DFT-guided mixed-linker UiO-66 MOFs for selective SPE-LC-MS/MS determination of emerging organic pollutants

Emerging organic pollutants (EOPs), such as bisphenol A (BPA), tetracycline (TC), and carbamazepine (CBZ), are widely detected in aquatic environments and pose significant risks due to their persistence and biological activity. Their trace-level occurrence in complex matrices requires highly sensitive and selective analytical methods. Solid-phase extraction (SPE) is commonly used for sample preparation; however, conventional polymeric and silica-based sorbents often suffer from limited selectivity and strong matrix interferences. Although metal-organic frameworks (MOFs) offer tunable structures for improved adsorption, their design is still largely empirical. Therefore, a rational strategy is needed to develop high-performance SPE sorbents for efficient extraction of diverse EOPs.

In this study, a mixed-linker UiO-66-NH₂/Py MOF was rationally designed using density functional theory (DFT). Adsorption energy calculations (-31.7 to -44.3 kcal mol^-1) and noncovalent interaction analysis identified an optimal BDC:NH₂-BDC:Py-BDC ratio of 1:1:2, enabling synergistic hydrogen bonding and π-π interactions. The synthesized MOF was applied as an SPE sorbent coupled with LC-MS/MS for EOP determination. The method achieved high enrichment factors (245-278), low detection limits (0.008-0.032 μg L^-1), and excellent recoveries (92.3-97.8%, RSD ≤6%). Matrix effects were significantly reduced (-3.1% to -12.4%) compared to direct injection. The sorbent also demonstrated good reusability, maintaining over 92% recovery after ten cycles. Application to river water samples from southern Vietnam revealed EOP concentrations ranging from 0.04 to 0.32 μg L^-1.

This work presents a DFT-guided design strategy for mixed-linker MOF sorbents, bridging molecular-level prediction with practical analytical application. Compared to conventional materials, the developed SPE platform offers enhanced selectivity, sensitivity, and matrix tolerance. The proposed approach provides a scalable and effective pathway for the development of next-generation MOF-based sample preparation techniques in environmental analysis.