Effective Arsenite Removal Using Green‐Synthesized Betel Nano Ceria: RSM and ANN Optimization With Structural Characterization

ABSTRACT Arsenite contamination of groundwater is a major environmental and public health concern, which creates an urgent need for sustainable and effective treatment methods. This study highlights the synthesis of cerium oxide nanoparticles, that is betel nano ceria (BNC), using betel leaf extract to evaluate arsenite removal from water. Batch adsorption studies were conducted with the help of response surface methodology (RSM) combined with artificial neural network (ANN) to optimize the process parameters and improve adsorption performance. The optimum conditions were found at 10 mg/L concentration, 100 mg/100 mL dose, and 60‐min contact time, with a maximum removal capacity of 18.22 mg/g and removal efficiency of 98.35%. Adsorption efficiency increased with concentration and dose up to the optimum level, and then stabilized due to saturation of active sites and nanoparticle accumulation. Both RSM and ANN models were applied to predict removal efficiency, with RSM showing slightly higher accuracy ( R 2 = 0.994). Characterization of the BNC using GC‐MS, FTIR, FESEM‐EDX, and XRD confirmed the formation of highly crystalline, porous CeO 2 nanoparticles having a surface area of 43.406 m 2 /g, average pore volume of 0.04 cm 3 /g, pore radius of 1.80 nm and average particle size of 12.2 nm. This study reveals that betel leaf‐derived cerium oxide nanoparticles are green, efficient adsorbent for the removal of arsenite, showing strong potential for practical application in real‐world water purification systems.