Exploiting intermediate wetting on superhydrophobic surfaces for efficient icing prevention
Bibliographic record
Abstract
HYPOTHESIS: Superhydrophobic surfaces can effectively prevent the freezing of supercooled droplets in technological systems. Droplets on superhydrophobic surfaces commonly not only wet the top asperities (Cassie State), but also partially penetrate into microstructure due to surface properties, environment, and droplet impact occurring in real-world applications. Implications on ice nucleation can be expected and are little explored. It remains elusive how anti-icing surfaces can be designed to exploit intermediate wetting phenomena. EXPERIMENTS: We utilized engineered micro-/nanostructures, specifically micropillars, to modulate the wetting fraction in the microstructure. The behavior of intermediate wetting with supercooling and resulting implications on ice nucleation delay when potential nucleation sites are formed in the microcavities were investigated using experimental, theoretical, and simulation components. FINDINGS: The temperature-dependent wetting fraction in the microstructure increased at supercooled temperatures, partly activated by condensation in the microcavities. At -10/-20 °C, a critical wetting fraction led to maximum ice nucleation delays, with experimental results consistent with theoretical predictions. This critical wetting fraction minimized the effective contact area solid-to-liquid along the partially wetted microstructure. The study establishes physical relations between ice nucleation delays, geometrical surface parameters and wettability properties in the intermediate wetting regime, providing guidance for the design of ice resistant microstructured surfaces.
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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".