Machine Learning-Based Optimization of Green-Synthesized MgO Nanoparticles Using Cardiospermum halicacabum for Antibacterial and Reactive Orange 16 Removal
J. Environ. Nanotechnol., Volume 15, No 3 (September 2026) pp. 741-756
Abstract
This study aimed to develop multifunctional magnesium oxide nanoparticles (MgO NPs) through a sustainable synthesis route using Cardiospermum halicacabum leaf extract and to evaluate their antibacterial and wastewater-treatment potential. The effects of magnesium nitrate concentration, extract concentration, reaction temperature, and reaction time on MgO yield were optimized using a Taguchi L27 design and ANOVA. The novelty lies in integrating Taguchi optimization with comparative artificial neural network (ANN) and support vector regression (SVR) modelling to optimize and predict plant-mediated MgO production. The optimized nanoparticles were characterized using UV–visible spectroscopy, FTIR, XRD, and SEM–EDX and evaluated for antibacterial activity and hydroxylamine-enhanced Reactive Orange 16 removal. Extract concentration was the dominant synthesis parameter, contributing 54.02% to yield variation. The optimum conditions of 0.10 M precursor, 100 g L⁻¹ extract, 65 °C, and 75 min produced 0.13913 ± 0.00035 g of MgO, with only 0.09% deviation from the Taguchi prediction. The ANN and SVR models achieved cross-validated R² values of 0.9828 and 0.9999, respectively, confirming the superior predictive performance of SVR. Characterization verified the formation of crystalline cubic-periclase MgO. The nanoparticles exhibited concentration-dependent antibacterial activity. The MgO/NaClO/hydroxylamine system achieved 96.8% Reactive Orange 16 removal within 30 min, with an apparent rate constant of 0.117 min⁻¹. These findings demonstrate the practical potential of green-synthesized MgO nanoparticles for rapid treatment of dye-bearing wastewater. Further studies should investigate catalyst recyclability, degradation-product toxicity, mineralization efficiency, reaction mechanisms, and performance in actual textile effluents.
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Reference
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