Statistical analysis of periodic fibrous structures by AFM

Researchers from Gliwice in Poland and Bielefeld in Germany looked more closely into the analysis of AFM images of nanofibers with gray-scale-resolved Hurst exponent distribution and particularly addressed the influence of standard AFM post-processing steps on the analysis.

Applied Sciences 2021, 11, 5, 2436

Tomasz Blachowicz, Krzysztof Domino, Michał Koruszowic, Jacek Grzybowski, Tobias Böhm, and Andrea Ehrmann

AFM is renowned for surface characterization of very smooth samples. Several statistical parameters are available to quantify the height distribution of a sample and distinguish between different samples. The most common height parameters are the arithmetic and root-mean-square (RMS) roughness, skewness, kurtosis, and peak-to-valley height and are iso-standardized (Iso 25178).

A challenging class of samples are periodic fibrous structures, as shown below. For these samples quantitative descriptions are scarce, and it is difficult to distinguish different samples by the conventional statistical analysis. Instead, analysis by Hurst exponent distributions has been used to overcome this problem. Shortly, in Hurst exponent analysis a surface image is sliced at different heights and a stack of black-and white images is generated, displaying where the surface crosses each of the slices. Next by random walk algorithms, exponents are determined giving information on the quantity and size of surfaces features at each height.

Researchers from Gliwice in Poland and Bielefeld in Germany looked more closely into the analysis of AFM images of nanofibers with gray-scale-resolved Hurst exponent distribution and particularly addressed the influence of standard AFM post-processing steps on the analysis. They found that post-processing like plane or line-wise background correction do shift Hurst exponents, but keep information about the 3D structure of the sample and are capable to distinguish between different samples.

Unprocessed AFM images
Processed AFM images

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