This study evaluates the effectiveness of MnFe2O4/g-C3N4 spinel ferrites nanoparticles, synthesized using Chrysopogon zizanioides (C. zizanioides) root powder as the base material, in removing Pb (II) from contaminated water through the adsorption process. Firstly, the nanoparticles were synthesized using the co-precipitation method. The MnFe2O4/g-C3N4 nanoparticles were characterized using various techniques, such as Fourier-transform infrared spectroscopy (FTIR), high-resolution transmission electron microscopy (HR-TEM), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), Brunauer–Emmett–Teller (BET) surface area analysis, dynamic light scattering (DLS), zeta potential measurement, Raman spectroscopy (RAMAN), and vibrating sample magnetometry (VSM). These analyses confirmed the successful synthesis and revealed the magnetic properties of the nanoadsorbent.

The adsorption capacity of the C. zizanioides/MnFe2O4/g-C3N4 nanoparticles was then tested, demonstrating a high removal efficiency for Pb (II) from contaminated water. The effects of various parameters on the adsorption process, including pH, adsorbent dosage, contact time, initial Pb (II) concentration, and temperature, were investigated. Kinetic studies revealed that the adsorption process followed a pseudo-second-order model with a coefficient of determination of 97.32%, indicating a strong correlation. Additionally, the Freundlich isotherm model best described the adsorption of Pb (II) by the nanoparticles. Thermodynamic studies indicated that the adsorption of Pb (II) was an endothermic and spontaneous process. This study also examined the desorption capability and reusability of the nanoadsorbent over multiple cycles, finding that it could be effectively regenerated and reused, with a high percentage of recovery of adsorbed Pb (II). Additionally, a machine learning algorithm was developed to predict and optimize the adsorption process, providing further insights and improving the efficiency of Pb (II) removal.