Researchers use various traditional techniques (e.g., scanning electron microscopy, energy dispersive spectroscopy and electron backscatter diffraction) to capture microstructural evolution in nuclear fuels and materials. However, these surface-based methods do not accurately capture the complex microstructure of the fuel. We utilize new tomography-based techniques on nuclear fuels which yield information on grains, fission products and porosity structure in 3D. This data can be used as input to new phase-field models to predict fuel performance in a reactor.
Faculty
Ph.D., 2017, University of California, Santa Barbara
Research Interests: Energy Materials, Advanced Materials Characterization, Electronic Materials, Ceramics, Machine Learning and AI; energy storage materials, functional and electronic materials, lithium-ion batteries, structure-property relationships, X-ray diffraction, pair distribution function analysis, local structure, amorphous and disordered materials
Lab Website: Butala Research Group
Ph.D., 2015, University of Illinois at Urbana-Champaign
Research Interests: Advanced Materials Characterization, Electronic Materials, Ceramics, Machine Learning and AI, Energy Materials, Nanomaterials; Advanced electron microscopy techniques, Quantitative analysis of electron microscopy data, Digital image processing, Understanding materials properties at the atomic scale, Functional oxides and semiconductors
Lab Website: Kim Electron Microscopy Group
Ph.D., McGill University, 2017
Research Interests: Metallurgy, Advanced Materials Characterization, Engineering Education
Ph. D., 2005, University of Wisconsin-Madison
Research Interests: Nuclear Fuels and Materials, Advanced Materials Characterization, Metallurgy, Machine Learning and AI, Materials Processing; structural and fuel materials for nuclear energy systems; LWRs sustainability and aging management, advanced fabrication and joining technologies, used fuel dry storage and disposition.
Lab Website: Nuclear Materials Degradation Research Group (NuMad)