| Scope |
Refractory high entropy alloys (RHEAs) are comprised of multiple principal elements (elements with concentrations between 5 - 35 at. %) from Group IV-VI refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, W) and are mostly BCC in crystal structure and known for their higher melting points and elevated-temperature strength. The recent development of RHEAs has offered remarkable potential to replace their current FCC counterparts and become the next-generation high-temperature alloys applied in energy, aerospace, and nuclear territories. In addition, the oxides, borides, carbides, nitrides, sulfides, and silicides of RHEAs, or refractory high entropy ceramics (RHEC), has been identified as a key class of materials for ultrahigh temperature ceramics (UHTC) used in aerospace.
Topics of interest for this symposium include, but are not limited to:
(1) Advanced processing of RHEAs and RHECs which includes but are not limited to high-throughput manufacturing, additive manufacturing, laser sintering, impurity control (e.g., O, N, C, P, S), machining and forming of nominally brittle materials, surface modification, and coating technologies.
(2) Advanced testing and characterization methods for materials applied under extreme conditions, including in-situ synchrotron/SEM/TEM mechanical testing, mechanical testing at high temperatures, small-scale mechanical testing for ion-irradiated materials, ultrahigh temperature (~4000 K) characterization, and advanced transmission electron microscopy, scanning electron microscopy (including electron backscatter diffraction), and atom probe tomography.
(3) Advances in the performance of RHEAs and RHECs under extreme conditions, which include but are not limited to creep, fatigue, wear, oxidation resistance, and radiation resistance.
(4) Advanced computational efforts on the high-throughput discovery and design of new RHEAs and RHEAs, which include but are not limited to high-throughput CALculation of PHAse Diagrams (CALPHAD) calculations, density functional theory-informed database construction, and machine learning. |