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Titanium dioxide (TiO₂) has been used in numerous paintings since its creation in the early 1920s. However, due to this relatively recent adoption by the art world, we have limited knowledge about the nature and risk of degradation in museum environments. This study expands on the existing understanding of TiO₂facilitated degradation of linseed oil, by examining the effect of visible light and crystallographic phase (either anatase or rutile) on the reactivity of TiO₂. The present approach is based on a combination of experimental chemical characterization with computational calculation through Density Functional Theory (DFT) modeling of the TiO₂-oil system. Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FT-IR) enabled the identification of characteristic degradation products during UV and visible light aging of both rutile and anatase based paints in comparison to BaSO₄and linseed oil controls. In addition, cratering and cracking of the paint surface in TiO₂based paints, aged under visible and UV–vis illumination, were observed through Scanning Electron Microscopy (SEM). Finally, Density Functional Theory (DFT) modeling of interactions between anatase TiO₂and oleic acid, a fatty acid component of linseed oil, to form a charge transfer complex explains one possible mechanism for the visible light activity observed in artificial aging. Visible light excitation of this complex sensitizes TiO₂by injecting an electron into the conduction band of TiO₂to generate reactive oxygen species and subsequent degradation of the oil binder by various mechanisms (e.g., formation of an oleic acid cation radical and other oxidation products).

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A kinetic Monte Carlo model of polyurethane polymerization which explicitly tracks the polymer sequences is developed and shared. This model is benchmarked against theoretical and experimental polyurethane data and used to investigate the effect on oligomer distributions of unequal reactivity of the first and second isocyanate to react. The reverse reactions using thermodynamic consistency are then added to the framework, and analogous to the addition polymerization concept of ceiling temperature, equilibrium chain length distributions at various temperatures are calculated. For a mixture of three monomers AA, BB, and CC, where BB and CC do not react with one another, are present in stoichiometric proportions, and have different enthalpies of reaction with AA, an odd‐even effect emerges. Odd length chains are more likely than even length chains for temperatures at which BB and CC have significantly different equilibrium conversions. The concept of ceiling temperature that is typically cited for addition polymers is extended here to provide a measure of conditions under which depolymerization for recycling is favored.

 

While the chemistry of artists’ paints has previously been studied and reviewed, these studies only capture a portion of the properties affecting the response of paint materials. The mechanical properties of artists’ paints relate to the deformation response of these materials when a stress is applied. This response is dependent on many factors, such as paint composition, pigment to binder ratio, temperature, relative humidity, and solvent exposure. Here, thirty years of tensile testing data have been compiled into a single dataset, along with the testing conditions, to provide future researchers with easy access to these data as well some general discussion of their trends. Alongside the more commonly used techniques of tensile testing and dynamic mechanical analysis, new techniques have been developed to more fully investigate the mechanical properties, and are discussed along with salient results. The techniques have been divided into two categories: those that are restricted to use on model systems and those that are applicable to historic samples. Techniques applied to model systems (tensile testing, dynamic mechanic analysis, quartz crystal microbalance, vibration studies) require too large of a sample to be taken from art objects or focus on the mechanical properties of the liquid state (shear rheometry). Techniques applied to historic samples incorporate the use of small sample sizes (nanoindentation), optical techniques (laser shearography), computational simulations (finite element analysis), and non-invasive comparative mechanical properties (single-sided nuclear magnetic resonance) to investigate and predict the mechanical properties of paints.

 

Optical coherence tomography (OCT) is an optical technique which allows for volumetric visualization of the internal structures of translucent materials. Additional information can be gained by measuring the rate of signal attenuation in depth. Techniques have been developed to estimate the rate of attenuation on a voxel by voxel basis. This depth resolved attenuation analysis gives insight into tissue structure and organization in a spatially resolved way. However, the presence of speckle in the OCT measurement causes the attenuation coefficient image to contain unrealistic fluctuations and makes the reliability of these images at the voxel level poor. While the distribution of speckle in OCT images has appeared in literature, the resulting voxelwise corruption of the attenuation analysis has not. In this work, the estimated depth resolved attenuation coefficient from OCT data with speckle is shown to be approximately exponentially distributed. After this, a prior distribution for the depth resolved attenuation coefficient is derived for a simple system using statistical mechanics. Finally, given a set of depth resolved estimates which were made from OCT data in the presence of speckle, a posterior probability distribution for the true voxelwise attenuation coefficient is derived and a Bayesian voxelwise estimator for the coefficient is given. These results are demonstrated in simulation and validated experimentally.

