Results for “eds” — Page 3

DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Major to trace element imaging and analysis of iron age glasses using stage scanning in the analytical dual beam microscope (tandem)

Dark and clear silicate glasses formed during an iron age vitrification event ≈ 1500 years ago at the Broborg hillfort near Uppsala, Sweden have been analyzed using a scanning electron microscope equipped with a micro-X-ray fluorescence (μXRF) spectrometer. Correlated µXRF and electron beam-induced energy dispersive spectrometry (EDS) X-ray maps were collected via stage-scanning at constant velocity. This coupled procedure represents a new approach for the cultural heritage community to conduct analytical studies of archaeometric specimens composed of metal, ceramic, or mixed inorganic/organic materials, where major and trace element compositions are registered in space for areas up to the centimeter-length scale at micrometer-scale resolution. Overview images were used to select areas for EDS beam scan maps correlated with multispectral cathodoluminescence (CL) imaging and co-located quantitative EDS and μXRF point analysis. Fe, Ca, Mg, Ti, P, Mn, Zr, Zn, and Y are enriched in the dark glass, while Si, Al, K, Na, Ba, Sr, Rb, and Ga are enriched in the clear glass. Unmelted material is comprised predominately of quartz (SiO 2 ) along with trace apatite (Ca 5 (PO 4 ) 3 [Cl,OH]) and zircon (ZrSiO 4 ). Multivariate statistical analysis was used to measure the area fractions of high variance components while lower variance components represented phase mixtures. Differences between calculated melt viscosities for the glass compositions are consistent with field and laboratory observations. Coupled large area EDS and μXRF imaging shows significant promise for informed selection of higher spatial resolution and higher sensitivity follow-up studies, e.g., those performed using synchrotron analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Lithium Recovery and Conversion from Wastewater Produced by Recycling of Li-Ion Batteries via Two-Stage Electrodialysis

Electrodialysis (ED) is a membrane separation technique that has been well-established in various applications such as desalination, drinking water production, wastewater treatment, and lithium salt production. A limited number of studies have explored its application in lithium salt production, especially from secondary resources like wastewater. This study investigated a route to recover lithium from wastewater generated from the recycling of end-of-life Li-ion batteries. Two electrodialysis methods, namely standard electrodialysis (ED) and bipolar-membrane electrodialysis (BPED), were combined to concentrate lithium ions and convert them to lithium hydroxide (LiOH), a valuable product that can be fed back into the supply chain for manufacturing Li-ion batteries. Lithium (Li⁺) concentration in recycling wastewater was successfully increased by 58% using ED and converted to LiOH (>96% purity) with a further increase in Li⁺ concentration by 67% using BPED. The Coulombic efficiency of the experiments was 91.0 and 92.2%, with specific energy consumption of 1 and 2.5 kWh/kg, and a production rate of 1.01 and 0.14 kg/h/m 2 for the ED and BPED processes, respectively. In addition, preliminary techno-economic and environmental impact analyses show a significant improvement (GHG emission reduction by 77% and total energy reduction by 53%) by producing LiOH via electrodialysis compared to conventional lithium production via brine extraction. The process was assessed to be beneficial for lithium extraction from secondary resources and to enhance overall battery recycling efforts.

25 ENERGY STORAGE↗

DOE OSTI2022

Crystal engineering of heterocyclic arylene(ethynylene) oligomers through programmed aromatic stacking

