363 research outputs found
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 SrAsN from a precise first-principles
study. The calculated equilibrium lattice parameters are in a good agreement
with the available data. We find that SrAsN 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 BC (=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, , of
MgBC is 51 K. The strong attractive interaction between -bonding
electrons and the B phonon mode gives rise to a larger electron-phonon
coupling constant (1.135) and hence high ; notably, higher than that of
MgB. The other compounds have a low superconducting critical temperature
(4-17 K) due to the interaction between -bonding electrons and low
energy phonons (E 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, zT1, 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 LiSn (=S,Se), a recently
synthesized class of lithium fast-ion conductors presenting high thermal
stability. In p-type LiSn, we estimate highly flat electronic valence
bands that render high Seebeck coefficients exceeding 400 VK at
700K. In n-type LiSn, 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 mWmK in p-type LiSnS and 8
mWmK in n-type LiSnSe at 300 K. Likewise, the thermal
conductivity in LiSn is low as compared to conventional thermoelectric
materials, 2-5 WmK at room temperature. As a result, we estimate
a maximum zT = 1.05 in p-type LiSnS at 700 K and an extraordinary 3.07
(1.5) in n-type LiSnSe at the same temperature (300 K). Our findings of
huge zT in LiSn 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
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