is low, droplets deform and detach from the surface, and then subsequent breakup may occur. The variation of droplet detachment time with the Weber and Ohnesorge numbers is further discussed in this paper. In the third regime, where the Ohnesorge number is high, there is no droplet detachment nor are rivulets formed. Finally, empirical correlations are developed to predict the droplet behavior on laser-patterned surfaces under the effect of shear flow. This work can be used as a baseline to study the droplet dynamics on a superhydrophobic surface in cases where temperature changes the liquid properties.
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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".