A Cluster-Based Method for Improving Analysis of Polydisperse Particle Size Distributions Obtained by Nanoparticle Tracking | Zendy

Thorsten Wagner | Zendy; Martin Wiemann | Zendy; Inge Schmitz | Zendy; HansGerd Lipinski | Zendy

Open Access

A Cluster-Based Method for Improving Analysis of Polydisperse Particle Size Distributions Obtained by Nanoparticle Tracking

Author(s) -

Thorsten Wagner,

Martin Wiemann,

Inge Schmitz,

HansGerd Lipinski

Publication year - 2013

Publication title -

journal of nanoparticles

Language(s) - English

Resource type - Journals

eISSN - 2314-4858

pISSN - 2314-484X

DOI - 10.1155/2013/936150

Subject(s) - materials science , particle (ecology) , cluster (spacecraft) , tracking (education) , particle size , nanoparticle , monte carlo method , dispersion (optics) , biological system , logarithm , particle size distribution , nanoparticle tracking analysis , statistical physics , suspension (topology) , interpretability , nanotechnology , statistics , optics , physics , computer science , mathematics , chemical engineering , machine learning , chemistry , pedagogy , microrna , microvesicles , mathematical analysis , oceanography , engineering , biology , psychology , biochemistry , homotopy , programming language , geology , pure mathematics , gene

Optical tracking methods are increasingly employed to characterize the sizeof nanoparticles in suspensions. However, the sufficient separation of differentparticle populations in polydisperse suspension is still difficult. In this work, Nanosight measurements of well-defined particle populations and Monte-Carlosimulations showed that the analysis of polydisperse particle dispersion could beimproved with mathematical methods. Logarithmic transform of measured hydrodynamicdiameters led to improved comparability between different modal valuesof multimodal size distributions. Furthermore, an automatic cluster analysisof transformed particle diameters could uncover otherwise hidden particlepopulations. In summary, the combination of logarithmically transformed hydrodynamicparticle diameters with cluster analysis markedly improved the interpretabilityof multimodal particle size distributions as delivered by particletracking measurements

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