Verification of Icephobic/Anti-icing Properties of a Superhydrophobic Surface
Bibliographic record
Abstract
Four aluminum surfaces with wettability varied from superhydrophilic to superhydrophobic were prepared by combining an etching and a coating process. The surface wettability was checked in terms of water contact angle (CA) and sliding angle (SA) under different humidity at -10 °C. High-speed photography was applied to study water droplet impact dynamics on these surfaces. It was found that single and successive water droplets could rebound on the superhydrophobic surface and roll off at a tilt angle larger than 30° under an extremely condensing weather condition (-10 °C and relative humidity of 85-90%). In addition, the superhydrophobic surface showed a strong icephobic property, the ice adhesion on this surface was only 13% of that on the superhydrophilic surface, though they had a similar nano/microtopological structure. Moreover, this superhydrophobic surface displayed an excellent durability of the icephobic property. The ice adhesion only increased to 20% and 16% of that on the superhydrophobic surface after the surface was undergone 20 icing/ice-breaking cycles and 40 icing/ice-melting cycles, respectively. Surface profile and XPS studies on these surfaces indicated a minor damage of the surface nano/microstructure and the coating layer upon these multiple ice-breaking and ice-melting processes. Therefore, this superhydrophobic surface could be a good candidate for icephobic applications.
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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".