Green-synthesized Zinc–Iron–Copper Oxide (ZFCO) Nanoparticles for Fast Green Photodegradation: RSM and Machine Learning
J. Environ. Nanotechnol., Volume 15, No 3 (September 2026) pp. 133-148
Abstract
The present study investigated the green synthesis of zinc–iron–copper oxide (ZFCO) nanoparticles using Ocimum tenuiflorum leaf extract for the photocatalytic degradation of Fast Green (FG) dye. Despite advances in green-synthesized ferrite photocatalysts, limited attention has been given to integrating Cu-modified ZFCO nanocatalysts with statistical optimization and machine-learning (ML) prediction for dye-contaminated wastewater treatment. Response Surface Methodology (RSM) based on a Central Composite Rotatable Design (CCRD 2³) was employed to evaluate the effects of dye concentration, catalyst dosage, and solution pH. ML was incorporated to capture nonlinear relationships among operating variables and provide reliable performance prediction beyond conventional statistical optimization. Gaussian Process Regression (GPR), Support Vector Regression (SVR), Ensemble Regression, and Random Forest (RF) models were evaluated, with GPR providing the highest predictive accuracy. Structural, surface, morphological, and optical characterization confirmed the successful formation of visible-light-responsive ZFCO nanostructures. Copper modification substantially enhanced photocatalytic performance compared with unmodified ZFCO, while RSM identified acidic conditions and appropriate catalyst loading as favorable for FG removal. The optimized experimental condition achieved 57.2% degradation, and Among the evaluated machine-learning models, SVR exhibited the best condition-wise nested cross-validation performance, achieving an R² of 0.9156, RMSE of 4.2915 and MAE of 2.9627. Overall, the integration of green synthesis, RSM optimization, and ML prediction provides a promising framework for developing and optimizing photocatalytic systems for practical treatment of dye-containing industrial wastewater.
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