Effects of Prolonged Exposure to UV and Water on Super-Hydrophobic Surfaces at Ambient and Icing Conditions
Bibliographic record
Abstract
The surfaces that shed drops helps with mitigation of icing. Shedding of drop depends on surface hydrophobicity, which becomes affected when exposed to water and/or UV. The hydrophobicity degradation of six (Spray SHS, Etched Al SHS, Hydrobead, Neverwet, Waterbeader, and WX2100) different super-hydrophobic surfaces (SHS), exposed to water or UV, were studied from the drop shedding perspective. Two methods were adopted for the hydrophobicity analysis. Among them, one is to study the contact angles (CA) and contact angle hysteresis (CAH) change at static state (i.e., no airflow) compared to the untreated surface. The other one is to analyze the change in critical air velocity (Uc) for a given drop exposed to airflow, on water/UV treated surfaces at room temperature (22 °C) and icing conditions (−1 and −7 °C). For the water/UV treated surfaces, the static CAH and Uc (for all temperatures tested) for drops on Etched Al SHS, Hydrobead, and Neverwet, were found to be less than the specified value for a surface to deem as under performing. Hence, from the overall analysis, Etched Al SHS, Hydrobead, and Neverwet, are considered retaining their performance fairly well at both room temperature and icing conditions after exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".