Brownian motion is the apparently random motion of small particles in a solution that results from the bombardment of molecules within the solution.The theoretical understanding of this motion was developed by Einstein in the early 1900s.Since then, features of Brownian motion, such as the fact that Brownian motion can be modeled using a random walk, or the fact that ensemble mean squared displacement (MSD) can be used to determine a diffusion coefficient and type of diffusive behavior, have been utilized to characterize a vast array of systems that are both naturally occurring and synthetic.In this thesis, I characterize three different types of systems using features of Brownian motion: naturally occurring nuclear pore complexes, synthetic molecular robots that are based on naturally occurring bipedal molecular walkers, and synthetic conductive nanoporous antimony-doped tin oxide (ATO).For the nuclear pore complex, the diffusion of particles through each region of the complex was modeled using a random walk in order to help determine the relative diffusion coefficients of the three regions.For the molecular robots, the movement of the robots was modeled using a more advanced random-walk simulation that utilizes the Gillespie algorithm; the movement of the robots was evaluated based on the MSDs, and the results were used to characterize the directional bias in the walking mechanism of the robots.For the ATO, fluorescent particles were monitored as they underwent Brownian motion while diffusing through the nanopores; MSDs were used to determine that these particles are embedded in the nanopores and that the diffusion coefficient depended in an unexpected way on the potential applied across the material.
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Using the Features of Brownian Motion to Characterize the Nuclear Pore Complex, Molecular Robots, and Antimony-Doped Tin Oxide.
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