Large-Scale Ab Initio Study of Size, Shape, and Doping Effects on Electronic Structure of Nanocrystals

  • Jingbo LiEmail author
  • Su-Huai Wei
Part of the Lecture Notes in Nanoscale Science and Technology book series (LNNST, volume 5)


Semiconductor nanocrystals, such as quantum dots (QDs) and wires (QWs), often contain from a few thousands to more than 106 atoms. It has been a great challenge to calculate the electronic structure of these large nanosystems using self-consistent first-principles method. In this chapter, recent development of calculations of nanocrystal physical properties using the large-scale ab initio pseudopotential or charge-patching methods is reviewed. The calculated size-dependent exciton energies and absorption spectra of QDs and QWs are in good agreement with experiments. The calculated ratios of bandgap increases between QWs and QDs are found to be material-dependent, and for most direct bandgap materials, this ratio is close to 0.586, as predicted by simple effective-mass approximation. We show that the electronic structure of a nanocrystal can be tuned not only by its size, but also by its shape. Therefore, the shape can be used as an efficient way to control the electronic structure of the nanocrystals. Changing the shape is expected to be more flexible and provides more variety of the electronic states than simply changing the size of the system. The special features of the electronic states obtained in different shapes of the nanocrystals can be used in various device applications. We also show that defect properties in QDs could be significantly different from those in bulk semiconductors. For example, although negatively charged DX center is unstable in bulk GaAs:Si with respect to the tetrahedral coordinated SiGa , when the dot size is small enough, it becomes stable.


Hole State Semiconductor Nanocrystals Bulk GaAs Bandgap Increase Valence Band State 
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.



J. Li gratefully acknowledges financial support from “One-hundred Talents Plan” of the Chinese Academy of Science. We would like to thank Dr. L. W. Wang for his contribution in this work and helpful discussions. The work is partially supported by the National Natural Science Foundation of China and by the Foundation of the Chinese Academy of Science. The work at NREL is supported by the U.S. DOE under contract No. DE-AC36-99GO10337. The use of computer resources of the NERSC is greatly appreciated.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.State Key Laboratory for Superlattices and MicrostructuresInstitute of Semiconductors, Chinese Academy of SciencesChina
  2. 2.National Renewable Energy LaboratoryGoldenUSA

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