 

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra pixel-wise across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping facilitated by the interpretation of measured spectra by experts. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging to implement automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pigment identification based on XRF on a pixel-by-pixel basis remains an obstacle due to the high noise level. Therefore, we developed a deep-learning based pigment identification framework to fully automate the process. In particular, this method offers high sensitivity to the underlying pigments and to the pigments present in low concentrations, therefore enabling robust mapping of pigments based on single-pixel XRF spectra. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Poèmes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899–1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model. 

X-ray fluorescence (XRF) spectroscopy is a common technique in the field of heritage science. However, data processing and data interpretation remain a challenge as they are time consuming and often require a priori knowledge of the composition of the materials present in the analyzed objects. For this reason, we developed an open-source, unsupervised dictionary learning algorithm reducing the complexity of large datasets containing 10s of thousands of spectra and identifying patterns. The algorithm runs in Julia, a programming language that allows for faster data processing compared to Python and R. This approach quickly reduces the number of variables and creates correlated elemental maps, characteristic for pigments containing various elements or for pigment mixtures. This alternative approach creates an overcomplete dictionary which is learned from the input data itself, therefore reducing the a priori user knowledge. The feasibility of this method was first confirmed by applying it to a mock-up board containing various known pigment mixtures. The algorithm was then applied to a macro XRF (MA-XRF) data set obtained on an 18th century Mexican painting, and positively identified smalt (pigment characterized by the co-occurrence of cobalt, arsenic, bismuth, nickel, and potassium), mixtures of vermilion and lead white, and two complex conservation materials/interventions. Moreover, the algorithm identified correlated elements that were not identified using the traditional elemental maps approach without image processing. This approach proved very useful as it yielded the same conclusions as the traditional elemental maps approach followed by elemental maps comparison but with a much faster data processing time. Furthermore, no image processing or user manipulation was required to understand elemental correlation. This open-source, open-access, and thus freely available code running in a platform allowing faster processing and larger data sets represents a useful resource to understand better the pigments and mixtures used in historical paintings and their possible various conservation campaigns. 

Abstract In this study we investigate the impact of the addition of colourless glass particles to red glazes, as seen in many 15th-17th-century easel paintings. With the use of reconstructions, we examined the influence of the paint preparation process on the morphological and mechanical properties of the paint film. Three sets of reconstructions were made, a control without ground glass, reconstructions with coarse or fine ground glass mixed in, and reconstructions where fine ground glass was ground jointly with the pigment oil mixture. The latter gave the desired consistency and colour based on visual inspection. The dried reconstructions were non-invasively imaged using optical coherence tomography (OCT). A data-analysis pipeline was developed for both the segmentation of the OCT images and the measurement of the size and spatial distributions of the glass particles within the glaze layer. Moreover, we used a nanoindentation protocol to measure the viscoelastic properties of the dried red glaze film. The OCT results show an expected decrease in median particle size with longer grinding-time, for which the additional grinding with pigment/oil resulted in a more narrow size distribution and a homogenous spatial distribution of the glass particles. The nanoindentation results indicate that the addition of glass particles increases the elastic and viscous moduli of the red glaze layers. The homogeneous size distribution, obtained by grinding the oil, pigment, and glass together, induces higher elastic and viscous moduli. Our imaging and analyses approach, combining OCT and nanoindentation, provides a non-invasive and quantitative investigation of glass particles in (semi-) transparent paint layers, and their effect on the mechanical properties of the glaze. The results of this study contribute to a better understanding of the artists’ addition of ground glass in paint layers.