Structural Characterization and Thermoelectric Properties of Br-Doped AgSn m [Sb 0.8 Bi 0.2 ]Te 2+ m Systems.
Daniela DelgadoSilvana MorisPaulina Valencia-GálvezMaría Luisa LópezInmaculada Álvarez-SerranoGraeme R BlakeAntonio Galdámez SilvaPublished in: Materials (Basel, Switzerland) (2023)
Herein, we report the synthesis, structural and microstructural characterization, and thermoelectric properties of AgSn m [Sb 0.8 Bi 0.2 ]Te 2+ m and Br-doped telluride systems. These compounds were prepared by solid-state reaction at high temperature. Powder X-ray diffraction data reveal that these samples exhibit crystal structures related to the NaCl-type lattice. The microstructures and morphologies are investigated by scanning electron microscopy, energy-dispersive X-ray spectroscopy (EDS), and high-resolution transmission electron microscopy (HRTEM). Positive values of the Seebeck coefficient (S) indicate that the transport properties are dominated by holes. The S of undoped AgSn m [Sb 0.8 Bi 0.2 ]Te 2+ m ranges from +40 to 57 μV·K -1 . Br-doped samples with m = 2 show S values of +74 μV·K -1 at RT, and the Seebeck coefficient increases almost linearly with increasing temperature. The total thermal conductivity ( κ tot ) monotonically increases with increasing temperature (10-300 K). The κ tot values of undoped AgSn m [Sb 0.8 Bi 0.2 ]Te 2+ m are ~1.8 W m -1 K -1 ( m = 4) and ~1.0 W m -1 K -1 ( m = 2) at 300 K. The electrical conductivity ( σ ) decreases almost linearly with increasing temperature, indicating metal-like behavior. The ZT value increases as a function of temperature. A maximum ZT value of ~0.07 is achieved at room temperature for the Br-doped phase with m = 4.
Keyphrases
- electron microscopy
- quantum dots
- solid state
- high resolution
- room temperature
- highly efficient
- high temperature
- ionic liquid
- metal organic framework
- visible light
- white matter
- mass spectrometry
- electronic health record
- computed tomography
- big data
- machine learning
- solid phase extraction
- dna methylation
- dual energy
- data analysis