Influence of Cu+ Substitution on the Structural, Ionic, and Thermal Transport Properties of Ag8-xCuxGeS6 Argyrodites

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Authors: Anupama Ghata, Paul Luis Eckert, Thorben Böger, Priyanka Priyanka, Wolfgang G. Zeier, Paul S. Till, Marvin A. Kraft
Cited by: 0

Abstract

Argyrodite-type Ag- and Cu-based chalcogenides have garnered considerable attention due to their combination of both low lattice thermal conductivity and high ionic conductivity. While low lattice thermal conductivity is advantageous for thermoelectric applications, high ionic conductivity can lead to device instability. The independent tunability of these two transport properties would open new strategies for designing high-performance functional materials. This dual behavior emphasizes the importance of understanding the underlying structure-property relationships in these systems. In this study, we investigate the mixed-cation argyrodite solid solution series Ag8-xCuxGeS6 (0 ≤ x ≤ 8), focusing on how substitution of Ag+ with Cu+ influences their structure and transport properties. To elucidate the microscopic origins of thermal and ionic transport a combined experimental and theoretical approach is applied here. A two-channel analytical model reveals that both propagating phonons and diffuson-like modes contribute to heat transport. Our findings demonstrate that the low lattice thermal conductivity and high ionic conductivity arise from shared structural features, such as strong lattice anharmonicity and dynamic disorder, rather than from direct interdependency between the two transport mechanisms. This decoupling enables independent optimization of thermal and ionic transport, offering valuable insights for the design of materials in next-generation thermoelectrics, ionic thermoelectrics, and solid-state batteries.

Fields of Study & Subjects

Advanced Thermoelectric Materials and Devices Thermal properties of materials Thermal Expansion and Ionic Conductivity

Publication details

Journal / source
Universitäts- und Landesbibliothek Münster
Publication date
9 February 2026
Language
EN
Document type
Dataset