Multifunctional polyurethane-based coating with corrosion resistance and anti-icing performance for AA2024-T3 alloy protection
Bibliographic record
Abstract
In this study, we present the development of advanced anticorrosive icephobic coatings tailored for AA2024-T3 surfaces. These coatings were engineered using polyurethane, incorporating fluoropolyol and isocyanate modified with hydroxyl-terminated silicone oil and fluoroalkyl polyhedral oligomeric silsesquioxane (F-POSS) particles. Surface characterization involved contact angle and sliding angle measurements, as well as atomic force microscopy. The icephobic efficacy was evaluated through a battery of tests including differential scanning calorimetry, delay of freezing time measurement, impact and non-impact ice adhesion measurement (push-off and centrifuge), and the ice accretion method. Electrochemical impedance spectroscopy served to evaluate the anticorrosion performance of the coatings. The integration of silicone oil and F-POSS led to notable improvements in contact angles, increasing from 92° to 127°. The lowest sliding angle (6°) was obtained for the coating containing both silicone oil and F-POSS. Topographical images showed the essential role of F-POSS in providing a rough surface structure. The optimal coating formulation consisted of 10 wt.% F-POSS particles and 5 wt.% silicone oil, resulting in a water contact angle of 127°, a sliding angle of 9°, approximately 42 days of surface protection, and an impedance value of 4.8 × 108 Ω·cm2. Remarkably, this coating demonstrated exceptional durability in terms of icephobic properties, maintaining a push-off ice adhesion strength of 9.3 kPa even after 15 icing/de-icing cycles, confirming its desirable icephobic performance.
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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".