In the last decades, the interest in the development of protective coatings for movable and immovable Cultural Heritage (CH) assets has decidedly increased. This has been mainly prompted by the raising consciousness on preservation requirements for cultural artefacts and monuments, which has consequently determined the development of new protective products. From acrylic resins used at the end of the last century to the up-to-date biomaterials and nanoparticles employed nowadays, the research has made a giant step forward. This article reviews the progresses, the technical challenges, and the most recent advances in protective coatings for archaeological metal, glass, and stone artefacts. It aims at offering a comprehensive and critical overview of the progressions in conservation science and displaying how research has optimized polymers in order to solve deterioration problems. Attention is given to recently developed materials, hybrid coatings, and corrosion inhibitors. This work seeks to provide a reference point for future research and to offer a wide-ranging introduction on the newly available material technologies to restorers and conservators.

It is known that oil paintings containing zinc white are subject to rapid degradation. This is caused by the interaction between the active groups of binder and the metal ions of the pigment, which gives rise to the formation of new zinc complexes (metal soaps). Ongoing studies on zinc white paints have been limited to the chemical mechanisms that lead to the formation of zinc complexes. On the contrary, little is known of the photo-physical changes induced in the zinc oxide crystal structure following this interaction. Time-resolved photoluminescence spectroscopy has been applied to follow modifications in the luminescent zinc white pigment when mixed with binder. Significant changes in trap state photoluminescence emissions have been detected: the enhancement of a blue emission combined with a change of the decay kinetic of the well-known green emission. Complementary data from molecular analysis of paints using Fourier transform infrared spectroscopy confirms the formation of zinc carboxylates and corroborates the mechanism for zinc complexes formation. We support the hypothesis that zinc ions migrate into binder creating novel vacancies, affecting the photoluminescence intensity and lifetime properties of zinc oxide. Here, we further demonstrate the advantages of a time-resolved photoluminescence approach for studying defects in semiconductor pigments.

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the “black-box” problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI. Graphical Abstract

In conservation science, the identification of painting materials is fundamental for the study of artists’ palettes, for dating and for understanding on-going degradation phenomena. For these purposes, the study of stratigraphic micro-samples provides unique information on the complex heterogeneity of the pictorial artworks. In this context, we propose a combined-microscopy approach based on the application of time-resolved photoluminescence (TRPL) micro-imaging and micro-Raman spectroscopy. The TRPL device is based on pulsed laser excitation (excitation wavelength = 355 nm, 1 ns pulse width) and time-gated detection, and it is suitable for the detection of photoluminescent emissions with lifetime from few nanoseconds to hundreds of microseconds. In this work, the technique is beneficially applied for identifying different luminescent semiconductor and mineral pigments, on the basis of their spectral and decay kinetic emission properties. The spatial heterogeneities, detected in the micro-sample, are investigated with Raman spectroscopy (785-nm in CW mode) for a further identification of the paint composition on basis of the molecular vibrations associated with the crystal structure. The effectiveness and limits of the proposed combined method is discussed through analysis of a corpus of stratigraphic micro-samples from Russian Avant-garde modern paintings. In the selected samples, the method allows the identification of modern inorganic pigments such as cadmium-based pigments, zinc white, titanium white, chrome yellow, ultramarine and cinnabar.