ABSTRACT
CONTEXT: Zeolites have attracted attention for their potential in adsorbing environmental contaminants. However, contaminants, such as acaricides used extensively in livestock production to control ticks and mites, have received limited exploration regarding their adsorption onto zeolite surfaces. This study aimed to identify the most appropriate zeolite frameworks for the adsorption of acaricide residues, deduce the mechanism underlying the adsorption process, and evaluate the impact of surface modification on the adsorption capabilities of zeolites. METHODS: Grand Canonical Monte Carlo (GCMC) was used to screen the entire zeolite database to analyze their adsorption properties, where the cloverite zeolite framework (CLO) exhibits the highest adsorption capacity (percentage weight, 54%). Machine learning was employed to rank structural feature importance on adsorption. Density and helium void fraction appeared to be the most important structural features. Thus, engineering these features is of utmost significance in harvesting the desired acaricides. The second step involved engineering the structural and electronic properties of the shortlisted zeolite frameworks via cation substitution with suitable atoms. DFT calculations involving natural bond orbital (NBO) analysis and quantum theory of atoms in molecules (QTAIM) have been done to understand the influence of cation substitution on the electronic structure.