Thermodynamic analysis and modeling of novel ternary Ni-S-based bulk metallic-forming systems

• Applicants: Professor Dr. Ralf Busch; Professorin Dr. Isabella Gallino, Ph.D.; Professor Dr. Hans Jürgen Seifert
• Subject Area: Thermodynamics and Kinetics as well as Properties of Phases and Microstructure of Materials
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 441308809

We propose to apply the CALPHAD (CALculation of PHAse Diagrams) approach to model the thermodynamics in multicomponent metallic glass-forming systems. The novel ternary bulk metallic glass (BMG)-forming systems Ni-X-S, with X=Pd, Ti, Nb are the focus of this project. The relative simplicity of the ternary systems in comparison to the commonly found quinary BMG-formers facilitates the application of the CALPHAD modeling. While our experimental investigations will include binary and ternary compositions for all of the three Ni-X-S systems, emphasis will be given first to the modeling of the Ni-Pd-S system, because it is a simple system in terms of equilibrium thermodynamics and extensive experimental data of the equilibrium phases are already available in the literature. Existing CALPHAD-type models will be extended and new will be developed not only for the description of the thermodynamic stable phase diagrams (binaries and ternary), but also for the thermodynamic description of metastable states of the undercooled liquid. Our own experimental data will be assessed and processed, so they can serve as the crucial input data for CALPHAD modeling and calculations. The experimental work includes calorimetry (DSC/DTA, Flash-DSC, MDSC) and structural and microstructural examinations (XRD, SEM/TEM). A physical property measurement system (PPMS) will also be used for temperatures down to 50 K. As a result of this project, this will be the first time that for ternary systems the thermodynamics of the undercooled liquid down to the glass transition in relation to the competing crystalline phases will be realistically modeled, since we will underpin our fits with a large number of real experimental input data.

• DFG Programme: Research Grants