Green Synthesis of Sep-NiO Nanocomposite Using Mentha aquatica Leaf: Exploring Catalytic Efficiency, Kinetics, Thermodynamics, and Neural Network Prediction in Methylene Blue Degradation

In present research, small-sized Sep-NiO nanocomposites were synthesized using Mentha aquatica leaf extract as a reducing and capping agent. The nanocomposites were systematically characterized to determine their crystallographic structure, chemical composition, morphological features, thermal stability, and luminescence properties. X-ray diffraction (XRD) was employed to assess the crystal structure and phase purity, while FTIR spectroscopy and X-ray photoelectron spectroscopy (XPS) provided insights into the chemical bonding and surface states. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX) were used to examine the nanoscale morphology and bulk elemental distribution. Atomic force microscopy (AFM) offered additional topographical information, and thermogravimetric analysis (TGA) evaluated the thermal stability. Zetasizer Nano and zeta potential measurements were conducted to assess particle size distribution and colloidal stability, respectively. Photoluminescence (PL) studies were performed to explore the optical and electronic properties of the nanocomposites. The nanocomposite was applied to the catalytic reduction of methylene blue, with a deep neural network model predicting degradation efficiency based on variables including catalyst mass, NaBH4 concentration, MB concentration, and reaction time. The model demonstrated excellent predictive accuracy (R² = 0.99), with RMSE, MAE, and MSE values of 1.95, 1.71, and 3.83, respectively. Kinetic studies showed that methylene blue degradation increased with catalyst mass and NaBH4 concentration but decreased at higher MB concentrations. Thermodynamic analysis indicated that the process was endothermic, involving physical adsorption on the catalyst surface, and led to increased system order.