Effect of HMDSO flow rate in nitrogen atmospheric plasma on the superhydrophobic characteristics of organosilicon-based coatings
Bibliographic record
Abstract
Summary form only given. An atmospheric pressure plasma reactor is used to generate a superhydrophobic surface using nitrogen as the ionization gas and hexamethyldisiloxane (HMDSO) as the monomer. A total of 14 different flow rates ranging from 3 gr/h to 70 gr/h are chosen for plasma polymerization in order to study the wetting behavior of the coatings along with their chemical composition and morphological structure.Static contact angle measurement shows an increase in contact angle up to a point, after which it remains relatively constant as the flow rate is increased. FTIR results show that by increasing the HMDSO injection rate into the plasma, the intensity of both Si-O-Si band and Si-C bands increases. While silicon oxide is a hydrophilic function, it is responsible for the dendrite-like structure that is necessary for low wetting behavior. Si-C bands, on the other hand, are indicative of the presence of organic functions on the surface which are responsible for lowering the surface energy. In order to study the mechanism under which the superhydrophobic structure evolves, developed surfaces using 6 different flow rates (3, 5, 10, 15, 30 and 70 gr/h) were studied using scanning electron microscopy. The results show that the superhydrophobic dendrite-like structure may be the outcome of particle agglomeration caused by increasing the flow rate. At flow rates close to the end limit in this experiment (70 gr/h), these agglomerates form a white silicon dioxide powder which is mechanically unstable and can be easily removed from the surface. Thus, it can be suggested that a middle ground exists, where the flow rate is high enough for silicon oxide to form a nano roughened structure while maintaining the presence of organic functions on the surface. The method introduced here is a relatively cheap, fast and environmentally friendly procedure which can be used in various applications, such as self-cleaning or icephobic surfaces.
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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 | 0.001 |
| 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) | 0.003 | 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".