In recent years, combinatorial materials science has had a major impact helping to accelerate the discovery and development of new materials. The combinatorial approach encompasses the combinatorial synthesis and high-throughput characterization methods. Among the different techniques
for combinatorial synthesis, magnetron sputtering stands out. This deposition method allows the synthesis of thin films with varying gradients of composition. In combination with high through put characterization methods to determine the local composition, structure, and properties, this information can be used to generate material libraries. The generation of thin film libraries, in combination with machine learning algorithms, might constitute a key tool for discovering new materials because it covers a wide range of material compositions.
In this work, these ideas are applied to investigate shape memory alloys. Shape memory alloys are attracting a lot of attention both from the scientific community and from different industries, such as aerospace or biomedical sectors. In particular, NiTi-based shape memory alloys, due to their good biocompatibility, are already in use in different biomedical applications that benefit from their superelastic behaviour and/or shape memory effects. The main objectives of this work are two-fold. Firstly, to explore combinatorial synthesis based on magnetron sputtering to search for new NiTi-based shape memory alloys with improved properties. And secondly, to transfer the knowledge generated to the fabrication of new biomedical devices by additive manufacturing, particularly selective laser melting. Selective laser melting is a prototyping method that allows the fabrication of complex structures that could benefit from either the superelastic or shape memory effects of the new NiTi-based alloys, in applications such as cardiac valves or coronary stents.