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  1. Abstract

    Engineering semiconductor devices requires an understanding of charge carrier mobility. Typically, mobilities are estimated using Hall effect and electrical resistivity meausrements, which are are routinely performed at room temperature and below, in materials with mobilities greater than 1 cm2V‐1s‐1. With the availability of combined Seebeck coefficient and electrical resistivity measurement systems, it is now easy to measure the weighted mobility (electron mobility weighted by the density of electronic states). A simple method to calculate the weighted mobility from Seebeck coefficient and electrical resistivity measurements is introduced, which gives good results at room temperature and above, and for mobilities as low as 10−3cm2V‐1s‐1,Here, μwis the weighted mobility, ρ is the electrical resistivity measured in mΩ cm,Tis the absolute temperature in K,Sis the Seebeck coefficient, andkB/e = 86.3 µV K–1. Weighted mobility analysis can elucidate the electronic structure and scattering mechanisms in materials and is particularly helpful in understanding and optimizing thermoelectric systems.

  2. Abstract

    Nanostructuring to reduce thermal conductivity is among the most promising strategies for designing next‐generation, high‐performance thermoelectric materials. In practice, electrical grain boundary resistance can overwhelm the thermal conductivity reduction induced by nanostructuring, which results in worse overall performance. Since a large body of work has characterized the transport of both polycrystalline ceramics and single crystals of SrTiO3, it is an ideal material system for conducting a case study of electrical grain boundary resistance. An effective mass model is used to characterize the transport signatures of electrical grain boundary resistance and evaluate thermodynamic design principles for controlling that resistance. Treating the grain boundary as a secondary phase to the bulk crystallites explains the transport phenomena. Considering that the interface can be engineered by controlling oxygen partial pressure, temperature, and the addition of extrinsic elements into the grain boundary phase, the outlook for SrTiO3as a nanostructured thermoelectric is promising, and thezTcould be greater than 0.5 at room temperature.

  3. Abstract

    Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across bothp- andn-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.

  4. Abstract

    Highly resistive grain boundaries significantly reduce the electrical conductivity that compromises the thermoelectric figure‐of‐meritzTin n‐type polycrystalline Mg3Sb2. In this work, discovered is a Mg deficiency near grain boundaries using atom‐probe tomography. Approximately 5 at% of Mg deficiency is observed uniformly in a 10 nm region along the grain boundary without any evidence of a stable secondary or impurity phase. The off‐stoichiometry can prevent n‐type dopants from providing electrons, lowering the local carrier concentration near the grain boundary and thus the local conductivity. This observation explains how nanometer scale compositional variations can dramatically determine thermoelectriczT, and provides concrete strategies to reduce grain‐boundary resistance and increasezTin Mg3Sb2‐based materials.

  5. Abstract

    Bismuth telluride is the working material for most Peltier cooling devices and thermoelectric generators. This is because Bi2Te3(or more precisely its alloys with Sb2Te3for p‐type and Bi2Se3for n‐type material) has the highest thermoelectric figure of merit,zT, of any material around room temperature. Since thermoelectric technology will be greatly enhanced by improving Bi2Te3or finding a superior material, this review aims to identify and quantify the key material properties that make Bi2Te3such a good thermoelectric. The largezTcan be traced to the high band degeneracy, low effective mass, high carrier mobility, and relatively low lattice thermal conductivity, which all contribute to its remarkably high thermoelectric quality factor. Using literature data augmented with newer results, these material parameters are quantified, giving clear insight into the tailoring of the electronic band structure of Bi2Te3by alloying, or reducing thermal conductivity by nanostructuring. For example, this analysis clearly shows that the minority carrier excitation across the small bandgap significantly limits the thermoelectric performance of Bi2Te3, even at room temperature, showing that larger bandgap alloys are needed for higher temperature operation. Such effective material parameters can also be used for benchmarking future improvements in Bi2Te3or new replacement materials.