Conjugated oligomers and polymers consisting of aromatic heterocycles are examples of next-generation organic electronic materials. However, control and optimization of the self-assembly of these materials in the solid-state, which is a crucial parameter for high efficiency performance, remains an unsolved challenge in materials development. In this work, we provide valuable insight into aromatic stacking interactions between fluorinated arene rings (ArF) and heterocycles with different electronic character (Het), and elaborate on the prospect of using these interactions to control the solid-state configurations of three-ring phenyleneethynylene oligomers (PEs). Oligomers possessing heterocycles typically thought of as electron-rich (ER) show blue-shifted optical spectra in the solid-state, while those incorporating heterocycles typically thought of us electron deficient (ED) show red-shifted solid-state optical spectra. Crystal structures show ArF-Het stacking interactions between the fluorinated side-chains and ER units, which twist the backbone out of planarity and prevent chromophore aggregation. Further, the interactions are absent in ED oligomers, highlighting cofacial incompatibility between ArF rings and ED units. A combination of TD-DFT computations and novel heterocycle descriptors reinforce our assignment of the oligomers as ED or ER, while exhaustive conformer analysis shows ArF-Het stacking interactions are a significant contributor to compound stability. Overall, this work describes the importance of heterocycle electronics in conformational control in the solid state, understanding of which can be a valuable asset in the development of novel optoelectronic materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Combining MicroED and native mass spectrometry for structural discovery of enzyme–small molecule complexes

With the goal of accelerating the discovery of small molecule–protein complexes, we leverage fast, low-dose, event-based electron counting microcrystal electron diffraction (MicroED) data collection and native mass spectrometry. This approach, which we term electron diffraction with native mass spectrometry (ED-MS), allows assignment of protein target structures bound to ligands with data obtained from crystal slurries soaked with mixtures of known inhibitors and crude biosynthetic reactions. This extends to libraries of printed ligands dispensed directly onto TEM grids for later soaking with microcrystal slurries, and complexes with noncovalent ligands. ED-MS resolves structures of the natural product, epoxide-based cysteine protease inhibitor E-64, and its biosynthetic analogs bound to the model cysteine protease, papain. It further identifies papain binding to its preferred natural products, by showing that two analogs of E-64 outcompete others in binding to papain crystals, and by detecting papain bound to E-64 and an analog from crude biosynthetic reactions, without purification. ED-MS also resolves binding of the CTX-M-14 β-lactamase, a target of active drug development, to the non-β-lactam inhibitor, avibactam, alone or in a cocktail of unrelated compounds. These results illustrate the utility of ED-MS for natural product ligand discovery and for structure-based screening of small molecule binders to macromolecular targets, promising utility for drug discovery.

MicroED↗

DOE OSTI2024

A Redox-Electrodialysis Model with Zero Fitting Parameters: Insights into Process Limitations, Design, and Material Interventions

Redox-Electrodialysis (r-ED) is an electrochemical desalination cell architecture that has recently received considerable interest, due to its low energy demand relative to electrochemical desalination technologies that rely on electrode-based ion removal. To further improve the energy efficiency of r-ED, we developed a lumped mathematical model with no adjustable parameters to investigate the various sources of overpotential within the cell. Existing models of electrodialysis and r-ED cells either do not accurately incorporate all phenomena contributing to the overpotential or utilize empirical fitting parameters. The model developed here indicates that ohmic overpotentials, especially in the diluate chamber, are the most significant contributors to energy losses. Based on this insight, we hypothesized that adding an ion exchange resin wafer in the diluate compartment would increase the ionic conductivity and decrease the energy demand. Experimental results showed an 18% reduction in specific energy use while achieving the same degree of salt removal (20 mM to 12 mM). Furthermore, the resin wafer enabled complete desalination to potable drinking levels at a current density previously unachievable within practical operating voltage limits (4.93 mA cm -2 ). We also expanded the model to explore differences in r-ED energy use between configurations using multiple cells and a single cell with increased area.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Energy Equity and Environmental Justice Summit Report 2022

