Perfluoroalkylated substances in food matrices: development of mass spectrometry based analytical methods and preliminary monitoring

Perfluoroalkylated substances are a group of chemicals that have been largely employed during the last 60 years in several applications, widely spreading and accumulating in the environment due to their extreme resistance to degradation. As a consequence, they have been found also in various types of food as well as in drinking water, proving that they can easily reach humans through the diet. The available information concerning their adverse effects on health has recently increased the interest towards these contaminants and highlighted the importance of investigating all the potential sources of human exposure, among which diet was proved to be the most relevant. This need has been underlined by the European Union through Recommendation 2010/161/EU: in this document, Member States were called to monitor their presence of in food, producing accurate estimations of human exposure. The purpose of the research presented in this thesis, which is the result of a partnership between an Italian and a French laboratory, was to develop reliable tools for the analysis of these pollutants in food, to be used for generating data on potentially contaminated matrices. An efficient method based on liquid chromatography-mass spectrometry for the detection of 16 different perfluorinated compounds in milk has been validated in accordance with current European regulation guidelines (2002/657/EC) and is currently under evaluation for ISO 17025 accreditation. The proposed technique was applied to cow, powder and human breast milk samples from Italy and France to produce a preliminary monitoring on the presence of these contaminants. In accordance with the above mentioned European Recommendation, this project led also to the development of a promising technique for the quantification of some precursors of these substances in fish. This method showed extremely satisfying performances in terms of linearity and limits of detection, and will be useful for future surveys.

Study of the inter-annual variability of particle vertical fluxes in two moorings in the Ross Sea (Antarctica)

The most ocean - atmosphere exchanges take place in polar environments due to the low temperatures which favor the absorption processes of atmospheric gases, in particular CO2. For this reason, the alterations of biogeochemical cycles in these areas can have a strong impact on the global climate. With the aim of contributing to the definition of the mechanisms regulating the biogeochemical fluxes we have analyzed the particles collected in the Ross Sea in different years (ROSSMIZE, BIOSESO 1 and 2, ROAVERRS and ABIOCLEAR projects) in two sites (mooring A and B). So it has been developed a more efficient method to prepare sediment trap samples for the analyses. We have also processed satellite data of sea ice, chlorophyll a and diatoms concentration. At both sites, in each year considered, there was a high seasonal and inter-annual variability of biogeochemical fluxes closely correlated with sea ice cover and primary productivity. The comparison between the samples collected at mooring A and B in 2008 highlighted the main differences between these two sites. Particle fluxes at Mooring A, located in a polynia area, are higher than mooring B ones and they happen about a month before. In the mooring B area it has been possible to correlate the particles fluxes to the ice concentration anomalies and with the atmospheric changes in response to El Niño Southern Oscillations. In 1996 and 1999, years subjected to La Niña, the concentrations of sea ice in this area have been less than in 1998, year subjected to El Niño. Inverse correlation was found for 2005 and 2008. In the mooring A area significant differences in mass and biogenic fluxes during 2005 and 2008 has been recorded. This allowed to underline the high variability of lateral advection processes and to connect them to the physical forcing.

Homoleptic and Heteroleptic Metal Carbonyl Clusters and Nanoclusters

This Thesis aims at presenting the general results achieved during my PhD, that was focused on the study and characterisation of new homoleptic and heteroleptic metal carbonyl clusters. From a dimensional point of view, the nuclearity of such species ranges from 2 to 44 metal atoms. Lower nuclearity compounds may be viewed as polymetallic complexes, whereas higher nuclearity species can reach the nanocluster size, by resembling to ultrasmall nanoparticles (USNPs). Initially, my research was focused on the investigation of small MCCs stabilised by N-Heterocyclic carbene (NHCs) ligands. At this regard, a general strategy for the synthesis of mono-anionic [Fe(CO)4(MNHC)]− and neutral Fe(CO)4(MNHC)2, Co(CO)4(MNHC) (M = Cu, Ag, Au; NHC = IMes, IPr) species has been developed. Furthermore, during this investigation, neutral trimetallic Fe(CO)4(MNHC)(M’NHC) (M, M’ = Cu, Ag, Au; M ≠ M'; NHC = IPr) and neutral heteroleptic Fe(CO)4(MNHC)(MNHC’) (M = Au; NHC = IMes, IPr) compounds have been isolated. Thermal treatment turned out to be an efficient method for the growth of the dimension of MCCs. Indeed, species of the type [M3Fe3(CO)12]3– and [M4Fe4(CO)16]4– (M = Ag, Au) as well as larger clusters were formed during the thermal treatment of the new Fe-M (M = Ag, Cu, Au) carbonyl compounds. These species inspired the investigation of promising reaction paths for the synthesis of Fe-M (M = Ag, Cu, Au) carbonyl compounds devoid of ancillary ligands and alloy MCCs, such as the heterometallic [MxM’5-xFe4(CO)16]3− (M, M' = Cu, Ag, Au; M ≠ M'; x = 0-5) carbonyl clusters. The second part of this Thesis regards high nuclearity MCCs. In particular, new strategies for the growth of platinum carbonyl clusters involving, for instance, the employment of bidentate phosphines are described, as well as the syntheses and the thermal decomposition of new Ni-M (Pd, Pt) carbonyl clusters.

