Additive manufacturing and characterization of ceramic insulators for radioactive beams generation

The Isotope Separation On-Line (ISOL) technique is widely used to produce radioactive ion beams for fundamental nuclear physics research and multidisciplinary applications, including nuclear medicine. In the ISOL process, the target–ion source (TIS) unit represents one of the main sub-assemblies, where the interaction of a proton beam with a production target generates a characteristic set of radionuclides. These nuclei are released from the target and migrate toward an ion source, where they are ionized, extracted, and subsequently purified through electromagnetic mass separation. At the SPES facility at INFN-LNL, the Forced Electron Beam Induced Arc Discharge (FEBIAD) ion source represents a key component of the TIS unit. It is composed two refractory-metal components, a cathode and an anode, that must remain electrically insulated under extreme operating temperatures (1500–2000 °C). However, the complexity of conventional manufacturing and assembly processes limits the reproducibility and reliability of the system. To address these challenges, additive manufacturing is being investigated for the fabrication of both electrodes and insulators, with the aim of simplifying assembly while maintaining the required thermal and electrical performance. The aim of this thesis was to explore the feasibility of producing alumina insulators for the FEBIAD ion source exploiting additive manufacturing (direct pellet extrusion), using commercial feedstocks. Firstly, the printing process parameters were optimized to ensure complete densification after debinding and sintering treatments. The physical properties of produced samples were characterized using the Archimedes method and scanning electron microscopy, whereas thermal properties at high temperature were evaluated with a custom experimental apparatus. Finally, the mechanical properties of the insulators were assessed through bending tests, and the obtained data were analyzed by means of Weibull statistical analysis.