Considerations for Electromagnetic Simulations for a Quantitative Correlation of Optical Spectroscopy and Electron Tomography of Plasmonic Nanoparticles

TL;DR

This paper evaluates electromagnetic simulation methods for accurately linking nanoparticle morphology to optical properties, emphasizing the importance of meshing, dielectric functions, and image segmentation in simulations based on electron tomography data.

Contribution

It introduces a workflow for electromagnetic simulations using electron tomography data, analyzing how reconstruction and segmentation affect optical cross section accuracy.

Findings

Optimal results with TVM reconstruction and Otsu thresholding

Mesh simplification improves simulation reliability

Substrate inclusion affects boundary element method accuracy

Abstract

The optical cross sections of plasmonic nanoparticles are intricately linked to the morphology of the particle. If this connection can be made accurately enough, it would become possible to determine a particles shape solely from its measured optical cross sections. For that, electromagnetic simulations can be used to bridge the morphology and optical properties assuming that they can be performed in an accurate manner. In this paper, we study key factors that influence the accuracy of electromagnetic simulations. First, we compare several standard electromagnetic simulation methods and discuss in detail the effects of the meshing accuracy, choice of dielectric function and inclusion of a substrate for the boundary element method. To help the boundary element methods complex parametrization, we develop a workflow including reconstruction, meshing and mesh simplification steps to be able to use electron tomography data as input for these simulations. In particular, we analyze how the choice of reconstruction algorithm and the intricacies of image segmentation influence the simulated optical cross sections and correlate it to induced shape errors, which can be minimized in the data processing pipeline. In our case, optimal results could be obtained by using the Total Variation Minimization (TVM) reconstruction method in combination with Otsu thresholding and slight smoothing, which was important to create a reliable and watertight surface mesh using the marching cubes algorithm, especially for more complex shapes.