Mavrogonatos A, Papia E-M, Dimitrakellis P, Constantoudis V
Institute of Nanoscience and Nanotechnology, NCSR Demokritos, Agia Paraskevi, Greece.
Nanometrisis p.c., Neapoleos 27, Agia Paraskevi, ATTIKI, 153 41, Greece.
J Microsc. 2023 Jan;289(1):48-57. doi: 10.1111/jmi.13149. Epub 2022 Oct 17.
The quantitative characterisation 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, that is, the Nearest Neighbour Index (NNI). Using 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) binarisation 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.
表面微观和纳米结构的随机性和聚集程度的定量表征对于评估它们对目标功能的影响至关重要。为此，在生态学和医学成像中广泛使用的点模式分析（PPA）方法似乎提供了一套强大的工具集。然而，这些技术的应用需要从显微镜图像中提取纳米结构的点模式。在这项工作中，我们解决了扫描电子显微镜（SEM）图像处理可能对PPA的基本度量即最近邻指数（NNI）产生的影响问题。使用从二次电子和背散射电子获取的聚合物微观和纳米结构的典型SEM图像，我们报告了（a）噪声过滤和（b）二值化阈值对NNI值的影响以及图像有限尺寸效应的影响。基于这些结果，我们得出关于安全选择SEM设置的结论，以便通过NNI估计准确测量纳米结构的随机性。
