4.7 Article

Fe3O4/graphene molecularly imprinted composite for selective separation of catecholamine neurotransmitters and their analysis in rat brain tissues

Journal

TALANTA
Volume 224, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.talanta.2020.121843

Keywords

Magnetic surface molecularly imprinted polymers; Multi-targets recognizing function; Magnetic solid-phase extraction; Catecholamine neurotransmitters; Rat brain tissues

Funding

  1. National Natural Science Foundation of China [81773694]

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A novel of magnetic molecularly imprinted polymers with multi-targets recognizing function was successfully prepared in this study, demonstrating high efficiency in recognizing and separating catecholamine neurotransmitters. The combination of this polymer with UPLC-MS/MS detection after solid-phase extraction showed accurate determination of target substances in complex biological matrices.
In this study, a novel of magnetic molecularly imprinted polymers (Fe3O4/GO/DMIPs) with multi-targets recognizing function were prepared by surface molecular imprinting technique adopting isoprenaline as the dummy-template molecule and graphene oxide (GO) as the carrier. The morphology, structures and magnetic properties of nanosorbents were characterized and assessed in detail and the results indicated that the 3D recognition cavities and matching functional groups with catecholamine neurotransmitters (CNs) were successfully fabricated on Fe3O4/GO surface. Moreover, the kinetic, isothermal and selective adsorption experiments were conducted to further reveal the adsorption behavior of adsorbent toward CNs and the results showed that the Fe3O4/GO/DMIPs possessed high adsorption capacity, rapid binding rate and excellent selectivity for CNs. On this basis, the Fe3O4/GO/DMIPs were further applied as adsorbent of magnetic solid-phase extraction (MSPE) for selective recognition and separation of CNs (dopamine, epinephrine, norepinephrine) followed by UPLC-MS/MS detection. The crucial parameters affecting the extraction efficiency were systematically optimized by Box-Behnken statistical design. Under the optimum conditions, satisfactory linearity (r > 0.99) was obtained with the lower limit of quantification from 0.53 to 1.93 ng mL(-1). The accuracy (RE) ranged from -7.6% to 6.4% and the intra- and inter-day precisions were not more than 8.7% and 10.2%, respectively. Hence, the strategy proposed in this study might be used for high selectivity recognition and determination of CNs in complex biological matrices, which would provide a basis and reference for its application in the fields of separation and clinical monitoring.

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