Monitoring Biopolymer Degradation by Taylor Dispersion Analysis

This work aims at demonstrating the interest of modern Taylor dispersion analysis (TDA), performed in narrow internal diameter capillary, for monitoring biopolymer degradations. Hydrolytic and enzymatic degradations of dendrigraft poly-l-lysine taken as model compounds have been performed and monitored by TDA at different degradation times. Different approaches for the data processing of the taylorgrams are compared, including simple integration of the taylorgram, curve fitting with a finite number of Gaussian peaks, cumulant-like method and Constrained Regularized Linear Inversion approach. Valuable information on the kinetics of the enzymatic/hydrolytic degradation reactions and on the degradation process can be obtained by TDA

Measuring Arbitrary Diffusion Coefficient Distributions of Nano-Objects by Taylor Dispersion Analysis

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

Polydispersity Analysis of Taylor Dispersion Data: The Cumulant Method

Taylor dispersion analysis is an increasingly popular characterization method that measures the diffusion coefficient, and hence the hydrodynamic radius, of (bio)­polymers, nanoparticles, or even small molecules. In this work, we describe an extension to current data analysis schemes that allows size polydispersity to be quantified for an arbitrary sample, thereby significantly enhancing the potentiality of Taylor dispersion analysis. The method is based on a cumulant development similar to that used for the analysis of dynamic light scattering data. Specific challenges posed by the cumulant analysis of Taylor dispersion data are discussed, and practical ways to address them are proposed. We successfully test this new method by analyzing both simulated and experimental data for solutions of moderately polydisperse polymers and polymer mixtures

Structure of Nanoparticles Embedded in Micellar Polycrystals

We investigate by scattering techniques the structure of water-based soft composite materials comprising a crystal made of Pluronic block-copolymer micelles arranged in a face-centered cubic lattice and a small amount (at most 2% by volume) of silica nanoparticles, of size comparable to that of the micelles. The copolymer is thermosensitive: it is hydrophilic and fully dissolved in water at low temperature (<i>T</i> ∼ 0 °C), and self-assembles into micelles at room temperature, where the block-copolymer is amphiphilic. We use contrast matching small-angle neuron scattering experiments to independently probe the structure of the nanoparticles and that of the polymer. We find that the nanoparticles do not perturb the crystalline order. In addition, a structure peak is measured for the silica nanoparticles dispersed in the polycrystalline samples. This implies that the samples are spatially heterogeneous and comprise, without macroscopic phase separation, silica-poor and silica-rich regions. We show that the nanoparticle concentration in the silica-rich regions is about 10-fold the average concentration. These regions are grain boundaries between crystallites, where nanoparticles concentrate, as shown by static light scattering and by light microscopy imaging of the samples. We show that the temperature rate at which the sample is prepared strongly influence the segregation of the nanoparticles in the grain-boundaries