Robust superhydrophobic coatings from modified siloxane resin
Bibliographic record
Abstract
Superhydrophobic coatings were produced from a modified siloxane resin that served as the low-surface energy (LSE) material needed to make superhydrophobic coatings. The authors used nanosilica to provide the desired surface texture needed for superhydrophobicity. They hypothesized that chemically bonding the LSE material to the surface of nanosilica will improve the durability of the coating. The mixture of nanoparticles and LSE material was applied on an aluminum surface, and it was heated to 150°C. A tin catalyst was employed to increase the reaction rate. The Fourier transform infrared spectra confirmed the chemical reaction between nanosilica and resin. The results showed that the coatings had contact angles higher than 150°C and a contact angle hysteresis (CAH) of less than 8°. The mechanical robustness of the coatings was investigated by an abrasion test. The CAH of the coatings with the catalyst after the abrasive test was 12°, while this angle was 22° without the catalyst. The coatings indicated good water/ultraviolet (UV) durability – for example, the CAH after UV treatment was 8°. Superhydrophobic coatings were applied using two different methods: spin-coating and spray-coating. For each application method, the weathering durability and mechanical properties of the coatings were compared. Considering the overall data, spray-coating is better than spin-coating in the terms of durability.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".