Improvement in Corrosion and Adhesion Resistance of a Al<sub>2</sub>O<sub>3</sub>-CeO<sub>2</sub> Nanocomposite Coating on the Aluminum Alloy AA6061 via Surface Pretreatment
Bibliographic record
Abstract
In the present study, a surface pretreatment method consisting of KOH etching followed by oxide thickening in boiling water was used to improve the corrosion and adhesion resistance of the coating. The coating morphology on non-pretreated and pretreated Al substrates was characterized by means of scanning electron microscopy (SEM), atomic force microscopy (AFM) and water contact angle measurement. FT-IR spectra was obtained by Fourier transform infrared spectrometer. The corrosion resistance of the coating in 3.5 wt.% NaCl solution was evaluated with potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) techniques. The adhesion resistance of the coating was tested using ISO-2409 standard. Results show that KOH etching followed by oxide thickening in boiling water can effectively improves the corrosion resistance and durability of the coating. Besides, this surface pretreatment method can also improve significantly the adhesion 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) | 0.001 | 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".