Durable Icephobic and Erosion Resistant Coatings Based on Quasicrystals
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Quasicrystalline (QC) coatings were evaluated as leading-edge protection materials for rotor craft blades. The QC coatings were deposited using high velocity oxy-fuel thermal spray and predominantly Al-based compositions. Ice adhesion, interfacial toughness with ice, wettability, topography, and durability were assessed. QC-coated sand-blasted carbon steel exhibited better performance in terms of low surface roughness (S<sub>a</sub> ~ 0.2 μm), liquid repellency (water contact angles: θ<sub>adv</sub> ~85°, θ<sub>rec</sub> ~23°), and better substrate adhesion compared to stainless steel substrates. To enhance coating performance, QC-coated sand-blasted carbon steel was further exposed to grinding and polishing, followed by measuring surface roughness, wettability, and ice adhesion strength. This reduced the surface roughness of the QC coating by 75%, resulting in lower ice adhesion strengths similar to previously reported values (~400 kPa). The durability of polished QC coating was evaluated using sand and rain erosion. The sand erosion test was conducted per ASTM D823. The thickness of the QC coating remained unchanged post-erosion, indicating the QC coating is quite resistant to abrasion from sand. Rain erosion tests were conducted following the Icephobic Comparative Jet Pulsating Rain Erosion test (ICPjet) at the Anti-icing Materials International Laboratory, Quebec. The coating remained intact even after 190,000 impacts demonstrating extreme durability against rain erosion, and the coating outperformed current erosion-resistant aircraft paint (SAE AMS-C-83231A). Overall, the extreme erosion resistance of the easy-to-spray coating, combined with its de-icing properties and ability to be repaired using standard polishing techniques, makes the developed quasicrystalline coatings extremely promising for the protection of rotor-craft blades and other aircraft components.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".