JOURNAL ARTICLE

Enhanced thermoelectric performance in AgSbTe2 with extremely low thermal conductivity via grain boundary defects.

  • Published In: Applied Physics Letters, 2025, v. 126, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Kaiqi; Wang, Jing; Liu, Shuang; Ji, Xiao; Gao, Chenhao; ZHANG, Bin; Wang, Guiwen; Wang, Guoyu; Wang, Yuqing; Zhou, Yun; Wang, Honghui; Lu, Xu; Zhou, Xiaoyuan 3 of 3

Abstract

This article focuses on enhancing the thermoelectric (TE) performance of AgSbTe₂ by Fe doping to optimize electrical conductivity and reduce lattice thermal conductivity through grain boundary defect engineering. The Fe-doped AgSbTe₂ exhibits a synergistic effect where grain boundary trapping states enable thermally activated carrier release and an energy filtering effect, resulting in a high power factor and a 33% reduction in glass-like thermal conductivity. This leads to a peak dimensionless figure of merit (zT) of 1.8 at 623 K and an average zT of 1.4 between 323–623 K, marking significant improvement over the pristine material. Additionally, nickel (Ni) was identified as an effective contact layer material due to its matched thermal expansion coefficient and low interfacial resistivity, enabling single-leg TE devices with approximately 10% conversion efficiency. The study provides insights into defect engineering and device fabrication strategies for mid-temperature TE applications using lead-free AgSbTe₂-based materials.

Additional Information

  • Source:Applied Physics Letters. 2025/02, Vol. 126, Issue 8, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:0003-6951
  • DOI:10.1063/5.0255308
  • Accession Number:183389140
  • Copyright Statement:Copyright of Applied Physics Letters is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.