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Nanocomposite Materials for Radionuclide Sequestration from Groundwater Environments
The half-lives of radionuclides range from fractions of a second to billions of years. Since no practical method of altering radioactive decay exists, and since exposure to either the energy emitted from radioactive decay or chemical properties of radionuclides poses dire health risks, radioactive materials must be segregated and controlled. The capture, treatment, and disposition of radioactive materials remain an extraordinary challenge. In here, we focus our attention on the synthesis and characterization of a unique class of nanocomposite materials that have potential for removal of radionuclide contamination. Specifically, we report a simple approach to decorate the surface of iron-based (Fe/FexOy) material with various nano-catalysts. Specifically, copper (Cu), tin (Sn), and silver (Ag) nanoparticles were prepared through two different reduction approaches, namely, citrate and cetyltrimethylammonium bromide (CTAB) methods, on the iron-based material surface. All samples were characterized by a variety of analytical tools, which included scanning electron microscopy (SEM), electron-dispersive X-ray microanalysis (EDS), and EDS mapping to elucidate materials’ morphology as well as nano-catalysts’ loading and location on the iron-based structures.
Probing individual single atom electrocatalyst sites by advanced analytical scanning transmission electron microscopy
Single atom electrocatalysts (SAEs) are promising next-generation materials for promoting a variety of important reactions, such as the oxygen reduction, nitrogen reduction, and CO 2 reduction reactions. While bulk characterization techniques such as X-ray absorption spectroscopy and Mössbauer spectroscopy have significantly enhanced our understanding of these catalysts, direct probing of individual single metal atom sites at the atomic scale is necessary to understand local variations in the properties of these sites and accelerate design and synthesis of improved SAEs. Aberration-corrected scanning transmission electron microscopy (STEM) has become a powerful tool for providing this type of atomic-scale information about SAE metal sites. These sites are typically unstable under the electron beam, however, which, in combination with conventional acquisition methods and detectors, has limited the type and quantity of information obtainable by spectroscopic STEM techniques. Here, we map multiple individual SAE metal sites in a nitrogen-doped carbon containing atomically dispersed Fe and Re (FeReNC) at the atomic scale by direct electron detection electron energy-loss spectroscopy (EELS). Direct electron detection provides an improved signal-to-noise ratio over conventional scintillator-based detectors and enables detection and real space localization of weak signals. In addition, we demonstrate an automated method for identification of metal atom positions, placement of the probe on these sites, and simultaneous EELS and energy dispersive X-ray spectroscopic (EDS) signal acquisition. This simultaneous acquisition of EELS and EDS provides access to the composition and bonding of a wide range of SAE metal sites. In this study, focusing the probe directly on the metal sites also increases the relevant data acquisition rate by more than an order of magnitude over two-dimensional mapping, enabling improved statistical measurements of site properties. The versatility, sensitivity, and speed that these techniques provide enhances our ability to probe the local elemental and chemical environment of a large number of individual SAE metal site structures at the atomic scale, enabling an improved understanding of the variations in the local properties of these electrocatalysts to be gained. As a result, significantly increased information about individual metal sites will be available to future electrochemical studies through these techniques, accelerating the development of advanced SAEs.
Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification
We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.
Search for meteoritic GEMS II: Comparison of inclusions in amorphous silicates from the Paris chondrite and from anhydrous chondritic interplanetary dust particles
Amorphous silicates containing abundant nano-inclusions have been reported in the Paris CM chondrite (Leroux et al., 2015). They have chemical and morphological similarities to glass with embedded metal and sulfides (GEMS) found in interplanetary dust particles (IDPs) and micrometeorites believed to originate from comets. We used scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) and nanodiffraction to study the chemistry and mineralogy of these inclusions in order to understand the origin of the GEMS-like material in Paris and its possible relationships to other materials found in primitive chondritic materials including IDP GEMS. EDS and diffraction analyses indicate compositional and mineralogical differences between the nanophase inclusions in cometary GEMS and Paris GEMS-like material. Metal inclusions are notably absent within Paris amorphous silicate. Ni-rich sulfides, including pentlandite, are common in even the least altered matrix material of Paris, while they are absent in GEMS-bearing IDPs and Ultracarbonaceous Antarctic Micrometeorites (UCAMMs). From examination of the inclusions, we cannot yet confirm or refute the possibility that GEMS-like material in Paris is related to cometary GEMS. The distinct compositions and mineralogy of the Paris material may be due to aqueous alteration of cometary GEMS precursors, but they may also denote an independent origin for meteoritic GEMS-like assemblages.
Sphalerite weathering and controls on Zn and Cd migration in mine waste rock: An integrated study fr
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Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis
Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.
