Towards the taxonomic categorization and recognition of nanoparticle shapes

The shape of nanoparticles and nanomaterials is a fundamental characteristic that has been shown to influence a number of their properties and effects, particularly for nanomedical applications. The information related with this feature of nanoparticles and nanomaterials is, therefore, crucial to exploit and foster in existing and future research in this area. We have found that descriptions of morphological and spatial properties are consistently reported in the nanotechnology literature, and in general, these morphological properties can be observed and measured using various microscopy techniques. In this paper, we outline a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts that can be used to address the problem of nanomaterial categorization. We employ an image segmentation technique, belonging to the mathematical morphology field, which is capable of identifying shapes in images that can be used to (semi-) automatically annotate nanoparticle images.

This team of authors outlines a taxonomy of nanoparticle shapes constructed according to nanotechnologists' descriptions and formal geometric concepts enabling nanomaterial categorization. They also employ a mathematical morphology-based image segmentation system, capable of identifying shapes and can be utilized in semi-automated annotation of nanoparticle images.