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Core-shell (CS) particles have been increasingly used for a wide range of applications due to their unique properties by merging individual characteristics of the core and the shell materials. The functionality of the designed particles is strongly influenced by their surface roughness. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task for Scanning Electron Microscopy (SEM).
The SEM images contain two-dimensional (2D) information providing contour roughness data only from the projection of the particle in the horizontal plane. This study presents a practical procedure to achieve more information by tilting the sample holder, hence allowing images of different areas of a single particle to be recorded at different orientations under the same view angle. From the analysis of these images, quasi three-dimensional (3D) information is obtained.
Three types of home-made particles were investigated: i) bare polystyrene (PS) particles, ii) PS particles decorated with a first magnetic iron oxide (Fe3O4) nanoparticle shell forming CS microbeads, iii) PS/Fe3O4 particles closed with a second silica (SiO2) shell forming core-shell-shell (CSS) microbeads. A series images of a single particle were taken with stepwise tilted sample holder up to 10° by an SEM with high-resolution and surface sensitive SE-InLens® mode.
A reliable analysis tool has been developed by a script in Python to analyze SEM images automatically and to evaluate profile roughness quantitatively, for individual core-shell microparticles. Image analysis consists of segmentation of the images, identifying contour and the centre of the particle, and extracting the root mean squared roughness value (RMS-RQ) of the contour profile from the particle projection within a few seconds.
The variation in roughness from batch-to-batch was determined with the purpose to set the method as a routine quality check procedure. Flow cytometry measurements provided complementary data. Measurement uncertainties associated to various particle orientations were also estimated.
This study presents 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 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.
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.