Understanding the Role of Surface Micro-Texture on the Delayed Freezing of Drops on Cold Surfaces
Bibliographic record
Abstract
Freezing of drops on surfaces has many consequences in icing of various systems, e.g. micro-condensers. It is known that when a water drop is placed on a cold surface and the surface temperature is reduced, it will not necessarily freeze when the surface temperature has reached zero degrees Celsius. The delay in freezing of a drop on a cold surface is not well understood; especially the effect that micro- and nano-texture of a surface has this delay. In this study, freezing and melting points of water drops on various micro-textured surfaces, i.e. superhydrophilic, and superhydrophobic have been measured by differential scanning calorimetry (DSC). A comparison of the experimental results with smooth hydrophilic and hydrophobic surfaces allows us to understand the roles of surface chemistry and roughness in freezing of drops in contact with such surfaces. It is found that when the surface chemistry is hydrophobic, roughness will delay the freezing and a drop may not freeze until the surface temperature has been lower than −15 ° C. On the contrary, for hydrophilic surfaces, roughness will shorten the freezing delay and facilitate formation of ice on the surface. This can explain the benefit of the superhydrophobic surfaces (SHS) in preventing ice formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".