Inorganic Thermoelectric Fibers: A Review of Materials, Fabrication Methods, and Applications

Toughening Thermoelectric Materials: From Mechanisms to Applications

With the tendency of thermoelectric semiconductor devices towards miniaturization, integration, and flexibility, there is an urgent need to develop high-performance thermoelectric materials. Compared with the continuously enhanced thermoelectric properties of thermoelectric materials, the understanding of toughening mechanisms lags behind. Recent advances in thermoelectric materials with novel crystal structures show intrinsic ductility. In addition, some promising toughening strategies provide new opportunities for further improving the mechanical strength and ductility of thermoelectric materials. The synergistic mechanisms between microstructure-mechanical performances are expected to show a large set of potential applications in flexible thermoelectric devices. This review explores enlightening research into recent intrinsically ductile thermoelectric materials and promising toughening strategies of thermoelectric materials to elucidate their applications in the field of flexible thermoelectric devices.

Electron Paramagnetic Resonance Study of PbSe, PbTe, and PbTe:In Semiconductors Obtained by the Pulsed Laser Deposition Method

The magnetic properties of lead selenide (PbSe) and indium-doped lead telluride (PbTe:In) composites have been studied by using the electron paramagnetic resonance (EPR) technique. The samples were obtained by using the pulsed laser deposition method (PLD). Temperature dependences of the EPR spectra were obtained. The analysis of the temperature dependencies of the integral intensity of the EPR spectra was performed using the Curie–Weiss law. In these materials, the paramagnetic centers of Pb¹⁺ and Pb³⁺ ions were identified. The results are discussed.

Enhanced Thermoelectric Properties of Composites Prepared With Poly(3,4-Ethylenedioxythiophene) Poly(Styrenesulfonate) and Vertically Aligned Se Wire

Controlling the electronic transport behavior in thermoelectric composites is one of the most promising approaches to enhance their power factor because this enables decoupling of the correlation between the electrical conductivity and Seebeck coefficient. Herein, we show that the unexpected high power factor of the Se nanowire array embedded in poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) can be achieved by controlling the interfacial band structure engineering. The electrical conductivity and Seebeck coefficient simultaneously increased, confirming that the synthesis of organic/inorganic hybrid thermoelectric materials with improved performance was possible. Our exploration can be helpful for the rational design of high-performance thermoelectric composites through interface engineering.

Applications of machine learning in computational nanotechnology

Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.