Nanoparticle characterisation via 2D classification using single particle averaging

Characterising the size and morphology of nanoparticles (NPs), especially in complex systems like core-shell particles and nanocapsules, remains a significant challenge due to limitations in resolution and applicability of traditional methods. Here, we explore a novel approach to image-based NP characterisation using 2D class averaging (2D-CA) techniques used in single particle analysis. By leveraging well-established software originally developed in structural biology, our method provides detailed size distribution analysis for diverse NP systems, including bimodal particle size distributions, nanocapsules and nanorods. To validate the efficacy and accuracy of this technique, we conduct a comparative study against established characterisation methods, highlighting the potential of 2D-CA to enhance the analysis of challenging NP systems that are otherwise inaccessible using conventional methods, such as highly agglomerated NPs. Our results indicate that single particle averaging techniques offer a sound statistical basis for NP size distribution determination, coupled with a streamlined workflow that utilises established software. This method facilitates the processing of large numbers of micrographs, yielding statistically robust results with minimal human bias through automated particle identification.