A robust superhydrophobic coating of SER/ ZnO/MWCNTs with high corrosion resistance was prepared by one-step spraying method
Bibliographic record
Abstract
Multifunctional integration is the basic feature of artificial superhydrophobic coatings widely used in many fields. This paper presents a simple and economical experimental method to prepare a super hydrophobic coating with super strong corrosion resistance by spraying a mixed solution composed of epoxy resin (EP) as the base, multi-walled carbon nanotubes (MWCNTs), sericite (SER), nano-zinc oxide (ZnO) and octadecyl trimethoxysilane (ODTMS) on an aluminum substrate. Firstly, the superhydrophobic property of the coating prepared in this experiment is better than that of the ordinary coating, and its contact angle reaches 167.3° and rolling angle is as low as 2.7°. Secondly, the superhydrophobic coating can not only maintain the superhydrophobic property in harsh environment (such as: soaking in boiling water for 10 hours, soaking in acid, alkali and salt solution for 7 days, high temperature, burning and strong ultraviolet irradiation, etc.), but also can withstand a variety of mechanical damage without losing the superhydrophobic property (such as: 1000 sandpaper wear cycles, 100 tape stripping cycles and 1000 g grit sustained impact). In addition, the excellent non-wettability of superhydrophobic coating can make it have excellent performance in the field of selfcleaning and anti-fouling. It is worth mentioning that the electrochemical workstation experiment proved that the coating also has good corrosion resistance, and the anticorrosion efficiency reached 99.924%. This strong superhydrophobic coating has many functions, such as self-cleaning, antifouling and corrosion prevention, and will have good application prospects in many fields in the future.
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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) | 0.001 | 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".