Multifunctional Silica–Silicone Nanocomposite with Regenerative Superhydrophobic Capabilities
Bibliographic record
Abstract
Superhydrophobic surfaces have been garnering increased interest because of their adaptive characteristics. However, concerns regarding their durability and complex fabrication techniques have limited their widespread adoption. In our study, we have developed an effective, durable, and versatile silica-silicone nanocomposite that can be applied through spray coating or bulk synthesized as superhydrophobic monoliths through a facile, economic, and scalable fabrication technique. For spray-coated samples, superhydrophobicity was achieved for concentrations above 9%. However, poor adhesion was observed for concentrations above 20%. Through extensive surface morphology studies, it was determined that a delicate balance between the polymer and dispersed superhydrophobic silica nanoparticles exists at a concentration of 14%. This concentration is necessary for developing the desired hierarchical structure and providing sufficient adhesion with the substrate. The monoliths were fabricated into complex geometries, with superhydrophobicity being observed in the 5 and 9% specimens. The hierarchical structure was formed through controlled surface abrasion, which created the microscale roughness and concurrently exposed the embedded silica nanoparticles. It was found that a monolith with a concentration of 9% provides excellent water repellency as well as a suitable emulsion viscosity to facilitate the molding process. Though compressive loading (up to 10 MPa) damages the monolith, the superhydrophobic performance can be quickly restored through abrasive layer removal. Both spray-coated and monolith specimens retained their superhydrophobicity after being subjected to high temperatures (up to 350 °C) and corrosive environments (pH 1-13) for 2 h.
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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) | 0.000 | 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".