4.6 Article

High-capacity magnetic hollow porous molecularly imprinted polymers for specific extraction of protocatechuic acid

期刊

JOURNAL OF CHROMATOGRAPHY A
卷 1404, 期 -, 页码 21-27

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.chroma.2015.05.038

关键词

Molecularly imprinted polymers; Hollow porous structure; Magnetic separation; Protocatechuic acid; Complex matrices

资金

  1. National Natural Science Foundation of China [21275163]
  2. Science and Technology Program of Hunan Province, China [2014RS4004]
  3. China Postdoctoral Science Foundation [2014M550426]
  4. Postdoctoral Science Foundation of Central South University
  5. Shenghua Yuying project of Central South University

向作者/读者索取更多资源

Magnetic hollow porous molecularly imprinted polymers (HPMIPs) with high binding capacity, fast mass transfer, and easy magnetic separation have been fabricated for the first time. In this method, HPMIPs was firstly synthesized using protocatechuic acid (PCA) as template, 4-vinylpyridine (4-VP) as functional monomer, glycidilmethacrylate (GMA) as co-monomer, and MCM-48 as sacrificial support. After that, epoxide ring of GMA was opened for chemisorbing Fe3O4 nanoparticles to prepare magnetic HPMIPs. The results of characterization indicated that magnetic HPMIPs exhibited large surface area (548 m(2)/g) with hollow porous structure and magnetic sensitivity (magnetic saturation at 2.9 emu/g). The following adsorption characteristics investigation exhibited surprisingly higher adsorption capacity (37.7 mg/g), and faster kinetic binding (25 min) than any previously reported PCA imprinted MIPs by traditional or surface imprinting technology. The equilibrium data fitted well to Langmuir equation and the adsorption process could be described by pseudo-second order model. The selective recognition experiments also demonstrated the high selectivity of magnetic HPMIPs towards PCA over analogues. The results of the real sample analysis confirmed the superiority of the proposed magnetic HPMIPs for selective and efficient enrichment of trace PCA from complex matrices. (C) 2015 Elsevier B.V. All rights reserved.

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