Texture analysis of self-structured surfaces in formation process using directional fractal signature method

Abstract

The range of applications for self-structured surfaces is growing. They are used to increase wear resistance, reduce friction and corrosion, and also used in design of biosensors and innovative coatings. However, to effectively manufacture these surfaces on a large scale, methods for their texture characterisation/description are required. Currently, we do not have any effective and accurate methods to characterise these surfaces. This is severely hindering their wider applications and further developments in this area. The texture of self-structured surfaces, like any texture of any other surface, would need to be characterised/described during the formation (production) process and for specific applications. During formation process, the self-structured surfaces change texture roughness and directionality. These changes are gradual, complex and occur over many scales. In this work, a recently developed method, called an augmented blanket with rotating grid method, is applied to microscopic images of real self-structured surface textures. Groups of isotropic and anisotropic texture images were analysed. In the first group the textures were formed through the growth of nanorods on indium oxide substrates while in another the laser beam irradiation was used to treat the polymer films. Results obtained showed that the augmented blanket with rotating grid method accurately quantifies minute decreases in roughness of isotropic surfaces and changes in roughness with directions of anisotropic surfaces observed during the formation process.