Pacific Northwest National Laboratory (PNNL) hosted a limited-engagement Energy Equity and Environmental Justice (EEEJ) Summit on September 28, 2022, with the Department of Energy (DOE) Office of Economic Impact and Diversity (DOE-ED) and DOE national laboratories: Argonne National Laboratory, Idaho National Laboratory, Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, Oak Ridge National Laboratory, Sandia National Laboratories, and Savannah River National Laboratory. The goal of the Summit was for labs to highlight their capabilities in the EEEJ space and connect them to DOE-ED’s mission and goals as well as share lessons learned and best practices in our various programmatic spaces. The event commenced with an opening address from Director Shalanda Baker (DOE-ED), which included a call to action for the national labs. Three presentation panels followed, giving insights to the programs stewarded by national laboratories that are currently taking steps to enhance the way science is implemented through equity and justice lenses. A roundtable discussion concluded the Summit and highlighted lessons learned, gaps, challenges, opportunities, and future needs that will improve the impact and partnership of EEEJ work at DOE, national laboratories, at other agencies and institutions, and in other programmatic areas. Interest among participants to stay connected and coordinated and to partner on EEEJ work across labs was emphasized, including a suggestion to establish an intra-laboratory community of practice.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel Jana Howard1,3, Guang Yang3, Haiyan Zhao1*, Patrick Warren4, Tiankai Yao3, Steven Cavazos4 Elizabeth Sooby Wood4*, Ching-heng Shiau2 1 University of Idaho, Environmental Science, Idaho Falls, Idaho, USA 2 Boise State University, Microscopy and Characterization Suite, Idaho Falls, Idaho, USA 3 Idaho National Laboratory, Idaho Falls, Idaho, USA 4 University of Texas San Antonio, Department of Physics and Astronomy, San Antonio, Texas, USA *Corresponding author: haiyanz@uidaho.edu; elizabeth.soobywood@utsa.edu Tri-structural Isotropic (TRISO) particle fuel is the preferred choice for the newest and next-generation high-temperature nuclear reactors due to its robust construction [1]. The fuel design effectively contains fission products inside the particle at extremely high temperatures [2]. However, the effect of transition metal fission products on fuel performance and structural integrity remains largely unknown, creating uncertainties in predicting fuel performance and potential failure mechanisms [2]. For example, palladium (Pd) has a strong tendency to form intermetallic phases with uranium (U). These new phases tend to be hard and brittle which could lead to fracture of fuel kernel during irradiation [3,4]. Pd is not normally produced in high concentrations in normal fission reactions however, driving the production of Pd into the locality of a Uranium Oxycarbide (UCO) nuclear fuel provides an opportunity to closely observe the diffusion and chemical interactions. This study focuses on detailed transmission electron microscopy characterization of the intermetallic phases formed by the interaction between a UCO kernel and a Pd bar after annealing at 1100 °C for 100 hours. Figure 1 provides an overview of the UCO kernel in contact with the Pd bar. The UCO-Pd sample surface was examined using a Focused Ion Beam (FIB) Quanta 3D FEG FEI in VCD detector mode at 10kV, 83pA to reveal surface features. Several key surface features in the interaction region were observed including: (1) a small area with dendritic microstructure, (2) color contrast near the UCO kernel and Pd boundary, and (3) darker and lighter regions throughout the sample surface. To analyze diffusion behavior, FIB was used to prepare lamellae from five different areas of the UCO-Pd sample, as well as one for the as-received sample. These lamellae were examined using a Scanning Transmission Electron Microscope ThermoFisher Spectra 300 (STEM-Spectra). Energy Dispersive Spectroscopy results confirmed the diffusion between the UCO fuel and Pd bar. Figure 2 shows the identified phases of UC, UO2, and Pd dispersed throughout the interaction region. It was also found that the Pd concentration decreases as the radial distance from the UCO-Pd interface increases. The Pd concentration was 36.93% atomic fraction 11µm from the interaction region then decreased to 7.51% atomic fraction at 257 µm away from the interaction region. Figure 3 shows how concentrations of U and Pd vary across the interaction zone, highlighting the extent of diffusion. This study confirms that Pd interacts with surrounding material to form U-Pd phase and diffusion zones. These diffusion zones vary in composition depending on its radial distance from the point of contact with the Pd bar. These findings contribute to a deeper understanding of the fission product behavior in high temperature nuclear fuels, aiding in the prediction and mitigation of potential fuel degradation mechanisms. A B C Fig. 1. SEM micrographs of UCO fuel kernel in contact with solid Pd bar and the lift out locations. Figure 1A shows UCO fuel kernel in contact with solid Pd bar. Figure 1B shows a higher magnification image of the UCO fuel-Pd interaction zone. Figure 1C shows lift out sites for the lamellae. A B C Fig 2. EDS maps reveal the distribution of U, O, and Pd in location 3. Figure 2A shows the distribution of uranium. Figure 2B shows the distribution of oxygen. Figure 2C shows the distribution of palladium. A B C Fig 3. Palladium and uranium concentration across interaction zone in location 2. Figure 3A shows EDS map of lift out number 2. Figure 3B shows a zoomed in EDS map taken from the interaction zone. Figure 3C shows a line graph of uranium and palladium concentrations across the interaction zone. References: 1. B.E. Wells, N.R. Phillips, K.J. Geelhood. Pacific West Laboratory. (2021). TRISO Fuel: Properties and Failure Modes. https://www.nrc.gov/docs/ML2117/ML21175A152.pdf (Accessed January 16, 2025). 2. American Nuclear Society. TRISO Fuel Development Progresses in INL, ORNL. https://www.gen-4.org/gif/upload/docs/application/pdf/2014-03/nov13nn_fuel_reprint.pdf (Accessed January 16, 2025) 3. Clark R.A., M.A. Con

