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Quantitative Investigation of the Rate of Intersystem Crossing in the Strong Exciton-Photon Coupling Regime.

Arpita MukherjeeJohannes FeistKarl Börjesson
Published in: Journal of the American Chemical Society (2023)
Strong interactions between excitons and photons lead to the formation of exciton-polaritons, which possess completely different properties compared to their constituents. The polaritons are created by incorporating a material in an optical cavity where the electromagnetic field is tightly confined. Over the last few years, the relaxation of polaritonic states has been shown to enable a new kind of energy transfer event, which is efficient at length scales substantially larger than the typical Förster radius. However, the importance of such energy transfer depends on the ability of the short-lived polaritonic states to efficiently decay to molecular localized states that can perform a photochemical process, such as charge transfer or triplet states. Here, we investigate quantitatively the interaction between polaritons and triplet states of erythrosine B in the strong coupling regime. We analyze the experimental data, collected mainly employing angle-resolved reflectivity and excitation measurements, using a rate equation model. We show that the rate of intersystem crossing from the polariton to the triplet states depends on the energy alignment of the excited polaritonic states. Furthermore, it is demonstrated that the rate of intersystem crossing can be substantially enhanced in the strong coupling regime to the point where it approaches the rate of the radiative decay of the polariton. In light of the opportunities that transitions from polaritonic to molecular localized states offer within molecular photophysics/chemistry and organic electronics, we hope that the quantitative understanding of such interactions gained from this study will aid in the development of polariton-empowered devices.
Keyphrases
  • energy transfer
  • quantum dots
  • high resolution
  • room temperature
  • single molecule
  • mass spectrometry
  • deep learning
  • ionic liquid