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In this work, a polyoxometalate (POM) incorporated polymer monolith microextraction (PMME) was successfully proposed and employed in the selective extraction of basic antidepressants in undiluted urine sample. This hybrid monolith exhibited strong cation-exchange interaction (SCX) with positively charged antidepressants when pH was 3.0, because of the multiple ionizable moieties on polyanionic POM. As such, antidepressants in complex sample matrices were efficiently extracted by the monolith, and the matrix effect was significantly reduced. In addition, due to the high amount of anionic POM, the monolith exhibited remarkable extraction capacities for target antidepressants ranging from 4.7 to 5.8mg/g. Further, the POM incorporated PMME was coupled with high-performance liquid chromatography-ultraviolet (HPLC-UV). Thus, antidepressants in undiluted urine sample was efficiently extracted under optimized extraction conditions online. The limits of detection (LODs) for the target antidepressants ranged from 0.7 to 1.4ng/mL, and the linear range was 5-1000ng/mL with determination coefficients (R 2 ) higher than 0.9960. The recoveries ranged from 86.8% to 104.0% with relative standard deviations (RSDs) of 0.4-10.1%. The proposed procedure was successfully applied to determine antidepressant in human urine. Taken together, the developed method presented a new strategy for the analysis of basic drugs in undiluted urine sample, which could be used for monitoring medicines in pharmacokinetic analysis., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Appropriate sample preparations prior to analysis can significantly enhance the sensitivity of ambient ionization techniques, especially during the enrichment or purification of analytes in the presence of complex biological matrix. Here in, we developed a rapid analysis method by the combination of thin film microextraction (TFME) and desorption corona beam ionization (DCBI) for the determination of antidepressants in human plasma. Thin films used for extraction consisted of sub-micron sized highly ordered mesoporous silica-carbon composite fibers (OMSCFs), simply prepared by electrospinning and subsequent carbonization. Typically, OMSCFs thin film was immersed into the diluted plasma for extraction of target analytes and then directly subjected to the DCBI-MS for detection. Size-exclusion effect of mesopores contributed to avoid of the protein precipitation step prior to extraction. Mass transfer was benefited from high surface-to-volume ratio which is attributed to macroporous network and ordered mesostructures. Moreover, the OMSCFs provided mixed-mode hydrophobic/ion-exchange interactions towards target analytes. Thus, the detection sensitivity was greatly improved due to effective enrichment of the target analytes and elimination of matrix interferences. After optimization of several parameters related to extraction performance, the proposed method was eventually applied for the determination of three antidepressants in human plasma. The calibration curves were plotted in the range of 5-1000 ng/mL with acceptable linearity (R(2) >0.983). The limits of detection (S/N=3) of three antidepressants were in ranges of 0.3-1 ng/mL. Reproducibility was achieved with RSD less than 17.6% and the relative recoveries were in ranges of 83.6-116.9%. Taken together, TFME-DCBI-MS method offers a powerful capacity for rapid analysis to achieve much-improved sensitivity., (Copyright © 2016 Elsevier B.V. All rights reserved.)

In this study, we proposed a method to fabricate magnetic carbon nitride (CN) nanosheets by simple physical blending. Low-cost CN nanosheets prepared by urea possessed a highly π-conjugated structure; therefore the obtained composites were employed as magnetic solid-phase extraction (MSPE) sorbent for extraction of polycyclic aromatic hydrocarbons (PAHs) in edible oil samples. Moreover, sample pre-treatment time could be carried out within 10 min. Thus, a simple and cheap method for the analysis of PAHs in edible oil samples was established by coupling magnetic CN nanosheets-based MSPE with gas chromatography-mass spectrometry (GC/MS) analysis. Limits of quantitation (LOQs) for eight PAHs ranged from 0.4 to 0.9 ng/g. The intra- and inter-day relative standard deviations (RSDs) were less than 15.0%. The recoveries of PAHs for spiked soybean oil samples ranged from 91.0% to 124.1%, with RSDs of less than 10.2%. Taken together, the proposed method offers a simple and cost-effective option for the convenient analysis of PAHs in oil samples., (Copyright © 2015 Elsevier B.V. All rights reserved.)

In this study, polyaniline coated SiO2 nanofibers (PANI/SiO2) was prepared by combining electrospinning technique with in-situ polymerization. The proposed strategy for the preparation of PANI/SiO2 can eliminate the aggregation of PANI and the yield of PANI/SiO2 was high. Scanning electron microscopy (SEM) images showed that PANI nanoparticles were uniformly coated on the surface of SiO2 nanofibers. The as-prepared PANI/SiO2 nanofibers were then applied as the sorbent for in-syringe dispersive solid-phase extraction (dSPE) for the extraction of fluoroquinolones (FQs) from honey samples. The influence of SiO2 amount on the formation of PANI/SiO2 and several parameters that affect the extraction efficiency were investigated. Under optimized conditions, a rapid, simple and effective method for the determination of FQs in honey sample was developed by coupling with liquid chromatography-fluorescence detector (LC-FLD) analysis. Due to the fast extraction equilibrium, the whole sample pretreatment process could be accomplished within 4 min. The limits of detection (LODs) for the target FQs were found to be 0.1-1.3 ng/g. The recoveries in honey sample were in the range of 81.4-118.1% with the RSDs of 0.8-14.4% (intra-day) and 1.4-14.9% (inter-day). This study offers a new strategy for the preparation of functional SiO2 nanofibers using post-electrospinning modification by in-situ polymerization, which could be generally applied in the preparation of various separation materials with electrospun nanofibers., (Copyright © 2015 Elsevier B.V. All rights reserved.)