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Free keywords:
Copper compounds, Differential scanning calorimetry, Inorganic compounds, Learning systems, Minerals, Phase change materials, Photovoltaic cells, Specific heat, Text mining, Thermal variables measurement, Thermoelectricity, Turing machines, Waste heat, Electronic and thermal transports, Machine learning methods, Powder X ray diffraction, Reversible phase transition, Thermoelectric figure of merit, Thermoelectric performance, Thermoelectric potential, Thermoelectric transport, Thermal conductivity
Abstract:
The binary copper chalcogenides Cu2−δX (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, Cu3−xTe2 rickardite mineral emerged as a candidate based on a purely text mining approach applied by a machine learning method. Polycrystalline samples of Cu3−xTe2 within the homogeneity range (x = 0.1, 0.2) were successfully synthesized from the raw elements by a solid-state method. High-temperature powder X-ray 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. © XXXX American Chemical Society
