Advanced Characterization of Materials for Nuclear, Radiation, and Extreme Environments: On-Demand Oral Presentations
Sponsored by: TMS Nuclear Materials Committee
Program Organizers: Cody Dennett, Commonwealth Fusion Systems; Samuel Briggs, Oregon State University; Christopher Barr, Department Of Energy; Michael Short, Massachusetts Institute of Technology; Janelle Wharry, Purdue University; Cheng Sun, Clemson University; Caitlin Kohnert, Los Alamos National Laboratory; Emily Aradi, University of Manchester; Khalid Hattar, University of Tennessee Knoxville
Friday 8:00 AM
October 22, 2021
Room: On-Demand Room 6
Location: MS&T On Demand
Session Chair: Caitlin Taylor, Los Alamos National Laboratory
Invited
Unraveling Solute-solvent Interactions in Molten Salt Environments Using X-ray Absorption Spectroscopy: Simerjeet Gill1; 1Brookhaven National Lab
Understanding the factors that control solubility and speciation of metal ions in molten salts is key for their successful deployment in molten salt reactors (MSR). We investigate the speciation and radiation-induced nanoparticle formation for metal ions in molten salts using X-ray absorption spectroscopy (XAS). XAS is an element specific technique that allows studying the local coordination environment and chemical structure of metal species (Ni, Co) in molten salt systems. XAS studies are combined with optical absorption spectroscopy and molecular dynamics simulations to investigate the coordination environment of metals in molten salt systems. The evolution of local structure of metal ions as function of temperature, metal ion concentration, and radiation dose in various molten salt systems will be discussed. Such knowledge of speciation of metals and radiation-induced nanoparticles in molten salt environments provides a critical understanding needed to predict and control physical and chemical properties of molten salts for MSR applications.
Invited
Speciation of Metal Ion Solutes in Molten Salt Matrices for Reactor Applications using Advanced Spectroscopy Techniques: Ruchi Gakhar1; Michael Woods1; Simerjeet Gill2; Anatoly Frenkel3; Mehmet Topsakal2; 1Idaho National Laboratory; 2Brookhaven National Lab; 3Stonybrook Unversity
Due to their large liquid temperature range and high actinide solubility, molten salts have regained attention for application in advanced nuclear fuel cycles and next generation nuclear reactors. A critical requirement for advancing molten salt technology is the fundamental understanding of the complex interplay between structure and dynamics of solutes (impurities, and corrosion and fission products) in the high-temperature media. The structure and speciation of salt components relate to the solute reactivity and is driven by the inherent radiation-induced processes, which in turn can degrade reactor performance. The team seeks to understand the speciation of metal ions in molten chloride salts and its variation with temperature and melt composition using advanced spectroscopy techniques as high temperature optical spectroscopy (UV-Vis-NIR), X-ray absorption spectroscopy (XAS) and X-ray absorption near-edge structure (XANES) modeling. This work was supported by Energy Frontier Research Center (EFRC-MSEE), funded by the U.S. Department of Energy Office of Science.
Invited
Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis: Stephen Taller1; Luke Scime1; Kurt Terrani1; 1Oak Ridge National Laboratory
Microstructural features such as precipitates, grain boundaries, and dislocations will dictate the mechanical properties of materials in extreme environments. This presents a significant challenge to use analytical electron microscopy to characterize the size, number density, composition, and volume fraction of each microstructural feature. The ultimate objective of this work is to demonstrate a generalized approach for characterization of complex microstructures at large scale. This approach is now possible through (1) advances in microscopy and its automation to assess large areas of material, and (2) advances in pixel-wise machine learning classification tools that are agnostic to the image size, type, and number of input channels. This presentation focuses on the application of a dynamic segmentation convolutional neural network for rapid microstructural analysis as demonstrated on an additively manufactured sample of Ni-superalloy 718. This generalized approach will provide for detailed microstructural characterization to facilitate more accurate property predictions.
