Preparation of nano-ZnO@polytetrafluoroethylene superhydrophobic coating and its anti-biological adhesion properties
Bibliographic record
Abstract
Multifunctional superhydrophobic surfaces that are resistant to biological adhesion have great application potential in marine science, biomedicine, and food engineering. In this study, a superhydrophobic surface was prepared by a simple spraying process with blended nano-ZnO and polytetrafluoroethylene (PTFE). The prepared surface was characterized by fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM), and the influence of the mass ratio of PTFE to nano-ZnO and the spraying distance on the morphology and wettability of the coating were investigated. In addition, the friction resistance of the coating and its antibacterial properties for Escherichia coli, Staphylococcus aureus, and Candida albicans were studied. Results showed that the optimal mass ratio of PTFE to nano-ZnO was 4:1 and that the optimal spraying method was spraying from near to far. SEM images indicated a compact surface structure of the surface with a thickness of about 100μm and the substrate was tightly bonded with the coating. The superhydrophobic properties of the coating surface were stable after friction testing. More importantly, the coating showed excellent antibacterial performance, which provides a reference for the research and application of superhydrophobic coatings with desirable anti-biological adhesion properties.
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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) | 0.000 | 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 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".