Peptide-based low molecular weight gelators for the preparation of self-assembled materials

Low molecular weight gelators (LMWGs) based on pseudo-peptides are here studied for the preparation of supramolecular materials. These compounds can self-assemble through non-covalent interactions such as hydrogen bonds and π-π stacking, forming fibres and gels. A wide variety of materials can be prepared starting from these building blocks, which can be tuned and functionalised depending on the application. In this work, derivatives of the three aromatic amino acids L-Phenylalanine, L-Tyrosine and L-DOPA (3,4-dihydroxiphenylalanine) were synthesised and tested as gelators for water or organic solvents. First, the optimal gelating conditions were studied for each compound, varying concentration, solvent and trigger. Then the materials were characterised in terms of mechanical properties and morphology. Water remediation from dye pollution was the first focus of this work. Organogels were studied as absorbent of dyes from contaminated water. Hydrogels functionalised with TiO2 nanoparticles and graphene platelets were proposed as efficient materials for the photo-degradation of dyes. An efficient method for the incorporation of graphene inside hydrogels using the gelator itself as dispersant was proposed. In these materials a high storage modulus coexists with good self-healing and biocompatibility. The incorporation of a mineral phase inside the gel matrix was then investigated, leading to the preparation of composite organic/inorganic materials. In a first study, the growth of calcium carbonate crystals was achieved inside the hydrogel, which preserved its structure after crystal formation. Then the self-assembled fibres made of LMWGs were used for the first time instead of the polymeric ones as reinforcement inside calcium phosphate cements (CPCs) for bone regeneration. Gel-to-crystal transitions occurring with time in a metastable gel were also examined. The formation of organic crystals in gels can be achieved in multicomponent systems, in which a second gelator constitutes the independent gel network. Finally, some compounds unable to gelate were tested as underwater adhesives.

Performance evaluation of low environmental impact asphalt concretes using the mechanistic empirical design method based on laboratory fatigue and permanent deformation models

