4.7 Review

Preparation and applications of metal-organic framework derived porous carbons as novel adsorbents in sample preparation

期刊

TRAC-TRENDS IN ANALYTICAL CHEMISTRY
卷 133, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.trac.2020.116093

关键词

MOF-derived magnetic porous carbons; Sample preparation; Solid phase extraction; Magnetic solid phase extraction; Solid phase microextraction

资金

  1. National Natural Science Foundation of China [21575148, 21505146, 21575149]

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Sample pretreatment is an important prerequisite for analyzing complex samples due to the low concentration of targets and numerous interferences in sample matrix. As a group of emerging absorbents, metal-organic frameworks (MOFs)-derived porous carbons (MPCs), obtained by high-temperature pyrolysis of MOFs, have been brought into stage recently due to their intrinsic advantages such as high porosity, permanent nanoscale cavities and open channels. The morphology of the original MOF precursor is well maintained after calcination produces. Furthermore, by directly calcinating MOFs comprised of magnetic metal centers (Co, Fe, Ni), magnetic porous carbon with excellent magnetism and separation efficiency can be fabricated. The uniformly distributed magnetic metal nanoparticles in the carbon matrix is helpful to reduce the agglomeration problem of magnetic nanoparticles and the porosity of carbon materials inherited from MOFs is beneficial to evaluate the adsorptive property. So MPCs has been widely used in sample preparation of environmental, biological samples and food matrices. In this review, we summarize the performances and different process for the preparation of MPCs. Moreover, this review mainly focuses on providing a detailed discussion about the application of MPCs for extracting analytes from complex matrix in different sample pretreatment modes such as solid-phase extraction, magnetic solid phase extraction and solid-phase microextraction. In addition, the efforts recently on the automation of magnetic solid phase extraction processes by means of MPCs are also described. (C) 2020 Published by Elsevier B.V.

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