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Title: Investigating the Effects on Adhesion of Surface Topography Across Length Scales
The performance of manufactured devices and tools involving contact is governed by surface properties such as adhesion, friction, and wear—all strongly influenced by surface topography. For perfectly flat surfaces, adhesion can be described by an interaction potential, where the minimum energy required to separate the bodies is the intrinsic work of adhesion, and the range of interaction is the length scale over which the interaction operates. These parameters reflect interfacial interactions—such as covalent, van der Waals, or capillary forces. However, for most natural and engineered surfaces, the surface interaction is strongly modified by surface topography, which exists across a wide range of length scales from the atomic scale to the device scale. Significant prior work has established theoretical strategies to describe topography-dependent adhesion. Yet there is a significant gap in understanding of how to experimentally measure and analyze surfaces in order to apply this theory to describe and improve surfaces in real-world applications. The first part of this research addresses the characterization of surfaces with multi-scale topography. To date, the standard practice often reduces surface topography to a single scalar metric (e.g., Ra), often without specifying the size over which it was measured or computed. Even while it is widely recognized that these scalar metrics do not correlate with surface performance. To address this limitation in surface characterization, we conducted the Surface-Topography Challenge. In this international effort, 153 people from 64 different institutions characterized statistically equivalent “smooth” and “rough” samples, creating a total of 2088 measurements using diverse characterization methods. Significant variation was observed when length scale was ignored, and consensus was only established using scale-dependent parameters and by controlling for artifacts and extreme values. The findings from this investigation suggest three best practices for characterizing and specifying topography. In the second part of this research, we explore the relationship between surface topography and surface adhesion. Specifically, sinusoidal patterned samples with varying roughness were created using grayscale electron-beam lithography and adhesion was measured using 250-µm silicon spheres. Conventional roughness parameters failed to explain the observed adhesion trends. However, a simple numerical model incorporating AFM measurements of both the patterned surfaces and the spherical tips successfully captured the trends in adhesion. Additionally, experimental measurements showed wide variability in adhesion not captured by the model. Similar variability was also observed in adhesion experiments on silicon wafers using sharp AFM probes of three materials: DLC, CrN and diamond. The model was improved using 3D probe geometries derived from TEM images and surface roughness reconstructed from the inverse Fourier transform of the full power spectral density (PSD). Simulations that varied both the intrinsic work of adhesion and the interaction range revealed that both the average value and the deviation in adhesion are strongly influenced by nanoscale roughness. Significant errors in predicted adhesion were obtained when nanoscale roughness was not taken into account.  more » « less
Award ID(s):
2400999
PAR ID:
10693693
Author(s) / Creator(s):
Publisher / Repository:
University of Pittsburgh
Date Published:
Format(s):
Medium: X
Institution:
University of Pittsburgh
Sponsoring Org:
National Science Foundation
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