The structure-bonding-property paradigm in thermoelectrics: Microscale insights from correlative characterization

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Authors: Huaide Zhang, Matthias Wuttig, Yuan Yu
Cited by: 2
References: 79

Abstract

Thermoelectric materials, capable of realizing direct conversion between thermal and electrical energy, hold great promise for waste heat recovery and solid-state cooling. Their performance is quantified by the dimensionless figure of merit (zT). For decades, enhancement strategies have focused on macro-scale engineering, often treating materials as homogeneous media. However, the properties of polycrystalline thermoelectrics are governed by a complex landscape of microstructural features, particularly grain boundaries (GBs), which exhibit a dual nature in transport. They can scatter phonons to reduce lattice thermal conductivity but also impede charge carriers, degrading electrical conductivity. Recent advances in correlative characterization techniques, combining electron backscatter diffraction, focused ion beam milling, micro-fabrication, physical property measurement systems, and atom probe tomography (APT), enable a "one-to-one" structure-property relationship at the micro- and nanoscale. This review demonstrates how these methods reveal that the electrical transport across individual GBs is dictated by misorientation angle, chemical segregation, and, crucially, the local chemical bonding character. The collapse of metavalent bonding (MVB) at GBs, detected by a drop in the probability of multiple events in APT, drastically reduces dielectric screening and creates high potential barriers. Conversely, strategic dopant segregation can passivate GBs, and MVB-based precipitates in elemental Te can enhance bulk conductivity via favorable band alignment. By unifying insights from macro-scale performance with micro/nano-scale mechanisms and quantum-mechanical bonding maps, this work charts a path for the bottom-up design of next-generation, high-efficiency thermoelectric materials.

Fields of Study & Subjects

Advanced Thermoelectric Materials and Devices Ferroelectric and Piezoelectric Materials Machine Learning in Materials Science

Publication details

Journal / source
Journal of Material Science and Technology
Publisher
Elsevier BV
Volume
271
Pages
167–176
Publication date
14 February 2026
Language
EN