Filters

2020 - 20212

More filters

20222
Reset filters

Settings

Author: Nielsch, Kornelius × End date: 2024 × Start date: 2020 × Has files: Yes × Publisher: Cambridge : RSC Publ. ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Unveiling the phonon scattering mechanisms in half-Heusler thermoelectric compounds
    (Cambridge : RSC Publ., 2020) He, Ran; Zhu, Taishan; Wang, Yumei; Wolff, Ulrike; Jaud, Jean-Christophe; Sotnikov, Andrei; Potapov, Pavel; Wolf, Daniel; Ying, Pingjun; Wood, Max; Liu, Zhenhui; Feng, Le; Perez Rodriguez, Nicolas; Snyder, G. Jeffrey; Grossman, Jeffrey C.; Nielsch, Kornelius; Schierning, Gabi
    Half-Heusler (HH) compounds are among the most promising thermoelectric (TE) materials for large-scale applications due to their superior properties such as high power factor, excellent mechanical and thermal reliability, and non-toxicity. Their only drawback is the remaining-high lattice thermal conductivity. Various mechanisms were reported with claimed effectiveness to enhance the phonon scattering of HH compounds including grain-boundary scattering, phase separation, and electron–phonon interaction. In this work, however, we show that point-defect scattering has been the dominant mechanism for phonon scattering other than the intrinsic phonon–phonon interaction for ZrCoSb and possibly many other HH compounds. Induced by the charge-compensation effect, the formation of Co/4d Frenkel point defects is responsible for the drastic reduction of lattice thermal conductivity in ZrCoSb1−xSnx. Our work systematically depicts the phonon scattering profile of HH compounds and illuminates subsequent material optimizations.
  • Thumbnail Image
    Item
    Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics
    (Cambridge : RSC Publ., 2021) Zhu, Taishan; He, Ran; Gong, Sheng; Xie, Tian; Gorai, Prashun; Nielsch, Kornelius; Grossman, Jeffrey C.
    Thermoelectric power generation represents a promising approach to utilize waste heat. The most effective thermoelectric materials exhibit low thermal conductivity κ. However, less than 5% out of about 105 synthesized inorganic materials are documented with their κ values, while for the remaining 95% κ values are missing and challenging to predict. In this work, by combining graph neural networks and random forest approaches, we predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database, and chart the structural chemistry of κ into extended van-Arkel triangles. Together with the newly developed κ map and our theoretical tool, we identify rare-earth chalcogenides as promising candidates, of which we measured ZT exceeding 1.0. We note that the κ chart can be further explored, and our computational and analytical tools are applicable generally for materials informatics.