Paola Rizzi

The Treasure of Marengo, a unique piece of art composed by 28 kg of Roman silver artifacts, was studied from a scientific point of view. In this paper, a comprehensive method of analysis of archeological metallic materials is proposed, allowing to determine: i) the gilding techniques used in antique; ii) the production techniques of the silver objects; iii) the Roman soldering composition; iv) the restoration techniques used at the beginning of the XX century; and v) the degradation state of the artifacts. In the proposed method, information obtained from different nondestructive techniques (i.e., energy–dispersive spectroscopy, X–ray fluorescence spectroscopy, scanning electron microscopy, optical and stereo microscopy) was combined with microstructural and compositional studies either on microsamples or on artifacts without sampling. The information obtained on the Treasure allowed to determine that the artifacts are embrittled due to an intergranular corrosion due to the presence of copper and lead precipitates at the grain boundaries in the as–solidified alloys. The presence of lead makes it possible to attribute silver extraction to cupellation processes. Gilding can be attributed to a leaf gilding for all the findings examined, except one for which fire–gilding technique was confirmed by the presence of mercury.

The development of hybrid materials combining functional polymers with nanostructured metals has gained significant attention for environmental and sensing applications. In this study, we present a proof-of-concept demonstration of the first-ever binding of a nanosponge polymer (NS) to nanoporous gold (NPG). The polymer was functionalized with a biodegradable lipoic acid linker, allowing efficient attachment to gold surfaces via sulfide bonds. To achieve successful binding, reducing agents were required during NS synthesis to generate free thiol (−SH) groups. Two synthesis approaches were tested, and optimal binding was confirmed with a polymer-to-surface ratio of 1.1 × 10^–3 mg/mm², as validated by surface-enhanced Raman spectroscopy (SERS) and colorimetric analysis. Additionally, the functionalized NPG was tested for its ability to detect methylene blue, a model contaminant, showing a 19% higher response compared to unmodified gold. These findings establish a foundation for further development of hybrid materials aimed at the detection and potential removal of emerging pollutants from water.

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB₂). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.

Dealloying technique has been used for centuries as an attractive method for producing porous surfaces by removing one or more undesirable elements from the surface. Since early 2000s, the technique has been further developed for understanding the dealloying mechanism and tailoring it to produce chemically homogeneous materials with nanoporous (np) morphology. Dealloying has found numerous applications such as sensors, catalysts, as well as in the biomedical field, which is fairly recent and has attracted great attention on this topic. This review investigates the dealloying technique for preparing nanoporous materials and nanoporous surfaces by using different modification routes on various types of Ti-based alloys for biomedical implant application. There has been significant growth in studying dealloying of crystalline, amorphous, shape memory, and composites-based Ti alloys. This review aims to summarise the findings from literature and discuss the scope of this technique and challenges involved for future aspects.

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.

In this study, we address the need for sustainable and scalable synthesis routes for hydrogen storage materials by developing a FeTi alloy in which vanadium (V) partially substitutes for titanium (Ti). The alloy was synthesized using mechanical alloying, compaction, and post-annealing, employing industrial-grade Fe and Ti powders and an alternative to pure vanadium, i.e., ferrovanadium (Fe–V). X-ray diffraction (XRD) analysis of the mechanically alloyed mixture revealed the partial formation of a Fe(V) solid solution, along with residual Ti. Subsequent compaction and annealing at 1000 °C led to the formation of the FeTi(V) phase, accompanied by two minor secondary phases, Fe₂Ti and Fe₂Ti₄O. A maximum phase yield of 90% for FeTi was achieved after 48 h of annealing. The novelty of this work lies in the demonstration of a sustainable and economical synthesis approach for V-substituted FeTi alloys using industrial-grade raw materials, offering a potential reduction in the carbon footprint compared with conventional melting techniques.