Estimation of mass thickness response of embedded aggregated silica nanospheres from high angle annular dark-field scanning transmission electron micrographs

TL;DR

This paper develops a method to estimate silica mass thickness in STEM micrographs, showing that a power function model improves accuracy over linear models, aiding 3D structural analysis of nanomaterials.

Contribution

It introduces a maximum likelihood approach to model intensity response, demonstrating the superiority of a power function for quantifying silica in STEM images.

Findings

Power function model outperforms linear in estimating silica thickness

Method enables direct 3D structural characterization from STEM images

Enhances quantitative analysis in electron microscopy

Abstract

In this study we investigate the functional behavior of the intensity in high-angle annular dark field (HAADF) scanning transmission electron micrograph (STEM) images. The model material is a silica particle (20 nm) gel at 5 wt%. By assuming that the intensity response is monotonically increasing with increasing mass thickness of silica, an estimate of the functional form is calculated using a maximum likelihood approach. We conclude that a linear functional form of the intensity provides a fair estimate but that a power function is significantly better for estimating the amount of silica in the z-direction. The work adds to the development of quantifying material properties from electron micrographs, especially in the field of tomography methods and three-dimensional quantitative structural characterization from a STEM micrograph. It also provides means for direct three-dimensional quantitative structural characterization from a STEM micrograph.