6 Materialchemie
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he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning.
Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method.
Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task.
The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms.
This presentation is structured in two parts: i) Hybrid metrology by combining SEM with AFM (N. Feltin) and ii) hybridization and corelative microscopy by SEM, STEM-in-SEM, TEM, EDS, Auger Electron Microscopy, TKD and more (D. Hodoroaba).
The first part is focused on the metrological part of the hybrid measurement SEM-AFM, the second part offers some further possibilities of correlative microscopy of nanoparticles based on practical examples.