Multiphase alloys such as advanced high strength steels show limited ductility due to interface decohesion at internal boundaries. This interface decohesion is caused by dislocations that pile-up at interfaces in the material, where they cause a stress concentration. This stress concentration in turn can lead to interface decohesion, resulting in the formation of voids, which, when they coalesce, can form a macroscopic crack. In order to understand the process of interface decohesion and the factors facilitating this, in this thesis interface decohesion at interfaces between the soft iron matrix of steel and hard precipitates is studied at the nano-scale with molecular dynamics simulations. From the nano-scale simulations cohesive laws are derived that relate the tractions at the interface to the separations at the interface. These cohesive laws can be used to describe interface decohesion in material models at the next larger length scale (micro-scale), such as discrete dislocation plasticity.

It is assumed that 17th century paintings were painted by a very systematic approach which involved the use of very specific palettes. For every pictorial unit, such a skin, a very specific palette was created. In this case the palette would then only be used to paint the skin and nothing else. The result is that for each pictorial unit a separate palette is used. This assumption can be verified by characterizing the all the paint mixtures present in a painting. Paint derives it colour from the pigments present within them. Many pigments used in the 17^th century contain characteristic heavy elements. The usage macro X-ray fluorescence (XRF) scanning is gaining popularity as a non-destructive method for determining elements present in a paintings. By the creation of elemental maps the pigments can be identified and localized in a painting. These maps already allow for visualizing many interesting features such as retouches, inpaintings and even hidden layers behind a painting. However the usage of the XRF data have never been used for identifying and visualizing the palettes used while painting.
In this work it is attempted to identify and visualize the palettes used in the Man with the red baret by Rembrandt van Rijn, by an in-depth analysis of raw XRF data. The raw data was model fitted using a simulated annealing algorithm to retrieve spectral line intensities in each point of the painting. The fitted spectral intensities was then visualized using t-distributed stochastic neighbour embedding (t-SNE). With help of the t-SNE visualisation the painting is divided into 24 sections. The suggestion is that these 24 sections each contain a different and unique paint mixture. This suggestion is further investigated by the use of the two-sample χ²-test between the sections’ spectral composition. The combination of model fitting alongside with t-SNE on a XRF spectra can indeed visualize different paint mixtures and give strong suggestion of the different palettes used in Man with the red baret.