Fluorine Based Superhydrophobic Coatings
Bibliographic record
Abstract
Superhydrophobic coatings, inspired by nature, are an emerging technology. These water repellent coatings can be used as solutions for corrosion, biofouling and even water and air drag reduction applications. In this work, synthesis of monodispersive silica nanoparticles of ~120 nm diameter has been realized via Stöber process and further functionalized using fluoroalkylsilane (FAS-17) molecules to incorporate the fluorinated groups with the silica nanoparticles in an ethanolic solution. The synthesized fluorinated silica nanoparticles have been spin coated on flat aluminum alloy, silicon and glass substrates. Functionalization of silica nanoparticles with fluorinated groups has been confirmed by Fourier Transform Infrared spectroscopy (FTIR) by showing the presence of C-F and Si-O-Si bonds. The water contact angles and surface roughness increase with the number of spin-coated thin films layers. The critical size of ~119 nm renders aluminum surface superhydrophobic with three layers of coating using as-prepared nanoparticle suspended solution. On the other hand, seven layers are required for a 50 vol.% diluted solution to achieve superhydrophobicity. In both the cases, water contact angles were more than 150°, contact angle hysteresis was less than 2° having a critical roughness value of ~0.700 µm. The fluorinated silica nanoparticle coated surfaces are also transparent and can be used as paint additives to obtain transparent coatings.
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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".