Parajuli P, Bhattacharya S, Rao R, Rao A M
Clemson Nanomaterials Institute, and Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
Air Force Research Laboratory, WPAFB, Ohio 45433, USA.
Mater Horiz. 2022 Jun 6;9(6):1602-1622. doi: 10.1039/d1mh01601f.
Thermoelectric (TE) materials have received much attention due to their ability to harvest waste heat energy. TE materials must exhibit a low thermal conductivity () and a high power factor (PF) for efficient conversion. Both factors define the figure of merit () of the TE material, which can be increased by suppressing without degrading the PF. Recently, binary chalcogenides such as SnSe, GeTe, and PbTe have emerged as attractive candidates for thermoelectric energy generation at moderately high temperatures. These materials possess simple crystal structures with low in their pristine forms, which can be further lowered through doping and other approaches. Here, we review the recent advances in the temperature-dependent behavior of phonons and their influence on the thermal transport properties of chalcogenide-based TE materials. Because phonon anharmonicity is one of the fundamental contributing factors for low thermal conductivity in SnSe, Sb-doped GeTe, and related chalcogenides, we discuss complementary experimental approaches such as temperature-dependent Raman spectroscopy, inelastic neutron scattering, and calorimetry to measure anharmonicity. We further show how data gathered using multiple techniques helps us understand and engineer better TE materials. Finally, we discuss the rise of machine learning-aided efforts to discover, design, and synthesize TE materials of the future.
热电（TE）材料因其能够收集废热能量而备受关注。热电材料必须具有低导热率（）和高功率因数（PF）才能实现高效转换。这两个因素决定了热电材料的品质因数（），可以通过在不降低PF的情况下抑制来提高该品质因数。最近，诸如SnSe、GeTe和PbTe等二元硫族化物已成为中高温下热电能量产生的有吸引力的候选材料。这些材料具有简单的晶体结构，其原始形式的较低，可通过掺杂和其他方法进一步降低。在此，我们综述了声子的温度依赖性行为及其对硫族化物基热电材料热输运性质的影响的最新进展。由于声子非简谐性是SnSe、Sb掺杂的GeTe及相关硫族化物中低导热率的基本贡献因素之一，我们讨论了诸如温度依赖性拉曼光谱、非弹性中子散射和量热法等互补实验方法来测量非简谐性。我们进一步展示了使用多种技术收集的数据如何帮助我们理解和设计更好的热电材料。最后，我们讨论了机器学习辅助的努力在发现、设计和合成未来热电材料方面的兴起。