The "sustainability" concept relates to the prolonging of human economic systems with as little detrimental impact on ecological systems as possible. Construction that exhibits good environmental stewardship and practices that conserve resources in a manner that allow growth and development to be sustained for the long-term without degrading the environment are indispensable in a developed society. Past, current and future advancements in asphalt as an environmentally sustainable paving material are especially important because the quantities of asphalt used annually in Europe as well as in the U.S. are large. The asphalt industry is still developing technological improvements that will reduce the environmental impact without affecting the final mechanical performance. Warm mix asphalt (WMA) is a type of asphalt mix requiring lower production temperatures compared to hot mix asphalt (HMA), while aiming to maintain the desired post construction properties of traditional HMA. Lowering the production temperature reduce the fuel usage and the production of emissions therefore and that improve conditions for workers and supports the sustainable development. Even the crumb-rubber modifier (CRM), with shredded automobile tires and used in the United States since the mid 1980s, has proven to be an environmentally friendly alternative to conventional asphalt pavement. Furthermore, the use of waste tires is not only relevant in an environmental aspect but also for the engineering properties of asphalt [Pennisi E., 1992]. This research project is aimed to demonstrate the dual value of these Asphalt Mixes in regards to the environmental and mechanical performance and to suggest a low environmental impact design procedure. In fact, the use of eco-friendly materials is the first phase towards an eco-compatible design but it cannot be the only step. The eco-compatible approach should be extended also to the design method and material characterization because only with these phases is it possible to exploit the maximum potential properties of the used materials. Appropriate asphalt concrete characterization is essential and vital for realistic performance prediction of asphalt concrete pavements. Volumetric (Mix design) and mechanical (Permanent deformation and Fatigue performance) properties are important factors to consider. Moreover, an advanced and efficient design method is necessary in order to correctly use the material. A design method such as a Mechanistic-Empirical approach, consisting of a structural model capable of predicting the state of stresses and strains within the pavement structure under the different traffic and environmental conditions, was the application of choice. In particular this study focus on the CalME and its Incremental-Recursive (I-R) procedure, based on damage models for fatigue and permanent shear strain related to the surface cracking and to the rutting respectively. It works in increments of time and, using the output from one increment, recursively, as input to the next increment, predicts the pavement conditions in terms of layer moduli, fatigue cracking, rutting and roughness. This software procedure was adopted in order to verify the mechanical properties of the study mixes and the reciprocal relationship between surface layer and pavement structure in terms of fatigue and permanent deformation with defined traffic and environmental conditions. The asphalt mixes studied were used in a pavement structure as surface layer of 60 mm thickness. The performance of the pavement was compared to the performance of the same pavement structure where different kinds of asphalt concrete were used as surface layer. In comparison to a conventional asphalt concrete, three eco-friendly materials, two warm mix asphalt and a rubberized asphalt concrete, were analyzed. The First Two Chapters summarize the necessary steps aimed to satisfy the sustainable pavement design procedure. In Chapter I the problem of asphalt pavement eco-compatible design was introduced. The low environmental impact materials such as the Warm Mix Asphalt and the Rubberized Asphalt Concrete were described in detail. In addition the value of a rational asphalt pavement design method was discussed. Chapter II underlines the importance of a deep laboratory characterization based on appropriate materials selection and performance evaluation. In Chapter III, CalME is introduced trough a specific explanation of the different equipped design approaches and specifically explaining the I-R procedure. In Chapter IV, the experimental program is presented with a explanation of test laboratory devices adopted. The Fatigue and Rutting performances of the study mixes are shown respectively in Chapter V and VI. Through these laboratory test data the CalME I-R models parameters for Master Curve, fatigue damage and permanent shear strain were evaluated. Lastly, in Chapter VII, the results of the asphalt pavement structures simulations with different surface layers were reported. For each pavement structure, the total surface cracking, the total rutting, the fatigue damage and the rutting depth in each bound layer were analyzed.

Fast and accurate numerical solutions in some problems of particle and radiation transport: synthetic acceleration for the method of short characteristics, Doppler-broadened scattering kernel, remote sensing of the cryosphere

The aim of this work is to present various aspects of numerical simulation of particle and radiation transport for industrial and environmental protection applications, to enable the analysis of complex physical processes in a fast, reliable, and efficient way. In the first part we deal with speed-up of numerical simulation of neutron transport for nuclear reactor core analysis. The convergence properties of the source iteration scheme of the Method of Characteristics applied to be heterogeneous structured geometries has been enhanced by means of Boundary Projection Acceleration, enabling the study of 2D and 3D geometries with transport theory without spatial homogenization. The computational performances have been verified with the C5G7 2D and 3D benchmarks, showing a sensible reduction of iterations and CPU time. The second part is devoted to the study of temperature-dependent elastic scattering of neutrons for heavy isotopes near to the thermal zone. A numerical computation of the Doppler convolution of the elastic scattering kernel based on the gas model is presented, for a general energy dependent cross section and scattering law in the center of mass system. The range of integration has been optimized employing a numerical cutoff, allowing a faster numerical evaluation of the convolution integral. Legendre moments of the transfer kernel are subsequently obtained by direct quadrature and a numerical analysis of the convergence is presented. In the third part we focus our attention to remote sensing applications of radiative transfer employed to investigate the Earth's cryosphere. The photon transport equation is applied to simulate reflectivity of glaciers varying the age of the layer of snow or ice, its thickness, the presence or not other underlying layers, the degree of dust included in the snow, creating a framework able to decipher spectral signals collected by orbiting detectors.

