Topological Data Analysis of Nanoscale Roughness in Brass Samples

Abstract

Rough surfaces possess complex topographies, which cannot be characterized by a single parameter. The selection of appropriate roughness parameters depends on a particular application. Large datasets representing surface topography possess orderliness, which can be expressed in terms of topological features in high-dimensional dataspaces reflecting properties such as anisotropy and the number of lay directions. The features are scale-dependent because both sampling length and resolution affect them. We study nanoscale surface roughness using 3 × 3, 4 × 4, and 5 × 5 pixel patches obtained from atomic force microscopy (AFM) images of brass (Cu Zn alloy) samples roughened by a sonochemical treatment. We calculate roughness parameters, correlation length, extremum point distribution, persistence diagrams, and barcodes. These parameters of interest are discussed and compared.

Sonochemical fabrication of gradient antibacterial materials based on Cu-Zn alloy

“…The antibacterial activity of Fraction III is very similar to that of the copper control. Thus, we can conclude that the main contribution to the antibacterial properties is made by copper ions released from the surface of ultrasonically treated samples [35] , [36] . As Fraction III has the least amount of copper and zinc phases so we can assume the Fraction III to be the most biocidal.…”

“…In our previous work, we found that ultrasonic treatment helps to obtain materials capable to inhibit predominantly the growth of a particular strain. This phenomenon is associated with the prevalent release of copper or zinc ions [35] . In this research, we assume that Fractions I–III will release copper and zinc ions differently.…”

Deposition of Nanostructured Tungsten Oxide Layers by a New Method: Periodic Modulation of the Deposition Angle

“…Based on the “Digital Materials Science” concept, numerical analysis of the AFM images (Figure ) was performed to calculate the lacunarity and autocorrelation functions and determine the persistence barcodes according to the TDA method described in detail earlier . The autocorrelation function is used to characterize the typical horizontal scale size of roughness details, while the barcodes are used to study scale-dependent surface anisotropy.…”

“…Furthermore, topological data analysis (TDA) of the obtained AFM images was performed to determine the persistence barcodes, and autocorrelation functions were calculated according to the method described in detail in ref .…”

Surface Wettability Prediction Using Image Analysis and an Artificial Neural Network

“…Atomic force microscopy (AFM) analysis is commonly employed to quantify surface nanoroughness from differences in the heights of surface structures but cannot give structural information in detail from a horizontal perspective, such as spacing between structures or the shape of the structure plane. As a complementary method, some studies have tried to use digital image processing to quantify various surface structural information captured in scanning electron microscopy (SEM) images employing substrates with irregular surface structures. − The combination of image data analysis and ML methods increases the sophistication at which surface topographies can be evaluated and enables the prediction or control of roughness-related properties that cannot be described using a single parameter. , …”