About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Transmutation Effects in Fusion Reactor Materials: Critical Challenges & Path Forward
|
Presentation Title |
Characterizing Transmutation Products in Materials via STEM and Machine Learning |
Author(s) |
Chad M. Parish |
On-Site Speaker (Planned) |
Chad M. Parish |
Abstract Scope |
Both fission and fusion materials will suffer significant transmutation of atoms under high-energy neutron irradiation. Here at ORNL, we are exploring scanning/transmission electron microscopy (STEM)-based methods to characterize and quantify these microstructural processes. In particular, STEM is a powerful tool for nanometer-scale characterization of elemental species present in a material. We are using a combination of high-throughput STEM X-ray spectrum imaging with automated mapping and machine learning to explore large volumes of material with higher statistical confidence. Examples from tungsten and fusion steels, irradiated in the High Flux Isotope Reactor, will be presented with discussions of strengths and limitations of the methods, and an eye to improvements for the future Fusion Prototypic Neutron Source (FPNS) transmutation science era, as well as synergies with fission materials studies. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Nuclear Materials, Machine Learning |