Title

Precession Electron Diffraction for SiC Grain Boundary Characterization in Unirradiated TRISO Fuel

Document Type

Article

Publication Date

8-15-2016

Abstract

Precession electron diffraction (PED), a transmission electron microscopy-based technique, has been evaluated for the suitability for evaluating grain boundary character in the SiC layer of tristructural isotropic (TRISO) fuel. This work reports the effect of transmission electron microscope (TEM) lamella thickness on the quality of data and establishes a baseline comparison to SiC grain boundary characteristics, in an unirradiated TRISO particle, determined previously using a conventional electron backscatter diffraction (EBSD) scanning electron microscope (SEM)-based technique. In general, it was determined that the lamella thickness produced using the standard focused ion beam (FIB) fabrication process (∼80 nm), is sufficient to provide reliable PED measurements, although thicker lamellae (∼120 nm) were found to produce higher quality orientation data. Also, analysis of SiC grain boundary character from the TEM-based PED data showed a much lower fraction of low-angle grain boundaries compared to SEM-based EBSD data from the SiC layer of a TRISO-coated particle made using the same fabrication parameters and a SiC layer deposited at a slightly lower temperature from a surrogate TRISO particle. However, the fractions of high-angle and coincident site lattice (CSL)-related grain boundaries determined by PED are similar to those found using SEM-based EBSD. Since the grain size of the SiC layer of TRSIO fuel can be as small as 250 nm (Kirchhofer et al., 2013), depending on the fabrication parameters, and since grain boundary fission product precipitates in irradiated TRISO fuel can be nano-sized, the TEM-based PED orientation data collection method is preferred to determine an accurate representation of the relative fractions of low-angle, high-angle, and CSL-related grain boundaries. It was concluded that although the resolution of the PED data is better by more than an order of magnitude, data acquisition times may be significantly longer or the number of areas analyzed needs to be significantly greater than the SEM-based method to obtain a statistically relevant distribution. Also, grain size could be accurately determined but significantly larger analysis areas would be required than those used in this study.