Monte-Carlo-based electromagnetic modeling of nanoscale structures: accounting for inhomogeneous broadening in polydisperse ensembles

TL;DR

This paper presents a Monte-Carlo-based electromagnetic modeling approach to account for inhomogeneous broadening effects in polydisperse nano-ensembles, improving the understanding of their optical spectra in nano-optics and plasmonics.

Contribution

It introduces a combined FDTD and Monte-Carlo simulation method to predict broadened spectra in polydisperse nanosystems, addressing computational challenges.

Findings

Significant spectral broadening observed in nano-polymers due to polydispersity.

The method effectively predicts inhomogeneous broadening effects in complex nanosystems.

Qualitative differences between broadened and unbroadened spectra demonstrated.

Abstract

Many experimental systems consist of large ensembles of uncoupled or weakly interacting elements operating as a single whole; this is the case in many experimental systems in nano-optics and plasmonics including colloidal solutions, plasmonic nanoparticles, dielectric resonators, antenna arrays, and others. In such experiments, measurements of the optical spectra of ensembles will differ from measurements of the independent elements even if these elements are designed to be identical as a result of small variations from element to element, known as polydispersity. In particular, sharp spectral features arising from narrow-band resonances will tend to appear broader and can even be washed out completely. Here, we explore this effect of inhomogeneous broadening as it occurs in colloidal nano-polymers comprising self-assembled nanorod chains in solution. Using a technique combining finite-difference time-domain (FDTD) simulations and Monte-Carlo sampling, we predict the inhomogeneously-broadened optical spectra of these colloidal nano-polymers, and observe significant qualitative differences compared to the unbroadened spectra. The approach combining an electromagnetic simulation technique with Monte-Carlo sampling is widely applicable for quantifying the effects of inhomogeneous broadening in a variety of physical systems, including those with many degrees of freedom which are otherwise computationally intractable.