  The functional properties of many technological surfaces in biotechnology,
electronics, and mechanical engineering depend to a large degree on the
individual features of their nanoscale surface texture, which in turn are a
function of the surface manufacturing process. Among these features, the
surface irregularities and self-similarity structures at different spatial
scales, especially in the range of 1 to 100 nm, are of high importance because
they greatly affect the surface interaction forces acting at a nanoscale
distance. An analytical method for parameterizing the surface irregularities
and their correlations in nanosurfaces imaged by atomic force microscopy (AFM)
is proposed. In this method, flicker noise spectroscopy - a statistical physics
approach - is used to develop six nanometrological parameters characterizing
the high-frequency contributions of jump- and spike-like irregularities into
the surface texture. These contributions reflect the stochastic processes of
anomalous diffusion and inertial effects, respectively, in the process of
surface manufacturing. The AFM images of the texture of corrosion-resistant
magnetite coatings formed on low-carbon steel in hot nitrate solutions with
coating growth promoters at different temperatures are analyzed. It is shown
that the parameters characterizing surface spikiness are able to quantify the
effect of process temperature on the corrosion resistance of the coatings. It
is suggested that these parameters can be used for predicting and
characterizing the corrosion-resistant properties of magnetite coatings.
