Iain Harley, Anke Kaltbeitzel, Francesca Mazzotta, Kaloian Koynov, Sarah S. Lembke, Thao P. Doan-Nguyen, Katharina Landfester and Ingo Lieberwirth
{"title":"Nanoparticle characterisation via 2D classification using single particle averaging†","authors":"Iain Harley, Anke Kaltbeitzel, Francesca Mazzotta, Kaloian Koynov, Sarah S. Lembke, Thao P. Doan-Nguyen, Katharina Landfester and Ingo Lieberwirth","doi":"10.1039/D5NH00094G","DOIUrl":null,"url":null,"abstract":"<p >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.</p>","PeriodicalId":93,"journal":{"name":"Nanoscale Horizons","volume":" 8","pages":" 1642-1652"},"PeriodicalIF":6.6000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/nh/d5nh00094g?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanoscale Horizons","FirstCategoryId":"88","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/nh/d5nh00094g","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Nanoscale Horizons stands out as a premier journal for publishing exceptionally high-quality and innovative nanoscience and nanotechnology. The emphasis lies on original research that introduces a new concept or a novel perspective (a conceptual advance), prioritizing this over reporting technological improvements. Nevertheless, outstanding articles showcasing truly groundbreaking developments, including record-breaking performance, may also find a place in the journal. Published work must be of substantial general interest to our broad and diverse readership across the nanoscience and nanotechnology community.