Journal of Materials Science

, Volume 52, Issue 12, pp 7067–7076 | Cite as

Vacancy-mediated ferromagnetism in Co-implanted ZnO studied using a slow positron beam

  • D. D. Wang
  • B. Zhao
  • N. Qi
  • Z. Q. ChenEmail author
  • A. Kawasuso
Original Paper


Co\(^+\) ions with multiple energies from 50 to 380 keV were implanted into ZnO single crystals up to a total dose of \(1.25\times 10^{17}\,\hbox {cm}^2\). The implanted samples were annealed in open air for 30 min between 200 and 1100 \(^{\circ }\)C. All the samples before and after implantation and annealing were characterized by X-ray diffraction (XRD), Raman scattering and positron annihilation measurements. XRD and Raman scattering measurements indicate that Co implantation induces severe lattice damage, and after annealing the damage recovers gradually. No Co clusters or Co-related second phase was observed in the implanted samples. Doppler broadening of positron annihilation radiation measurements using a slow positron beam reveals a large number of vacancy clusters introduced by Co implantation. After annealing up to 1000 \(^{\circ }\)C, almost all the defects induced by implantation are removed. The implanted samples show clear ferromagnetism measured at 5 K. It shows very slight decrease after annealing at 700 \(^{\circ }\)C and becomes much weaker after annealing at 1000 \(^{\circ }\)C. The origin of ferromagnetism is most probably due to substitution of Co\(^+\) ions at Zn lattice sites. However, it is apparent that the decrease in magnetization after annealing is consistent with the vacancy recovery process, indicating that the ferromagnetism in Co-implanted ZnO is mediated by defects such as Zn vacancy (V\(_{Zn}\)) or vacancy clusters. First principles calculations also support that Zn-related monovacancies and vacancy clusters can enhance the ferromagnetism in Co-doped ZnO.


Positron Annihilation Vacancy Cluster Vacancy Defect Positron Energy Positron Annihilation Spectroscopy 
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 work was supported by the National Natural Science Foundation of China under Grant Nos. 11305117, 11475130 and 11575131 and by Henan University of Science and Technology (Grant No. 2015GJB014).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • D. D. Wang
    • 1
    • 2
  • B. Zhao
    • 2
  • N. Qi
    • 2
  • Z. Q. Chen
    • 2
    Email author
  • A. Kawasuso
    • 3
  1. 1.School of Physics and EngineeringHenan University of Science and TechnologyLuoyangChina
  2. 2.Hubei Nuclear Solid Physics Key Laboratory, Department of PhysicsWuhan UniversityWuhanChina
  3. 3.Advanced Science Research CenterJapan Atomic Energy AgencyTakasakiJapan

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