Synthesis and Characterization of CeO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub> Nanocomposite Coating on the AA6061 Alloy
Bibliographic record
Abstract
In the present study, a thick, uniform and crack-free sol-gel coating embedded with Al2O3-CeO2 nanoparticles was successfully synthesized and deposited on aluminum alloy AA6061 by spin coating method. The coating morphology was characterized by using a scanning electron microscopy coupled with electron diffraction x-ray spectrometer (SEM-EDX), an atomic force microscopy (AFM) and water contact angle measurements. FT-IR spectra were obtained using a Fourier transformation infrared spectrometer. The corrosion resistance of this coating in 3.5 wt.% NaCl solution was evaluated with electrochemical methods including potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). The stability of the corrosion resistance of this coating was evaluated by immersion in 3.5 wt.% NaCl solution and by exposure to the UV radiation condition. In addition, the adhesion resistance of the coating was also assessed. SEM and AFM results showed that Al2O3-CeO2 nanoparticles dispersed uniformly in the room temperature vulcanized (RTV) silicon rubber matrix and formed a thick and crack-free coating. Both polarization and impedance results reveal that CeO2-Al2O3 nanoparticles embedded silicon rubber coating can improve the corrosion resistance of the AA6061 alloy by more than three orders of magnitude. Meanwhile, the corrosion resistance of this coating was found to be stable under immersion in 3.5 wt.% NaCl solution and UV exposure conditions. However, excessive content of CeO2 nanoparticles in the coating made the coating morphology porous and decreased the thickness of the coating, which resulted in the decrease in the corrosion resistance of the 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) | 0.000 | 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".