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Journal of Materials Science

, Volume 51, Issue 4, pp 1978–1984 | Cite as

Synthesis and characterization of CuInS2 nanocrystalline semiconductor prepared by high-energy milling

  • Erika Dutková
  • María J. Sayagués
  • Jaroslav Briančin
  • Anna Zorkovská
  • Zdenka Bujňáková
  • Jaroslav Kováč
  • Jaroslav KováčJr.
  • Peter Baláž
  • Jana Ficeriová
Original Paper

Abstract

Nanocrystalline CuInS2 particles have been synthesized from copper, indium, and sulfur powders by high-energy milling in a planetary mill in an argon atmosphere. Structural characterization of the prepared nanoparticles, including phase identification, Raman spectroscopy, specific surface area measurement, and particle size analysis were performed. The optical properties were studied using UV–Vis absorption and photoluminescence (PL) spectroscopy. The production of CuInS2 (JCPDS 027-0159) particles with a crystallite size of about 17.5–23.5 nm was confirmed by X-ray diffraction. The crystal structure has a tetragonal body-centered symmetry belonging to the I-42d space group. The Raman spectra also proved the formation of pure CuInS2 nanoparticles. TEM and HRTEM measurements revealed the presence of nanoparticles of different dimensions (10–20 nm) and their tendency to form agglomerates. The nanoparticles tend to agglomerate due to their large specific surface area. The average size of the synthesized particles was determined by photon cross-correlation spectroscopy to be in the range of 330–530 nm (bimodal size distribution). The band gap of the CuInS2 particles is 2 eV which is wider than that in bulk materials. The decrease in size leads to the blue-shift of the PL spectra. Therefore, CuInS2 nanoparticles are promising candidates for optical applications, and they have high potential in solar energy conversion.

Keywords

Milling Chalcopyrite Mechanochemical Synthesis Bimodal Size Distribution Planet Carrier 
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.

Notes

Acknowledgements

The support through the Slovak Grant Agency VEGA (projects 2/0027/14, 1/0439/13, 2/0051/14) and APVV 14-0103 is gratefully acknowledged. The authors also acknowledge the support of the European Union through the CT-2011-1-REGPOT285895 AL-NANOFUN project (Advanced Laboratory for the Nano-Analysis of novel Functional materials), for the microscopy facilities sited at the Institute of Materials Science in Seville.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Erika Dutková
    • 1
  • María J. Sayagués
    • 2
  • Jaroslav Briančin
    • 1
  • Anna Zorkovská
    • 1
  • Zdenka Bujňáková
    • 1
  • Jaroslav Kováč
    • 3
  • Jaroslav KováčJr.
    • 3
  • Peter Baláž
    • 1
  • Jana Ficeriová
    • 1
  1. 1.Institute of GeotechnicsSlovak Academy of SciencesKosiceSlovakia
  2. 2.Institute of Material Science of Seville (CSIC-US)SevilleSpain
  3. 3.Institute of Electronics and PhotonicsSlovak University of Technology and International Laser CentreBratislavaSlovakia

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