Liquid-like thermal conductivity in solid materials: Dynamic behavior of silver ions in argyrodites

Pai-Chun Wei*, Cheng-Rong Hsing, Chun-Chuen Yang, Yung-Hsiang Tung, Hsin-Jay Wu, Wan-Ting Yen, Yen-Chung Lai, Jey-Jau Lee, Chin-Wei Wang, Hung-Cheng Wu, Hung-Duen Yang, Venkatesh Singaravelu, Xiaohe Miao, Andrea Giugni, Jia-Kai Hu, Jui-Han Fu, Vincent Tung, Jian He, Ching-Ming Wei*, Jr-Hau He*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Citations (Scopus)

Abstract

Argyrodite-type compounds are renowned for their exceptional thermoelectric performance and ultralow thermal conductivity. While the latter is commonly attributed to the superionic behavior of cations, there has been limited research into how cations' static or dynamic behavior affects the thermal transport properties of argyrodites. To address and bridge this research gap, we employ a wide range of measurements and develop ab-initio based machine-learning interatomic potentials to perform large-scale molecular dynamics simulations on Ag8SiTe6 under different temperatures. We highlight the symmetry breaking and lattice-distortion scattering caused by chilled ions at low temperatures and the enhanced ionic diffusion behavior at elevated temperatures endowing argyrodites with superior superionicity and liquid-like thermal conductivity. Our findings also provide valuable insights into the ionic diffusion kinetics and the exotic lattice dynamics of liquid-like thermoelectrics. © 2024 Published by Elsevier Ltd.
Original languageEnglish
Article number109324
JournalNano Energy
Volume122
Online published22 Jan 2024
DOIs
Publication statusPublished - Apr 2024

Funding

Funding: Pai-Chun Wei would like to acknowledge the financial support from the National Science and Technology Council of Taiwan under the grant MOST 111-2112-M-006-034-MY3 and MOST 112-2112-M-008-028. Ching-Ming Wei would like to acknowledge the financial support from the National Science and Technology Council of Taiwan through the grant MOST 111-2112-M-001-084-MY3. Chun-Chuen Yang would like to acknowledge the financial support from the National Science and Technology Council of Taiwan under the grant MOST 111-2112-M-008-034.

Research Keywords

  • AI machine learning
  • Argyrodites
  • Liquid-like thermoelectric materials
  • Molecular dynamics simulation
  • Superionicity
  • Ultralow thermal conductivity

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