Chemical synthesis of Ni-Co through hydrazine reduction method

Abstract

Soft magnetic materials, generally consisting of iron, nickel or cobalt, are used in numerous electrical applications such as transformers and media storage. Recently, machine learning has become a popular research field in research and development. In traditional research methods, high costs, high manual labor, and enormous amount of time are needed to obtain results and data required for a specific material. With the application of machine learning, fewer experiments will be required as predictions can be made through an algorithm. This shortens the time required to discover new materials and processes, compared to traditional experimental approaches. Within the Iron-Nickel-Cobalt ternary system, there were limited literature available for Nickel-Cobalt. Therefore, in this project, Nickel-Cobalt of various compositions will be studied to understand the physical properties. In this project, hydrazine reduction synthesis is used to produce the various compositions of Nickel-Cobalt. Different compositions of Nickel-Cobalt were analyzed, determining its percentage yield, morphology, crystal structure, hardness, electrical and magnetic properties. The results showed that the percentage yield for all composition variations are close to 100%, except Ni0.1Co0.9 and Ni0.2Co0.8. Nickel-Cobalt can be either face centered cubic with spherical particles, or hexagonal closed packed with dendritic particles. The crystal structure has a correlation and strong impact on the materials’ properties. Face centered cubic structures tend to be ductile, of higher electrical resistivity and have softer magnetic properties. On the other hand, hexagonal close packed ones have higher hardness, lower electrical resistivity, and larger hysteresis loops. Despite the higher coercivity and low yield, compositions with hexagonal closed packed structure has better soft magnetic properties. Further research can be done to lower coercivity values and increase yield percentage.