Résumé
Taylor dispersion analysis is an absolute andstraightforward characterization method that allows determiningthe diffusion coefficient, or equivalently the hydrodynamicradius, from angstroms to submicron size range. In this work,we investigated the use of the Constrained Regularized LinearInversion approach as a new data processing method to extractthe probability density functions of the diffusion coefficient (orhydrodynamic radius) from experimental taylorgrams. Thisnew approach can be applied to arbitrary polydisperse samplesand gives access to the whole diffusion coefficient distributions,thereby significantly enhancing the potentiality of Taylordispersion analysis. The method was successfully applied toboth simulated and real experimental data for solutions ofmoderately polydisperse polymers and their binary and ternary mixtures. Distributions of diffusion coefficients obtained by thismethod were favorably compared with those derived from size exclusion chromatography. The influence of the noise of thesimulated taylorgrams on the data processing is discussed. Finally, we discuss the ability of the method to correctly resolvebimodal distributions as a function of the relative separation between the two constituent species