4.7 Article

Direct growth of MnOOH nanorod arrays on a carbon cloth for high-performance non-enzymatic hydrogen peroxide sensing

期刊

ANALYTICA CHIMICA ACTA
卷 913, 期 -, 页码 128-136

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2016.01.055

关键词

Manganite; Carbon cloth; Nanorod array; Non-enzymatic sensor; Hydrogen peroxide

资金

  1. NSFC [51572040, 51402112]
  2. Chongqing University Postgraduates' Innovation Project [CYB15044]
  3. National High Technology Research and Development Program (863 program) of China [2015AA034801]
  4. Fundamental Research Funds for the Central Universities [CQDXWL-2014-001, CQDXWL-2013-012]
  5. large-scale equipment sharing fund of Chongqing University

向作者/读者索取更多资源

Novel MnOOH nanorod arrays directly growing on a flexible carbon cloth substrate (MnOOH/CC) is first synthesized through a facile hydrothermal technique and utilized as an electrocatalyst for nonenzymatic detection of hydrogen peroxide. The as-prepared MnOOH nanorods are uniformly distributed on the carbon cloth with a 3D porous network structure, which provides a high specific surface area and numerous electroactive sites. The electrode based on the carbon cloth-supported MnOOH nanorod arrays exhibits a higher sensitivity (692.42 mA mM(-1) cm(-2)) and a wider linear range (20 mu m-9.67 mM) with a detection limit of 3.2 mu M (S/N = 3) compared with the electrode based on the rigid graphite substrate supported the random distributed MnOOH nanorods. Further, the MnOOH/CC possesses an outstanding flexibility and can conveniently be assembled into the required shape for a specific use, thus the arc-shaped MnOOH/CC electrodes are fabricated whose electrocatalytic activity toward the hydrogen peroxide reduction remains nearly unchanged in comparison with the unbent state. Due to its excellent sensitivity, reproducibility, anti-interference and stability, the electrode based on the carbon clothsupported MnOOH nanorod arrays is believed to be promising for applications in high efficiency flexible hydrogen peroxide sensing. (C) 2016 Elsevier B. V. All rights reserved.

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