Phase-Transition-Enhanced Thermoelectric Transport in Rickardite Mineral Cu_3–xTe₂

Abstract

The binary copper chalcogenides Cu 2−δ X (X = S, Se, and Te) have recently gained significant interest due to their high thermoelectric performance at moderate temperatures. In an effort to unveil new Cu-based compounds with promising thermoelectric potential, Cu 3−x Te 2 rickardite mineral emerged as a candidate based on a purely text mining approach applied by a machine learning method. Polycrystalline samples of Cu 3−x Te 2 within the homogeneity range (x = 0.1, 0.2) were successfully synthesized from the raw elements by a solid-state method. High-temperature powder Xray diffraction combined with differential scanning calorimetry and specific heat measurements showed several reversible phase transitions at around 458, 640, and 647 K. Signatures of these transitions were observed on the electronic and thermal transport properties, measured over a broad range of temperatures (5−733 K). The transition undergone by this compound at 647 K results in a crossover from metallic-like to semiconducting-like properties. The combination of high power factor and low thermal conductivity in the high-temperature phase results in improved thermoelectric performances with a peak dimensionless thermoelectric figure-of-merit zT of ∼0.14 at 733 K. The synthetic rickardite mineral is an exciting candidate to be used as a phase change material in broad application areas such as in waste heat harvesting and photovoltaic systems.

Recent advances and applications of deep learning methods in materials science

“…The researchers demonstrated that such an approach, if applied in the past using historical data, may have subsequently predicted many recently reported thermoelectric materials; they also presented a list of potentially interesting thermoelectric compositions using the known literature at the time. Since then, several of these predictions have been tested either computationally [277][278][279][280][281][282] or experimentally 283 as potential thermoelectrics. Such approaches have recently been applied to search for understudied areas of metallocene catalysis 284 , although challenges still remain in such direct approaches to materials prediction.…”

Thermal Management Systems and Waste Heat Recycling by Thermoelectric Generators—An Overview

“…The efficiency of thermoelectric generators is very much dependent on the conversion efficiency of the constituent thermoelectric (TE) compounds [25][26][27][28][29]. Therefore, the introduction of advanced thermoelectric materials is crucial in increasing the waste heatelectricity conversion rate.…”

Recent Advances and Applications of Deep Learning Methods in Materials Science

“…The researchers demonstrated that such an approach, if applied in the past using historical data, may have subsequently predicted many recently reported thermoelectric materials; they also presented a list of potentially interesting thermoelectric compositions using the known literature at the time. Since then, several of these predictions have since been tested either computationally [322][323][324][325][326][327] or experimentally [328] as potential thermoelectrics. Recently, such approaches have also been applied to search for understudied areas of metallocene catalysis [329], although challenges still remain in such direct approaches to materials prediction.…”