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Non-equilibrium processing leads to record high thermoelectric figure of merit in PbTe–SrTe

Author

Listed:
  • Gangjian Tan

    (Northwestern University)

  • Fengyuan Shi

    (Northwestern University)

  • Shiqiang Hao

    (Northwestern University)

  • Li-Dong Zhao

    (Northwestern University
    School of Materials Science and Engineering, Beihang University)

  • Hang Chi

    (University of Michigan)

  • Xiaomi Zhang

    (Northwestern University)

  • Ctirad Uher

    (University of Michigan)

  • Chris Wolverton

    (Northwestern University)

  • Vinayak P. Dravid

    (Northwestern University)

  • Mercouri G. Kanatzidis

    (Northwestern University
    Argonne National Laboratory)

Abstract

The broad-based implementation of thermoelectric materials in converting heat to electricity hinges on the achievement of high conversion efficiency. Here we demonstrate a thermoelectric figure of merit ZT of 2.5 at 923 K by the cumulative integration of several performance-enhancing concepts in a single material system. Using non-equilibrium processing we show that hole-doped samples of PbTe can be heavily alloyed with SrTe well beyond its thermodynamic solubility limit of

Suggested Citation

  • Gangjian Tan & Fengyuan Shi & Shiqiang Hao & Li-Dong Zhao & Hang Chi & Xiaomi Zhang & Ctirad Uher & Chris Wolverton & Vinayak P. Dravid & Mercouri G. Kanatzidis, 2016. "Non-equilibrium processing leads to record high thermoelectric figure of merit in PbTe–SrTe," Nature Communications, Nature, vol. 7(1), pages 1-9, November.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12167
    DOI: 10.1038/ncomms12167
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    Cited by:

    1. Ming Liu & Muchun Guo & Haiyan Lyu & Yingda Lai & Yuke Zhu & Fengkai Guo & Yueyang Yang & Kuai Yu & Xingyan Dong & Zihang Liu & Wei Cai & Matthias Wuttig & Yuan Yu & Jiehe Sui, 2024. "Doping strategy in metavalently bonded materials for advancing thermoelectric performance," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Zhu, Yuxiao & Newbrook, Daniel W. & Dai, Peng & de Groot, C.H. Kees & Huang, Ruomeng, 2022. "Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator," Applied Energy, Elsevier, vol. 305(C).

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