First-principles study of the surface properties of U-Mo system

Journal Article · Thu Feb 01 00:00:00 EST 2018 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2017.10.033· OSTI ID:1421975

Mei, Zhi-Gang; Liang, Linyun; Yacout, Abdellatif M.

U-Mo alloys are promising fuels for future high-performance research reactors with low enriched uranium. Surface properties, such as surface energy, are important inputs for mesoscale simulations (e.g., phase field method) of fission gas bubble behaviors in irradiated nuclear fuels. The lack of surface energies of U-Mo alloys prevents an accurate modeling of the morphology of gas bubbles and gas bubble-induced fuel swelling. To this end, we study the surface properties of U-Mo system, including bcc Mo, alpha-U, gamma-U, and gamma U-Mo alloys. All surfaces up to a maximum Miller index of three and two are calculated for cubic Mo and gamma-U and non-cubic alpha-U, respectively. The equilibrium crystal shapes of bcc Mo, alpha-U and gamma-U are constructed using the calculated surface energies. The dominant surface orientations and the area fraction of each facet are determined from the constructed equilibrium crystal shape. The disordered gamma U-Mo alloys are simulated using the Special Quasirandom Structure method. The (1 1 0) and (1 0 0) surface energies of gamma U-7Mo and U-10Mo alloys are predicted to lie between those of gamma-U and bcc Mo, following a linear combination of the two constituents' surface energies. To better compare with future measurements of surface energies, the area fraction weighted surface energies of alpha-U, gamma-U and gamma U-7Mo and U-10Mo alloys are also predicted. (C) 2017 Published by Elsevier B.V.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA) - Office of Defense Nuclear Nonproliferation - Office of Material Management and Minimization (NA-23) Reactor Conversion Program
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1421975
• Journal Information: Computational Materials Science, Vol. 142, Issue C; ISSN 0927-0256
• Publisher: Elsevier
• Country of Publication: United States
• Language: English