Investigating the failure behavior of cast Al-11Ce-0.4Mg alloys using in-situ scanning electron microscopy tensile testing
Within the last decade, research on Al-Ce-Mg alloys has reported promising results for use in cast part applications. In this paper, the failure behavior of cast Al-11Ce-0.4Mg (wt%) was investigated experimentally with focus on the effect the matrix and intermetallic phases have on the fracture propagation behavior at failure. For the first time, in-situ SEM tensile testing was used to study the failure behavior of cast Al-Ce alloys, reporting results for uniaxial, DIC, and single edge notch tensile tests. The results of the in-situ SEM tensile testing were compared with the materials characterization experiments, which included serial sectioning, EBSD, EDS, and fractography. Analysis of EBSD and EDS mapping of cast Al-11Ce-0.4Mg showed that the cast microstructure was a hypereutectic two phase Al-Ce alloy with grains encompassing large complex colonies of laminar eutectic Al 11 Ce 3 intermetallic. The uniaxial tensile results reported the effect casting defects have on the strength and ductility of the alloy, and DIC in-situ testing showed that the eutectic colonies plastically deform less than the matrix phase. In-situ SEM single edge notch tensile testing displayed how the strength of an individual phase affected the crack propagation direction in the alloy. The results of both the materials characterization and in-situ tensile testing experiments on the failure of this alloy revealed further directions for future alloy development that can improve both the strength and fracture toughness of Al-Ce-Mg alloys.
Effects of the method of active site characterization for determining structure-sensitivity in Ag-catalyzed ethylene epoxidation
We report catalysts have been prepared on a low surface area α-Al 2 O 3 support used commercially for previous generation olefin epoxidation catalysts. Prescreening of the low surface area alumina (0.73 m 2 /g) indicated the absence of acid catalyzed isomerization of EO at an evaluation temperature of 210 °C. A 0.1 wt% Ag base material synthesized by incipient wetness impregnation of AgNO 3 was used as a base material for electroless deposition (ED) of additional Ag to increase particle sizes by controlled reduction of Ag + directly onto the preexisting Ag surface to form weight loadings between 0.3 and 5.0 wt% metal. A 12 wt% Ag/α-Al 2 O 3 using Ag 2 C 2 O 4 as the Ag precursor was also prepared to compare performance of the ED samples with a catalyst more typical of industrial formulations. Characterization by SEM, STEM, and hydrogen titration of oxygen precovered Ag characterized before and after catalytic evaluation indicated that microscopy is required to accurately represent distributions of Ag particle sizes, but H 2 titration of O-precovered Ag gives the best representation of active sites since it directly counts the number of Ag surface sites. Larger particles >100 nm are resistant to both Ag sintering and carbon foulant; TOF values were relatively insensitive to particle size with only a 2.2 × difference between the best and worst performing samples. Selectivity, which is not a function of TOF, shows the most significant structure sensitivity effect where particle sizes follow the trend 67 nm ≈ 92 nm (58% EO) < 157 nm (67% EO) < 211–542 nm (73% EO). The lower EO selectivities were also correlated with increased fouling for the smaller Ag sizes, suggesting that more strongly bound EO precursor(s) leads to combustion and CO 2 /H 2 O formation.
U 3 Si 2 and UO 2 composites densified by spark plasma sintering for accident-tolerant fuels
This work reports the synthesis and characterization of the U 3 Si 2 and UO 2 composites sintered by spark plasma sintering (SPS) with controlled microstructures for accident-tolerant fuel application. The U 3 Si 2 and UO 2 composites with different silicide and oxide ratios were synthesized by SPS at temperatures from 1000 to 1300 °C for 5 minutes. The microstructure and phase composition of the SPS densified composite fuels were characterized with scanning electron microscopy, X-ray diffraction (XRD), and energy dispersed spectroscopy (EDS). A systematic study of the thermal and mechanical properties was conducted using microhardness testing and laser flash apparatus, along with oxidation resistance measurements using thermogravimetric analysis (TGA). The results show that the synthesis of composite fuels can be achieved with a 90% theoretical density (TD) at 1000 °C and over 95% TD when sintered at 1300 °C. XRD and EDS results confirmed that the dominant phases in the composites are U 3 Si 2 and UO 2 . Improved physical density generally leads to improved hardness, fracture toughness, thermal diffusivity, and onset temperature during the oxidation process. U 3 Si 2 was found to play a dominant role in determining the mechanical and oxidation properties of the composite fuels, whereas UO 2 had a more important impact on controlling the thermal diffusivity of the composites. The composite with 50 wt% UO 2 sintered at 1300 °C displayed the onset oxidation temperature of 500 °C by dynamic oxidation testing using TGA at a ramp degree of 10 °C/min. The composite also achieved a high fracture toughness of ~3.5 MPa m ½ . Finally, these results highlight the potential of composite fuel forms densified by SPS with simultaneously enhanced fissile element density, fracture toughness, thermal transport properties, and oxidation resistance.
