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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it