Fabrication of high-durability superhydrophobic coatings based on dual-sized SiC particles
Bibliographic record
Abstract
In recent years, inspired by “biomimicry”, superhydrophobic surfaces have gained significant attention. Superhydrophobic surfaces demonstrate notable advantages in addressing interfacial issues, and superhydrophobic coatings exhibit excellent waterproofness, anti-fouling, self-cleaning, anti-corrosion, and additional capabilities, making them promising next-generation waterproof materials. However, the complex preparation process, coupled with poor wear resistance and environmental durability, severely limits their practical applications. Therefore, this article started from simplifying the preparation process and improving the durability of the coatings. Epoxy resin (E51) was used as the film-forming material, and carbon nanotubes (CNTs) and dual-sized SiC particles (nano-SiC and micro-SiC) were used as the fillers. Room temperature vulcanized silicone rubber (RTV) was used as a binder interacting with epoxy resin to promote the interface interaction between the fillers and the polymers. This process resulted in the successful preparation of superhydrophobic coatings with outstanding comprehensive performance. When the ratio of μ-SiC to n-SiC was 1:1, the prepared coating exhibited the best superhydrophobic properties with a water contact angle (WCA) of 167.4° and a sliding angle (SA) of 4.6°. Even after undergoing severe mechanical tests, such as sandpaper abrasion for 1000 cycles, sand impact for 100 cycles, cross-cut test, and tape-peeling for 70 cycles, the coatings still maintained their non-wetting Cassie-Baxter state. Furthermore, even after immersion in strong acid, strong alkali and 3.5 wt% NaCl solutions for 6 days, keeping at 500 ℃ for 2 hours, and exposure to ultraviolet for 6 days, the coatings still exhibited excellent superhydrophobicity. This suggested that the prepared coating had excellent chemical stability and high-temperature resistance. In addition, the superhydrophobic coating exhibited exceptional capabilities in self-cleaning, anti-corrosion, anti-icing, and de-icing properties. Furthermore, this coating, applicable to diverse substrates including board, steel, paper, and glass, demonstrated an impressive water contact angle (WCA) and sliding angle (SA). The spraying method offers the benefits of simplicity and cost-effectiveness. This is poised to significantly broaden its practical applications in various fields, including construction, transportation, and the chemical industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".