Molecular Iodine Interactions with Metal Substrates: Towards the Understanding of Iodine Interactions in the Environment Following a Nuclear Accident
In order to evaluate the potential impacts to the public from radioiodine in a nuclear event, it is vital to expand our understanding of the interaction of molecular iodine with various surfaces. There are many potential surfaces that iodine could interact with in and around a nuclear facility, including stainless steel. This study, carried out at ambient temperature, pressure and humidity, demonstrates the highly adsorptive nature of molecular iodine on two types of austenitic stainless steel, 304L and 316L. In the authors review of available literature, Fe is the only metal in stainless steel that is assumed to react with gas-phase molecular iodine. By using a novel approach which combines Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) with surface Energy Dispersive X-ray Spectroscopy (EDS) there is evidence of the formation of metal iodides that have not previously been verified or quantified. Samples exposed to gaseous molecular iodine formed an iodine containing corrosion product visible by scanning electron microcopy (SEM). Evaluation of the metals in the corrosion region using EDS was compared to a water leach of the same samples analyzed using ICP-OES. A comparison of the results provide evidence that the water leachate is representative of the corrosion layer and not the base material. Furthermore, it provides confirmation of metal iodide formation with minor stainless-steel constituents including: FeI2, NiI2, MnI2 and CrI2.
An examination of the precipitation behavior of proton irradiated dual phase 308L weldment filler materials
Voids, G phase particles, and Ni-Si rich clusters in proton irradiated dual phase 308L groove filler of a SA508–304 L dissimilar metal weldment are analyzed using advanced characterization techniques. These weldments are often used in light water nuclear reactors and are subject to enhanced corrosion and associated stress corrosion cracking (SCC). Radiation damage is known to accelerate SCC. Ni-Si enriched clusters were observed in proton irradiated γ austenite, while G phase M 6 Ni 16 Si 7 (where M transition metal element) precipitates were observed in proton irradiated δ ferrite. Compositional analysis of the G phase precipitates and Ni-Si clusters from STEM-EDS and APT are compared. Unlike G phase particles in proton irradiated δ ferrite, Ni-Si clusters in proton irradiated γ austenite are not rich in Mn. Both STEM-EDS and APT line-scan profiles of the Ni-Si clusters show that the Fe and Cr concentration gradient between matrix γ austenite and the Ni-Si clusters is not as sharp as those between matrix δ ferrite and G phase. Further, HR-STEM imaging indicates that the lattice parameter of the Ni-Si clusters is commensurate with γ austenite and the clusters do not represent the precipitation of a second phase. Finally, our analysis demonstrates the density and volume fraction of G phase particles and the density of voids scales with proton irradiation damage and the energy to recoils.
Contributions to the mechanistic understanding of the microstructural evolution in irradiated U-Mo dispersion fuel
Here, advanced microstructural characterization techniques, such as scanning electron microscopy (SEM) and scanning transmission electron microscopy - energy dispersive x-ray spectroscopy (STEM-EDS), were used to interpret the fuel microstructure evolution and fission products behavior in U-Mo dispersion fuel irradiated in the Advanced Test Reactor (ATR) as part of the European Mini-Plate Irradiation Experiment (EMPIrE) test. The larger as-fabricated fuel grain size achieved by heat-treating the U-Mo powder resulted in slower high burnup structure (HBS) development and reduced fission gas porosity. Slower HBS kinetics was observed at the fuel kernels’ periphery, which contained smaller and less fission gas bubbles at all fission densities (FDs) investigated and was attributed to a locally reduced damage density and fission products concentration, as corroborated with Monte Carlo simulations. The non-refined grains at the fuel kernel periphery hosted a perfectly ordered fission Gas Bubble Superlattice (GBS) up to 6.3 × 10 21 fissions/cm 3 . Nano-scale STEM-EDS analysis presented in this study provided useful information on the GBS characteristic morphology and evolution in U-Mo fuel. The concentration of fission gas in the GBS progressively increased with FD, pointing to an evolution of the nanobubble pressure status with irradiation. A possible connection between the GBS collapse and HBS onset is proposed for which there exists a threshold in the misorientation of the refined sub-grains above which the GBS stability during irradiation is no longer preserved, resulting in the GBS collapse.
