Evaluation Of Anti-Icing Polymer Coatings
Bibliographic record
Abstract
Ice accumulation on aircraft, wind turbines and power lines can have detrimental effects, including efficiency reduction, mechanical failures and the creation of safety hazards.The aim of this study is to investigate the ice adhesion and wear resistance of three hydrophobic and icephobic coatings applied onto an aluminum substrate.Ethylene-tetrafluoroethylene (ETFE) coating was deposited using a plasma spray method while advanced liquid glass (ALG) and silicone R-2180 were both applied using dipping followed by furnace curing.Water was applied and frozen between both bare and coated surfaces using a custom built jig at -20 °C for 24 hours.The ice adhesion strength was measured using a lap shear test done inside an insulated chamber.The results showed low ice adhesion strengths for both ALG and silicone R-2180 coatings when compared to the bare surface.It was also found the silicone R-2180 coating had a higher wear rate than both the ETFE and ALG coatings.By combining icephobic coatings with an ultrasonic de-icing system, the power required by the system can be reduced, creating a low-powered active approach to the de-icing problem.
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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".