Journal of Materials Science

, Volume 52, Issue 7, pp 3893–3905 | Cite as

Synthesis of NiCo2O4 nanoneedle@polypyrrole arrays supported on 3D graphene electrode for high-performance detection of trace Pb2+

  • Xiaobing Wei
  • Chao Wang
  • Peng Dou
  • Jiao Zheng
  • Zhenzhen Cao
  • Xinhua Xu
Original Paper


In this work, we reported the highly ordered NiCo2O4 nanoneedle@polypyrrole arrays anchored on three-dimensional graphene (NiCo2O4@PPy/3D graphene) for high-sensitivity detection of trace lead ions (Pb2+). The 3D graphene was prepared by a hydrothermal process and then decorated with NiCo2O4 nanoneedle arrays by another hydrothermal process. This two-step hydrothermal method is simple and mild. Furthermore, the highly conductive PPy was coated on NiCo2O4 via a chemical vapor-phase polymerization to prepare NiCo2O4@PPy/3D graphene. The free-standing NiCo2O4@PPy/3D graphene can be directly utilized as 3D electrochemical working electrode without being decorated onto working electrode like Au and glassy carbon electrode. High conductivity, large specific surface area, short ion diffusion path and excellent adsorption capacity of NiCo2O4@PPy/3D graphene efficiently improved electrochemical property for the detection of Pb2+. Using square-wave anodic stripping voltammetry (SWASV), a linear range between the currents and the concentrations of Pb2+ of 0.0125–0.709 μM with a high sensitivity of 115.621 μA μM−1 was obtained. The limit of detection can reach to 0.2 nM. In addition, the fabricated sensor of Pb2+ also had good selectivity, reproducibility and long-term stability. Finally, NiCo2O4@PPy/3D graphene electrode was utilized for determining Pb2+ in tap water sample using the standard addition method, revealing a promising application for the quantitative detection of certain concentration ranges of Pb2+ in real sample.


Graphite Oxide Deposition Potential NiCo2O4 Graphene Electrode Hydrothermal Time 
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 project was financially supported by the National Natural Science Foundation of China (Nos. 51143009 and 51273145).

Supplementary material

10853_2016_650_MOESM1_ESM.docx (13.6 mb)
Supplementary material 1 (DOCX 13915 kb)


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringTianjin UniversityTianjinPeople’s Republic of China
  2. 2.Tianjin Key Laboratory of Composite and Functional MaterialsTianjinPeople’s Republic of China

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