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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".