Improving Hydrophobicity of Glass Surface Using Dielectric Barrier Discharge Treatment in Atmospheric Air
Bibliographic record
Abstract
Non-thermal plasmas under atmospheric pressure are of great interest in industrial applications, especially in material surface treatment. In this paper, the treatment of a glass surface for improving hydrophobicity using the non-thermal plasma generated by dielectric barrier discharge (DBD) at atmospheric pressure in ambient air is conducted, and the surface properties of the glass before and after the DBD treatment are studied by using contact angle measurement, surface resistance measurement and wet flashover voltage tests. The effects of the applied voltage and time duration of DBD on the surface modification are studied, and the optimal conditions for the treatment are obtained. It is found that a layer of hydrophobic coating is formed on the glass surface after spraying a thin layer of silicone oil and undergoing the DBD treatment, and the improvement of hydrophobicity depends on DBD voltage and treating time. It seems that there exists an optimum treating time for a certain applied voltage of DBD during the surface treatment. The test results of thermal aging and chemical aging show that the hydrophobic layer has quite stable characteristics. The interaction mechanism between the DBD plasma and the glass surface is discussed. It is concluded that CH3 and large molecule radicals can react with the radicals in the glass surface to replace OH, and the hydrophobicity of the glass surface is improved accordingly.
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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".