Environmentally Sustainable Alkyd-Based SiO₂–CuO Nanocoatings for Industrial Corrosion Protection: Synergistic, Structural, and Electrochemical Evaluation
Bibliographic record
Abstract
This study investigates the development of anticorrosion alkyd coatings enhanced with a hybrid nanofiller system comprising silicon dioxide (SiO₂) and copper oxide (CuO) nanoparticles.The primary objective was to determine the optimal nanoparticle ratio and loading concentration to improve the protective performance on mild steel substrates.Electrochemical impedance spectroscopy (EIS) revealed that a SiO₂: CuO weight ratio of 0.61:0.39 at a total concentration of 0.84 wt.% exhibited the highest corrosion resistance, achieving an impedance of 8.79 × 10⁶ Ω•cm².Scanning electron microscopy (SEM) confirmed a uniform nanofiller distribution with no microstructural defects.Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses verified the successful chemical incorporation and crystallinity of the nanocomposite.Thermogravimetric analysis (TGA) indicated enhanced thermal stability, while adhesion testing following ASTM D3359 Method A demonstrated improved bonding with the substrate.Compared to a commercial epoxy-phenolic coating (TK™-34), the developed coating retained 69% of its initial impedance after 72 hours of salt spray exposure, indicating superior durability.The synergistic interaction between hydrophobic CuO and insulating SiO₂ significantly contributed to enhanced barrier and electrochemical properties.These findings highlight the practical viability of SiO₂-CuO nanocomposite coatings in extending the service life of steel infrastructures, offering a cost-effective and sustainable alternative to conventional protective systems.
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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".