Measuring Arbitrary Diffusion Coefficient Distributions of nano-Objects by Taylor Dispersion Analysis
Résumé
Taylor dispersion analysis is an absolute and
straightforward characterization method that allows determining
the diffusion coefficient, or equivalently the hydrodynamic
radius, from angstroms to submicron size range. In this work,
we investigated the use of the Constrained Regularized Linear
Inversion approach as a new data processing method to extract
the probability density functions of the diffusion coefficient (or
hydrodynamic radius) from experimental taylorgrams. This
new approach can be applied to arbitrary polydisperse samples
and gives access to the whole diffusion coefficient distributions,
thereby significantly enhancing the potentiality of Taylor
dispersion analysis. The method was successfully applied to
both simulated and real experimental data for solutions of
moderately polydisperse polymers and their binary and ternary mixtures. Distributions of diffusion coefficients obtained by this
method were favorably compared with those derived from size exclusion chromatography. The influence of the noise of the
simulated taylorgrams on the data processing is discussed. Finally, we discuss the ability of the method to correctly resolve
bimodal distributions as a function of the relative separation between the two constituent species