Invited
Successful, Unsuccessful, and Partially-successful Attempts at Understanding Alloy Corrosion in Molten Salts: Stephen Raiman1; 1Texas A&M University
We present a combined experimental and computational strategy aimed at fundamentally understanding corrosion of alloys in molten salts. Since important phenomena may be present in both the materials and in the salt, efforts at understanding and controlling each are presented. Model Ni-Cr alloys were fabricated with controlled microstructures, and exposed to various conditions to provide the evidence basis for 1D and 2D engineering-scale diffusion models. X-ray spectroscopy and rapid solidification were explored to identify the speciation of corrosion products in the salts, and to develop a thermodynamic description of the alloy-salt system. In an effort to maintain greater control of the molten salt environment, new processes to control and measure salt purity are presented. This talk will showcase a loosely-connected hodgepodge of materials science and salt chemistry aimed at advancing our understanding of alloy corrosion in molten salts
High Temperature Mechanical Properties of WC/W2C Composites Fabricated by Reactive Sintering of Powders Colloidally Processed: Antonio Javier Sanchez-Herencia1; Macarena Garcia-Ayala1; Sandra Tarancon2; Begoņa Ferrari1; Jose Ygnacio Pastor2; 1Institute for Ceramic and Glass; 2ETSI Caminos-UPM
Fusion and fission reactors require of new materials that could withstand severe conditions of temperature and neutronic damage. Tungsten carbide is proposed as an alternative to high temperature service due to its dimensional stability and resistance. But fracture toughness of binderless tungsten carbide is still low. In this work all ceramic composite materials of carbide (WC) and semicarbide (W2C) are obtained after SPS sintering of WC/W powders with volume ratios of 100/0, 90/10, 20/80 and 50/50. Mixture of powders is achieved by colloidal processing methods. WC/W2C composites were mechanically test in bending at temperatures from 600 at 1200 ēC under vacuum conditions similar to a fusion reactor. Results shows that composites behave as elastic ceramics with brittle fracture up to 1000 ēC. Indicating a self-reinforcement mechanism based on the transformation of the W2C phase.
Radiation Damage Suppression in AISI-316 Steel Nanoparticles: Implications for the Design of Future Nuclear Materials: Emily Aradi1; Matheus Tunes2; Jacob Lewis-Fell3; Graeme Greaves3; Steven Donnelly3; Jonathan Hinks3; 1University of Manchester; 2Montanuniversitaet Leoben; 3University of Huddersfield
Self-healing capability of defects introduced by energetic particle irradiation is a desired behaviour to be attained in the design of materials for application in extreme environments. Nanoporous materials have a potential for achieving higher radiation tolerance due to the presence of active unsaturable surfaces that may diffuse and thus effectively annihilate defects. The effects of heavy ion collisions in the lattice of AISI-316 steel nanoparticles (NPs)—which serve as a model for the ligaments in a nanoporous—are herein investigated in-situ within a transmission electron microscope. Compared with AISI-316 steel foils, fewer radiation-induced defect clusters form in the NPs. Scanning-TEM Post-irradiation characterization revealed that AISI-316 steel NPs may develop a radiation-induced self-passivation driven by a solute-drag mechanism: an effect that can potentially enhance their radiation-corrosion resistance in extreme conditions. The capability of an NP to self-heal irradiation-induced point defects is investigated using the cellular model for active internal sinks.
Effects of He on Nanoscale Mechanical Properties of Er: Eric Lang1; Caitlin Taylor2; Riley Parrish1; Patrick Price1; Raj Tandon1; Khalid Hattar1; 1Sandia National Laboratories; 2Los Alamos National Lab
In nuclear energy systems, materials exposed to helium will form bubbles and cavities within the microstructure. Combined with high temperatures and irradiation conditions, degradation of mechanical properties can occur, affecting material safety and bulk performance. In this work, we investigate the influence of helium implantation on the mechanical property performance of erbium and erbium hydride, which exhibits bubble formation and blistering following helium accumulation. Utilizing in-situ transmission electron microscopy and scanning electron microscopy mechanical testing on helium-implanted erbium hydride, we investigate the mechanical performance under various nano/micro-scale straining conditions, including tension, compression, and bending. With real-time imaging and stress-strain measurements, the microstructural features are correlated with deformation behavior to quantify the effects of helium loading to be able to correlate nanoscale helium bubbles with the micro-to-nano scale mechanical performance. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
Characterizing the Spatial and Temporal Evolution of Iron Thin Films during Coupled Irradiation and Corrosion: Benjamin Derby1; Trevor Clark2; Junsoo Han3; Khalid Hattar2; John Scully3; Matthew Janish1; Cortney Kreller1; Nan Li1; 1Los Alamos National Laboratory; 2Sandia National Laboratory; 3University of Virginia
This work describes a novel technique for the in situ characterization of an Fe sample exposed to ion irradiation in a corrosive salt medium. A thick, epitaxial Fe sample grown by physical vapor deposition was exposed to a 0.0001 M HCl + 0.1 M NaCl + H2O (pH = 4.0) salt solution for 90 minutes while simultaneously being irradiated by a 6 MeV Al3+ ion beam. The ion beam damage profile was centered at the liquid-solid interface. SEM images of the sample surface at 300 second intervals reveal the coupling nature of the irradiation and corrosion process. Intergranular voids from Fe leaching into the salt were enhanced by the heavy-ion irradiation. As such, a coupling effect was observed and the corrosion process was accelerated. Post-mortem TEM characterization and ex-situ EIS of the sample allow us to reason these observations with a physical model.
