Journal
APPLIED SURFACE SCIENCE
Volume 581, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.apsusc.2021.152092
Keywords
Surface topography; Roughness; Hurst exponent; Monte-Carlo simulations
Categories
Funding
- Welch Foundation, United States [F-2002-20190330]
- National Science Foundation Faculty Early Career Development Program, United States [2042304]
- Taiho Kogyo Tribology Research Foundation, United States [20A03]
- 2018 Ralph E. Powe Junior Faculty Enhancement Award - Oak Ridge Associated Universities (ORAU), United States
- Walker Department of Mechanical Engineering at the University of Texas at Austin
- Texas Materials Institute at the University of Texas at Austin
- National Science Foundation, United States through the 2020 Graduate Research Fellowship Program [2020308624]
- College of Engineering at the University of Texas at Austin, United States
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [2042304] Funding Source: National Science Foundation
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Surface topography affects various surface properties. A Fourier filtering algorithm combined with a novel method is used to simulate AFM topography images with known Hurst exponent, and a Monte Carlo experiment is conducted to evaluate the accuracy of this approach.
Surface topography influences several surface properties, including friction and adhesion. While a statistical description of surface topography can be obtained from a power spectral density (PSD) analysis of atomic force microscopy (AFM) height maps and fitting the self-affine region of the PSD to determine the Hurst exponent (H), the accuracy of this approach has not been rigorously evaluated yet. Here, we use a Fourier filtering algorithm combined with a novel approach to simulate typical AFM scan-line anisotropy to generate synthetic AFM topography images with known input Hurst exponent. These synthetic AFM images are used as a Monte Carlo experiment to evaluate the variance and bias in H estimation from PSDs across different hypothetical experimental approaches, including the case of a cluster of images collected at one scan size (scale) and the case of a cluster of images collected at different scales. Our analysis reveals that estimates of the Hurst exponent from images collected at a single scale are persistently biased in a scale-dependent fashion despite misleading convergence in variance. This bias can be reduced by combining images collected at least at three different scales across the range of scales accessible to AFM.
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