A comprehensive investigation of oxygen/graphite carbon nitride smart Nano coatings for sustainable maintenance and sacrificial anode protection of mild steel
Bibliographic record
Abstract
Abstract BACKGROUND This study enhancing the corrosion resistance of mild steel (MS) by incorporating photo‐catalyst‐doped graphitic carbon nitride (g‐C₃N₄) nano‐sheets containing silver (Ag) and oxygen (O₂) into a solar light‐active photoelectrochemical anticorrosion paint with polyester (PE). The objective is to provide sacrificial anode protection through visible light activation. RESULTS The developed paint forms a uniform film on the MS surface, effectively providing anodic protection under visible light. Characterization techniques, including energy‐dispersive X‐ray spectroscopy (EDX), transmission electron microscopy (TEM), Fourier‐transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy, confirm the molecular structure of the nano‐sheets. Corrosion resistance, assessed through weight loss measurements and open circuit potential (OCP) tests, shows significant improvement with the addition of Ag/g‐C₃N₄ and Ag‐O₂/g‐C₃N₄ nano‐sheets at an optimized concentration of 15 mg. The protection efficiency is ranked as follows: O₂‐g/C₃N₄ > O‐g/C₃N₄ > Ag‐O₂/g‐C₃N₄ > Ag‐g/C₃N₄ > g‐C₃N₄. After 28 days of immersion in seawater, MS coated with O₂/g‐C₃N₄ exhibited the least weight loss, with an inhibition efficiency of up to 99%. Electrochemical impedance spectroscopy (EIS) further demonstrated enhanced coating resistance for the O₂/g‐C₃N₄ coating (Rct = 3.76 kΩ.cm 2 , Rcoat = 1.69 kΩ.cm 2 ) compared to pure PE (Rct = 0.005 kΩ.cm 2 , Rcoat = 0.096 kΩ.cm 2 ). CONCLUSION The synthesized O₂/g‐C₃N₄ and Ag‐O₂/g‐C₃N₄ nano‐sheets exhibit high surface areas and enhanced water dispersibility, which contribute to significant corrosion protection of MS. The electrochemical performance, including weight loss and OCP measurements, highlights the potential of these nano‐sheet photo‐catalysts in advancing corrosion resistance technologies, with broad implications for materials science and engineering applications. © 2024 Society of Chemical Industry (SCI).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".