03125nas a2200181 4500000000100000000000100001008004100002100002300043700002100066700002200087700002100109700001700130700001900147245010600166856004900272490000700321520261500328 2021 d1 aTomasz Błachowicz1 aKrzysztof Domino1 aMichal Koruszowic1 aJacek Grzybowski1 aTobias Böhm1 aAndrea Ehrmann00aStatistical analysis of nanofiber mat AFM images by gray-scale-resolved Hurst exponent distributions uhttps://www.mdpi.com/2076-3417/11/5/2436/pdf0 v113 a

Two-dimensional structures, either periodic or random, can be classified by diverse mathematical methods. Quantitative descriptions of such surfaces, however, are scarce since bijec-tive definitions must be found to measure unique dependency between described structures and the chosen quantitative parameters. To solve this problem, we use statistical analysis of periodic fibrous structures by Hurst exponent distributions. Although such a Hurst exponent approach was suggested some years ago, the quantitative analysis of atomic force microscopy (AFM) images of nanofiber mats in such a way was described only recently. In this paper, we discuss the influence of typical AFM image post-processing steps on the gray-scale-resolved Hurst exponent distribution. Examples of these steps are polynomial background subtraction, aligning rows, deleting horizontal errors and sharpening. Our results show that while characteristic features of these false-color images may be shifted in terms of gray-channel and Hurst exponent, they can still be used to identify AFM images and, in the next step, to quantitatively describe AFM images of nanofibrous surfaces. Such a gray-channel approach can be regarded as a simple way to include some infor-mation about the 3D structure of the image.