Understanding the anti-icing behavior of superhydrophobic surfaces
Bibliographic record
Abstract
Superhydrophobic surfaces (SHS) are promising nature-inspired materials in low-temperature-condition applications to resist frost, ice, snow and water drop freezing. However, other observations bring into question the advantage of using SHS. In particular, the water supercooling phenomenon and the lack of characterization methods make it difficult to understand icing phenomena on SHS. Here, the authors bring about clarity by measuring water kinetic freezing points on surfaces of various wettabilites through differential scanning calorimetry technique. It is shown that under relatively dry environment, the freezing delay on cold surfaces is attributed to the water-freezing-point depression, which is determined by the combined effect of surface chemistry and roughness. In addition, drop-freezing tests using a thermoelectric cooler under humid atmosphere showed that the freezing delay is dependent on the frosting, as well. Frost crystals growing on the cold surface will act as nucleus initiating drop freezing. The results of this study provide new insights into the mechanism responsible for the drop-freezing delay on SHS. This article contains supporting information that is available online.
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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) | 0.000 | 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".