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Due to their unique physico-chemical properties, nanoparticles are well established in research and industrial applications. A reliable characterization of their size, shape, and size distribution is not only mandatory to fully understand and exploit their potential and develop reproducible syntheses, but also to manage environmental and health risks related to their exposure and for regulatory requirements. To validate and standardize methods for the accurate and reliable particle size determination nanoscale reference materials (nanoRMs) are necessary. However, there is only a very small number of nanoRMs for particle size offered by key distributors such as the National Institute of Standards and Technology (NIST) and the Joint Research Centre (JRC) and, moreover, few provide certified values. In addition, these materials are currently restricted to polymers, silica, titanium dioxide, gold and silver, which have a spherical shape except for titania nanorods. To expand this list with other relevant nanomaterials of different shapes and elemental composition, that can be used for more than one sizing technique, we are currently building up a platform of novel nanoRMs relying on iron oxide nanoparticles of different shape, size and surface chemistry. Iron oxide was chosen as a core material because of its relevance for the material and life sciences.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
By means of an effective medium (EMA) based approach, it is possible to use spectroscopic ellipsometry to determine the mixing ratios between air and material in porous dielectrics and calculate a quantitative value of the porosity for these materials. As this method is model-based, it is very difficult to provide a quantitative measure for the accuracy of porosity values determined by this method. Valuable additional information can often be obtained by combining ellipsometry with other complementary methods. In the present study, the ellipsometry results were validated by Electron Probe Microanalysis (EPMA) with the option of layer analysis (by the socalled “STRATAGem” approach), gravimetry, and electron microscopy. We analysed porous TiO2 thin layers synthesised by means of a template synthesis approach (evaporation induced self assembly).
Ellipsometry measurements on porous samples are difficult to analyse due to the complexity of the models necessary. Often it is difficult to decide if the measured data contains enough information to successfully determine the target quantities (mixing ratio, dielectric function of the matrix material). One method to decrease the complexity of the fit and therefore use the measurement data most efficiently is a multi-sample analysis. In the present case, multi-sample analysis was used for determining the porosity factors of the individual layers while using one common set of dielectric function values for the matrix in all cases.
While the results of the fit analysis in this case can be used to show the feasibility and also the limitations of the multi-method approach, the porosity values themselves show a promising agreement between the independent methods. It can therefore be concluded that determining porosity values with ellipsometry is in accordance to other methods to the level of accuracy provided by the complementary methodologies.