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A novel Infinitesimal Columns (IC) simulation model is introduced in this study for the quantitativeanalysis of core-shell nanoparticles (CSNP) by means of XPS, which combines the advantages of exist-ing approaches. The IC model is applied to stabilised Lumidot™CdSe/ZnS 610 CSNP for an extensiveinvestigation of their internal structure, i.e. calculation of the two shell thicknesses (ZnS and stabiliser)and exploration of deviations from the idealised CSNP composition. The observed discrepancies betweendifferent model calculations can be attributed to the presence of excess stabiliser as well as synthesisresidues, demonstrating the necessity of sophisticated purification methods. An excellent agreement isfound in the comparison of the IC model with established models from the existing literature, the Shardmodel and the software SESSA.
Advances in scanning electron microscopy (SEM) enable the high-resolution imaging of single nanoparticles (NPs) with sizes well below 10 nm. The SEM analysis in transmission mode (T-SEM) of NPs on thin film supports has many benefits when compared to the analysis of NPs on bulk substrates. The enhanced material (mass – thickness) contrast of the T-SEM imaging mode is well suited for in-depth and, particularly valuable, to very accurate, traceable, lateral dimensional measurements of NPs. Compared to samples prepared on bulk substrates, T-SEM with energy dispersive X-ray spectroscopy (EDS) achieves a drastically improved spatial resolution of the emitted X-rays. The poor signal-to-noise ratio of the X-ray spectra emitted by a single nanoparticle (NP) can be improved by the use of high-sensitivity (high collection solid angle) silicon drift (SDD), energy-dispersive X-ray spectrometers (EDS). The EDS spectral imaging of a single NP with a spatial resolution below 10 nm has become possible. This is demonstrated by means of various examples of nanostructures. Advanced data processing of T-SEM/EDS results sets the stage for the automated classification of NPs by feature analysis. This method combines the detection of morphological structures of interest by image processing of T-SEM micrographs with the chemical classification by EDS.