A New Separation Metric to Assess the Optimization of AF4 Separations of Polydisperse Nanoparticles

Published

June 15, 2014

Author(s)

Wyatt N. Vreeland, Anne A. Galyean, James J. Filliben, Richard D. Holbrook, Dean C. Ripple, Howard S. Weinberg

Abstract

The development of standardized and optimized methods to characterize polydisperse nanoparticles in complex matrixes is considered a critical component in many fields. Robust, rigorously optimized methods are currently lacking, in part because of matrix variability, sample polydispersity, and a fundamental lack of understanding of how primary instrument factors affect method performance. We have devised a robust, practical, and effective assessment metric that provides optimization tools for separation techniques that use a light scattering detector for the measurement of particle size. Here, this assessment is applied to optimization of cross flow (Vx) in asymmetric flow field flow fractionation (AF4) separation protocols with online quasi-elastic light scattering (QELS) detection. Following the separation of complex nanomaterial samples using a variety of Vx protocols, the differential in hydrodynamic radius determines relative separation protocol improvement. A separation protocol is considered improved if the measured hydrodynamic radius is lower in average size. Further, if the measured radius differential is smaller than the associated uncertainty, the separation has the optimal cross-flow protocol. While we demonstrate this in optimizing cross-flow protocols in AF4 separations, it can be applied to any separation variable that employs light scattering detection and characterization.

Citation

TechConnect Briefs

Volume

1

Pub Type

Journals

Keywords

Asymmetric flow field flow fractionation, AF4, separation, complex matrix, polydispersity, quasi- elastic light scattering, QELS, particle sizing, differential number fraction

Citation

Vreeland, W. , Galyean, A. , Filliben, J. , Holbrook, R. , Ripple, D. and Weinberg, H. (2014), A New Separation Metric to Assess the Optimization of AF4 Separations of Polydisperse Nanoparticles, TechConnect Briefs (Accessed September 18, 2024)