Polyurethane Nanocomposite Coatings Coupled with Titanium-Based Conversion Layers for Enhanced Anticorrosion, Icephobic Properties, and Surface Protection
Bibliographic record
Abstract
This study examines the efficacy of icephobic polyurethane nanocomposite coatings in mitigating corrosion on an aluminum substrate. A titanium-based conversion coating is applied to modify the substrate, and the research focuses on optimizing the dual functionalities of icephobicity and anticorrosion within the polyurethane coatings while ensuring strong substrate adhesion. The coatings are formulated using fluoropolyol, isocyanate, and silica nanoparticles treated with polydimethylsiloxane. Surface properties are analyzed using contact angles, contact angle hysteresis measurements, and atomic force microscopy, and the coatings’ icephobicity is evaluated through differential scanning calorimetry, freezing time delay, ice adhesion under impact and non-impact conditions, and ice accretion tests. The corrosion resistance and adhesive strength of the coatings are assessed using electrochemical impedance spectroscopy and cross-cut tests, respectively. Increasing the concentration of silica nanoparticles to 10 wt.% increases contact angles to 167°, although the 4 wt.% coating produces the lowest contact angle hysteresis (3° ± 0.5°) and ice nucleation temperature (−23 °C). The latter coating is then applied to a substrate pretreated with a titanium/cerium-based conversion coating. This prepared surface maintains an ice adhesion of about 15 kPa after 15 icing/de-icing cycles and provides approximately 90 days of surface protection (|Z|lf = 1.6 × 109 Ω·cm2). Notably, the impedance value exceeds that of untreated substrates, underscoring the effectiveness of the titanium/cerium-based conversion coating in enhancing both corrosion resistance and coating adhesion to the substrate.
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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".