Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/6587
Title: Dielectric Confinement, Structure, and Luminescence of 2D Layered Hybrid Lead Halide Perovskites
Authors: NAG, ANGSHUMAN
GHOSH, PRASENJIT
CHAKRABORTY, RAYAN
Dept. of Chemistry
20152025
Keywords: Semiconductor
photophysics
perovskites
Issue Date: Feb-2022
Citation: 245
Abstract: Two-dimensional (2D) layered hybrid perovskites of the type A2PbX4 (X = Cl, Br, I; A = organic ammonium cation) have attracted research interest due to their potential in optoelectronic applications. These materials resemble multi-quantum well structures with semiconducting Pb-X (inorganic) and insulating A (organic) layers. Consequently, the organic/inorganic interface gives rise to many interesting excitonic properties tuned by varying A- and X-site ions. In this thesis, the optical properties of these materials are explored using temperature-dependent luminescence spectroscopy, with a particular focus on the dielectric confinement and structural distortions. Overall, a roadmap has been provided that guides in identifying potential A2PbX4 compositions as per the need for specific optoelectronic applications. At first, we explored the effect of dielectric contrast on the exciton binding energies by systematically changing high-frequency dielectric constants of A- and X-site ions. Exciton binding energies are tuned in the range of 65-450 meV. Then by varying A-site cations, we explored the role of non-covalent interactions at the organic/inorganic interface and the Pb-XPb bond angle on the exciton emission energies. Introduction of new non-covalent interaction in the structure was found to enhance structural rigidity and stabilize exciton emission energy. Next, we investigated the luminescence signatures of exciton self-trapping in compositions with different confinement and structural distortions. We showed that the luminescence from exciton trapping indicates the presence of two distinctly different trap states in these materials. In the last chapter, we developed a method using generative machine learning that can be used to interpolate optical datasets reducing the time of spectroscopic data acquisition and analysis.
URI: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/6587
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