Development and application of metal organic framework/chitosan foams based on ultrasound-assisted solid-phase extraction coupling to UPLC-MS/MS for the determination of five parabens in water.

In this work, a variety of highly porous metal organic framework/chitosan (MOF/CS) foams (MIL-53(Al)/CS, MIL-53(Fe)/CS, MIL-101(Cr)/CS, MIL-101(Fe)/CS, UiO-66(Zr)/CS, and MIL-100(Fe)/CS) were designed and prepared by an ice-templating process. The introduction of MOFs made these foams achieve excellent inherent characters in terms of strength, stability, and adsorption ability. The MOFs incorporated in the foams retained their unique properties. Additionally, the foams were durable and their adsorption abilities had only a little loss after being recycled several times. MIL-53(Al)/CS foam was selected as an adsorbent candidate to develop an ultrasound-assisted solid-phase extraction (UA-SPE) method for the first time, owing to its particularly noteworthy performance among the prepared MOF/CS foams. The method was then successfully applied to extract trace amount of five parabens (methylparaben, ethylparaben, propylparaben, butylparaben, benzylparaben) in water samples, followed by ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) detection. Several experimental parameters were investigated. Under the optimal conditions, the linear ranges were 0.5-200 μg/L with regression coefficients (r2) from 0.9948 to 0.9983. The method detection limits were between 0.09 and 0.45 μg/L. The recoveries ranged from 78.75 to 102.1% with relative standard deviations (RSDs) < 7.4%. Furthermore, the molecular interactions and free binding energies between MOFs and parabens were calculated by means of molecular docking to explain the adsorption mechanism deeply. The novel method proposed in this work exhibited many benefits such as easy operation, high enrichment efficiency, less solvent consuming, and higher sensitivity. Such a strategy would expand the application prospect of MOFs in sample pretreatment. Graphical abstract ᅟ.