The role of physical collisions in shaping Saturn's F ring is explored using a mixture of dynamical theory, image analysis and computer simulations. The F ring is highly dynamic, being perturbed by the nearby moons, Prometheus and Pandora, and by a population of small bodies, whose presence is inferred by their influence on the ring, charged particle data and, occasionally, direct detection. Small-scale features, termed `mini-jets', are catalogued from images taken by the Imaging Science Subsystem of the Cassini spacecraft. More than 1000 are recorded, implying a population of 100 objects on nearby orbits, colliding with the ring at velocities of a few ms 1. Many are seen to collide several times, forming repeated structures, and must have enough physical strength, or self-gravity, to survive multiple passages through the core. Larger features, called `jets', share a similar morphology. They are likely caused by a more distant population which collide at higher velocities ( 10 ms 1) and are roughly an order of magnitude less common. Differential orbital motion causes jets to shear out over time, giving the ring its multi-stranded appearance. Jets have different orbital properties to mini-jets, probably because they result from multiple, overlapping collisions. Simulations using an N-body code show that the shape of collisional features depends heavily on the coefficient of restitution, particularly the tangential component. When both components are < 1 large objects merely sweep up small particles. Features like jets and mini-jets require large particles in both the target and impactor, as is the case for two similarly-sized aggregates colliding. A single population of aggregates is proposed, ranging from large, unconsolidated clumps, embedded in the core, through mini-jet-forming objects to the more distant, jet-forming colliders. Prometheus may be ultimately responsible for all of these features as its gravity can trigger clump formation as well as perturb particles.

Polymer coatings on periodic nanostructures have facilitated advanced applications in various fields. The performance of these structures is intimately linked to their nanoscale characteristics. Smart polymer coatings responsive to environmental stimuli such as temperature, pH level, and ionic strength have found important uses in these applications. Therefore, to optimize their performance and improve their design, precise characterization techniques are essential for understanding the nanoscale properties of polymer coating, especially in response to stimuli and interactions with the surrounding medium. Due to their layered compositions, applying non-destructive measurement methods by X-ray/neutron scattering is optimal. These approaches offer unique insights into the structure, dynamics, and kinetics of polymeric coatings and interfaces. The caveat is that scattering methods require non-trivial data modeling, particularly in the case of periodic structures, which result in strong correlations between scattered beams. The dynamical theory (DT) model offers an exact model for interpreting off-specular signals from periodically structured surfaces and has been validated on substrates measured by neutron scattering. In this dissertation, we improved the model using a computational optimization approach that simultaneously fits specular and off-specular scattering signals and efficiently retrieves the three-dimensional sample profile with high precision. In addition, we extended this to the case of X-ray scattering. We applied this approach to characterize polymer brushes for nanofluidic applications and protein binding to modulated lipid membranes. This approach opens new possibilities in developing soft matter nanostructured substrates with desired properties for various applications. / Doctor of Philosophy / Polymer coatings on nanopatterned surfaces have recently facilitated advanced applications in various fields, particularly biotechnology. For example, multichannel surfaces coated with polymer can serve as nanofluidic devices for precise control of fluid flow in drug screening and detection of specific biomolecules. Moreover, polymer-coated nanopatterned surfaces, which possess similar properties to the extracellular matrix, provide excellent substrates for biological studies. The performance of these systems is closely tied to their nanoscale features, such as the thickness and conformation of the polymer layers. Therefore, high-resolution non-invasive nanoscale characterization techniques are essential for investigating these coatings to optimize their performance and enhance their design. X-ray/neutron scattering offers a non-destructive measurement method with unique capabilities in the nanoscale characterization of polymer coatings. However, scattering methods require non-trivial data modeling, particularly in the case of layered coatings on patterned surfaces. To tackle this challenge, we improved a dynamical theory (DT) model that allows for precise modeling of neutron and X-ray scattering signals from such systems. Using a computational optimization approach, the model enables efficient retrieval of the three-dimensional sample profile with high accuracy. We applied this approach to characterize polymer brushes for nanofluidic applications and protein binding to modulated lipid membranes. This methodology opens up new avenues for developing customizable, nanostructured substrates made from soft materials that possess tailored properties for a wide range of uses.

A comprehensive hydrological study of large watersheds in Africa e.g. the Congo basin and the Nile basin has not been vigorously pursued for various reasons. One of the major reasons is the lack of adequate modeling tools that would not be very demanding in terms of input data needs and yet inclusive enough to cover such wide extents (over 3 million square kilometers for the Congo basin). Using a coupled run of the Community Atmospheric model (CAM3) and Community Land Model (CLM3) components of the Community Climate System of Models (CCSM), this study looks into the spatial and temporal variation of precipitation and river runoff in the Congo basin in the light of increasing trends in deforestation of the tropical forests. The effect of deforestation on precipitation and runoff is investigated by changing the land cover-type from the current configuration of broadleaf evergreen/deciduous, non-Artic grass and corn to a mostly grass type of vegetation. Discharge simulation for the river Congo is centered at the point of entrance to the Atlantic Ocean. Although the CLM3 does not presently simulate the observed river runoff to within at least one standard deviation it gives an opportunity to iteratively improve on the land surface parameterization with a possibility of future accurate prediction of mean monthly river runoffs under varying climate scenarios and land use practices. When forced with the National Center for Environment and Prediction (NCEP) re-analysis data the CLM3 runoff simulation results are relatively more stable and much closer to the observed. An improved CLM3 when coupled to CAM3 or other Global Climate Models is definitely a better tool for investigative studies on the regional hydrological cycle in comparison to the traditional methods. There was a slight reduction in rainfall in the first experiment which mimicked a severe form of deforestation and a slight increase in rainfall following low level of deforestation. These changes in rainfall were however statistically insignificant when compared to the control simulation. There was notable heterogeneity in the spatial distribution of the changes in rainfall following deforestation.