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel

Chemical Interaction of Palladium in Uranium Oxycarbide Nuclear Fuel Kernel Jana Howard1,3, Guang Yang3, Haiyan Zhao1*, Patrick Warren4, Tiankai Yao3, Steven Cavazos4 Elizabeth Sooby Wood4*, Ching-heng Shiau2 1 University of Idaho, Environmental Science, Idaho Falls, Idaho, USA 2 Boise State University, Microscopy and Characterization Suite, Idaho Falls, Idaho, USA 3 Idaho National Laboratory, Idaho Falls, Idaho, USA 4 University of Texas San Antonio, Department of Physics and Astronomy, San Antonio, Texas, USA *Corresponding author: haiyanz@uidaho.edu; elizabeth.soobywood@utsa.edu Tri-structural Isotropic (TRISO) particle fuel is the preferred choice for the newest and next-generation high-temperature nuclear reactors due to its robust construction [1]. The fuel design effectively contains fission products inside the particle at extremely high temperatures [2]. However, the effect of transition metal fission products on fuel performance and structural integrity remains largely unknown, creating uncertainties in predicting fuel performance and potential failure mechanisms [2]. For example, palladium (Pd) has a strong tendency to form intermetallic phases with uranium (U). These new phases tend to be hard and brittle which could lead to fracture of fuel kernel during irradiation [3,4]. Pd is not normally produced in high concentrations in normal fission reactions however, driving the production of Pd into the locality of a Uranium Oxycarbide (UCO) nuclear fuel provides an opportunity to closely observe the diffusion and chemical interactions. This study focuses on detailed transmission electron microscopy characterization of the intermetallic phases formed by the interaction between a UCO kernel and a Pd bar after annealing at 1100 °C for 100 hours. Figure 1 provides an overview of the UCO kernel in contact with the Pd bar. The UCO-Pd sample surface was examined using a Focused Ion Beam (FIB) Quanta 3D FEG FEI in VCD detector mode at 10kV, 83pA to reveal surface features. Several key surface features in the interaction region were observed including: (1) a small area with dendritic microstructure, (2) color contrast near the UCO kernel and Pd boundary, and (3) darker and lighter regions throughout the sample surface. To analyze diffusion behavior, FIB was used to prepare lamellae from five different areas of the UCO-Pd sample, as well as one for the as-received sample. These lamellae were examined using a Scanning Transmission Electron Microscope ThermoFisher Spectra 300 (STEM-Spectra). Energy Dispersive Spectroscopy results confirmed the diffusion between the UCO fuel and Pd bar. Figure 2 shows the identified phases of UC, UO2, and Pd dispersed throughout the interaction region. It was also found that the Pd concentration decreases as the radial distance from the UCO-Pd interface increases. The Pd concentration was 36.93% atomic fraction 11µm from the interaction region then decreased to 7.51% atomic fraction at 257 µm away from the interaction region. Figure 3 shows how concentrations of U and Pd vary across the interaction zone, highlighting the extent of diffusion. This study confirms that Pd interacts with surrounding material to form U-Pd phase and diffusion zones. These diffusion zones vary in composition depending on its radial distance from the point of contact with the Pd bar. These findings contribute to a deeper understanding of the fission product behavior in high temperature nuclear fuels, aiding in the prediction and mitigation of potential fuel degradation mechanisms. A B C Fig. 1. SEM micrographs of UCO fuel kernel in contact with solid Pd bar and the lift out locations. Figure 1A shows UCO fuel kernel in contact with solid Pd bar. Figure 1B shows a higher magnification image of the UCO fuel-Pd interaction zone. Figure 1C shows lift out sites for the lamellae. A B C Fig 2. EDS maps reveal the distribution of U, O, and Pd in location 3. Figure 2A shows the distribution of uranium. Figure 2B shows the distribution of oxygen. Figure 2C shows the distribution of palladium. A B C Fig 3. Palladium and uranium concentration across interaction zone in location 2. Figure 3A shows EDS map of lift out number 2. Figure 3B shows a zoomed in EDS map taken from the interaction zone. Figure 3C shows a line graph of uranium and palladium concentrations across the interaction zone. References: 1. B.E. Wells, N.R. Phillips, K.J. Geelhood. Pacific West Laboratory. (2021). TRISO Fuel: Properties and Failure Modes. https://www.nrc.gov/docs/ML2117/ML21175A152.pdf (Accessed January 16, 2025). 2. American Nuclear Society. TRISO Fuel Development Progresses in INL, ORNL. https://www.gen-4.org/gif/upload/docs/application/pdf/2014-03/nov13nn_fuel_reprint.pdf (Accessed January 16, 2025) 3. Clark R.A., M.A. Con

