Modeling the Liquid-Phase Adsorption of Cephalexin onto Coated Iron Nanoparticles Using Response Surface and Molecular Modeling

Abstract

In order to assess the interactions between process factors, the experiments involving the liquid-phase adsorption of cephalexin (CEX) onto silicon-coated iron nanoparticles (Fe3O4@SIO2) were designed using the Box-Behnken Design-Response surface methodology (BBD-RSM). Optimal circumstances were used to investigate the synergistic influence on the process’s efficiency. In addition, the data was used to test and fit an artificial neural network (ANN) model. Molecular-level DFT calculations on the CEX molecule were carried out. The PW6B95D3/Def2-TZVP level of theory was used to build DFT-based descriptors for the CEX molecule. At 25°C, pH 5.83, 37.67 min, a dosage of 0.8 g Fe3O4@SIO2 and 118.01 mg/L CEX, the removal efficiency achieved a maximum of 99.01 percent. For example, we found that OH --- O, NH --- O, CH --- O hydrogen bonds, NH --- π, OH --- π, CH --- π interactions as well as dipole-dipole interactions between CEX and the nanoparticles could all be used to connect the CEX and the nanoparticles. There is a strong correlation between the output and target values acquired by BBD-RSM and ANN fits. Fe3O4@SIO2 proved to be an excellent tool for eliminating CEX.

Cephalexin Adsorption by Acidic Pretreated Jackfruit Adsorbent: A Deep Learning Prediction Model Study

“…Several studies have been conducted on antibiotic adsorption; however, no studies have investigated the efficiency of APJPA to remove CFX [20][21][22][23]. Furthermore, most studies used RSM for optimization, which evaluates the removal efficiency based on the coefficient (R 2 ).…”