Nanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions.
Surface roughness plays an important role in various fields of nanoscience and nanotechnology. However, the present practices in roughness measurements, typically based on some Atomic Force Microscopy measurements for nanometric roughness or optical or mechanical profilometry for larger scale roughness significantly bias the results. Such biased values are present in nearly all the papers dealing with surface parameters, in the areas of nanotechnology, thin films or material science. Surface roughness, most typically root mean square value of irregularities Sq is often used parameter that is used to control the technologies or to link the surface properties with other material functionality. The error in estimated values depends on the ratio between scan size and roughness correlation length and on the way how the data are processed and can easily be larger than 10% without us noting anything suspicious. Here we present a survey of how large is the problem, detailed analysis of its nature and suggest methods to predict the error in roughness measurements and possibly to correct them. We also present a guidance for choosing suitable scan area during the measurement.
We present a large area high-speed measuring system capable of rapidly generating nanometre resolution scanning probe microscopy data over mm2 regions. The system combines a slow moving but accurate large area XYZ scanner with a very fast but less accurate small area XY scanner. This arrangement enables very large areas to be scanned by stitching together the small, rapidly acquired, images from the fast XY scanner while simultaneously moving the slow XYZ scanner across the region of interest. In order to successfully merge the image sequences together two software approaches for calibrating the data from the fast scanner are described. The first utilizes the low uncertainty interferometric sensors of the XYZ scanner while the second implements a genetic algorithm with multiple parameter fitting during the data merging step of the image stitching process. The basic uncertainty components related to these high-speed measurements are also discussed. Both techniques are shown to successfully enable high-resolution, large area images to be generated at least an order of magnitude faster than with a conventional atomic force microscope.
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