Reducing Ice Adhesion on Nonsmooth Metallic Surfaces: Wettability and Topography Effects
Bibliographic record
Abstract
The effects of ice formation and accretion on external surfaces range from being mildly annoying to potentially life-threatening. Ice-shedding materials, which lower the adhesion strength of ice to its surface, have recently received renewed research attention as a means to circumvent the problem of icing. In this work, we investigate how surface wettability and surface topography influence the ice adhesion strength on three different surfaces: (i) superhydrophobic laser-inscribed square pillars on copper, (ii) stainless steel 316 Dutch-weave meshes, and (iii) multiwalled carbon nanotube-covered steel meshes. The finest stainless steel mesh displayed the best performance with a 93% decrease in ice adhesion relative to polished stainless steel, while the superhydrophobic square pillars exhibited an increase in ice adhesion by up to 67% relative to polished copper. Comparisons of dynamic contact angles revealed little correlation between surface wettability and ice adhesion. On the other hand, by considering the ice formation process and the fracture mechanics at the ice-substrate interface, we found that two competing mechanisms governing ice adhesion strength arise on nonplanar surfaces: (i) mechanical interlocking of the ice within the surface features that enhances adhesion, and (ii) formation of microcracks that act as interfacial stress concentrators, which reduce adhesion. Our analysis provides insight toward new approaches for the design of ice-releasing materials through the use of surface topographies that promote interfacial crack propagation.
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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".