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Modeling temperature dependent singlet exciton dynamics in multilayered organic nanofibers.

Leonardo Evaristo de SousaPedro Henrique de Oliveira NetoJakob Kjelstrup-HansenDemétrio Antônio da Silva Filho
Published in: The Journal of chemical physics (2018)
Organic nanofibers have shown potential for application in optoelectronic devices because of the tunability of their optical properties. These properties are influenced by the electronic structure of the molecules that compose the nanofibers and also by the behavior of the excitons generated in the material. Exciton diffusion by means of Förster resonance energy transfer is responsible, for instance, for the change with temperature of colors in the light emitted by systems composed of different types of nanofibers. To study in detail this mechanism, we model temperature dependent singlet exciton dynamics in multilayered organic nanofibers. By simulating absorption and emission spectra, the possible Förster transitions are identified. Then, a kinetic Monte Carlo model is employed in combination with a genetic algorithm to theoretically reproduce time-resolved photoluminescence measurements for several temperatures. This procedure allows for the obtainment of different information regarding exciton diffusion in such a system, including temperature effects on the Förster transfer efficiency and the activation energy of the Förster mechanism. The method is general and may be employed for different systems where exciton diffusion plays a role.
Keyphrases
  • energy transfer
  • quantum dots
  • monte carlo
  • machine learning
  • deep learning
  • genome wide
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  • copy number
  • density functional theory
  • climate change
  • neural network