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Single‐Particle Tracking for Understanding Polydisperse Nanoparticle Dispersions
Author(s) -
Gong Xun,
Park Minkyung,
Parviz Dorsa,
Silmore Kevin S.,
Gordiichuk Pavlo,
Lew Tedrick Thomas Salim,
Strano Michael S.
Publication year - 2019
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.201901468
Subject(s) - materials science , nanoparticle , characterization (materials science) , dispersity , nanoparticle tracking analysis , nanomaterials , polystyrene , particle (ecology) , nanotechnology , particle size , chemical engineering , polymer , chemistry , composite material , polymer chemistry , microrna , biochemistry , oceanography , microvesicles , geology , engineering , gene
Colloidal dispersions of nanomaterials are often polydisperse in size, significantly complicating their characterization. This is particularly true for materials early in their historical development due to synthetic control, dispersion efficiency, and instability during storage. Because a wide range of system properties and technological applications depend on particle dimensions, it remains an important problem in nanotechnology to identify a method for the routine characterization of polydispersity in nanoparticle samples, especially changes over time. Commonly employed methods such as dynamic light scattering or analytical ultracentrifugation (AUC) accurately estimate only the first moment of the distribution or are not routine. In this work, the use of single‐particle tracking (SPT) to probe size distributions of common nanoparticle dispersions, including polystyrene nanoparticles, single‐walled carbon nanotubes, graphene oxide, chitosan‐tripolyphosphate, acrylate, hexagonal boron nitride, and poly(lactic‐ co ‐glycolic acid), is proposed and explored. The analysis of particle tracks is conducted using a newly developed Bayesian algorithm that is called Maximum A posteriori Nanoparticle Tracking Analysis. By combining SPT and AUC techniques, it is shown that it is possible to independently estimate the mean aspect ratio of anisotropic particles, an important characterization property. It is concluded that SPT provides a facile, rapid analytical method for routine nanomaterials characterization.