36 - MATERIALS SCIENCE↗

DOE OSTI2026

A PRACTICAL ELECTRODIALYSIS MODEL FOR ACCELERATING SYSTEM DEVELOPMENT

Empirical optimization of electrodialysis (ED) is dependent on repetitive experiments with incremental adjustments, which is cost prohibitive at scale. While models can reduce the costs associated with optimization and scale-up, existing ED models are limited in application to specific use cases and tend to be developed for the exploration of specific transport phenomena. The field requires a practical system-level model, generalized for the broad range of ED systems. This work presents a modeling framework that enables rapid evaluation of membrane stack design, flow configuration, scale, and operational inputs. Across applications spanning 1 L to 5400 L; use of conventional and bipolar membranes; operation in continuous, batch and fed-batch modes; and feedstocks including seawater, brine, wastewater, and manure hydrolysate, the model achieves a mean R2 of 0.978 for concentration-time profiles and links design choices to techno-economic trade-offs, enabling cost-aware prioritization of system configurations.

Bipolar Membrane↗

DOE OSTI2024

Smart manufacturing approach to manufacture bulk nanocrystalline aluminum for lightweight applications

In this research, a smart manufacturing approach was used to enhance the mechanical properties of aluminum (Al) for lightweight applications. The smart manufacturing involved cryomilling of Al powders with and without 5 wt.% magnesium (Mg) powders for varying durations followed by a high-pressure cold spray (HPCS) additive manufacturing process to prepare bulk components. The morphological changes, crystallite size, and composition of the cryomilled powders and cold sprayed (CS’ed) components were examined using scanning electron microscopy (SEM), x-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The results showed that the crystallite size reduces with an increase in cryomilling time and the addition of Mg dopant. To test the mechanical properties of the bulk CS’ed components, microhardness tests were performed using a Vickers microhardness tester. Uniaxial tensile tests were also carried out to ascertain the material’s tensile properties. The mechanical testing results showed great improvement in the hardness and tensile strength of CS’ed Al–Mg samples as compared to pure Al samples. Subsequently, fractography analysis of the tensile failed samples was carried out to determine the nature of the failure. Here, the research article also discusses the inherent mechanisms for the improvement in mechanical properties of smart manufactured components as a result of Mg doping and cryomilling.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Nonlinear programming optimization of a single-stack electrodialysis desalination system for cost efficiency

