Abstract

Differential centrifugal sedimentation (DCS) is based on physical separation of nanoparticles in a centrifugal field prior to their analysis. It is suitable for resolving particle populations, which only slightly differ in size or density. Agglomeration presents a common problem in many natural and engineered processes. Reliable data on the agglomeration state are also crucial for hazard and risk assessment of nanomaterials and for grouping and read-across of nanoforms. Agglomeration results in polydisperse mixtures of nanoparticle clusters with multimodal distributions in size, density, and shape. These key parameters affect the sedimentation coefficient, which is the actual physical quantity measured in DCS, although the method is better known for particle sizing. The conversion into a particle size distribution is, however, based on the assumption of spherical shapes. The latter disregards the influence of the actual shape on the sedimentation rate. Sizes obtained in this way refer to equivalent diameters of spheres that sediment at the same velocity. This problem can be circumvented by focusing on the sedimentation coefficient distribution of complex nanoparticle mixtures. Knowledge of the latter is essential to implement and optimize preparative centrifugal routines, enabling precise and efficient sorting of complex nanoparticle mixtures. The determination of sedimentation coefficient distributions by DCS is demonstrated based on supracolloidal assemblies, which are often referred to as “colloidal molecules”. The DCS results are compared with sedimentation coefficients obtained from hydrodynamic bead-shell modeling. Furthermore, the practical implementation of the analytical findings into preparative centrifugal separations is explored.

Highlights

  • Pursuant to the International Union of Pure and Applied Chemistry, colloidal particles are defined as objects that have “a dimension roughly between 1 nm and 1 μm” [1]

  • The agglomeration of nanoparticles yields a large variety of supraparticles, which differ in their aggregation numbers, their compositions, their spatial dimensions, and, their shapes

  • Brownian motion and have well-defined geometries if prepared by a template-based assembling strategy. This makes the latter ideal model systems for testing analytical tools that are suitable for exploring multimodal mixtures of complex nanoparticles

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Summary

Introduction

Pursuant to the International Union of Pure and Applied Chemistry, colloidal particles are defined as objects that have “a dimension roughly between 1 nm and 1 μm” [1]. This includes the nanoscale, which is associated to the size range of approximately 1 nm to. Surface energy will promote the agglomeration of small particles in the absence of kinetic stabilization. The latter is strongly influenced by external parameters, such as pH, salinity, or the presence of depletants [4].

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