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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.
Low-temperature co-fired ceramics (LTCC) are used to fabricate multilayer circuits which are robust in harsh environments. Thick-film technology is well established for the metallization of circuit boards and microsystems. For specific sensor applications, the combination of LTCC and thin-film technology is advantageous to reach higher structure resolutions. Due to the high roughness of as-fired LTCC surfaces compared with silicon-wafers, the deposition of low-defect- films with narrowly specified properties is challenging. There is spare literature about thin films on commercial LTCC comparing different material systems or sintering techniques. For developing thin film sensors on multilayer circuits it is crucial to identify thin-film-compatible commercial LTCC material as well as the crucial surface properties. In this work we evaluate the thin-film capability of different LTCC surfaces.
The as-fired surfaces of free-sintered, constrained-sintered (sacrificial tape), and pressure-assisted sintered commercial LTCCs (DP951, CT708, CT800), as well as respective polished surfaces, were analyzed by tactile and optical roughness measurements and scanning electron microscopy. The thin-film capability of the LTCC surfaces was assessed by sheet resistance and temperature coefficient of resistance (TCR) of deposited Ni thin-film layers. Contrary to the expectations, no correlation between roughness and thin-film capability was found. Ni thin films on constrained sintered DP951 show the lowest sheet resistance and highest TCR within the experimental framework of the as-fired surfaces. The influence of surface morphology on the film properties is discussed.
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.