Electrodialysis (ED) presents a competitive method for desalinating brackish waters. In this work, we perform cost optimization of a single-stack ED system across a range of feed salinities and water recoveries while optimizing operating voltage, number of cell pairs, and cell length. The results of our optimization show that the levelized cost of water (LCOW) increases with an increase in feed salinity. The outcomes of our optimization show that cost-optimal design generally increases cell length while decreasing cell pair number and operating voltage with an increase in salinity. These trends are nonlinear, with the number of cell pairs and applied voltage exhibiting local maxima when operating at low salinity and high recovery. We discuss the underlying mechanism for cell length becoming a leveraging design parameter by inspecting the length-dependent profiles of key electrochemical properties of the ED cell. Finally, we present how increasing performance metrics and decreasing costs impact LCOW, demonstrating that innovations that decrease counter-current diffusion have resulted in the highest decrease of LCOW.

42 ENGINEERING↗

DOE OSTI2025

Fabrication of porous transport electrodes: Development of quantitative approach for quality control

This work focuses on porous transport electrodes (PTEs), which integrate the anodic catalyst with the adjacent Ti porous transport layer (PTL). Challenges in catalyst deposition on PTLs, particularly at low loadings, motivated this study to evaluate various fabrication methods and characterization approaches. This work investigated Pt-treated PTLs coated with Ir-based catalysts using several common methods, including airbrush coating, rod coating, ultrasonic spray coating, electrodeposition, and sputter deposition, with catalyst loadings ranging from 2.9 to 0.1 mg/cm 2 , providing the opportunity for comparisons across a large set of samples produced by different methods. Two widely accessible characterization techniques: X-ray computed tomography (XCT) and scanning electron microscopy energy dispersive X-ray spectroscopy (SEM-EDS) were explored. Initial evaluation of selected samples with XCT provided qualitative insights into catalyst distribution, however comprehensive quantitative analysis was limited. SEM-EDS enabled detailed information on the catalyst distribution both qualitatively and quantitatively using two metrics. Atomic and surface area % ratios of Pt:Ir and Ti:Ir revealed trends in catalyst loading and losses into the PTL pores, as well as evaluating the homogeneity of catalyst coatings. The analysis demonstrated that ultrasonic spray coating, electrodeposition, and sputter coating produced the most homogeneous coatings, with minimal catalyst losses observed for electrodeposition and sputter coating. By adapting common techniques with novel, standardized methodologies, this work establishes a universally applicable framework for cross-study comparison of PTEs. The SEM-EDS approach provides a practical, accessible tool for PTE characterization and contributes a reference dataset supporting both research development and rapid quality control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Multi-technique characterization of spray coated and roll-to-roll coated gas diffusion fuel cell electrodes

Here this work reports the characterization of catalyst layer (CL) structure and composition for a set of gas-diffusion electrodes (GDEs) fabricated by different coating methods with the goal of developing a better understanding of CL processing-structure-property-performance relationships. The CL coating techniques used in this study were ultrasonic spray coating, and two roll-to-roll (R2R) methods - gravure and slot die. Scanning transmission electron microscopy (STEM) coupled with X-ray energy dispersive spectroscopy (EDS) analysis of GDE cross-sections provided bulk information, while scanning electron microscopy (SEM) with EDS and X-ray photoelectron spectroscopy (XPS) were used to investigate the near surface and surface composition of the CLs that will be in direct contact with the membrane once the membrane electrode assembly (MEA) is constructed. This study demonstrates that common quantification parameters, namely the F:Pt ratio extracted from cross-sectional STEM-EDS maps and top-down CL measurements with SEM and XPS, along with roughness parameters quantified from SEM micrographs are useful parameters in explaining and potentially predicting performance trends. Additionally, it highlights the importance of understanding key CL surface properties for advancing the manufacturability of high-performance components for polymer electrolyte membrane (PEM) technologies.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Enhanced ZIF-8-enabled colorimetric CO 2 sensing through dye-precursor synthesis

