Optoelectronic Properties of Chalcogenide Perovskites by Many-Body Perturbation Theory
arXiv:2103.17264 · doi:10.1021/acs.jpclett.1c01034
Abstract
Chalcogenide perovskites have emerged as non-toxic and stable photovoltaic materials, acting as an alternative to lead halide hybrid perovskites having similar optoelectronic properties. In the present work, we report the electronic and optical properties of chalcogenide perovskites AZrS (A=Ca, Sr, Ba) by using the density functional theory (DFT) and many-body perturbation theory (MBPT viz. GW and BSE). This study includes excitonic analysis for the aforementioned systems. The exciton binding energy (E) is found to be larger than that of the halide perovskites, as the ionic contribution to dielectric screening is negligible in the former. We also observe a more stable charge-separated polaronic state as compared to that of the bound exciton. Finally, on the basis of direct gap and absorption coefficient, the estimated spectroscopic limited maximum efficiency (SLME) of the solar cells is large and suggests the applicability of these perovskites in photovoltaics.
31 pages, 11 figures
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Cited by in corpus (7)
- Chalcogenide Perovskites: An Emerging Class of Semiconductors for Optoelectronics
- Computational study of structural, elastic, electronic, phonon dispersion relation and thermodynamic properties of orthorhombic CaZrS for optoelectronic applications
- Post-Transition Metal Sn-Based Chalcogenide Perovskites: A Promising Lead-Free and Transition Metal Alternative for Stable, High-Performance Photovoltaics
- Exploring Exciton and Polaron Dominated Photo-physical Phenomena in Ruddlesden-Popper Phases of Ban+1ZrnS3n+1 (n=[1-3]) from Many Body Perturbation Theory
- Optimizing Lead-Free Chalcogenide Perovskites for High-Efficiency Photovoltaics via Alloying Strategies
- Sn/Ge substitution in ((CHNH)PbI; n=3): An emerging 2D layered hybrid perovskites with enhanced optoelectronic properties
- Discovery of Novel Silicon Allotropes with Optimized Band Gaps to Enhance Solar Cell Efficiency through Evolutionary Algorithms and Machine Learning