A comparative study between alkyl- and perfluoroalkyl silane coatings for glass
Bibliographic record
Abstract
Surface modification is a crucial strategy to enhance material performance and expand their applications across diverse fields. Among these, nature-inspired hydrophobic coatings have gained attention for their ability to address challenges such as environmental pollution, surface degradation, and efficiency loss in various industries, including optoelectronics, automotive, and outdoor structures. In this work, we present a comparative study of commercial alkyl- and perfluoroalkyl silanes, (H 3 C(H 2 C) 16 H 2 CSiCl 3 ) and (F 3 C(F 2 C) 5 (H 2 C) 2 SiCl 3 ), dip coated on regular glass, with a focus on their stability and performance upon waterfall simulation and outdoor exposure. These coatings’ wettability, optical properties, and stability on nanotextured glass are also studied. A video-based goniometer was used to study the wetting properties. Roughness, topography, and optical properties of the resulting surfaces were investigated by atomic force microscopy and UV–Vis–NIR spectroscopy. At low deposition times, we were able to spot the presence of agglomerated regions of polymerized short perfluoroalkyl chains, leading to a rougher and less uniform film. In contrast, we observed smoother coating with fewer polymerized aggregates for long alkyl chains. We found that an enhancement in hydrophobicity and a decrease in reflectance was achieved with a short perfluorinated alkyl chain. When applied to nanotextured glass, the observed increase in reflectance for both coatings, at 500 nm, was likely due to the coating’s thickness effect. Texturing combined with surface roughness significantly increased the water contact angle, while only lowering the surface energy of regular glass without altering its structure resulted in less increase in water contact angle. These coatings can serve as hydrophobic surfaces and protective coatings against outdoor conditions, including dust accumulation, UV irradiation, ice adhesion, and corrosion.
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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) | 0.000 | 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".