journal article·2013-10

Determination of nano-roughness of carbon fibers by atomic force microscopy

Jan Jäger, Judith Moosburger-Will, Siegfried Horn

Journal of Materials Science· Volume 48· Issue 19
Publisher page

Abstract

We present a novel approach to determine the surface roughness on varying scales using atomic force microscopy data. The key factor is to find a suitable background correction for the desired scale. Using the example of the surface of sized and unsized high-tenacity carbon fibers, we present an easy method to find backgrounds for widely varying scales and to evaluate respective topography and surface roughness with the same lateral resolution as the microscope itself. The analysis is done by subtracting a tunable background from the respective height data. By choosing an appropriate background to investigate the surface topography of a carbon fiber on a nm-scale, only small nano-structures with a width of around 20 nm remain after the background subtraction. Evaluating the mean roughness R a of these nano-structures, sized carbon fibers show an overall value of around 0.1 nm while unsized carbon fibers a value of around 0.4 nm. Total background corrected height analysis shows an even distribution of these nano-structures along the fibrils of the unsized fibers, whereas for the sized fibers the nano-structures are not present. The presented method allows analysis and visualization of the distribution of nano-structures on a carbon fiber surface for the first time. This feature is used to visualize the distribution of the sizing and can further be used to investigate the influence of different production parameters on the fiber topography or to evaluate the contribution of mechanical interlocking to the interfacial strength.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Fiber-reinforced polymer composites · Surface Modification and Superhydrophobicity · Graphene research and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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