Drop Shedding by Shear Flow for Hydrophilic to Superhydrophobic Surfaces
Bibliographic record
Abstract
A balance of surface science and aerodynamic knowledge is brought to bear to elucidate the fundamental parameters determining the incipient motion (runback) for a drop exposed to shearing airflow. It was found that wetting parameters such as contact angle are very influential in determining the minimum required air velocity for drop shedding. On the basis of experimental results for drops of water and hexadecane (0.5-100 microL) on PMMA, Teflon, and a superhydrophobic aluminum surface, an exponential function is proposed that relates the critical air velocity for shedding to the ratio of drop base length to projected area. The results for all of the water systems can be collapsed to self-similar curves by normalization. Results from other researchers also conform to the exponential self-similar functional form proposed. It was shown that the data for hexadecane drops can be matched relatively well to those for water drops by means of a corrective factor based on fluid properties and contact angles. Also, the critical air velocity for shedding from the superhydrophobic surface is seen to be more constant over a range of volumes than for the other surfaces. Finally, contact angle measurements from airflow shedding experiments are compared to measurements made by tilted plate and quasi-static advancing and receding tests. The observed differences between contact angles from different measurement methods show that the transfer of contact angle data among various applications must be done with care.
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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 | 0.000 |
| 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".