4.7 Article

Sulfur vacancy promoted peroxidase-like activity of magnetic greigite (Fe3S4) for colorimetric detection of serum glucose

期刊

ANALYTICA CHIMICA ACTA
卷 1127, 期 -, 页码 246-255

出版社

ELSEVIER
DOI: 10.1016/j.aca.2020.06.056

关键词

Fe3S4 nanosheets; S-vacancy; Peroxidase-like activity; Glucose detection; Diabetes; Smartphone app

资金

  1. National Science Foundation of China [21707105, 21876125]
  2. Natural Science Foundation of Zhejiang Province [LY19B070010]
  3. Zhejiang Province Public Welfare Technology Application Research Project [LGF19B070008, LGF19B070009]
  4. Research and Development Fund of Wenzhou Medical University [QTJ16013]

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Herein, sulfur vacancies in magnetic greigite (SVs-Fe3S4) nanosheets were synthesized by a one-step solvothermal method by adjusting the ethylene glycol: water ratio. Electron paramagnetic resonance spectroscopy (EPR) and X-ray photoelectron spectroscopy (XPS) revealed that SV-rich Fe3S4 and SV-poor Fe3S4 were acquired using 100% ethylene glycol and 100% water as solvent, respectively. A peroxidase-like activity assay demonstrated that maximum reaction rates for H2O2-mediated oxidation of 3,3',5,5'-tetramethyl-benzidine (TMB) catalyzed by the SV-rich Fe3S4 was 2.3 times higher than SV-poor Fe3S4. Density functional theory (DFT) calculations and reactive oxygen species (ROS) detection confirmed that the enhanced peroxidase-like activity by SV-rich Fe3S4 was attributed to efficient adsorption of H2O2 and subsequent decomposition to hydroxyl radicals (center dot OH) on the SVs sites of Fe3S4. The SV-rich Fe3S4 nanozyme was employed to develop a simple, highly sensitive and selective assay for glucose detection with a linear range of 0.5-150 mu M and a detection limit of 0.1 mu M (S/N = 3). A smartphone application (App) was designed and applied to efficiently detect serum glucose with the integrated analytical system based on the SV-rich Fe3S4. These new findings highlight the important role of surface defects in nanozymes on generating peroxidase-like activity for glucose detection in point-of-care diagnosis. (C) 2020 Elsevier B.V. All rights reserved.

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