Matrix Designs and Methods for Secure and Efficient Compressed Sensing

The idea of balancing the resources spent in the acquisition and encoding of natural signals strictly to their intrinsic information content has interested nearly a decade of research under the name of compressed sensing. In this doctoral dissertation we develop some extensions and improvements upon this technique's foundations, by modifying the random sensing matrices on which the signals of interest are projected to achieve different objectives. Firstly, we propose two methods for the adaptation of sensing matrix ensembles to the second-order moments of natural signals. These techniques leverage the maximisation of different proxies for the quantity of information acquired by compressed sensing, and are efficiently applied in the encoding of electrocardiographic tracks with minimum-complexity digital hardware. Secondly, we focus on the possibility of using compressed sensing as a method to provide a partial, yet cryptanalysis-resistant form of encryption; in this context, we show how a random matrix generation strategy with a controlled amount of perturbations can be used to distinguish between multiple user classes with different quality of access to the encrypted information content. Finally, we explore the application of compressed sensing in the design of a multispectral imager, by implementing an optical scheme that entails a coded aperture array and Fabry-Pérot spectral filters. The signal recoveries obtained by processing real-world measurements show promising results, that leave room for an improvement of the sensing matrix calibration problem in the devised imager.

Exploring New Material and Analytical Method for Characterization and Conservation of Artifacts

In this dissertation, we focus on developing new green bio-based gel systems and evaluating both the cleaning efficiency and the release of residues on the treated surface, different micro or no destructive techniques, such as optical microscopy, TGA, FTIR spectroscopy, HS-SPME and micro-Spatially Offset Raman spectroscopy (micro-SORS) were tested, proposing advanced analytical protocols. In the first part, a ternary PHB-DMC/BD gel system composed by biodiesel, dimethyl carbonate and poly-3 hydroxybutyrate was developed for cleaning of wax-based coatings applied on indoor bronze. The evaluation of the cleaning efficacy of the gel was carried out on a standard bronze sample which covered a layer of beeswax by restores of Opificio delle Pietre Dure in Florence, and a real case precious indoor bronze sculpture Pulpito della Passione attributed to Donatello. Results obtained by FTIR analysis showed an efficient removal of the wax coating. In the second part, two new kinds of combined gels based on electrospun tissues (PVA and nylon) and PHB-GVL gel were developed for removal of dammar varnish from painting. The electrospun tissue combined gels exhibited good mechanical property, and showed good efficient in cleaning over normal gel. In the third part, green deep eutectic solvent which consists urea and choline chloride was proposed to produce the rigid gel with agar for the removal of proteinaceous coating from oil painting. Rabbit glue and whole egg decorated oil painting mock-ups were selected for evaluating its cleaning efficiency, results obtained by ATR analysis showed the DES-agar gel has good cleaning performance. Furthermore, we proposed micro-SORS as a valuable alternative non-destructive method to explore the DES diffusion on painting mock-up. As a result, the micro-SORS was successful applied for monitoring the liquid diffusion behavior in painting sub-layer, providing a great and useful instrument for noninvasive residues detection in the conservation field.

An efficient Jeans modelling of axisymmetric galaxies with multiple stellar components

Dynamical models of stellar systems represent a powerful tool to study their internal structure and dynamics, to interpret the observed morphological and kinematical fields, and also to support numerical simulations of their evolution. We present a method especially designed to build axisymmetric Jeans models of galaxies, assumed as stationary and collisionless stellar systems. The aim is the development of a rigorous and flexible modelling procedure of multicomponent galaxies, composed of different stellar and dark matter distributions, and a central supermassive black hole. The stellar components, in particular, are intended to represent different galaxy structures, such as discs, bulges, halos, and can then have different structural (density profile, flattening, mass, scale-length), dynamical (rotation, velocity dispersion anisotropy), and population (age, metallicity, initial mass function, mass-to-light ratio) properties. The theoretical framework supporting the modelling procedure is presented, with the introduction of a suitable nomenclature, and its numerical implementation is discussed, with particular reference to the numerical code JASMINE2, developed for this purpose. We propose an approach for efficiently scaling the contributions in mass, luminosity, and rotational support, of the different matter components, allowing for fast and flexible explorations of the model parameter space. We also offer different methods of the computation of the gravitational potentials associated of the density components, especially convenient for their easier numerical tractability. A few galaxy models are studied, showing internal, and projected, structural and dynamical properties of multicomponent galaxies, with a focus on axisymmetric early-type galaxies with complex kinematical morphologies. The application of galaxy models to the study of initial conditions for hydro-dynamical and $N$-body simulations of galaxy evolution is also addressed, allowing in particular to investigate the large number of interesting combinations of the parameters which determine the structure and dynamics of complex multicomponent stellar systems.

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.