The accumulation of carbon dioxide (CO 2 ) within enclosed spaces, along with volatile organic compounds, under certain humidity, temperature, and ventilation conditions is associated with detrimental human health symptoms such as fatigue. Color-based chemical sensing is a promising approach to detect CO 2 levels relevant to indoor air quality through producing fast, quantifiable output visible to the naked eye. In a prior work, a colorimetric gas sensor was fabricated through synthesizing the metal-organic framework, ZIF-8, as the adsorbent, followed by post-synthetic mixing with a dye, phenol red (PSP), and primary amine, ethylenediamine (ED). While this sensor (termed PSP-ED/ZIF-8) maintained its structural integrity in atmospheric conditions and exhibited an increasing fuchsia-to-yellow color change with increasing CO 2 levels in dry environment, the colorimetric response greatly suffered in the presence of humid CO 2 . Here in this work, a significantly improved colorimetric CO 2 sensor (referred to as ED/PSP:ZIF-8) is accomplished through directly incorporating phenol red in the ZIF-8 metal and linker precursor solutions and then blending with ethylenediamine. MATLAB-generated color distributions and in-situ ultraviolet-visible (UV-Vis) spectroscopic studies quantitatively demonstrate an enhanced colorimetric gas response of ED/PSP:ZIF-8 compared to that of PSP-ED/ZIF-8 across an important range of CO 2 for indoor air quality monitoring (500 – 3500 ppm) and across a range of humidity. The new sensor also exhibits high selectivity to CO 2 compared to select volatile organic compounds, such as acetone and ethanol, which contribute to human health symptoms experienced indoors. The enhanced performance is attributed to the proposed incorporation of phenol red within ZIF-8, while maintaining the chemical stability of the MOF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Impact of Concurrent Solubilization and Fines Migration on Fracture Aperture Growth in Shales during Acidized Brine Injection

Acidic hydraulic fracturing fluid chemically and physically alters shale rock fabric during injection and shut-in, creating a “reaction-altered zone” along the fracture faces. To better characterize the variable thickness and composition of this reaction-altered zone under advective flow, we take a coupled experimental and modeling approach. A fluidic cell, with six fiducial markers, is first fabricated to keep the rock sample in place during the core floods and to allow image alignment of acquired images. Then, we conduct a series of reactive core floods in a clay-rich siliceous Wolfcamp shale sample with 10 wt % carbonate, using a synthetic fracturing fluid under no confining stress and at room temperature. High-resolution computed tomography (CT) scans are periodically conducted to observe the spatial alteration of the fracture network. We then perform scanning electron microscopy (SEM-EDS) on the two orthogonal surfaces (fracture surface and freshly cut profile face) to generate high-resolution elemental maps that show the change in mineralogy, both with distance along a given flow path along a fracture surface and with depth from the fracture surface into the shale matrix. These results are contrasted against a two-dimensional (2D) advection-diffusion reaction model developed previously for batch reactions between shale and synthetic fracturing fluids. The model simulates the geochemical interaction occurring at the fracture/matrix interface and penetrating into the shale matrix during the reactive core flood. Both model and experimental results show that the acidic brine is neutralized during the core flood, corresponding to an increase in fracture aperture as a function of fluid volume injected with the greatest change near the inlet. SEM-EDS scans reveal significant dissolution of carbonates on the fracture surface without pyrite oxidation. The reactive transport model indicates that carbonate depletion into the shale interior should be observable, yet SEM-EDS shows no discernible loss of carbonate in the orthogonal profile face. The combination of these observations suggests an additional fracture evolution mechanism in the reactive system, i.e., fines migration. We show that fines migration enhances the access of fracturing fluid to the matrix resulting in a more pronounced fracture widening. Finally, we conclude that coupled mineral dissolution and fines migration govern fracture aperture growth during acidized brine injection. In this work, we effectively show the underlying risk of relying solely on models that do not include an important (transport) process that can alter the system significantly and propose a combined chemomechanical mechanism for fracture evolution appropriate for this shale mineralogy.

58 GEOSCIENCES↗

DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