Cultural heritage includes an extraordinary variety of heterogeneous materials, each of which tells a unique story and requires specific approaches for its study, conservation, and enhancement. Inorganic materials include, for example, stones, metals, ceramics, and glass, while organic materials are classified as wood, paper, textiles, leather, and parchment. Most materials of interest are subject to degradation phenomena of a biological and/or physical-chemical nature, reacting to various degradation phenomena in different ways.

Often, the coexistence of different materials leads to the development of other compounds, known as secondary compounds, which are formed through chemical reactions or physical processes between the original materials, altering their characteristics and giving rise to new properties or structures. Furthermore, microclimatic conditions of conservation, the presence of contaminants, and the application of restoration products add an additional element of complexity to the system to be investigated. Manuscripts, for example, are composed of different parts including a paper support based on vegetable fibers, iron-gall inks, pigments, and bindings generally made with other organic materials. Due to the acidity of the inks and the denaturation of the paper support, such as depolymerization, they are often subject to periodic deacidification processes with products whose long-term stability is unknown and which could compromise the legibility of the works.

The in-depth knowledge of the constituent materials and degradation phenomena therefore requires an interdisciplinary approach that integrates physics, chemistry, and materials science. Advanced analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray fluorescence (XRF), and hyperspectral imaging play a fundamental role in the study of material properties, allowing for the identification of elements, compounds, and their spatial distribution.

The more recent use of machine learning techniques in this context is transforming the way these problems are addressed, providing new data interpretation solutions. This new approach makes it possible to handle more complex issues by providing new investigation tools with the ability to manage large amounts of data and to simultaneously consider multiple parameters.

The Physics for Cultural Heritage research line is dedicated to analyzing historical and artistic artifacts. It integrates advanced analytical methodologies and artificial intelligence techniques to deepen our knowledge, optimize conservation efforts, and promote the value of cultural heritage.

Research activities are structured into four operational phases:

• a) Identifying specific emerging issues in cultural heritage through ongoing dialogue with professionals in related fields like archaeologists, museologists, conservators, anthropologists, and historians.

• b) Researching and developing new analytical methodologies and protocols that go beyond the state of the art.

• c) Developing and applying methods for classifying and extracting microscopic benchmarks using machine learning techniques to enhance investigation effectiveness.

• d) Interpreting the microscopic results within the specific context using an integrated, interdisciplinary approach.

To support these research topics, we have established mid- to long-term collaborations with museum and academic institutions that hold the artifacts under study, formalizing these relationships through framework collaboration agreements. Our areas of interest include:

• Manuscripts: The study of raw materials (inks, pigments, adhesives, binders, drawings, prints, and photographic emulsions) and their interaction with new restoration products, as well as the evaluation of their functionality and eco-sustainability.

• Metals: The study of the production and raw materials that characterized ancient technological history.

• Bone and Wood Artifacts: The analysis of organic materials, such as bones, wood, and seeds, found in prehistoric and protohistoric contexts.

• Ceramics: The study of production techniques and the sourcing of materials in antiquity, as well as the analysis of organic and plant residues.

Over the next three years, the goals of this research line will be achieved through the following strategic-scientific objectives:

• Developing innovative methodologies and protocols for characterizing composite materials in the indicated fields.

• Implementing protocols for classifying and extracting microscopic benchmarks using machine learning and artificial intelligence techniques.

• Studying and characterizing the short- and long-term effects and sustainability of new restoration products applied to materials of interest.

• Enhancing collaborations through new research agreements, participation in national and international networks, and joint projects.

The objectives of this research line are designed to generate innovation, making a decisive contribution to the advancement of knowledge and technologies in the field of cultural heritage conservation. The development of advanced protocols for material characterization and the implementation of techniques based on machine learning and artificial intelligence will optimize diagnostic processes, significantly strengthening our analytical capabilities to meet the needs of a continuously evolving sector. Strengthening national and international collaborations will foster knowledge exchange and the launch of joint projects that will enrich scientific research and increase our international visibility.

The expected results of our research are divided into several areas of application, with a particular focus on methodological innovation, enhancing restoration/conservation practices, and developing new investigation protocols.

Over the next three years, we expect to make significant contributions to the study of manuscripts and historical books. We’ll optimize the innovative methodology we’ve already begun to use for text attribution. This will involve generalizing our analysis of ink composition to include pigments, aiming to correlate microscopic and macroscopic information. Simultaneously, we’ll introduce a new approach to analyze oil paintings and artistic prints, with the goal of identifying the artistic techniques used. This method will integrate data from advanced characterization techniques and machine learning, correlating information about microscopic structure, original “recipes” for materials, methods of execution, and the works’ state of conservation.

We will also conduct an in-depth study on the effects of applying new restoration products to various materials of historical and artistic interest. The primary goal is to understand the interaction mechanisms and application methods of these products in real-world scenarios, which will help define restoration practices based on quantitative data. As part of this, we plan an initial study on the medium- and long-term effects of nanoparticle-based products applied to paper supports. This will help us find more effective intervention methods and identify innovative solutions for safeguarding our literary heritage and works of art.