Rf-Sputtered Teflon®-Modified Superhydrophobic Nanostructured Titanium Dioxide Coating on Aluminum Alloy for Icephobic Applications
Bibliographic record
Abstract
Icing on surfaces such as cables or high-voltage insulators may often lead to severe safety issues such as power outages in cold winter conditions. Conventional methods used to tackle such icing problems include mechanical deicing, where the ice is scraped or broken, and chemical deicing, where deicers such as ethylene glycol are used. However, the best approach to addressing these issues is to prevent ice formation in the first place. Research in the past few decades have shown hydrophobic and superhydrophobic surfaces to be effective in reducing ice adhesion. We used the concept of water repellency to turn an aluminum surface superhydrophobic to minimize ice adhesion on these surfaces. However, to render these surfaces also applicable to insulating surfaces, we also demonstrated the adaptability of the concept on a low dielectric oxide, TiO2, to an aluminum surface with icephobic properties. This work demonstrates the importance of the coexistence of rough nanostructures as well as low-surface-energy compositions on a surface to make it superhydrophobic and icephobic and is applicable on metals and insulating 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".