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Applied Physics A

, Volume 115, Issue 3, pp 953–960 | Cite as

Growth mechanism studies of ZnO nanowire arrays via hydrothermal method

  • Clotaire Chevalier-César
  • Martine Capochichi-Gnambodoe
  • Yamin Leprince-Wang
Article

Abstract

Well-controlled ZnO nanowire arrays have been synthesized using the hydrothermal method, a low temperature and low cost synthesis method. The process consists of two steps: the ZnO buffer layer deposition on the substrate by spin-coating and the growth of the ZnO nanowire array on the seed layer. We demonstrated that the microstructure and the morphology of the ZnO nanowire arrays can be significantly influenced by the main parameters of the hydrothermal method, such as pH value of the aqueous solution, growth time, and solution temperature during the ZnO nanowire growth. Scanning electron microscopy observations showed that the well oriented and homogeneous ZnO nanowire arrays can be obtained with the optimized synthesis parameters. Both x-ray diffraction spectra and high-resolution transmission electron microscopy (HRTEM) observations revealed a preferred orientation of ZnO nanowires toward the c-axis of the hexagonal Wurtzite structure, and HRTEM images also showed an excellent monocrystallinity of the as-grown ZnO nanowires. For a deposition temperature of 90 °C, two growth stages have been identified during the growth process with the rates of 10 and 3 nm/min, respectively, at the beginning and the end of the nanowire growth. The ZnO nanowires obtained with the optimized growth parameters owning a high aspect ratio about 20. We noticed that the starting temperature of seed layer can seriously influence the nanowire growth morphology; two possible growth mechanisms have been proposed for the seed layer dipped in the solution at room temperature and at a high temperature, respectively.

Keywords

Growth Time Seed Layer Nanowire Array Growth Solution HMTA 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Clotaire Chevalier-César
    • 1
  • Martine Capochichi-Gnambodoe
    • 1
  • Yamin Leprince-Wang
    • 1
  1. 1.Université Paris-Est, Laboratoire Physique des Matériaux Divisés et des Interfaces (LPMDI-EA 7264)UPEMLVMarne-la ValléeFrance

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