Search

Efficient and precise calculations of thermal transport properties and figures of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material Tl9SbTe6by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors, such as population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m–1K–1at room temperature, a result that closely correlates with experimental observations. Notably, we observe that the off-diagonal terms of heat flux operators play a significant role in shaping the overall lattice thermal conductivity of Tl9SbTe6, where the ultralow thermal conductivity resembles that of glass due to limited group velocities. Furthermore, we achieve a maximum ZTvalue of 3.17 in the c-axis orientation for p-type Tl9SbTe6at 600 K and an optimal ZTvalue of 2.26 in the a-axis and b-axis direction for n-type Tl9SbTe6at 500 K. The crystalline Tl9SbTe6not only showcases remarkable thermal insulation but also demonstrates impressive electrical properties owing to the dual-degeneracy phenomenon within its valence band. These results not only elucidate the underlying reasons for the exceptional thermoelectric performance of Tl9SbTe6but also suggest potential avenues for further experimental exploration.

Recently, it has been reported for ultrathin 2D metals that positive surface stress of a clean surface (τ1) can induce considerable compressive lattice strain towards optimized adsorption energy and catalytic properties (L. Wang et al., Science, 2019, 363, 870). We report negative surface stress (τ2) induced by adsorbates at finite coverage counteracting the effects of τ1, by studying the hydrogen adsorption properties and hydrogen evolution reaction (HER) catalysis performance of 2D Pt. Due to the competition between τ1and τ2, the resultant lattice strain of 2D Pt is small. Consequently, modulation speed in adsorption energy and Tafel reaction barriers is significantly reduced as the Pt slab thickness decreases. Our study highlights the importance of properly including the effects of τ2in predicting and understanding the catalytic properties of 2D metals when coverage of adsorbates is high.

The article focuses on the use of simulation software from ANSYS by the automobile racing team of the Harbin Institute of Technology (HIT) in Harbin, China to design lightweight vehicle.

The article evaluates software including ANSYS Mechanical Software, ANSYS DesignXplorer and ANSYS Fluent from Ansys Inc. which were used by team of student from the Harbin Institute of Technology (HIT) in China to design a high speed car.