Green MnFe₂O₄/g-C₃N₄ Nanoparticles for Pb(II) Treatment in Contaminated Water: Experimental Insights and Machine Learning Integration
Saba Madadgar, Faramarz Doulati Ardejani, Zohreh Boroumand, Christoph Butscher, Reza Taherdangkoo
Abstract
Abstract This study evaluates the effectiveness of MnFe 2 O 4 /g-C 3 N 4 spinel ferrites nanoparticles, synthesized using Chrysopogon zizanioides root powder, in removing Pb (II) ions from contaminated water through the adsorption process. The nanoparticles were synthesized using the co-precipitation method. Firstly, the CZ/MnFe 2 O 4 /g-C 3 N 4 nanoparticles (CZMNCN) were characterized using various techniques such as Fourier-transform infrared spectroscopy (FTIR), 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). The analyses confirmed the successful synthesis and the magnetic properties of the nanosorbent. The adsorption capacity of the CZ/MnFe 2 O 4 /g-C 3 N 4 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. The study also examined the desorption capability and reusability of the nanosorbent 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, XGBoost tuned with Artificial Rabbits Optimization, was developed to predict the adsorption process, providing a robust tool for future use and improving the efficiency of Pb (II) removal.