6 Materialchemie
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- Roughness (4)
- Core-shell particles (3)
- Electron microscopy (3)
- Image processing (3)
- MamaLoCA (2)
- Particle characterisation (2)
- Core–shell particles (1)
- Fast Fourier Transform (1)
- Image analysis (1)
- MamaLoCa (1)
Organisationseinheit der BAM
- 6.1 Oberflächen- und Dünnschichtanalyse (4) (entfernen)
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.
The roughness as a property of core–shell (CS) microparticles plays a key role in their functionality. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task with approaches using electron microscopy images being scarce and showing pronounced differences in terms of methodology and results. This work presents a generalized method for the reliable roughness determination of nonplanar specimens such as CS particles from electron microscopic images, the method being robust and reproducible with a high accuracy. It involves a self-written software package (Python) that analyzes the recorded images, extracts corresponding data, and calculates the roughness based on the deviation of the identified contour. Images of single particles are taken by a dual mode scanning electron microscopy (SEM) setup which permits imaging of the same field-of-view of the sample with high resolution and surface sensitive in SE InLens mode as well as in transmission mode (TSEM). Herein, a new type of polystyrene core–iron oxide shell–silica shell particles is developed to serve as a set of lower micrometer-sized study objects with different surface roughness; the analysis of their images by the semiautomatic workflow is demonstrating that the particles’ profile roughness can be quantitatively obtained.
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.