On the Icephobic Behavior of Organosilicon-Based Surface Structures Developed Through Atmospheric Pressure Plasma Deposition in Nitrogen Plasma
Bibliographic record
Abstract
In many regions around the world, atmospheric icing during freezing rains and ice storms can cause severe damage to exposed infrastructure. Subsequently, protective coatings capable of ice accumulation prevention or ice adhesion reduction, often referred to as icephobic coatings, have gained a significant amount of interest. In this study, we examine an atmospheric-pressure plasma jet technique for the development of organosilicon-based icephobic coatings on aluminum substrates. Initially, Al-6061 samples are exposed to multiple passes of air plasma treatment at very short jet-to-substrate distances to create a microporous alumina-based surface structure. These surfaces are then used for plasma deposition of superhydrophobic coatings in the same jet with hexamethyldisiloxane (HMDSO) as the precursor and nitrogen as the plasma gas. Several samples are created with varying plasma precursor flow rates and number of deposition passes. All samples are exposed to three cycles of icing/de-icing to estimate coatings’ stability in aggressive natural conditions. The effects of multiple icing/de-icing cycles on surface chemistry, surface morphology, and wetting behavior is studied. It is shown that the most remarkable mechanism through which icing affects surface properties is coating removal during aggressive de-icing procedure. Finally, the icephobic properties of the most efficient coating (referred to as PT5x3) is further studied through 10 cycles of icing/de-icing, and it is shown that this coating can reduce ice adhesion strength by a factor of at least two for up to nine cycles of icing/de-icing.
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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) | 0.002 | 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".