Synthesis of Monodisperse Fluorinated Silica Nanoparticles and Their Superhydrophobic Thin Films
Bibliographic record
Abstract
Monodispersive silica nanoparticles have been synthesized via the Stöber process and further functionalized by adding fluorinated groups using fluoroalkylsilane in an ethanolic solution. In this process, six different sizes of fluorinated silica nanoparticles of varying diameter from 40 to 300 nm are prepared and used to deposit thin films on aluminum alloy surfaces using spin coating processes. The functionalization of silica nanoparticles by fluorinated group has been confirmed by the presence C-F bonds along with Si-O-Si bonds in the thin films as analyzed by Fourier transform infrared spectroscopy (FTIR). The surface roughnesses as well as the water contact angles of the fluorinated silica nanoparticle containing thin films are found to be increased with the increase of the diameter of the synthesized fluorinated silica nanoparticles. The thin films prepared using the fluorinated silica nanoparticles having a critical size of 119 ± 12 nm provide a surface roughness of ∼0.697 μm rendering the surfaces superhydrophobic with a water contact angle of 151 ± 4°. The roughness as well as the water contact angle increases on the superhydrophobic thin films with further increase in the size of the fluorinated silica nanoparticles in the films.
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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".