Preparation and anti-biological adhesion performance evaluation of ZNO@PSBMA super hydrophilic coating
Bibliographic record
Abstract
In this study, zinc oxide (ZnO) nanoparticles were modified by means of dispersion using a γ-aminopropyl triethoxysilane coupling agent (KH550), obtaining KH550-ZnO. Then a certain amount of SBMA powder was put into the KH550-ZnO solution, and the ensuing polymerization reaction produced super hydrophilic PSBMA-KH550-ZnO powder. Finally, several polished aluminum sheets were immersed in the PSBMA-KH550-ZnO aqueous solution to start deposition. Upon completion of the deposition, the aluminum sheets were taken out and dried to obtain samples of the ZnO@PSBMA super hydrophilic coating. The structure, morphology and chemical composition of the powders and coatings were investigated by SEM, IR and EDS. The dispersion of KH550-ZnO in aqueous solution was analyzed. The results show that the particle size of KH550-ZnO decreases greatly in aqueous solution. The substrate can be superhydrophilic when deposited in PSBMA-KH550-ZnO aqueous solution for 130 minutes，After 48 hours of coating deposition, the compactness, roughness and friction resistance of the coating are greatly improved. ZnO@PSBMA superhydrophilic coating has good anti-protein, anti-bacterial and anti-algal 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".