Determining conductivity and mobility values of individual components in multiphase composite Cu_(1.97)Ag_(0.03)Se

The intense interest in phase segregation in thermoelectrics as a means to reduce the lattice thermal conductivity and to modify the electronic properties from nanoscale size effects has not been met with a method for separately measuring the properties of each phase assuming a classical mixture. Here, we apply effective medium theory for measurements of the in-line and Hall resistivity of a multiphase composite, in this case Cu_(1.97) Ag_(0.03)Se. The behavior of these properties with magnetic field as analyzed by effective medium theory allows us to separate the conductivity and charge carrier mobility of each phase. This powerful technique can be used to determine the matrix properties in the presence of an unwanted impurity phase, to control each phase in an engineered composite, and to determine the maximum carrier concentration change by a given dopant, making it the first step toward a full optimization of a multiphase thermoelectric material and distinguishing nanoscale effects from those of a classical mixture

Effect of Isovalent Substitution on the Thermoelectric Properties of the Cu_2ZnGeSe_(4−x)S_x Series of Solid Solutions

Knowledge of structure–property relationships is a key feature of materials design. The control of thermal transport has proven to be crucial for the optimization of thermoelectric materials. We report the synthesis, chemical characterization, thermoelectric transport properties, and thermal transport calculations of the complete solid solution series Cu_2ZnGeSe_(4–x)S_x (x = 0–4). Throughout the substitution series a continuous Vegard-like behavior of the lattice parameters, bond distances, optical band gap energies, and sound velocities are found, which enables the tuning of these properties adjusting the initial composition. Refinements of the special chalcogen positions revealed a change in bonding angles, resulting in crystallographic strain possibly affecting transport properties. Thermal transport measurements showed a reduction in the room-temperature thermal conductivity of 42% triggered by the introduced disorder. Thermal transport calculations of mass and strain contrast revealed that 34% of the reduction in thermal conductivity is due to the mass contrast only and 8% is due to crystallographic strain

Enhanced thermoelectric performance in the very low thermal conductivity Ag_2Se_(0.5)Te_(0.5)

In this letter, we report the high-temperature thermoelectric properties of Ag_2Se_(0.5)Te_(0.5). We find that this particular composition displays very low thermal conductivity and competitive thermoelectric performance. Specifically, in the temperature region 520 K ≤ T ≤ 620 K, we observe non-hysteretic behavior between the heating and cooling curves and zT values ranging from 1.2 to 0.8. Higher zT values are observed at lower temperatures on cooling. Our results suggest that this alloy is conducive to high thermoelectric performance in the intermediate temperature range, and thus deserves further investigation

Influence of Compensating Defect Formation on the Doping Efficiency and Thermoelectric Properties of Cu_(2-y)Se_(1–x)Br_x

The superionic conductor Cu_(2−δ)Se has been shown to be a promising thermoelectric at higher temperatures because of very low lattice thermal conductivities, attributed to the liquid-like mobility of copper ions in the superionic phase. In this work, we present the potential of copper selenide to achieve a high figure of merit at room temperature, if the intrinsically high hole carrier concentration can be reduced. Using bromine as a dopant, we show that reducing the charge carrier concentration in Cu_(2−δ)Se is in fact possible. Furthermore, we provide profound insight into the complex defect chemistry of bromine doped Cu_(2−δ)Se via various analytical methods and investigate the consequential influences on the thermoelectric transport properties. Here, we show, for the first time, the effect of copper vacancy formation as compensating defects when moving the Fermi level closer to the valence band edge. These compensating defects provide an explanation for the often seen doping inefficiencies in thermoelectrics via defect chemistry and guide further progress in the development of new thermoelectric materials

Excavations at Azoria, 2003–2004, Part 2: The Final Neolithic, Late Prepalatial, and Early Iron Age Occupation

This article constitutes the second of two reports on fieldwork conducted at Azoria in eastern Crete during the 2003 and 2004 excavation seasons. Evidence of Final Neolithic and Early Iron Age occupation and traces of Late Prepalatial activity were found underlying the Archaic civic buildings on the South Acropolis, particularly along the southwest terrace. The recovery of substantial Final Neolithic architectural and habitation remains contributes to our understanding of the 4th millennium in eastern Crete. Stratigraphic excavations have also clarified the spatial extent of the settlement from Late Minoan IIIC to the Late Geometric period, and brought to light evidence for the transition from the Early Iron Age to the Archaic period, and the transformation of the site in the 7th century B.C

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration