Prof. Tamas Ungar
Senior Research Fellow
Area of Specialisation: X-Ray Diffraction; Convolutional Multiple Whole Profile (CMWP)
Tamas Ungar obtained his PhD in 1980 at the Eötvös University Budapest in Hungary, studying the GP zone formation in Al-base alloys and developed a model for the interaction between GP zones and more stable precipitates in this alloy system. After his PhD, after which he was a postdoctoral fellow as Humboldt stipend holder at the Max-Planck-Institute of Metallforschung in Stuttgart, where he discovered the characteristically asymmetric X-ray line broadening and gave direct evidence for the long-range internal stresses prevailing in heterogeneous dislocation structures.
Following a career as professor at the Materials Science Department of the Eötvös University Budapest, including receiving the the Dr of Science (DSc) title issued by the Hungarian Academy of Science in 1988, Tamas has held visiting positions at research institutions around the world, including Los Alamos National Lab and City University of Hong Kong. Since 2016, Tamas has been at the University of Manchester, as a Senior Research Fellow with the Zirconium Group. In 2007 he obtained the Hanawalt award from ICDD for the dislocation model of strain anisotropy. In 2008/2009 he was awarded by the Humboldt Research Prize to work at different institutes in Germany. He published over 200 research papers and has more than 4000 independent citations with H=36.
Tamas developed the CMWP method, for XRD line profile analysis, along with Gabor Ribarik, one of his previous PhD students. In MIDAS, all BOR-60 samples will be initially analysed using CMWP, to provide information about the defect structures present, including <a< and <c> dislocation loop densities, to be benchmarked against data for proton-irradiated specimens, and complement subsequent TEM analysis.
Next Steps: Following his invaluable contributions to the MIDAS team, Tamas is undertaking an invited teaching engagement at the University of Vienna. Tamas remains closely involved in the MIDAS community, and will continue to work with the team, bringing his expertise to the programme’s utilisation of CMWP.