DFT based study on structural stability and transport properties of Sr3AsN: A potential thermoelectric material

Antiperovskite materials are well known for their high thermoelectric performance and gained huge research interest. Here, we report the structural stability and transport properties of Sr$_3$AsN from a precise first-principles study. The calculated equilibrium lattice parameters are in a good agreement with the available data. We find that Sr$_3$AsN is a mechanically, energetically and dynamically stable at ambient condition. Our calculated electronic structure indicates that it is a direct bandgap semiconductor, with a value ~1.2 eV. Sr-4d and N-2p orbitals mainly formulate the direct bandgap. This antiperovskite possesses a high Seebeck coefficient. Although its lattice thermal conductivity is comparatively low, electronic thermal conductivity is very high. The calculated maximum TE figure of merit is 0.75 at 700 K, indicating that it is a potential material for thermoelectric applications.Comment: 22 pages, 11 figure

High Seebeck coefficient and ultra-low lattice thermal conductivity in Cs2InAgCl6

The elastic, electronic and thermoelectric properties of indium-based double-perovskite halide, Cs2InAgCl6 have been studied by first principles study. The Cs2InAgCl6 is found to be elastically stable, ductile, anisotropic and relatively low hard material. The calculated direct bandgap 3.67 eV by TB-mBJ functional fairly agrees with the experimentally measured value 3.3 eV but PBE functional underestimates the bandgap by 1.483 eV. The relaxation time and lattice thermal conductivity have been calculated by using relaxation time approximation (RTA) within the supercell approach. The lattice thermal conductivity (\k{appa}l) is quite low (0.2 Wm-1K-1). The quite low phonon group velocity in the large weighted phase space, and high anharmonicity (large phonon scattering) are responsible for small \k{appa}l. The room temperature Seebeck coefficient is 199 {\mu}VK-1. Such high Seebeck coefficient arises from the combination of the flat conduction band and large bandgap. We obtain power factors at 300K by using PBE and TB-mBJ potentials are ~29 and ~31 mWm-1K-2, respectively and the corresponding thermoelectric figure of merit of Cs2BiAgCl6 are 0.71 and 0.72. However, the maximum ZT value obtained at 700K is ~0.74 by TB-mBJ potential. The obtained results implies that Cs2InAgCl6 is a promising material for thermoelectric device applications.Comment: 19 pages. arXiv admin note: text overlap with arXiv:1801.0370

First-principles prediction of phonon-mediated superconductivity in XBC (X= Mg, Ca, Sr, Ba)

From first-principles calculations, we predict four new intercalated hexagonal $X$BC ($X$=Mg, Ca, Sr, Ba) compounds to be dynamically stable and phonon-mediated superconductors. These compounds form a LiBC like structure but are metallic. The calculated superconducting critical temperature, $T{_c}$, of MgBC is 51 K. The strong attractive interaction between $\sigma$-bonding electrons and the B${_{1g}}$ phonon mode gives rise to a larger electron-phonon coupling constant (1.135) and hence high $T_c$; notably, higher than that of MgB$_2$. The other compounds have a low superconducting critical temperature (4-17 K) due to the interaction between $\sigma$-bonding electrons and low energy phonons (E${_{2u}}$ modes). Due to their energetic and dynamic stability, we envisage that these compounds can be synthesized experimentally.Comment: 7 pages, 6 figure

First-principles prediction of extraordinary thermoelectric efficiency in superionic Li2SnX3(X=S,Se)

Thermoelectric materials create an electric potential when subject to a temperature gradient and vice versa hence they can be used to harvest waste heat into electricity and in thermal management applications. However, finding highly efficient thermoelectrics with high figures of merit, zT$\geq$1, is very challenging because the combination of high power factor and low thermal conductivity is rare in materials. Here, we use first-principles methods to analyze the thermoelectric properties of Li$_2$Sn$X_3$ ($X$=S,Se), a recently synthesized class of lithium fast-ion conductors presenting high thermal stability. In p-type Li$_2$Sn$X_3$, we estimate highly flat electronic valence bands that render high Seebeck coefficients exceeding 400 ${\mu}$VK$^{-1}$ at 700K. In n-type Li$_2$Sn$X_3$, the electronic conduction bands are slightly dispersive however the accompanying weak electron-acoustic phonon scattering induces high electrical conductivity. The combination of high Seebeck coefficient and electrical conductivity gives rise to high power factors, reaching a maximum of 4 mWm$^{-1}$K$^{-2}$ in p-type Li$_2$SnS$_3$ and 8 mWm$^{-1}$K$^{-2}$ in n-type Li$_2$SnSe$_3$ at 300 K. Likewise, the thermal conductivity in Li$_2$Sn$X_3$ is low as compared to conventional thermoelectric materials, 2-5 Wm$^{-1}$K$^{-1}$ at room temperature. As a result, we estimate a maximum zT = 1.05 in p-type Li$_2$SnS$_3$ at 700 K and an extraordinary 3.07 (1.5) in n-type Li$_2$SnSe$_3$ at the same temperature (300 K). Our findings of huge zT in Li$_2$Sn$X_3$ suggest that lithium fast-ion conductors, typically employed as electrolytes in solid-state batteries, hold exceptional promise as thermoelectric materials.Comment: 21 Page

Detection of Outliers and Influential Observations in Regression Models

Observations arising from a linear regression model, lead one to believe that a particular observation or a set of observations are aberrant from the rest of the data. These may arise in several ways: for example, from incorrect or faulty measurements or by gross errors in either response or explanatory variables. Sometimes the model may inadequately describe the systematic structure of the data, or the data may be better analyzed in another scale. When diagnostics indicate the presence of anomalous data, then either these data are indeed unusual and hence helpful, or contaminated and, therefore, in need of modifications or deletions. Therefore, it is desirable to develop a technique which can identify unusual observations, and determine how they influence the response variate. A large number of statistics are used, in the literature, to detect outliers and influential observations in the linear regression models. Two kinds of comparison studies to determine an optimal statistic are done in this dissertation: (i) using several data sets studied by different authors, and (ii) a detailed simulation study. Various choices of the design matrix of the regression model are considered to study the performance of these statistics in the case of multicollinearity and other situations. Calibration points using the exact distributions and the Bonferroni\u27s inequality are given for each statistic. The results show that, in general, a set of two or three statistics is needed to detect outliers, and a different set of statistics to detect influential observations. Various measures have been proposed which emphasize different aspects of influence upon the linear regression model. Many of the existing measures for detecting influential observations in linear regression models have natural extensions to the multivariate regression. The measures of influence are generalized to the multivariate regression model and multivariate analysis of variance models. Several data sets are considered to illustrate the methods. The regression models with autocorrelated errors are also studied to develop diagnostic statistics based on intervention analysis