Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).
Statistical Method Improves Single-Particle Tracking
For Immediate Release: November 10, 2010
Andrew Berglund, a Project Leader in the CNST Nanofabrication Research Group, has developed an improved data analysis method for determining the diffusion coefficient of a single nanoparticle using a series of images captured in a microscope.1 In fluidic systems, the diffusion coefficient provides a sensitive measure of nanoparticle size and shape and also contains information about the material properties of the suspending medium, making it an important nanoscale characterization tool. Berglund’s new method introduces a maximum likelihood estimator (MLE), which is computationally simple and accounts for technical difficulties such as finite spatial resolution and motion blur during image acquisition, making it useful in realistic experimental settings. Furthermore, for particles undergoing pure (i.e. not anomalous) diffusion, the MLE is asymptotically optimal, which means it gives an estimate of the diffusion coefficient with the smallest possible variance if the measurement contains enough data points. Analyzing an experiment by collaborators at the University of Maryland, aimed at controlling the position of individual quantum dots in solution with nanometer-scale accuracy, the method was successfully applied to determine the accuracy of the particle tracking and calibrate the diffusion coefficient measurements.2