Dramatically enhanced Seebeck coefficient in GeMnTe2–NaBiTe2 alloys by tuning the Spin's thermodynamic entropy

Literature Information

Publication Date 2021-07-28
DOI 10.1039/D1CP02545G
Impact Factor 3.676
Authors

Liang Xu, Jianfeng Cai, Yinong Yin


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Abstract

The emerging material GeMnTe2 provides a rare example to study the spin degree of freedom in thermoelectric transport, as it exhibits an anomalous Seebeck coefficient driven by the spin's thermodynamic entropy. This work presents an unconventional strategy to optimize the thermoelectric performance of GeMnTe2 by manipulating the spin degree of freedom. NaBiTe2 is alloyed into GeMnTe2 to disorder the spin orientation under finite temperature, and the obtained Seebeck coefficient is confirmed to be dramatically enhanced by more than 150%. The measurements of XRD and magnetic susceptibility indicate that the increased Seebeck coefficient is due to the increase of the spin's thermodynamic entropy. Finally, the maximum ZT of 1.06 at 820 K is obtained in Ge0.8Na0.1Bi0.1MnTe2. This work enriches the physical picture of spin degree of freedom in thermoelectric materials.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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