Surface hydrophobicity of composite films containing polymer and nanoparticles has been studied as a function of composition. We show that the hydrophobicity can be tuned by adjusting the amount of particles in the two-component system. A sharp transition from a polymer-rich surface to a nanoparticles-rich surface was observed with increasing mass fraction of particles in spin-coated thin films. Water drops on the films did not slide down even at tilt angles of 90 degrees . Contact angle hysteresis increased with the mass fraction of particles indicating that the surface roughness increased as the surfaces remained in the Wenzel regime. Contact angle hysteresis data were quantitatively consistent with predictions of a recent theory.
A self-control mechanism that stabilizes the size of Rhodamine B-doped water microdroplets standing on a superhydrophobic surface is demonstrated. The mechanism relies on the interplay between the condensation rate that was kept constant and evaporation rate induced by laser excitation which critically depends on the size of the microdroplets. The radii of individual water microdroplets (>5 µm) stayed within a few nanometers during long time periods (up to 455 seconds). By blocking the laser excitation for 500 msec, the stable volume of individual microdroplets was shown to change stepwise.
Trajectories of individual molecules moving within complex environments such as cell cytoplasm and membranes or semiflexible polymer networks provide invaluable information on the organization and dynamics of these systems. However, when such trajectories are obtained from a sequence of microscopy images, they can be distorted due to the fact that the tracked molecule exhibits appreciable directed motion during the single-frame acquisition. We propose a new model of image formation for mobile molecules that takes the linear in-frame motion into account and develop an algorithm based on the maximum likelihood approach for retrieving the position and velocity of the molecules from single-frame data. The position and velocity information obtained from individual frames are further fed into a Kalman filter for interframe tracking of molecules that allows prediction of the trajectory of the molecule and further improves the precision of the position and velocity estimates. We evaluate the performance of our algorithm by calculations of the Cramer-Rao Lower Bound, simulations, and model experiments with a piezo-stage. We demonstrate tracking of molecules moving as fast as 7 pixels/frame~12.6 mm/s! within a mean error of 0.42 pixel~37.43 nm!.
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