Modification of Nano-<i>α</i>-Al<sub>2</sub>O<sub>3</sub> and Its Influence on the Surface Properties of Waterborne Polyurethane Resin Composite Passivation Films
Bibliographic record
Abstract
Silane coupling agent KH560 was used to modify the surface of nano-α-Al2O3 in ethanol-aqueous solution with different proportions. The particle size of nano-α-Al2O3 was determined by nano-particle size analyzer, and the effects of nano-α-Al2O3 content, ethanol-aqueous solution ratio and KH560 dosage on the dispersion and particle size of nano-α-Al2O3 were investigated. The material structure before and after modification was determined by Fourier transform infrared spectroscopy (FTIR). Aqueous polyurethane resin and inorganic components are combined with modified nano-α-Al2O3 dispersion to form chromium-free passivation solution. The solution is coated on the galvanized sheet, the adhesion and surface hardness are tested, the bonding strength of the coating and the surface hardness of the substrate are discussed. The corrosion resistance and surface morphology of the matrix were investigated by electrochemical test, neutral salt spray test and scanning electron microscope test. The chromium-free passivation film formed after the modification of nano-α-Al2O3 increases the surface hardness of galvanized sheet by about 85%. The corrosion resistance of the film is better than that of a single polyurethane film. The results show that the surface hardness and corrosion resistance of polyurethane resin composite passivation film are significantly improved by the introduction of nano-α-Al2O3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".