Evaluation of doped amorphous carbon coatings for hydrophobic applications in aerospace
Bibliographic record
Abstract
To improve aircraft performance in adverse service environments, specific coatings are applied to critical surfaces to improve (i) erosion, (ii) corrosion, (iii) hydrophobic, or (iv) icephobic properties. Although protective coatings are available in all four groups, aerospace applications require combinations of properties that are not readily available, for example hydrophobicity together with erosion resistance. The combination of hydrophobic (or icephobic) and erosion properties is difficult to obtain. Known hydrophobic or icephobic materials are usually polymers that are too soft to provide required erosion resistance and durability in aircraft service conditions. Conversely, physical vapour deposited coatings that are erosion resistant and durable, typically do not have adequate hydrophobicity. However, recent studies on amorphous carbon (a-C) coatings indicate that these materials have potential for combined hydrophobic and erosion applications through doping or compositional changes. Thus, this paper reports on improving the hydrophobic and erosion resistance properties of nonhydrogenated a-C coatings doped with a single element such as chromium (Cr), silicon (Si), aluminum (Al), or titanium (Ti) up to 12 at.%. All coatings are produced using unbalanced magnetron sputtering and characterized by scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, nanoindentation, and water contact angle measurements. The most prospective compositions are evaluated for erosion resistance and ice adhesion strength testing. Overall, the best combination of hydrophobic and erosion properties is obtained for a-C doped with 10 at.% of Al, whereas the ice adhesion results against bare steel substrate have been inconclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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".