  6. Abstract

    The discovery of a semiconducting ground stateXyYZ(y= 0.8 or 0.75) in nominally 19‐electron half‐Heusler materials warrants a closer look at their apparently metallic properties that often make them good thermoelectric (TE) materials. By systematically investigating the temperature dependence of off‐stoichiometry (x) in V0.8+xCoSb, Nb0.8+xCoSb, and Ti0.75+xNiSb it is found thatxinvariably increases with increasing temperature, leading to an n‐type self‐doping behavior. In addition, there is also a large phase width (range ofx) associated with each phase that is temperature dependent. Thus, unlike in typical 18‐electron half‐Heuslers (e,g, TiNiSn), the temperature dependence of vacancy and carrier concentration (n) in nominally 19‐electron half‐Heuslers links its transport properties to synthesis conditions. The temperature dependence ofxandnare understood using density functional theory based defect energies (Ed) and phase diagrams.Edare calculated for 21 systems which can be used in predicting solubility in this family of compounds. Using this simple strategy, suitable composition and temperature synthesis conditions are devised for obtaining an optimizednto engineer TE properties in phase‐pure V0.8+xCoSb, and the previously unexplored Ta0.8+xCoSb.

  7. Discovery of novel high-performance materials with earth-abundant and environmentally friendly elements is a key task for civil applications based on advanced thermoelectric technology. Advancements in this area are greatly limited by the traditional trial-and-error method, which is both time-consuming and expensive. The materials genome initiative can provide a powerful strategy to screen for potential novel materials using high-throughput calculations, materials characterization, and synthesis. In this study, we developed a modified diffusion-couple high-throughput synthesis method and an automated histogram analysis technique to quickly screen high-performance copper chalcogenide thermoelectric materials, which has been well demonstrated in the ternary Cu–Sn–S compounds. A new copper chalcogenide with the composition of Cu 7 Sn 3 S 10 was discovered. Studies on crystal structure, band gap, and electrical and thermal transport properties were performed to show that it is a promising thermoelectric material with ultralow lattice thermal conductivity, moderate band gap, and decent electrical conductivity. Via Cl doping, the thermoelectric dimensionless figure of merit zT reaches 0.8 at 750 K, being among the highest values reported in Cu–Sn–S ternary materials. The modified diffusion-couple high-throughput synthesis method and automated histogram analysis technique developed in this study also shed light on the development of other advanced thermoelectric andmore »functional materials.« less
  8. The full-Heusler VFe 2 Al has emerged as an important thermoelectric material in its thin film and bulk phases. VFe 2 Al is attractive for use as a thermoelectric materials because of it contains only low-cost, non-toxic and earth abundant elements. While VFe 2 Al has often been described as a semimetal, here we show the electronic and thermal properties of VFe 2 Al can be explained by considering VFe 2 Al as a valence precise semiconductor like many other thermoelectric materials but with a very small band gap ( E g = 0.03 ± 0.01 eV). Using a two-band model for electrical transport and point-defect scattering model for thermal transport we analyze the thermoelectric properties of bulk full-Heusler VFe 2 Al. We demonstrate that a semiconductor transport model can explain the compilation of data from a variety of n and p-type VFe 2 Al compositions assuming a small band-gap between 0.02 eV and 0.04 eV. In this small E g semiconductor understanding, the model suggests that nominally undoped VFe 2 Al samples appear metallic because of intrinsic defects of the order of ∼10 20 defects per cm −3 . We rationalize the observed trends in weighted mobilities ( μmore »w ) with dopant atoms from a molecular orbital understanding of the electronic structure. We use a phonon-point-defect scattering model to understand the dopant-concentration (and, therefore, the carrier-concentration) dependence of thermal conductivity. The electrical and thermal models developed allow us to predict the zT versus carrier concentration curve for this material, which maps well to reported experimental investigations.« less
  9. Solid-solution alloy scattering of phonons is a demonstrated mechanism to reduce the lattice thermal conductivity. The analytical model of Klemens works well both as a predictive tool for engineering materials, particularly in the field of thermoelectrics, and as a benchmark for the rapidly advancing theory of thermal transport in complex and defective materials. This comment/review outlines the simple algorithm used to predict the thermal conductivity reduction due to alloy scattering, as to avoid common misinterpretations, which have led to a large overestimation of mass fluctuation scattering. The Klemens model for vacancy scattering predicts a nearly 10× larger scattering parameter than is typically assumed, yet this large effect has often gone undetected due to a cancellation of errors. The Klemens description is generalizable for use in ab initio calculations on complex materials with imperfections. The closeness of the analytic approximation to both experiment and theory reveals the simple phenomena that emerges from the complexity and unexplored opportunities to reduce thermal conductivity.
  10. Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ - or X -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ -, L -, or W -point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W -point to the valence band maximum. We do this by constructing an “orbital phase diagram” to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineermore »band structures with band convergence and high valley degeneracy.« less