Extrusion-printed La3-xTe4 legs with interlocking Ni electrode for high-temperature thermoelectric devices

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Authors: Alexander Pröschel, Yunjia Zhang, Ming Chen, Kurt E. Star, Duncan Zavanelli, Sabah K. Bux, G. Jeffrey Snyder, David C. Dunand
Cited by: 1
References: 75

Abstract

Thermoelectric materials, despite their desirable heat-to-electricity conversion properties, have seen limited commercial viability due to their labor-intensive and high-cost conventional synthesis and assembly processes. Here, we demonstrate an ink printing/sintering approach which unlocks cost-effective fabrication of high-performing, geometrically-complex La3-xTe4 thermoelectric legs with high relative densities and phase purity. LaTe1.47 legs are created by ink-extrusion printing of pre-alloyed powders, followed by debinding and sintering at high temperature; they achieve a high figure of merit (zT = 1.49 ± 0.24 at 1250 K), on par with state-of-the-art LaTe1.46 synthesized via traditional hot pressing. Furthermore, the ink printing methodology enables printing and diffusion bonding of non-flat interfaces between a LaTe1.47 leg and a Ni electrode, which are designed to achieve high mechanical interlocking with minimal chemical interdiffusion. After measuring creep properties on dense La3-xTe4, we perform simulations of the thermomechanical stress at these LaTe1.47/Ni interlocking interfaces during operation; this demonstrates the importance of creep in relaxing the stress state of both phases and the potential for designed interfaces to mitigate crack propagation at the thermoelectric-metal junction. This additive approach addresses the key challenges with thermoelectric device fabrication, enabling thermoelectric devices which are economical, scalable and thermomechanically-robust at very high temperatures. Thermoelectric devices require more scalable, cost-effective methods of manufacturing. Here, the authors ink print high-performing La3-xTe4 legs with interlocked electrodes and evaluate their interfaces with creep property measurements and modeling.

Fields of Study & Subjects

Advanced Thermoelectric Materials and Devices Advanced Sensor and Energy Harvesting Materials Machine Learning in Materials Science

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
5 May 2026
Language
EN