Measuring the randomness of micro and nanostructure spatial distributions: Effects of Scanning Electron Microscope image processing and analysis

TL;DR

This paper investigates how SEM image processing affects the measurement of micro and nanostructure randomness using Point Pattern Analysis, providing guidelines for accurate SEM settings to ensure reliable NNI calculations.

Contribution

It analyzes the impact of SEM image filtering and binarization on NNI, offering practical recommendations for accurate nanostructure randomness assessment.

Findings

Noise filtering influences NNI values significantly.

Binarization threshold affects the measurement of randomness.

Finite size effects can bias NNI estimation.

Abstract

The quantitative characterization of the degree of randomness and aggregation of surface micro and nanostructures is critical to evaluate their effects on targeted functionalities. To this end, the methods of Point Pattern Analysis (PPA), largely used in ecology and medical imaging, seem to provide a powerful toolset. However, the application of these techniques requires the extraction of the point pattern of nanostructures from their microscope images. In this work, we address the issue of the impact that Scanning Electron Microscope (SEM) image processing may have on the fundamental metric of PPA, i.e. the Nearest Neighbour Index (NNI). Using as examples two typical SEM images of polymer micro- and nanostructures taken from secondary and backscattered electrons, we report the effects of the a) noise filtering and b) binarization threshold on the value of NNI as well as the impact of the image finite size effects. Based on these results, we draw conclusions for the safe choice of SEM settings to provide accurate measurement of nanostructure randomness through NNI estimation.