Hierarchical MXene@ZIF-67 heterostructure for selective adsorption of tetracycline antibiotics: Synergistic mechanism and application in SPME Arrow.

The selective determination of tetracycline antibiotics (TCs) in animal-derived food matrices remains challenging due to severe matrix interference and insufficient extraction specificity. Efficient sample pretreatment materials with high selectivity and stability are therefore urgently required to enable reliable trace-level analysis. In this study, a MXene@ZIF-67 heterostructure was rationally designed and applied for selective adsorption and enrichment of TCs. The MXene@ZIF-67 composite was synthesized via in-situ growth of ZIF-67 on MXene nanosheets and systematically characterized. Adsorption experiments combined with kinetic, isotherm, spectroscopic analyses, and density functional theory calculations revealed that the enhanced selectivity originates from synergistic interactions, including surface complexation, π-π interactions, and electrostatic attraction. The optimized MXene@ZIF-67-25% material was further integrated into a solid-phase microextraction (SPME) Arrow coating via electrospinning. Coupled with high performance liquid chromatography-ultraviolet detection, the developed method exhibited good linearity (15-1000 μg L-1), with limits of detection of 5-7 μg L-1. The method showed satisfactory precision with intra-day and inter-day relative standard deviations of 5.5%-8.9% and 6.3%-11.2%, respectively, and maintained stable performance over 200 extraction-desorption cycles. The developed SPME Arrow-based analytical platform demonstrated strong resistance to matrix interference and reliable applicability in real animal-derived food samples. This work not only provides mechanistic insight into the design of MXene@MOF heterostructures for selective adsorption, but also establishes a robust and efficient sample pretreatment strategy for the sensitive determination of antibiotic residues in complex food matrices, highlighting its potential for food safety monitoring applications.