Development of a Stable TiO<sub>2</sub>Nanocomposite Self-Cleaning Coating for Outdoor Applications
Bibliographic record
Abstract
A convenient and low-cost approach for the elaboration of a stable superhydrophobic coating is reported, involving the use of TiO2nanoparticles via the spray coating method. This method can be used for preparing self-cleaning superhydrophobic coatings on large areas for different kinds of substrates. The synergistic effect of the micro/nanobinary scale roughness was produced by a multilayer RTV SR/TiO2composite. The influence of the nanofiller concentration in a specific frequency range (40 Hz to 2 MHz) on the dielectric behavior was analyzed as well. It was found that the real relative permittivity ( εr′ ) increases as the nanofiller concentration increases. Superhydrophobic behavior is analyzed by contact angle measurements, scanning electron microscopy (SEM), and profilometer. The stability of the developed coating also has been evaluated in terms of immersion in various aqueous solutions, heating, adhesion, and exposure to UV irradiation, and the results showed good stability against these factors. The coating retained its superhydrophobicity after several days of immersion in solutions of different pH levels (2, 4, 6, and 12) and different conductivities. In addition, they also exhibited exceptional stability against UV radiation and heating, as well as good mechanical stability.
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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".