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CS particles show unique properties by merging individual characteristics of the core and the shell materials. An alteration particularly in their surface roughness affects the final performance of the particles in the targeted application. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task employing microscopic techniques being scarce and showing large differences in terms of methodology and results.
In our previous work, we have reported a systematic study with a reliable analysis tool, which evaluates profile roughness quantitatively, for individual core-shell microparticles using electron microscopy (EM) images of both types, Scanning Electron Microscopy (SEM) and transmission mode SEM (or TSEM). The SEM images contain two-dimensional (2D) information, therefore, provide profile roughness data only from the projection in the horizontal plane (in other words, from the “belly”) of a spherical particle. The present study offers a practical procedure to give access to more information by tilting the sample holder and hence allowing images of a single particle to be recorded at different orientations under the same view angle. From the analysis of these images, extended information on surface roughness of the particle can be extracted. Thus, instead of obtaining 2D information from a single SEM image, three-dimensional (3D) information is obtained from 2D projections recorded at different particle orientations.
A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
A particle roughness analysis tool, based on electron microscopy images (SEM and TEM). The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
Welcome to the poster Towards Determination of Surface Roughness from a SEM Image, a contribution from BAM in Berlin, Germany. This work is part of the MamaLoCA project, which aims to develop a biosensor for the detection of mycotoxins in cereals. Biosensors come in a great variety, one of which makes use of microscopic beads produced by homogenous coating of polystyrene microspheres. The beads are functionalized by decorating them with bioreceptors – in our case antibodies – which then specifically react with the analyte – in our case mycotoxins – and emit an electrical or optical signal. The functionalization of the beads depends on the surface roughness because this determines the amount and orientation of binders. In other words, the surface roughness affects the accessibility to the binding sites and influences device sensitivity, hence its quantitative determination is an important step in evaluating the quality of the biosensor in general.
The presented solution to the problem of the estimation of surface roughness relies in the repetitive characteristics on the surface of the beads. A SEM image of the bead shows a raspberry like microparticle with a variation of grayscale values, which arise from the secondary electron yield. The principle of the measurement measures the variation of grayscale values along a circumference of a circle centred in the centre of the particle and with an arbitrary radius. The grayscale value variation along the given circumference gives the so-called z-modulation or the lateral profile. By performing Fast Fourier Transform on this profile we obtain the power spectrum as a function of the spatial frequencies through which the grayscales vary. The maximal value for spatial frequency then reveals the most common feature along one given circumference. Surface roughness then is the feature frequency in the spatial domain. This calculation is repeated for several concentric circles with different radii over the particle.
The results for the same particle but recorded at two different accelerating voltages show that the applied method has a potential to reveal the roughness. Interpretation of results from an SE InLens SEM image obtained using 3 kV shows that surface roughness is about 21 nm, which is in a good agreement with an alternative method given in a different presentation. The results from the 10 kV are underestimated due to the loss of surface sensitivity on the SE InLens detection at high voltages.
In conclusion, this method shows promise in determining quantitatively the surface roughness from a single SEM image and its validation is being sought using 3D SEM images and AFM methods.
A particle roughness analysis tool, based on electron microscopy (EM) images. The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
Extracting statistical characteristics from radiographic films is vital for film system classification and contrast sensitivity evaluation and serves as a basis for film noise simulation. A new method for digitising radiographic films in order to extract these characteristics is presented. The method consists of a camera-based setup and image processing procedure to digitise films. Correct optical density values and granularity can be extracted from the digitised images, which are equal to results obtained by standardised measurement procedures. Specific statistical characteristics of film noise are theoretically derived and subsequently verified by the obtained data, including characteristics such as Gaussianity and spatial spectral characteristics of the optical density fluctuations. It is shown that the presented method correctly measures the granularity of film noise and can therefore replace time-consuming microdensitometer measurements traditionally required for film system classifications. Additionally, the inherent unsharpness of film systems was investigated and compared with literature data. This comparison serves as another validation approach of the presented method.
Digital Volume Correlation (DVC) is a powerful set of techniques used to compute the local shifts of 3D images obtained, for instance, in tomographic experiments. It is utilized to analyze the geometric changes of the investigated object as well as to correct the corresponding image misalignments for further analysis. It can therefore be used to evaluate the local density changes of the same regions of the inspected specimens, which might be shifted between measurements. In recent years, various approaches and corresponding pieces of software were introduced. Accuracies for the computed shift vectors of up to about 1‰of a single voxel size have been reported. These results, however, were based either on synthetic datasets or on an unrealistic setup. In this work, we propose two simple methods to evaluate the accuracy of DVC-techniques using more realistic input data and apply them to several DVC programs. We test these methods on three materials (tuff, sandstone, and concrete) that show different contrast and structural features.