Corrosion susceptibility and chromium loss in Austenitic steels and Nickel-based alloys in molten FLiNaK at 700 °C
A comparative study was conducted to evaluate the corrosion susceptibility of 316L, 316H, Ni 200, Inconel 625, and Hastelloy N in molten FLiNaK at 700 °C for 100 h. Top-view and cross-sectional scanning electron microscopy (SEM) imaging, combined with energy-dispersive X-ray spectroscopy (EDS) mapping, was performed to investigate microstructural and compositional changes. SEM images were further processed by introducing a contrast threshold to map cavity distribution. Using EDS mapping, intergranular and intragranular Cr loss were separately characterized. The ranking of mass loss after corrosion, from highest to lowest, is as follows: 316H > 316L > Inconel 625 > Hastelloy N > Ni 200. Cr loss was found to be correlated with Mo concentration, in agreement with many previous studies, with higher Mo content resulting in reduced Cr loss. On the other hand, there is no evidence that the high carbon content in 316H enhances corrosion resistance. The relatively low solubility of carbon in austenite at the testing temperature limits the amount of dissolved carbon. Therefore, the carbon-retarded vacancy diffusion, and consequently the reduced Cr diffusion, were not observed.
Multi-modal characterization methods of solid-electrolyte interphase in silicon-graphite composite electrodes
Composite silicon-graphite (Si-Gr) anodes can improve battery energy density, due to Si's high gravimetric capacity, while mitigating mechanical degradation of the anode and solid-electrolyte interphase (SEI) caused by Si volumetric expansion. Optimizing these anodes is challenging, in part due to difficulty characterizing the SEI structure and composition. In this work, we present multi-modal characterization of the SEI on composite Si-Gr anodes to relate SEI chemical composition and structure to functional properties. Discrepancies in elemental concentrations from X-ray photoelectron spectroscopy, Auger electron spectroscopy, and energy-dispersive X-ray spectroscopy (EDS) are attributed to varying information depth and lateral resolution of the individual probes. However, by combining quantitative composition information with spatially resolved element mapping from scanning transmission electron microscopy, EDS, and electron energy loss spectroscopy, a holistic picture of the SEI emerges. We observe the bilayer SEI structure and a direct correlation between elemental Li and F, suggesting that most Li in the SEI exists as lithium fluoride (LiF). Further, LiF concentration is directly proportional to the maximum SEI resistivity, as determined by scanning spreading resistance microscopy. Lastly, there is an inverse relationship between lithium carbonate and LiF concentration in the SEI, providing insight into the detailed chemistry of SEI formation and evolution.
Voltage cycling as a dynamic operation mode for high temperature electrolysis solid oxide cells
Solid Oxide Electrolysis Cells (SOECs) have emerged as a promising technology for the efficient production of H2 via high-temperature electrolysis. However, power input from dynamic energy sources remains a significant challenge for their long-term stability. It is important to analyze the tolerance of cells under dynamic operation conditions. This study focuses on evaluating the impact of voltage cycling on the performance and durability of electrode-supported SOECs. We explore the operational limits and degradation mechanisms of SOECs subjected to various voltage conditions and find that the cells have high tolerance for dynamic voltage. Voltage cycling between 1.3 V and 1.5 V for 9000 cycles does not damage the cell. Conversely, cycling to higher voltages (≥1.7 V) results in accelerated degradation. Advanced characterization is used to screen for various degradation modes post operation. Within the oxygen electrode, XRD and STEM EDS find compositional and phase evolution in all voltage cycled samples including increased decomposition of the air electrode resulting in cation migration. Microstructural analysis of the fuel electrode from nano-CT data shows minimal change throughout the sample set and no evidence of Ni migration, indicating the fuel electrode is stable and not impacted by cycling to higher voltages within the timeframe studied.
XRD and SEM/EDS characterization of two quaternary fuel alloys (U-2.5Mo-2.5Ti-5.0Zr and U-1.5Mo-1.5Ti-7.0Zr in wt. %) for fast reactors
The current study focuses on characterization of two potential fuel alloys for sodium-cooled fast reactors: U-2.5Mo-2.5Ti-5.0Zr (U-MT5Z) and U-1.5Mo-1.5Ti-7.0Zr (U-MT7Z) in wt. %. As-cast alloys and annealed (600°C, 500h) alloys are studied by X-ray powder diffraction (XRD) for their bulk structures, and scanning electron microscopy / energy dispersive X-Ray spectroscopy (SEM/EDS) for their microstructures and phase constituents. For as-cast alloys, the XRD results show that U-MT5Z mainly contains γ, while U-MT7Z alloy contains a. In both alloys, there are three phases indicated by EDS, a U-rich phase, secondary phase, and Zr-rich phase. Further, the elemental compositions varied in different phases and in different alloys. After annealing, both alloys exhibit the structures of α-U and U 2 Ti according to XRD. The EDS results suggest there are five phases found in both alloys.
Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning
Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.