Surface Modification and Functionalization of Oxide Nanoparticles for Superhydrophobic Applications
Bibliographic record
Abstract
Oxide nanoparticles have tremendous technological applications in the present days in diverse fields. In this study, the surface modification and functionalization of hydrophilic silica (SiO2) and zinc oxide (ZnO) nanoparticles were performed to obtain superhydrophobicity. Monodispersive nanoparticles of SiO2 were prepared by Stöber process using tetraethoxysilane (Si (OC2H5)4) as a precursor and ammonium hydroxide as a catalyst in a ethanolic solution. The surface modification of the silica nanoparticles were performed using fluoroalkylsilane (FAS-17: C16H19F17O3Si) molecules to obtain fluorinated silica nanoparticles of diameter varying from 50nm to 300nm. On the other hand, surface modification of zinc oxide (ZnO) nanoparticles was performed using stearic acid (C18H36O2) molecules to obtain methylated ZnO nanoparticles. These functionalized nanoparticles were characterized both in the form of powder as well as thin films. The bonding characteristics of FAS-17 molecules with SiO2 and stearic acid molecules with ZnO were investigated using Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Nanostructured thin films of these functionalized oxide nanoparticles exhibit superhydrophobicity with contact angles over 150° with water roll-off properties. Such functionalized oxides nanoparticles, therefore, can be easily incorporated in coatings and paints for various applications in emerging technologies like biomedical applications, anti-corrosion, anti-icing, drag reduction and energy consumption reduction.
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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".