Preparation of stable and highly hydrophobic coatings via one-step spray method and study of their anti-icing performance
Bibliographic record
Abstract
In daily life, phenomena such as icing, corrosion, and dust accumulation on metal surfaces significantly reduce the operational efficiency of working systems. Research into stable and functional waterproof protective layers on metal surfaces has become a focal point of attention. The ability of superhydrophobic surfaces to remain dry is attractive for their self-cleaning, corrosion-resistant, anti-icing, and deicing properties. However, some superhydrophobic surfaces are highly susceptible to damage from external forces or even light touches, causing them to lose their superhydrophobic properties. Furthermore, the complex preparation process and stringent equipment requirements for superhydrophobic surfaces also limit their application prospects. Here, we filled epoxy resin with modified SiC particles mixed with different particle sizes (E-51) and added silica sol and silica sol mixed with SiC particles of varying sizes. Due to the excellent adhesive properties of epoxy resin (E-51), micro/nanoparticles are firmly adhered to the substrate surface. As a result, the coating withstands various mechanical stability tests, including 250 cycles of sandpaper abrasion, 25 impacts from gravel, 240 water jet impacts, and 30 cycles of adhesive tape peeling. It also passed the 4H pencil hardness test and scratch resistance test (level 1). Moreover, when immersed in acid/alkali/salt solutions or exposed to UV radiation and high temperatures, its superhydrophobic properties exhibit minimal changes over certain time and temperature ranges. With an inhibitory efficiency of 99.52%, the electrochemical polarization curve (Tafel) results verify a notable improvement in the ability of the coating to withstand corrosion. Benefiting from the excellent photothermal conversion performance of nanosilicon carbide, the surface temperature of the coating can rapidly rise under sunlight, promising excellent prospects in anti-icing/deicing applications. This highlights the functional potential of the superhydrophobic coating to a great extent.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".