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Journal of Materials Science

, Volume 50, Issue 2, pp 801–807 | Cite as

Compounds based on Group 14 elements: building blocks for advanced insulator dielectrics design

  • A. Mannodi-Kanakkithodi
  • C. C. Wang
  • R. RamprasadEmail author
Original Paper

Abstract

Being in the group with the most diverse set of properties among all in the periodic table, the Group 14 elements (C, Si, Ge, Sn, and Pb) are particularly interesting candidates for structure–property investigation. Motivated by the need to create new insulators for energy storage and electronics applications, we study a few compounds based on Group 14 elements in this work, namely the dihydrides, dichlorides, and difluorides. Using density functional theory (DFT) calculations, we establish patterns in their properties, including favored coordination chemistry, stability, electronic structure, and dielectric behavior. While a coordination number (CN) of 4 is commonly associated with Group 14 elements, there is a significant deviation from it down the group, with CNs as high as 7 and 8 common in Pb. Further, there is an increase in the relative stability of the +2 oxidation state as opposed to +4 when we go from C to Pb, a direct consequence of which is the existence of the di-compounds of C and Si as polymers, whereas the compounds of Ge, Sn, and Pb are strictly 3D crystalline solids. The coordination chemistries are further linked with the band gaps and dielectric constants (divided into two components: the electronic part and the ionic part) of these compounds. We also see that the more stable difluorides and dichlorides have large band gaps and small electronic dielectric constants, and most of the Ge and Sn compounds have remarkably large ionic dielectric constants by virtue of having polar and more flexible bonds. The staggering variation in properties displayed by these parent compounds offers opportunities for designing derivative materials with a desired combination of properties.

Keywords

Dichloride PbI2 PbF2 GeCl2 PbBr2 
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

This work is part of a Multi-disciplinary University Research Initiative (MURI) sponsored by the Office of Naval Research (ONR). We are much obliged to XSEDE for their computational resources. Discussions with H. D. Tran and V. Sharma are greatly appreciated.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • A. Mannodi-Kanakkithodi
    • 1
  • C. C. Wang
    • 1
  • R. Ramprasad
    • 1
    Email author
  1. 1.Department of Materials Science and Engineering, Institute of Materials ScienceUniversity of ConnecticutStorrsUSA

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