Supercooled Water Droplet Impacting Superhydrophobic Surfaces in the Presence of Cold Air Flow
Bibliographic record
Abstract
In the present work, an investigation of stagnation flow imposed on a supercooled water drop in cold environmental conditions was carried out at various air velocities ranging from 0 (i.e., still air) to 10 m/s along with temperature spanning from −10 to −30 °C. The net effect of air flow on the impacting water droplet was investigated by controlling the droplet impact velocity to make it similar with and without air flow. In cold atmospheric conditions with temperatures as low as −30 °C, due to the large increase of both internal and contact line viscosity combined with the presence of ice nucleation mechanisms, supercooled water droplet wetting behavior was systematically affected. Instantaneous pinning for hydrophilic and hydrophobic surfaces was observed when the spread drop reached the maximum spreading diameter (i.e., no recoiling phase). Nevertheless, superhydrophobic surfaces showed a great repellency (e.g., contact time reduction up to 30% where air velocity was increased up to 10 m/s) at temperatures above the critical temperature of heterogeneous ice nucleation (i.e., −24 °C). However, the freezing line of the impacting water droplet was extended up to 2-fold at air velocity up to 10 m/s where substrate temperature was maintained below the aforementioned critical temperature (e.g., −30 °C).
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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".