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Statistical Method Improves Single-Particle Tracking

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

1Statistics of Camera-Based Single-Particle Tracking, A. J. Berglund, Physical Review E 82, 011917 (2010) .      [NIST Publication Database Entry]     [Journal Web Site]
2Manipulating Quantum Dots to Nanometer Precision by Control of Flow, C. Ropp, R. Probst, Z. Cummins, R. Kumar, A. J. Berglund, S. R. Raghavan, E. Waks, and B. Shapiro , Nano Letters 10, 2525–2530 (2010).      [NIST Publication Database Entry]     [Journal Web Site]

Released November 10, 2010, Updated December 27, 2022