Investigating the microenvironments of inhomogeneous soft materials with multiple particle tracking

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Original entry: Ian Burgess, Fall 2009


M. T. Valentine, P. D. Kaplan, D. Thota, J. C. Crocker, T. Gisler, R. K. Prud'homme, M. Beck, D. A. Weitz, "Investigating the microenvironments of inhomogeneous soft materials with multiple particle tracking," Phys. Rev. E, 64, 061506(9) (2001).


Microrheology, mean-square displacement, Brownian motion, diffusion coefficient.


This article describes a new particle tracking technique that allows localized probing of individual microenvironments in inhomogeneous soft materials. Their technique applies passive microrheology to a large ensemble (~100) of fluorescent-labeled particles. They simultaneously track the motion of each particle using video microscopy. The parallelism of this technique allows them to collect much data about several different types of microenvironments in the system in a short amount of time. The limitation of this technique is that the measured particle trajectories are too short to make statistically meaningful comparisons between individual particles, due to the fixed field of view. To overcome this obstacle, the authors group particles whose mean-square displacements are statistically indistinguishable and then combine the data within each group to gain more accurate statistics about each environment. To demonstrate the effectiveness of their technique, the authors apply it to three different types of media. First, a glycerol/water mixture, which should behave as a homogeneous viscous fluid, is used as a standard. The other media studies are agarose, which is an inhomogeneous and porous gel, and F actin, a component of cellular cytoplasm.



Shown above is the authors' comparison of van Hove correlation functions for the mean displacement of particles moving in Glycerol (top) and agarose (bottom). Ensemble averages for each are shown on the left (a) and individual particles are compared on the right. For glycerol, the ensemble average is fit well by a Gaussian distribution, whose variance increases with the time-window of the sample, and individual particles obey the same statistics, what is expected for a simple viscous fluid with a homogeneous diffusion coefficient. In the agarose sample, the ensemble average is not well fit by a Gaussian, however individual particles fit well to Gaussians with different variances. Thus, the different diffusion coefficients of the different microenvironments in the gel can be identified.

Using this type of analysis, the authors were able to generate a spatial map of the different microenvironments within the inhomogeneous agarose sample. They were able to identify individual microenvironments in which particle statistics correlated very well, by found no spatial correlation between microenvironments. In the F actin sample, they found that the ensemble-average statistics were non-normal (as with agarose) indicating inhomogeneity in the sample, but, unlike in agarose, they were unable to identify individual environments from distinguishable single-particle statistics. They concluded that this indicates that there is temporal and/or very short range spatial variation in the sample and that individual particles are experienceing multiple environments during the course of one experiment.