Photochemically induced formation of adhesive hydrogels from sodium alginate, acrylamide, and iron sandwich complexes.

Visible light irradiation of an aqueous solution of sodium alginate and organometallic complex [(C5H5)Fe(toluene)]BF4 transforms it into a rigid hydrogel due to crosslinking of the carboxylate groups by the iron ions. Irradiation of the same iron complex together with K2S2O8 initiates the polymerization of acrylamide, which provides an efficient method for light-controlled one-step preparation of alginate-polyacrylamide double network hydrogels, which are capable of gluing wet glass with 100-200 kPa shear strength.

Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts.

Additive manufacturing demonstrates tremendous progress and is expected to play an important role in the creation of construction materials and final products. Contactless (remote) mechanical testing of the materials and 3D printed parts is a critical limitation since the amount of collected data and corresponding structure/strength correlations need to be acquired. In this work, an efficient approach for coupling mechanical tests with thermographic analysis is described. Experiments were performed to find relationships between mechanical and thermographic data. Mechanical tests of 3D-printed samples were carried out on a universal testing machine, and the fixation of thermal changes during testing was performed with a thermal imaging camera. As a proof of concept for the use of machine learning as a method for data analysis, a neural network for fracture prediction was constructed. Analysis of the measured data led to the development of thermographic markers to enhance the thermal properties of the materials. A combination of artificial intelligence with contactless nondestructive thermal analysis opens new opportunities for the remote supervision of materials and constructions.