Exciton Diffusion in a Quantum Dot Ensemble

Fuente: arXiv
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Auteurs principaux: Kawa, Karol, Machnikowski, Paweł
Format: Preprint
Publié: 2023
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author Kawa, Karol
Machnikowski, Paweł
author_facet Kawa, Karol
Machnikowski, Paweł
contents In this theoretical study, we explore Förster resonant energy transfer of a single exciton within a two-dimensional array of self-assembled quantum dots arranged randomly on a circular mesa. Employing the stochastic simulation method, we solve the equation of motion for the density matrix, considering a specified decay rate. Our analysis quantifies diffusion through the mean-square displacement from the initially excited quantum dot, revealing distinct temporal stages: ballistic, normal diffusion, and saturation. Furthermore, we observe power-law localization of the exciton. Complementing our numerical investigations, we develop approximate analytical expressions that closely align with the numerical findings.
format Preprint
id arxiv_https___arxiv_org_abs_2305_02905
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Exciton Diffusion in a Quantum Dot Ensemble
Kawa, Karol
Machnikowski, Paweł
Mesoscale and Nanoscale Physics
Disordered Systems and Neural Networks
In this theoretical study, we explore Förster resonant energy transfer of a single exciton within a two-dimensional array of self-assembled quantum dots arranged randomly on a circular mesa. Employing the stochastic simulation method, we solve the equation of motion for the density matrix, considering a specified decay rate. Our analysis quantifies diffusion through the mean-square displacement from the initially excited quantum dot, revealing distinct temporal stages: ballistic, normal diffusion, and saturation. Furthermore, we observe power-law localization of the exciton. Complementing our numerical investigations, we develop approximate analytical expressions that closely align with the numerical findings.
title Exciton Diffusion in a Quantum Dot Ensemble
topic Mesoscale and Nanoscale Physics
Disordered Systems and Neural Networks
url https://arxiv.org/abs/2305.02905