Tamara Tomašegović

Photochromism refers to a reversible colour change induced by the irradiation of photochromic materials with ultraviolet (UV) or visible light that reverts to the original colour after the light source is removed. This effect arises from chemical transformations between two isomers with different absorption spectra, involving processes like proton transfer, chemical-bond formation, and isomerisation. These photochromic inks, appearing as crystalline powders with micro-sized particles, require dissolution in a suitable matrix to achieve the colour change. Photochromic inks are used in security, as functional coatings for paper and packaging, in the fabric industry, and in other ways. This study examines the influence of varying concentrations of micro-sized photochromic pigments and different ink-coating thicknesses on the photochromic effect on sustainable paperboard substrates. Artificial ageing was performed to assess the photochromic response and lightfastness in relation to pigment concentration, ink-coating thickness, and the influence of the paperboard substrates. The results of this research could contribute to enhancing knowledge on employing photochromic inks for diverse packaging applications.

In this work, three types of photopolymer printing plates for packaging printing were subjected to varied UV (ultraviolet radiation) post-treatments, and their surface free energy (SFE) components were calculated. SFE of the photopolymer printing plate is crucial in the process of transferring the ink from the printing plate to the substrate. Calculated polar and dispersive SFE components were used to build and optimize artificial neural networks for the prediction of the surface properties of different photopolymer materials after the performed UVA and UVC post-treatments. In this way, the production of printing plates with tailored SFE components could be automated and optimized. Consequently, products with improved qualitative properties could be printed. Results of the research have shown that the choice of the neural network’s activation function is most significant for the minimization of the mean squared error (MSE), while the number of neurons and hidden layers in neural networks has less influence on MSE. The optimized neural networks applied for common photopolymer materials in this work have the potential to be applied for the automation of the printing plates’ post-treatment process and the production of printing plates with surface properties tailored to specific printing systems.