Bhaskar Jyoti Krishnatreya

Measuring Boltzmann's constant through holographic video microscopy of a single colloidal sphere

The trajectory of a colloidal sphere diffusing in water records a history of the random forces exerted on the sphere by thermally driven fluctuations in the suspending fluid. The trajectory therefore can be used to characterize the spectrum of thermal fluctuations and thus to obtain an estimate for Boltzmanns constant. We demonstrate how to use holographic video microscopy to track a colloidal spheres three-dimensional motions with nanometer precision while simultaneously measuring its radius to within a few nanometers. The combination of tracking and characterization data reliably yields Boltzmanns constant to within two percent and also provides the basis for many other useful and interesting measurements in statistical physics, physical chemistry, and materials science.

Charged hydrophobic colloids at an oil–aqueous phase interface

Hydrophobic poly(methyl methacrylate) (PMMA) colloidal particles, when dispersed in oil with a relatively high dielectric constant, can become highly charged. In the presence of an interface with a conducting aqueous phase, image-charge effects lead to strong binding of colloidal particles to the interface, even though the particles are wetted very little by the aqueous phase. We study both the behavior of individual colloidal particles as they approach the interface and the interactions between particles that are already interfacially bound. We demonstrate that using particles which are minimally wetted by the aqueous phase allows us to isolate and study those interactions which are due solely to charging of the particle surface in oil. Finally, we show that these interactions can be understood by a simple image-charge model in which the particle charge q is the sole fitting parameter.

Fast feature identification for holographic tracking: the orientation alignment transform

The concentric fringe patterns created by features in holograms may be associated with a complex-valued orientational order field. Convolution with an orientational alignment operator then identifies centers of symmetry that correspond to the two-dimensional positions of the features. Feature identification through orientational alignment is reminiscent of voting algorithms such as Hough transforms, but may be implemented with fast convolution methods, and so can be orders of magnitude faster.