Durable superhydrophobic coatings based on CNTs-SiO2gel hybrids for anti-corrosion and thermal insulation
Bibliographic record
Abstract
When steel structure buildings appear more frequently in our lives, the anti-corrosion and thermal insulation capacity of steel as the substrate is an inevitable difficult problem. To solve this problem, we propose a carbon nanotube silica aerogel hybrid (CNTs-SiO 2 gel hybrids), which is a new type of nanoparticle obtained by grafting the modified silica aerogel onto carbon nanotubes. After that, the superhydrophobic coating was prepared on Q235 steel by simple one-step spraying. The prepared superhydrophobic coating has a stable micro nanolayered structure and excellent hydrophobicity , with a contact angle of 168.7 ± 1°and a sliding angle of 4.5 ± 0.4°. The superhydrophobic coating can withstand various mechanical and chemical durability tests and maintained good superhydrophobic properties. The electrochemical experimental analysis shows that the corrosion current density of CNTs-SiO 2 gel hybrids superhydrophobic coating is 1.55 × 10 −7 A/cm 2 is three orders of magnitude lower than steel plate (1.07 × 10 −4 A/cm 2 ), indicating that CNTs-SiO 2 gel hybrids have excellent corrosion resistance . CNTs-SiO 2 gel hybrids superhydrophobic coating has achieved certain thermal insulation performance compared with ordinary CNTs-SiO 2 coating. CNTs-SiO 2 gel hybrids superhydrophobic coating, which has both anti-corrosion and thermal insulation capabilities, will certainly expand the practical application of superhydrophobic coating.
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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) | 0.001 | 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".