Adsorption characteristics of metronidazole on CoZr-LDH and its GO nanocomposite: Experimental and theoretical study

CoZr-LDH and its Graphene Oxide (GO) nanocomposite were synthesized by mechanochemical and hydrothermal methods, respectively, and were applied to absorb metronidazole as a pollutant antibiotic from an aqueous solution. In this study, Tetravalent and divalent metal cations were employed. The LDH and LDH/GO were characterized by SEM, EDS, IR, BET, and XRD analyses. In the adsorption process of metronidazole using both the CoZr-LDH and CoZr-LDH/GO nanocomposite as adsorbents, parameters such as initial solution pH, adsorbent dose, initial drug concentration, and contact time were optimized to obtain the maximum adsorption capacity. The adsorption performance of CoZr-LDH and its nanocomposite with graphene oxide (GO) was thoroughly investigated for the removal of metronidazole (MNZ), a significant environmental contaminant. CoZr-LDH demonstrated optimal adsorption, yielding an experimental qe value of 189.1376 mg g-1. Conversely, the CoZr-LDH/GO nanocomposite exhibited superior adsorption performance under optimum conditions, achieving a remarkable experimental qe value of 906.5688 mg g-1. The adsorption isotherms, including Langmuir, Freundlich, and Redlich-Peterson models, were fitted to the experimental data to describe the interaction mechanisms and surface characteristics. Additionally, kinetic studies involving pseudo-first-order and pseudo-second-order models were performed, revealing critical insights into the adsorption process. Post-adsorption characterization confirmed the successful interaction between MNZ and the adsorbents. This study emphasizes the potential of GO-enhanced CoZr-LDH for effective removal of metronidazole, addressing its status as a critical pollutant in wastewater. To comprehensively study the adsorption process in CoZr-LDH and CoZr-LDH/GO, theoretical analyses were conducted using Monte Carlo computational method. This approach, known for simulating numerous particle trajectories and statistical averaging, allowed for an in-depth examination of adsorption mechanisms under various conditions. The findings, validated against experimental data, enhanced model accuracy and provided deeper insights into adsorption behaviors in complex environments.