Solvent-Free Fabrication of Robust Superhydrophobic Powder Coatings
Bibliographic record
Abstract
Superhydrophobicity originating from the "lotus effect" enables novel applications such as self-cleaning, anti-fouling, anti-icing, anti-corrosion, and oil-water separation. However, their real-world applications are hindered by some main shortcomings, especially the organic solvent problem, complex chemical modification of nanoparticles, and poor mechanical stability of obtained surfaces. Here, we report for the first time the solvent-free, chemical modification-free, and mechanically, chemically, and UV robust superhydrophobic powder coatings. The coatings were fabricated by adding commercially available polytetrafluoroethylene (PTFE) particles into powder coatings and by following the regular powder-coating processing route. The formation of such superhydrophobic surfaces was attributed to PTFE particles, which hindered the microscale leveling of powder coatings during curing. Through adjusting the dosage of PTFE, the hydrophobicity of obtained coatings can be tuned in a large range (water contact angle from 92 to 162°). The superhydrophobic coatings exhibited remarkable mechanical robustness against abrasion because of the unique hierarchical micro/nanoscale roughness and low surface energy throughout the coating and the solid lubrication effect of PTFE particles. The coatings also have robustness against chemical corrosion and UV irradiation owing to high bonding energy and chemical inertness of PTFE. Moreover, the coatings show attractive performances including self-cleaning, anti-rain, anti-snow, and anti-icing. With these multifaceted features, such superhydrophobic coatings are promising for outdoor applications. This study also contributes to the preparation of robust superhydrophobic surfaces in an environmentally friendly way.
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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 | 0.001 | 0.001 |
| 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".