半赫斯勒热电半导体的p型可掺杂性

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Lirong Hu, Shen Han, Tiejun Zhu, Tianqi Deng, Chenguang Fu
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引用次数: 0

摘要

具有高价带简并度的半赫斯勒半导体是一种很有前途的p型热电材料。然而,在HH半导体中有效的p型掺杂仍然具有挑战性,阻碍了高性能p型TE材料的进一步探索。在这项工作中,我们进行第一性原理计算,以确定三种具有代表性的HH化合物(nbbfesb, NbCoSn和ZrNiSn)中的主要天然缺陷和潜在p型掺杂物。我们的研究结果表明,4d间质强调了p型可亲和性。通过系统地研究NbFeSb、NbCoSn和ZrNiSn中三个Wyckoff位置的外源掺杂,我们强调了固定费米能级是p型掺杂的一个指标。计算结果表明,在co -贫条件下,Hf为NbCoSn中的p型掺杂剂,并通过实验进一步验证了这一结论。掺hf的NbCoSn显著提高了p型TE性能。这些结果可以指导HH半导体p型TE性能的掺杂剂选择和实验优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

P-type dopability in Half-Heusler thermoelectric semiconductors

P-type dopability in Half-Heusler thermoelectric semiconductors

Half-Heusler (HH) semiconductors with high valence band degeneracy are promising p-type thermoelectric (TE) materials. However, effective p-type doping in HH semiconductors remains challenging, hindering further the exploration of high-performance p-type TE materials. In this work, we conduct first-principles calculations to identify the dominant native defects and potential p-type dopants in three representative HH compounds, e.g., NbFeSb, NbCoSn, and ZrNiSn. Our findings reveal that 4d interstitials underline the p-type dopability. By systematically investigating the extrinsic doping at the three Wyckoff positions in NbFeSb, NbCoSn, and ZrNiSn, respectively, we highlight that the pinned Fermi level serves as an indicator of p-type dopability. The calculation results identify Hf as a p-type dopant in NbCoSn under the Co-poor condition, which is further validated by experiments. A significantly improved p-type TE performance is obtained in Hf-doped NbCoSn. These results could guide the dopant selection and experimental optimization of the p-type TE performance of HH semiconductors.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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