Innovative Solid Slippery Coating: Uniting Mechanical Durability, Optical Transparency, Anti-Icing, and Anti-Graffiti Traits
Bibliographic record
Abstract
Slippery coatings, such as the slippery liquid-infused porous surface (SLIPS), have gained significant attention for their potential applications in anti-icing and anti-fouling. However, they lack durability when subjected to mechanical impact. In this study, we have developed a robust slippery coating by blending polyurethane acrylate (PUA) with methyltriethoxysilane (MTES) and perfluoropolyether (PFPE) in the solvent of butyl acetate. The resulting mixture is homogeneous and allows for uniform coating on various substrates using a drop coating process followed by drying at 160 °C for 3 h. The cured coating exhibits excellent water repellency (contact angle of ~108° and sliding angle of ~8°), high transparency (average visible transmittance of ~90%), exceptional adherence to the substrate (5B rating according to ASTMD 3359), and remarkable hardness (4H on the pencil hardness scale). Moreover, the coating is quite flexible and can be folded without affecting its wettability. The robustness of the coating is evident in its ability to maintain a sliding angle below 25° even when subjected to abrasion, water jetting, high temperature, and UV irradiation. Due to its excellent nonwetting properties, the coating can be employed in anti-icing, anti-graffiti, and anti-sticking applications. It effectively reduces ice adhesion on aluminum substrates from approximately 217 kPa to 12 kPa. Even after 20 cycles of icing and de-icing, there is only a slight increase in ice adhesion, stabilizing at 40 kPa. The coating can resist graffiti for up to 400 cycles of writing with an oily marker pen and erasing with a tissue. Additionally, the coating allows for easy removal of 3M tape thereon without leaving any residue.
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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 | 0.001 | 0.001 |
| 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".