Journal of Materials Science

, Volume 47, Issue 4, pp 1737–1744 | Cite as

The preparation of carbon-encapsulated Fe/Co nanoparticles and their novel applications as bifunctional catalysts to promote the redox reaction for p-nitrophenol

  • Jun Xue
  • Houkui Xiang
  • Kaipeng Wang
  • Xiaorong Zhang
  • Shengjun Wang
  • Xuehua Wang
  • Hong Cao


As a common organic pollutant in industrial and agricultural wastewater, p-Nitrophenol (p-NP) is difficult to be degraded naturally. Though, various methods have been developed for degradation of p-NP, the utilization of catalysts to electrochemically degrade p-NP became a novel effective way. In this article, two magnetic nanoparticles (carbon-encapsulated iron, Fe/C; carbon-encapsulated cobalt, Co/C) were prepared. Through a series of physical phase characterization, we found that the average dimension of the prepared magnetic nanoparticles arrived at 60 nm for Fe/C and 80 nm for Co/C, and within such small dimensions, the prepared nanoparticles might have some remarkable catalytic characters in electrochemical degradation of p-NP. Therefore, two novel types of Fe/C and Co/C modified glassy carbon electrodes were fabricated to investigate their catalytic activity for p-NP degradation. In our results, both of the two modified electrodes showed favorable stability and excellent electro-catalytic activity for p-NP degradation. In addition, two modified electrodes also exhibited favorable electro-catalytic reductive ability for p-NP. The electrochemical reactions on the surface of the two modified electrodes were all diffusion controlled processes. Therefore, Fe/C and Co/C were excellent bifunctional catalysts which could be considered as a practical way to be applied in industrial hydrogenation and oxidative degradation of organic compounds.


Glassy Carbon Electrode Oxidation Peak Oxidation Peak Current Carbon Shell Reduction Peak Current 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by the Natural Science Foundation of China (Grant No. 50774071) and the Research Foundation of Education Bureau of Hubei Province, China (Grant No. D20091505).


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jun Xue
    • 1
    • 2
  • Houkui Xiang
    • 1
  • Kaipeng Wang
    • 1
  • Xiaorong Zhang
    • 1
  • Shengjun Wang
    • 1
  • Xuehua Wang
    • 1
  • Hong Cao
    • 1
  1. 1.School of Materials Science and EngineeringWuhan Institute of TechnologyWuhanChina
  2. 2.Wuhan Research Institute of Materials ProtectionWuhanChina

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