NSUF 23-4764: Probing Interface Metastability as a Response to Radiation in Compositionally Complex Alloys
This project aims to understand metastability in complex alloys to tailor radiation damage tolerance. A key challenge in gaging metastability lies in characterizing non-equilibrium materials and understanding defect interaction with the metastable interfaces (i.e., grain boundaries). In-situ TEM irradiation experiments and high-resolution defect analysis are crucial steps to the success of this project. Dr. Taheri’s Dynamic Characterization Group specializes in complex experiments and the hybridization of state-of-the-art methods integral to the successful execution of this work. TEM specimen preparation and initial characterization including high-resolution (HR) TEM imaging and precession electron diffraction (PED) techniques such as automated crystal orientation mapping (ACOM) and relative elastic strain analysis can be performed at Johns Hopkins University’s Materials Characterization and Processing Facility. Sample preparation will be completed at Johns Hopkins University prior to any in situ and ex situ experiments. In anticipation of sample heavy-ion bombardment, irradiation simulations will also be performed at our home institution to calculate damage dose parameters. Post-experimental analysis of time resolved in situ irradiation experimental data will be performed at Johns Hopkins to inspect the rates of sink efficiency evolution. Atomic-resolution microstructural images will be correlated with strain measurements and defect morphologies. These methods have given us new insight to material recovery under irradiation conditions and observed effects can be translated to analysis of in-situ TEM videos. Based on prior work, these videos will illustrate the defect evolution in specimens to identify changes in defect landscapes near grain boundaries. Following our hypothesis, changes in the defect landscapes can be correlated to sink efficiency changes in real-time. By preforming intermittent HR-TEM analysis, structural transformations can be benchmarked and quantified. To date, the relationship between irradiation dose and species, resultant grain boundary microstates, and a grain boundary’s ability to mitigate damage are completely unknown, especially in the context of radiation-tolerant compositionally complex alloys (CCAs). Simulations suggesting grain boundary classification in varying microstates present an opportunity to experimentally define variations in sink efficiency through microscopic DOFs in the grain boundary. This involves detection and quantification of atomic density at the grain boundary plane. In CCAs, emphasized nuances in grain boundary stability and the defect morphology near the grain boundary are likely to occur due to variance in defect formation energies, matrix energy landscapes, chemical segregation and ordering, and complicated defect diffusion pathways. Current work led by PI Taheri is utilizing deep learning-based defect visualization methods for in situ transmission electron microscopy (TEM) video analysis to uncover the connection between point defect landscapes with nuanced grain boundary responses to irradiation damage. Utilization of deep learning techniques provide an opportunity to extract an abundance of information at a speed never-before achieved by human analysis methods, including frame to frame defect evolution, giving temporal insight to fluctuating GB absorption behavior. The intention of this proposal is to leverage suggested experimental and deep learning visualization techniques on a new class of promising materials for radiation tolerance. As we move beyond pure and dilute systems, we seek to generate a holistic view of the grain boundary and grain matrix response to radiation damage through the employment of ongoing collaborations to directly compare experimental results with multiscale models and simulations.
Additional Info
| Field | Value |
|---|---|
| Abstract | This project aims to understand metastability in complex alloys to tailor radiation damage tolerance. A key challenge in gaging metastability lies in characterizing non-equilibrium materials and understanding defect interaction with the metastable interfaces (i.e., grain boundaries). In-situ TEM irradiation experiments and high-resolution defect analysis are crucial steps to the success of this project. Dr. Taheri’s Dynamic Characterization Group specializes in complex experiments and the hybridization of state-of-the-art methods integral to the successful execution of this work. TEM specimen preparation and initial characterization including high-resolution (HR) TEM imaging and precession electron diffraction (PED) techniques such as automated crystal orientation mapping (ACOM) and relative elastic strain analysis can be performed at Johns Hopkins University’s Materials Characterization and Processing Facility. Sample preparation will be completed at Johns Hopkins University prior to any in situ and ex situ experiments. In anticipation of sample heavy-ion bombardment, irradiation simulations will also be performed at our home institution to calculate damage dose parameters. Post-experimental analysis of time resolved in situ irradiation experimental data will be performed at Johns Hopkins to inspect the rates of sink efficiency evolution. Atomic-resolution microstructural images will be correlated with strain measurements and defect morphologies. These methods have given us new insight to material recovery under irradiation conditions and observed effects can be translated to analysis of in-situ TEM videos. Based on prior work, these videos will illustrate the defect evolution in specimens to identify changes in defect landscapes near grain boundaries. Following our hypothesis, changes in the defect landscapes can be correlated to sink efficiency changes in real-time. By preforming intermittent HR-TEM analysis, structural transformations can be benchmarked and quantified. To date, the relationship between irradiation dose and species, resultant grain boundary microstates, and a grain boundary’s ability to mitigate damage are completely unknown, especially in the context of radiation-tolerant compositionally complex alloys (CCAs). Simulations suggesting grain boundary classification in varying microstates present an opportunity to experimentally define variations in sink efficiency through microscopic DOFs in the grain boundary. This involves detection and quantification of atomic density at the grain boundary plane. In CCAs, emphasized nuances in grain boundary stability and the defect morphology near the grain boundary are likely to occur due to variance in defect formation energies, matrix energy landscapes, chemical segregation and ordering, and complicated defect diffusion pathways. Current work led by PI Taheri is utilizing deep learning-based defect visualization methods for in situ transmission electron microscopy (TEM) video analysis to uncover the connection between point defect landscapes with nuanced grain boundary responses to irradiation damage. Utilization of deep learning techniques provide an opportunity to extract an abundance of information at a speed never-before achieved by human analysis methods, including frame to frame defect evolution, giving temporal insight to fluctuating GB absorption behavior. The intention of this proposal is to leverage suggested experimental and deep learning visualization techniques on a new class of promising materials for radiation tolerance. As we move beyond pure and dilute systems, we seek to generate a holistic view of the grain boundary and grain matrix response to radiation damage through the employment of ongoing collaborations to directly compare experimental results with multiscale models and simulations. |
| Award Announced Date | 2023-09-14T13:40:51.587 |
| Awarded Institution | None |
| Facility | None |
| Facility Tech Lead | Wei-Ying Chen |
| Irradiation Facility | None |
| PI | Mitra Taheri |
| PI Email | [email protected] |
| Project Type | RTE |
| RTE Number | None |