Abstract.We explore the influence of particle softness and internal structure on both the bulk and interfacial rheological properties of colloidal suspensions. We probe bulk stresses by conventional rheology, by measuring the flow curves, shear stress vs strain rate, for suspensions of soft, deformable microgel particles and suspensions of near hard-sphere-like silica particles. A similar behavior is seen for both kind of particles in suspensions at concentrations up to the random close packing volume fraction, in agreement with recent theoretical predictions for sub-micron colloids. Transient interfacial stresses are measured by analyzing the patterns formed by the interface between the suspensions and their own solvent, due to a generalized Saffman-Taylor hydrodynamic instability. At odd with the bulk behavior, we find that microgels and hard particle suspensions exhibit vastly different interfacial stress properties. We propose that this surprising behavior results mainly from the difference in particle internal structure (polymeric network for microgels vs compact solid for the silica particles), rather than softness alone.

Abstract. We introduce a temporal scheme for data sampling, based on a variable delay between two successive data acquisitions. The scheme is designed so as to reduce the average data flow rate, while still retaining the information on the data evolution on fast time scales. The practical implementation of the scheme is discussed and demonstrated in light scattering and microscopy experiments that probe the dynamics of colloidal suspensions using CMOS or CCD cameras as detectors.

We introduce a new estimator of particle size polydispersity for dynamic light scattering data, which quantifies the relative width of the intensity-weighted distribution of diffusion coefficients. Simulated dynamic light scattering data are analyzed to (i) compare the accuracy and precision of the new polydispersity indicator to polydispersity measurements from standard cumulant and moment analysis (MA) fits and (ii) establish for each method the optimum data range for fitting. Although MA is preferable at low polydispersity, the new estimator is the most accurate and precise at intermediate and large polydispersities. Finally, we successfully apply the method proposed here to real data from colloidal particles, microgels, and polymer solutions.