Celia Duce

New Insights into the Ageing of Linseed Oil Paint Binder: A Qualitative and Quantitative Analytical Study

This paper presents an analytical investigation of paint reconstructions prepared with linseed oil that have undergone typical 19th century treatments in preparation for painting. The oil was mechanically extracted from the same seed lot, which was then processed by various methods: water washing, heat treatments, and the addition of driers, with and without heat. A modern process lead white (Dutch source, Schoonhoven) and a commercially available vine black were used as pigments. The reconstructions were prepared in 1999, and naturally aged from then onwards. We compared thermogravimetric analysis (TG), which yields macromolecular information, with gas chromatography-mass spectrometry (GC-MS) and direct exposure mass spectrometry (DEMS), which both provide molecular information. The study enabled us to quantitatively demonstrate, for the first time, that the parameters used to identify drying oils are deeply influenced by the history of the paint. In particular, here we show that the ratio between the relative amounts of palmitic and stearic acid (P/S), which is used as an index for differentiating between drying oils, is extremely dependent on the pigments present and the age of the paint. Moreover the study revealed that neither the P/S parameter nor the ratios between the relative amounts of the various dicarboxylic acids (azelaic over suberic and azelaic over sebacic) can be used to trace the sorts of pre-treatment undergone by the oil investigated in this study. The final results represent an important milestone for the scientific community working in the field, highlighting that further research is still necessary to solve the identification of drying oils in works of art.

VLE and LLE of perfluoroalkane + alkane mixtures

Abstract Vapour–liquid equilibria (VLE) of binary mixtures of n -perfluorohexane plus a n -alkane (C 5 –C 8 ) and of n -hexane plus a n -perfluoroalkane (C 5 –C 8 ) were determined using a head-space gas-chromatographic technique. Excess molar Gibbs energies, G E , for the systems investigated have been obtained by a least-square treatment of equilibrium data. Liquid–liquid equilibria (LLE) were determined by turbidimetry for n -perfluorohexane+ n -C n H 2 n +2 ( n =6–8) mixtures and the observed T – x curves compared with those predicted by the temperature dependence of activity coefficients. All mixtures show strong positive deviations from ideality, which increase with the size of the second component. Solute–solvent and solute–solute interactions have been examined by calculating and discussing solvation Gibbs energies and Kirkwood–Buff integrals. Perfluoroalkanes proved to be inert molecules that interact weakly with themselves as well as with alkanes.

Physico-chemical characterization of protein–pigment interactions in tempera paint reconstructions: casein/cinnabar and albumin/cinnabar

AbstractIn this work, we characterized paint reconstructions using ovalbumin and casein as binders, and cinnabar (HgS) as a pigment, before and after artificial ageing. Egg and casein are common paint binders that were used historically in the technique of tempera painting. Despite extensive research on the identification of proteinaceous binders in paintings, there is a substantial lack of knowledge regarding the ageing pathway of their protein content, and their chemical interaction with inorganic pigments. Thermogravimetric analysis, infrared spectroscopy and size-exclusion chromatography (SEC) were used to reveal the physico-chemical processes involved in the ageing of proteins in paintings. Taken together, the three techniques highlighted that proteins are subject to both cross-linking and hydrolysis upon ageing, and to a lesser extent, to oxidation of the side chains. Mercury–protein interactions were also revealed using a cold vapour generation atomic fluorescence spectrometer mercury-specific detector coupled to SEC. The study clearly showed that HgS forms stable complexes with proteins and acts as a sensitizer in cross-linking, hydrolysis and oxidation. FigureA multi-techinque approach to the study of protein/cinnabar tempera paint recontructions: thermogravimetric analysis, Fourier Transform Infrared Spectroscopy and size exclusion chromatography

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

A multi-analytical approach to studying binding media in oil paintings

This article presents a multi-analytical approach to investigating the drying, polymerisation and oxidative degradation of linseed oil, which had undergone various treatments known to be undertaken during the nineteenth century in preparation for painting. The oil was mechanically extracted from the same seed lot then processed by different methods: water washing, heat treatments, and the addition of driers, with and without heat. The oil was prepared in 1999 within the framework of the MOLART project. We compared thermogravimetric analysis (TG), which yields macromolecular information, with gas-chromatography mass-spectrometry (GC/MS) and direct exposure mass spectrometry (DE-MS), which provide molecular information. This comparison enabled us to elucidate the role of pre-treatment on the composition of the oil. TG and oxygen uptake curves registered at a constant temperature helped us to identify the different physical behaviour of the oil samples, thus highlighting the presence of hydrolysed, oxidised and crosslinked fractions, as a consequence of the different pre-treatments. GC/MS was used to characterise the soluble and non-polymeric fraction of the oil, to calculate the ratios of palmitic to stearic acid (P/S), and azelaic to palmitic acid (A/P), and to further evaluate the effects of oil pre-treatments. DE-MS using chemical ionisation with CH4, enabled us to establish the chemical composition of the oil in different stages of ageing. DE-MS proved to be a useful tool for a simultaneous semi-quantitative characterisation of the free fatty acids, monoglycerids, diglycerides and triglycerides present in each sample. The combination of thermal analysis with GC/MS and DE-MS enabled a model to be developed, which unravelled how oil pre-treatments produce binders with different physical–chemical qualities.