Effect of Ion Irradiation on the Corrosion of 304SS in PWR Simulated Water Chemistry: Fu-Yun Tsai1; Ryan Schoell1; Khalid Hattar2; Djamel Kaoumi1; 1North Carolina State University; 2Sandia National Laboratories
A study on the effects of ion irradiated microstructure on corrosion of 304SS in simulated Pressurized Water nuclear Reactor conditions was conducted. Coupons of 304SS were irradiated with 10 MeV Au ions to 10 displacements per atom on average over 1 μm thickness below surface. Corrosion was conducted in a recirculating autoclave in simulated PWR primary water condition (325 ℃, 2200 psi, 30 cc H2/kg of H2O at STP) for a total of 6 weeks and samples were investigated at intervals of about 7 days. Transmission Electron Microscopy (TEM) of cross-sectional samples was used to study the oxide formation through diffraction analysis and kinetics through oxide-thickness measurements in both irradiated and non-irradiated regions. Results showed differences in oxide depth and shape between irradiated and non-irradiated regions although the oxide phases were similar. A discussion was conducted to substantiate the effects of irradiation.
Material Degradation Pathways of UO2 under Oxygen, Humidity, and Temperature Probed by XAFS: Juejing Liu1; Aiping Chen2; Joanne Stubbs3; Peter Eng3; Hongwu Xu2; Steven Conradson1; Xiaofeng Guo1; 1Washington State University; 2Los Alamos National Laboratory; 3University of Chicago
UO2 is the major component material for nuclear fuel rods for LWR or PWR. Understanding uranium oxide – water interfacial structures and chemical reactions are critical for addressing a wide range of high-profile issues in the nuclear fuel cycle, related to reactor fuel safety and geological disposal. Herein, we performed glazing incident X-ray absorption fine structure to probe changes of lattice structures of UO2 thin films under various humidity, oxygen level, and temperature conditions. Our preliminary results show that the UO2 lattice is distorted even in low humidity. Increasing humidity introduces two or three extra O atoms in the interstitial site (2.4~2.8 Å from U), suggesting the gradual formation of non-stoichiometric UO2+x or U4O9. This study provided an insight into the potential impacts of external conditions on UO2 from the atomic scale, which helped unveil all possible material degradation pathways.
Deep Learning Pipeline for Cavity Segmentation in Transmission Electron Microscopy: Chun Yin Wong1; Xing Wang2; Zhe Fan3; Karren More4; Sergei Kalinin4; Maxim Ziatdinov4; 1University of Tennessee, Knoxville; 2The Pennsylvania State University, Oak Ridge National Laboratory; 3Lamar University, Oak Ridge National Laboratory; 4Oak Ridge National Laboratory
A physics-informed deep learning (DL) pipeline is proposed to perform cavity segmentation in transmission electron microscopy (TEM) images of irradiated concentrated solid solution alloy (CSA) systems. Irradiation-induced cavities threaten the mechanical integrity of structural materials in nuclear reactors and CSAs, particularly high entropy alloys, have shown to resist cavity growth. The challenge of measuring large numbers of cavities can be overcome by automated analyses yet traditional machine learning (ML) methods are inadequate. The proposed DL pipeline outperforms traditional ML algorithms by incorporating ensemble learning in the pixelwise identification of cavities. Cavity dynamics are incorporated via computer vision techniques—Laplacian of Gaussian and Hough Transform—to measure the cavities. The DL pipeline achieved an intersection-over-union of 0.80 and overlapping cavities were individually identified. The cavities were identified instantaneously and only five images were labeled for training. The success of the DL pipeline paves the way for automated microscopy experiments in the future.