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A Round Robin (RR) exercise on selected coated materials has been carried out with the aim of finding the optimal conditions for the analysis of nitride layers with GD-OES. Such pre-normative work is necessary for the evaluation of parallel development of the production of nitride layers as certified reference materials (CRMs). Two types of samples, TiN layer and VN layer, respectively, with chemical compositions close to stoichiometry and a thickness of ~3 µm, deposited on a steel substrate, have been provided to the RR participants. Additionally, another type of sample, a (100 nm CrNi/100 nm Cu) multilayer (ML) stack deposited on silicon wafer has also been included in the RR. This sample can be used as a CRM for checking GD spectrometer conditions and it has been proved helpful in further development of GDS instrumentation and methodology. The RR exercise has been performed in the frame of the EC Thematic Network on Glow Discharge Spectroscopy for Spectrochemical Analysis (GDS-Net).
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.