Development of Mesoporous Cu-Doped ZnO/rGO Nanocomposites with High Surface Area for Efficient Ethyl Violet Removal: Kinetics, Isotherms, and AI-Driven Predictive Modeling
J. Environ. Nanotechnol., Volume 15, No 2 (June 2026) pp. 490-511
Abstract
A mesoporous Cu-doped ZnO/rGO nanocomposite were made by one-step co-precipitation process and employed effective adsorbent for elimination of ethyl violet (EV) from aqueous solutions. Structural, morphological, and surface properties of synthesized material was thoroughly examined utilizing X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET), Raman spectroscopy, scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDS), and small-angle X-ray diffraction (SAXRD) methodologies. The findings validated improvement of partially ordered mesoporous structure exhibiting a substantial surface area of 115.8 m²/g, average pore diameter of 29.6 nm, and distinctive SAXRD peak at 0.83°, offering several active sites for adsorption. Batch adsorption investigations revealed that the removal effectiveness varied from 72.4% to 93.3%, contingent upon operational parameters including starting concentration, pH, sonication duration, and adsorbent dosage. The process was statistically analyzed by response surface methodology (RSM), demonstrating non-significant lack of fit (p = 0.321) and high significance (F = 82.44, p < 0.0001), so affirming model adequacy. To improve predictive accuracy, artificial intelligence models such as BP-ANN, RBF, RF, and ANN-GA were utilized. The BP-ANN model demonstrated enhanced performance with an MSE of 0.00096915 and a high correlation value (R = 0.99888), while the RBF model indicated worse predictive capability (R² = 0.83812). Optimization by ANN-GA determined the ideal parameters as a concentration of 348.92 mg/L, pH 4.91, a sonication duration of 9.82 minutes, and a dosage of 17.03 mg, resulting in a predicted removal efficiency of 93.33%, which closely aligned with the experimental value of 92.10%, exhibiting a negligible error of 1.23%. Kinetic investigation revealed that adsorption adhered to pseudo-second-order model (R² = 0.997), indicating a chemisorption mechanism, whereas equilibrium tests demonstrated that the Langmuir isotherm (R² = 0.9502) most accurately characterized the process, with highest adsorption capacity of 965 mg/g. The separation factor (RL = 0.0127 to 0.0372) further validated the advantageous adsorption behavior. The amalgamation of statistical modeling and hybrid AI optimization establishes a formidable framework for process improvement, indicating that Cu-doped ZnO/rGO nanocomposites are very effective and promising materials for advanced wastewater treatment applications.
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Reference
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