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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.
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.
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.
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.
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.
Core-shell (CS) particles with a polymeric core and a silica shell play an important role in the materials and (bio)analytical sciences. Besides the establishment of reliable synthesis procedures, comprehensive particle characterization is essential for batch-to-batch reproducibility and objective performance assessment across architectures, protocols, and laboratories. Particle characterization with respect to size, size distribution, shell thickness and texture, surface area and roughness or materials composition is commonly conducted with different analytical methods, often on different samples. Our approach uses a dual-mode TSEM/SEM set-up with an EDX detector to obtain a complementary data set with sufficient statistical confidence of one and the same sample on a single instrument. Our protocol reveals information about size, size distribution and shell thickness of the various particles employed from overview images, while an increased field of view (FOV) and high-resolution EDX analysis yields detailed information on shell texture and elemental composition. An image analysis tool was developed to derive and quantify the profile roughness of CS particles from individual beads. Comparison with surface roughness data from AFM showed a similar trend in roughness across the series of particles. Reliable classification into smooth and rough is proposed and roughness changes within different particle batches were tracked systematically.