Comparative analysis of the electronic structure and magnetic properties of Zn(1-x)Tmx Se(Tm = Fe, V) using LDA and LDA+U approximations

In this article, we studied the structural, electrical, lattice vibrational, and magnetic properties of the quaternary compound Zn(1−x)FexSeyTe(1−y) using density functional theory. All the calculations have been performed based on first-principles calculations using Perdew–Zunger [local-density approximation (LDA)] and Hubbard parameter correction (LDA+U) functionals as employed in the Quantum Espresso package. The computed equilibrium lattice parameter for ZnTe is 6.01 Å, and the energy bandgap, Eg, is 1.362 eV, which is consistent with the experimental values as well as the previous reports, respectively. The influence of the co-doping of iron and selenium on electrical and magnetic properties in a ZnTe system is discussed in detail. The co-doping of iron and selenium affects metallic behavior in these systems by forming localized states between the conduction and valance bands. The presence of localized states is related to the metallic properties of the iron atom, specifically iron 3d orbitals. The spin-polarized density of state and band structure computations also confirmed that the iron and selenium co-doped ZnTe system exhibits significant half-metal ferromagnetic and dilute magnetic semiconductor features at room temperature. Furthermore, the phonon calculation of these systems indicated that the systems are dynamically stable and that localized frequency states are created at higher frequencies due to the presence of iron atoms. As a result, the iron and selenium co-doped ZnTe systems can be considered for magnetic and spintronic device applications at room temperature, pending further experimental research.

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Comparative analysis of the electronic structure and magnetic properties of Zn(1-x)Tmx Se(Tm = Fe, V) using LDA and LDA+U approximations

Cited by 2 publications

References 33 publications

Comprehensive study of spinel nanoparticles using experimental, theoretical and machine learning methods

Comprehensive study of spinel nanoparticles using experimental, theoretical and machine learning methods

Computational study on the structural, electronic, lattice vibration, and magnetism in Zn(1−x)FexSeyTe(1−y) quaternary materials

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