Twinning behavior and thermoelectric performance of Cu2SnS3
Source abstract
Abstract
With the perfect and orderly atomic arrangement, nanotwins could effectively scatter mid- and low-frequency phonons while minimizing the scattering for carriers, beneficial for enhancing the thermoelectric properties of materials. To unlock these features, we explored the twin structural characteristics in Cu 2SnS 3 and found that nanotwins formed along [100] direction on the glide-set plane, and further delved into the mechanism of twin formation and found that twinning possessed a lower nucleation barrier than the slipping. In addition, we reduced the unstable stacking fault energy in the stacking stage and amplified the probability of twin formation by introducing Se atoms at the S sites. In spite of more nanotwins, a slight increase in carrier mobility was realized, leading to a maximum power factor of 5.5 μW cm −1 K −2 at 773 K for the Cu 2SnS 2 Se material. Meanwhile, the lattice thermal conductivity across the entire temperature range was effectively reduced due to the introduction of nanotwins and Se S points defects, which is approximately 0.46 W m −1 K −1 at 773 K for Cu 2SnS 2Se. Further optimization of the power factor was realized by utilizing a high-efficient doping strategy via introducing Sn vacancies. The carrier concentration became significantly augmented while minimizing ionized impurities scattering, giving rise to an exceptionally high power factor of 8.2 μW cm −1 K −2 and a highly-ranked thermoelectric figure of merit of 0.98 at 773 K for Cu 2Sn 0.99S 2Se. Our work demonstrates an intriguing route towards the high thermoelectric performance of Cu 2SnS 3 through the introduction of nanotwins as well as high-efficient doping techniques.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Chalcogenide Semiconductor Thin Films · Quantum Dots Synthesis And Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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