Status on Microstructural Studies of Nuclear Graphite

Abstract

This report documents the completion of the Advanced Reactor Technologies (ART) Level 3 Milestone (M3AT-26OR0605054), “Provide status on microstructural studies of nuclear graphite,” due May 1, 2026. The contents of this report summarize recent progress in the development of the Nuclear Graphite Microstructure Library, including datasets that have been generated, curated, and submitted for publication, as part of the broader effort to establish and integrate the library within the NDMAS platform. The Nuclear Graphite Microstructure Library is being developed to provide a curated repository of microstructural datasets for graphite materials used in nuclear applications. This effort compiles multi-scale characterization data obtained from advanced imaging techniques, including X-ray computed tomography, electron microscopy, and complementary methods used to investigate the internal structure of nuclear graphite. The library aims to preserve and organize datasets from both historical and modern graphite grades, enabling systematic analysis of key microstructural features such as porosity, filler morphology, and structural anisotropy. A part of the library also focuses on datasets acquired from specimens exposed to in-service conditions, including oxidation, neutron irradiation, and molten salt impregnation. In addition, the library incorporates related carbon-based materials that can serve as reference systems for improving the interpretation and understanding of nuclear graphite behavior. To ensure long-term data accessibility, the microstructure library is being integrated into the Nuclear Data Management and Analysis System (NDMAS), a data repository maintained by Idaho National Laboratory. The integration process includes standardized data formatting, metadata documentation, and future expansion of dataset collection. Beyond supporting traditional characterization efforts, the library is intended to provide training datasets for artificial intelligence and machine learning approaches aimed at automated microstructural analysis. The resulting resource will support research, modeling, and industry efforts related to the development and qualification of graphite materials for advanced nuclear reactor systems.