Quantitative Modeling of Polaritonic Emission Using the Source Term Method

TL;DR

This paper introduces a new model to simulate polaritonic emission, validated through experiments with a BODIPY derivative in an optical cavity.

Contribution

The study presents a quantitative model using the source term method to effectively simulate and compare polaritonic emission data.

Findings

The source term method successfully simulated polaritonic emission in the ultrastrong coupling regime.

A BODIPY derivative in an optical cavity showed a collapse of polaritonic line width, indicating ideal polaritons.

Emission in transverse electric and magnetic polarizations was spectrally resolved and matched simulations.

Abstract

Strong exciton-photon coupling leads to the formation
of hybrid
states, polaritons, with properties different from those of their
constituents, making it a valuable tool for modifying the physical
and chemical properties of organic and inorganic materials. Despite
its potential, the field lacks a fundamental understanding of the
photophysics involved and the ability to model experimental data effectively.
In this study, we quantitatively simulate polaritonic emission using
the source term method. This model assumes that each molecular dipole
in the exciton reservoir emits as it would in free space, into the
optical environment formed by the polaritons. To benchmark theory
with experiments, a BODIPY derivative containing a suitable amount
of steric bulk was synthesized. Neat films of this molecule exhibited
close to unperturbed absorption and emission envelopes compared to
dilute solution. When placed in an optical cavity, the ultrastrong
coupling regime was reached, and a collapse of the polaritonic line
width was observed. Such a collapse is an indication of an ideal polariton,
and it allowed for the emission in the transverse electric and magnetic
polarizations to be spectrally resolved and thus successfully compared
to the simulated emission. This work hence describes an effective
model that fits experimental data, which is crucial for advancing
the field and for optimizing applications.

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