Abstract

The alignment of one-dimensional (1D) nanostructures, such as nanorods and nanowires, has had impact on the development of a variety of nanotechnology ranging from sensors to field-emission devices. These nanostructures may exhibit orientational order on surfaces (2D) and volumes (3D), which impact the properties and performance for the desired application. This chapter summarizes the theory of orientational order, relevant to 1D nanostructures and image processing methods needed to apply this theory to the characterization of experimental images. Key scalar measures of orientational order, orientational order parameters (OOPs), which approximate an orientational probability distribution function are described for both linear (nanorods) and curved (nanowire) 1D nanostructures. Image processing methods relevant to determination of 1D nanostructure alignment are described including standard object-fitting and more advanced mathematical morphology techniques. Finally, methods for computationally efficient identification of overlapping nanostructures are discussed and demonstrated.