2.2 The CALPHAD method
2.2.4 Evaluation of Gibbs energy parameters
2.2.4.4 The PARROT programme
The PARROT module [28] is integrated with the Thermo-Calc package [29] and is essentially based on the following governing principles:
- Establish a criterion for best fit: This criterion is based on a maximum likelihood principle where a likelihood function is chosen and must be maximized to obtain the best estimates of the model parameters. Simplification of this process is made by assuming that the joint probability density function of all the experimental data is Gaussian in form, and that there is no dependence or coupling between different experimental measurements.
- Data separation based on accuracies: The PARROT module allows the user to input exper- imental data that are significantly inaccurate compared with their true values in addition to the data which are free from such inaccuracies. Dependent and independent variables: Both types of variables can be used in the optimization process of the PARROT module. Dependent variables are those that describe the responses of the system to prescribed conditions, whereas independent vari- ables define the equilibrium conditions. The calculation of the Gibbs energies for different phases and the determination of equilibria are then performed by the Poly-3 module of the Thermo-Calc software that is coupled with PARROT. State variables such as temperature, pressure, and chem- ical potentials are treated as independent variables and are preselected to define the equilibrium conditions. Dependent variables, or responses of the system, are then written as functions of the independent variables and the model parameters. This makes it possible to use almost any type of experimental information in the evaluation of the model parameters. A significant advantage of using the CALPHAD method is that various other properties can be automatically obtained after the calculation procedure and can also be plotted as functions of the state variables.
In developing the thermodynamic model of the PbTe phase in the Pb-Te system (Chapter 3), the optimization process was started with a small number of iterations and very low statistical weights assigned to experimental and calculated data. The liquid and the PbTe phases were first entered in the optimization process. The weights were increased appropriately during the optimization process to be within an acceptable range of the input data. To aid in the calculation of a global equilibrium, another constraint that was used was the driving force for the formation of a particular phase.
Driving force is defined as the affinity between reacting chemical species, and its magnitude gives the equilibrium of a phase at particular composition and temperature. At particular temperature and composition it is negative for the stable phase, and positive for the phases that are not at equilibrium under those conditions. Also, the optimization was carried out until a balance was achieved between different sets of input data, which in the present work are the solidus, liquidus equilibrium lines, the formation enthalpies of the liquid and PbTe phases, and the defect formation energies.
Chapter 3
Ab initio study of intrinsic point defects in PbTe: an insight into phase stability through defect thermodynamics
Adapted from Ref. [30] with permission from Elsevier.
3.1 Introduction
One solution to the rapidly changing climate and global warming is to extract the available energy (or exergy) from waste heat, and convert it to electricity. Devices that perform such an operation are called thermoelectric generators, and materials used in these devices are known as thermoelec- tric (TE) materials. The lead chalcogenide salt PbTe, one of the most widely studied TE materials, is a IV-VI semiconductor with a small band-gap and is the best performing TE material for mid- temperature power generation (200-6000C) [13]. It exhibits both types of conductivity depending on the growth conditions-n-type (electrons) in a Pb-rich environment andp-type (holes) in a Te- rich environment [31–37], which makes it particularly advantageous since TE devices consist of both n- andp-type legs coupled together. Furthermore, tuning the carrier densities by adjusting the defect/dopant concentrations not only maximizeszT, but also gives control over where its peak occurs in the temperature space [13]. This coupling between carrier concentration, defects/dopants, andzT makes it important to understand the defect chemistry of a material for its application as
a thermoelectric material.
In a previous study by Ahmadet al[38], the physics and electronic structure of vacancy and im- purity induced deep defect states in PbTe was investigated byab initiocalculations. Another study by Xiong et al [39] investigated the impact of dopants on the density of states (DOS) and band structures between pure and extrinsically doped PbTe. Both these works have not investigated the energetics of different types of point defects in intrinsic PbTe. In a recent publication by Wanget al[40], spin-orbit coupling (SOC) calculations were performed that result in a band gap of 0.11 eV, which is in better agreement with the room temperature experimental band gap of 0.31 eV. How- ever, it was found that the fermi level is positioned where Te vacancyVT e+2donor defects for Pb-rich and Te substitutional T e+2P b donor defects for Te-rich conditions are most stable, thus leading to n-type conductivity for both growth conditions. p-type/hole conductivity for Te-rich conditions was found to only occur for a limited set of Te chemical potential. This result is inconsistent with the well-known observance of both electron and hole conductivity in PbTe from numerous experimental works. Additionally, no comparison with experimental results on carrier concentrations is made.
Thus, in this work, we investigate the role of intrinsic point defects in the conductivity and doping behavior of PbTe within the framework of Density Functional Theory (DFT). Specifically, we study the stability of vacancy, anti-site, and interstitial defects by calculating their formation energies for both Pb-rich and Te-rich growth conditions. Defects with the lowest formation energies will help us attribute the origins of n-type and p-type conductivity in PbTe to that specific type of point defect type.
The phase diagram of Pb-Te exhibits a very small range of non-stoichiometry in the PbTe phase to the order of x= 10−4 on both sides of the stoichiometric line at x = 0.50 [31–37] due to the presence of defects, which in turn leads to then- andp-type conductivity in this phase. A previously developed thermodynamic model of this system by Gierlotkaet al[41] describes the Gibbs energy of this compound using the Wagner-Schottky defect model [42] that assumes anti-site defects - (Pb, Te)1:(Te, Pb)1 to cause the very small non-homogeneity range. However, as we shall see later, it is actually the vacancy defects that have the lowest formation energy and hence are the most stable among the different types of defects. Thus, in this work, we use the CALculation of PHAse Diagrams (CALPHAD) method [16] to modify the description of the PbTe phase, and then use the
DFT vacancy formation energy values in its Gibbs free energy expressions, which result in solubility lines that agree very well with experimental data. This approach of using information from DFT to select a particular type of phase model and use the DFT calculated defect formation energies in the Gibbs free energy descriptions to calculate a phase diagram makes the process of building ther- modynamic models more physical. More importantly, in the field of TE materials where dopants are added in small concentrations to fine tune TE properties, this methodology helps determine the solubility ranges of those dopants in the base material, and is critical to the future development of unexplored phase diagram databases.
In the next section we first discuss the method used to calculate defect formation energies, and the correction schemes employed to account for using finite-sized supercells with periodic boundary conditions to model dilute defects. We also briefly describe the CALPHAD method, and the vacancy defect model used to model the PbTe phase in this section. The results obtained for both Pb-rich and Te-rich conditions are shown and discussed in Section 3.3 along with the calculated carrier concentrations that are compared with experimental data. The thermodynamic model for the PbTe system and the resulting thermodynamic data are compared with that from literature in the same section. Finally, concluding remarks are presented in Section 3.4.