从第一性原理研究二元硫化物锡的本征性质和可掺杂性对热电性能的影响

F. A. Bipasha, L. C. Gomes, Jiaxing Qu, E. Ertekin
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摘要

高性能热电(TE)材料依赖于具有合适固有特性的半导体,其载流子浓度可以被控制和优化。为了证明在同时考虑本征性质和可掺杂性时在计算分析中可以获得的见解,我们将TE质量因子(本征性质)的预测与二元Sn硫系化合物Sn、SnSe和SnTe的天然缺陷和载流子浓度的第一性原理模拟结合起来。将计算预测与之前报道的每种材料(包括p型和n型载流子)的TE值的综合数据集进行比较。综合分析表明,可溶性限制以不同的方式限制了每种锡硫化物的TE性能。在SnS中,低载流子浓度阻碍了p型和n型载流子的TE性能,只有通过合适的外源掺杂获得更高的载流子浓度才能提高性能。对于SnSe来说,Cmcm相的p型性能似乎已经达到了理论潜力,而通过调整Pnma相中的载流子浓度,n型性能可能会得到改善。同时,对SnTe缺陷化学的评估表明,p型TE性能受到材料简并p型性质的限制,而n型TE性能由于材料的简并p型性质而无法实现。该分析强调了在计算引导搜索中同时考虑内在和外在属性的好处,这种方法可以应用于TE应用的各种半导体材料集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrinsic properties and dopability effects on the thermoelectric performance of binary Sn chalcogenides from first principles
High-performance thermoelectric (TE) materials rely on semiconductors with suitable intrinsic properties for which carrier concentrations can be controlled and optimized. To demonstrate the insights that can be gained in computational analysis when both intrinsic properties and dopability are considered in tandem, we combine the prediction of TE quality factor (intrinsic properties) with first-principles simulations of native defects and carrier concentrations for the binary Sn chalcogenides SnS, SnSe, and SnTe. The computational predictions are compared to a comprehensive data set of previously reported TE figures-of-merit for each material, for both p-type and n-type carriers. The combined analysis reveals that dopability limits constrain the TE performance of each Sn chalcogenide in a distinct way. In SnS, TE performance for both p-type and n-type carriers is hindered by low carrier concentrations, and improved performance is possible only if higher carrier concentrations can be achieved by suitable extrinsic dopants. For SnSe, the p-type performance of the Cmcm phase appears to have reached its theoretical potential, while improvements in n-type performance may be possible through tuning of electron carrier concentrations in the Pnma phase. Meanwhile, assessment of the defect chemistry of SnTe reveals that p-type TE performance is limited by, and n-type performance is not possible due to, the material’s degenerate p-type nature. This analysis highlights the benefits of accounting for both intrinsic and extrinsic properties in a computation-guided search, an approach that can be applied across diverse sets of semiconductor materials for TE applications.
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