Interactions between inorganic pigments and rabbit skin glue in reference paint reconstructions

The thermal degradation of rabbit skin glue, a collagen-based proteinaceous material used as a paint binder in paintings, was investigated in this paper. Paint reconstructions of the glue on its own or mixed with azurite (Cu3(CO3)2(OH)2), calcium carbonate (CaCO3), hematite (Fe2O3·nH2O) and red lead (Pb3O4) were analysed using a thermoanalytical approach. This method enabled us to investigate the interactions between the glue and pigments before and after artificial indoor light ageing. The study was carried out using differential scanning calorimetry, thermogravimetry and thermogravimetry/FTIR analysis already successfully employed to characterize the paint binders. The results highlighted that all the inorganic pigments interact with rabbit skin glue, thus decreasing the thermal stability of the binder. Light ageing further decreased the thermal stability of pigmented paint replicas, suggesting a moderate increase in the rate of the degradation.

Surfactant recovery from mesoporous metal-modified materials (Sn-, Y-, Ce-, Si-MCM-41), by ultrasound assisted ion-exchange extraction and its re-use for a microwave in situ cheap and eco-friendly MCM-41 synthesis

Different metal substituted (Y, Sn and Ce) MCM-41 materials were synthesized and detemplated by a low temperature surfactant removal methodology. All metal substituted materials showed an increase in the d100 lattice parameter compared to the parent MCM-41 matrices. The increase depends on both the metal type and amount that is successfully incorporated by direct conventional hydrothermal synthesis. The metal modified MCM-41 materials were detemplated by an ultrasound assisted (US) ion-exchange process using methanol as the solvent (NH4NO3/US/MeOH). The effect of the ultrasound amplitude, extraction time and salt concentration were explored, and optimal values were determined for Y–MCM-41 detemplation (40 mM of NH4NO3, 60% of US amplitude and 15 min of adiabatic treatment). The removal percentage achieved with these values was in the following order: Y (97.7%) > Ce (94.4%) > Sn (92.1%) > Si (90.3%). Several techniques (SAXS, FTIR, TGA, 1H MAS, 29Si HPDEC MAS NMR and N2 physisorption) demonstrated that the mesoporous materials keep their hexagonal structure and high surface area after the NH4NO3/US/MeOH surfactant extraction. Moreover, the thermal shrinkage of the structure was reduced in the following order: Si (0.6%) < Sn (4%) < Ce (5%) < Y (9%) < calcined samples (from 9 to 15%). The surfactant recovered was successfully recycled in a consecutive microwave assisted hydrothermal synthesis cycle (MW-HT). The synergy of different strategies (MW-HT synthesis, NH4NO3/US/MeOH surfactant removal and surfactant recovery) produces considerable time, energy and cost abatement, environmental impact reduction and promising scale up projections in the eco-friendly synthesis of MCM-41 materials.

Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks

This paper reports some recent results from the empirical evaluation of different types of structured molecular representations used in QSPR analysis through a recursive neural network (RNN) model, which allows for their direct use without the need for measuring or computing molecular descriptors. This RNN methodology has been applied to the prediction of the properties of small molecules and polymers. In particular, three different descriptions of cyclic moieties, namely group, template and cyclebreak have been proposed. The effectiveness of the proposed method in dealing with different representations of chemical structures, either specifically designed or of more general use, has been demonstrated by its application to data sets encompassing various types of cyclic structures. For each class of experiments a test set with data that were not used for the development of the model was used for validation, and the comparisons have been based on the test results. The reported results highlight the flexibility of the RNN in directly treating different classes of structured input data without using input descriptors.

Novel configurations for a citrus waste based biorefinery: from solventless to simultaneous ultrasound and microwave assisted extraction

Innovative extraction configurations for the biorefining of a biomass waste (citrus peel) were developed in this work. Non-conventional energies, such as microwaves (MW) and ultrasounds (US), were directly irradiated to the fresh orange peel using a versatile MW coaxial dipole antenna. This particular MW configuration enabled us to build two new extraction systems: (1) a coaxial solventless MW-assisted extraction (SMWAE) approach and, (2) a simultaneous ultrasound coaxial MW-assisted hydrodistillation (US-MWHD) method. The yield and chemical composition of the essential oils (EOs) of the orange peel obtained by the two innovative approaches were analyzed as a function of the extraction time and compared with those from coaxial microwave hydrodistillation (MWHD) and conventional hydrodistillation (CH). The EOs were chemically characterized by GC and GC-MS analysis. The residue mash was then used to extract pectin by a MW-assisted procedure. The structure and thermal stability of the pectin were investigated by FTIR and TG. The biorefining of EOs and pectin from a citrus waste maximises the benefits of our proposed green methodologies, which involve safe operability, faster processing and easy scalability. Furthermore, the energy consumed per unit mass of products in each step of the orange peel biorefining clearly showed that the most promising approach is SMWAE (since it is around 27 times lower than the CH approach). MWHD and US-MWHD also showed more than 60% energy savings compared to CH.

Loading of halloysite nanotubes with BSA, α-Lac and β-Lg: A Fourier transform infrared spectroscopic and thermogravimetric study

Halloysite nanotubes (HNTs) are considered as ideal materials for biotechnological and medical applications. An important feature of halloysite is that it has a different surface chemistry on the inner and outer sides of the tubes. This property means that negatively-charged molecules can be selectively loaded inside the halloysite nanoscale its lumen. Loaded HNTs can be used for the controlled or sustained release of proteins, drugs, bioactive molecules and other agents. We studied the interaction between HNTs and bovine serum albumin, α lactalbumin and β -lactoglobulin loaded into HTNs using Fourier transform infrared spectroscopy and thermogravimetry. These techniques enabled us to study the protein conformation and thermal stability, respectively, and to estimate the amount of protein loaded into the HNTs. TEM images confirmed the loading of proteins into HTNs.