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Powder River Basin CORE-CM: Advancing Strategies for Carbon Ore, Rare Earth Element, and Critical Mineral Resource Development in the Nation's Largest Coal Producing Basin
A multiscale strengthening approach was employed to tailor the mechanical properties of a NiCoCr-based multi-principal element alloy (MPEA). Initially, the addition of Mo was utilized to strengthen the alloys by achieving grain size refinement, severe lattice distortion, and a reduction in stacking fault energy (SFE). Subsequently, the Suzuki segregation phenomenon was leveraged by carefully controlling stacking fault (SF) density via pre-straining and a subsequent 500°C heat treatment. Consequently, the Mo-added MPE alloys that were pre-strained and subsequently heat-treated exhibited a true yield stress of 2.3 GPa, with a corresponding fracture elongation of 12%. The Mo promotes complex SF interactions and the SFs formed during prior straining serve as sites for Suzuki segregation during subsequent heat treatment, varying the local SFE with Cr segregation, creating a roughened landscape and hindering dislocation dynamics. This multiscale strengthening approach presents a promising route for achieving desired mechanical properties in MPEAs.
This report details the development of a machine learning (ML)-driven framework to coarse-grain inter-particle collision dynamics from high-fidelity Discrete Element Method (DEM) simulations to Particle-in-Cell (PIC) scales for gas-solid systems. Traditional PIC models, while computationally efficient, rely on empirical granular stress formulations that fail to capture the full complexity of collision physics, particularly the heterogeneity in particle dynamics. This study adopts a bottom-up approach, integrating insights from DEM simulations to improve the physical fidelity and interpretability of PIC-scale models.
The University of North Dakota (UND), in collaboration with a comprehensive team of technical, business and host-site partners, built on prior technology development to complete a front-end engineering and design (FEED) and business planning study to recover and refine rare earth elements (REE) and critical minerals (CM) from North Dakota (ND) lignite mine wastes. The end of project goal was to have an investment quality project and a committed team ready to commercialize the proposed technologies in a future construction and operations phase.
Nanodiamonds embedded with nitrogen-vacancy (NV) centers are emerging as powerful tools in quantum sensing due to their remarkable sensitivity to local variations in electromagnetic fields, temperature, and pressure. These point defects in the diamond lattice can be probed using optically detected magnetic resonance (ODMR) and spin relaxometry techniques, allowing for the detection and characterization of magnetic nanoparticles and ions. The sensing mechanism relies on laser-induced excitation of the NV center's non-degenerate triplet ground state, which promotes electrons to an excited state. Subsequent photoluminescence (PL) emission, which is spin-state dependent, is then measured. These spin states are highly responsive to magnetic perturbations in the surrounding environment. By analyzing changes in the ODMR spectra and spin relaxation times, we can effectively monitor how magnetic species influence the NV center's spin dynamics, thus enabling sensitive and selective characterization of nanoscale magnetic materials. These techniques have been applied for the characterization of transition metals and rare earth elements which shows natural ferromagnetic properties.
This study investigates the potential for New Mexico basalts to serve as geologic CO₂ sequestration media through mineral carbonation. Batch-type experiments were conducted using crushed basalts from the Carrizozo and Taos regions at low PCO₂ (0.7–2 bar) and temperatures up to 40°C over 180 days. The experiments evaluated the kinetics of CO₂-water-rock interaction, cation release, and critical element mobility (Cu, Ni, Co, Zn). Solid and fluid samples were analyzed using SEM, EMPA, ICP-OES, and ICP-MS, and geochemical modeling was performed with GEM-Selektor and PHREEQC. Results indicated that up to 80% of the injected CO₂ was mineralized within 100 days, forming secondary carbonates such as siderite and ankerite. Additionally, significant mobilization of critical metals was observed, which has implications for resource recovery and mine-waste valorization in future CO₂ sequestration projects.
This three-year project established a new approach to calculating crystal field coefficients for rare-earth elements in solids, especially for quantum applications such as transduction. Crystal field values of erbium doping several oxide hosts were presented. In addition stoichiometric materials, erbium oxide and cerium vanadate and phosphate, were simulated to understand their properties for quantum information. A fundamental enhancement to transduction enabled by magnon interaction was presented, along with magnon calculations in several oxide hosts and 2D materials.
BISON is a finite element-based nuclear fuel performance code applicable to a variety of fuel forms including light water reactor fuel rods, TRISO particle fuel, and metallic rod and plate fuel. It is a multiphysics fuel analysis tool that solves fully-coupled thermomechanical problems. BISON is based on MOOSE and can efficiently solve problems using standard workstations or very large high-performance computers in a variety of different dimensions, including full 3D, 2D-RZ axisymmetric, layered axisymmetric 1D, and spherically symmetric 1D systems. It is developed by a team of scientists and engineers at Idaho National Laboratory and by collaborators. The development of BISON is supported by various funding agencies, principally the United States Department of Energy.
In this presentation, we first discuss the benefits of targeting end-of-life (EOL) products as a feedstock. We then discuss the problem statement of designing a processing facility for recovering rare earth elements (REEs) from EOL HDDs and motors from EOL EVs/HEVs. We then discuss the most profitable pathway, and then discuss the environmental impacts associated with the processing pathway, comparing it to a processing facility in China, and find it to have lower impacts overall. We also investigate the trade-off solutions for other pathways that are not as profitable, but have lower impacts by generating Pareto Fronts. In the next portion of the presentation, Critical Materials Recycling, Inc. is discussed. We discuss how their process was found to be the optimal pathway after performing superstructure optimization, and how we are currently working with them to further optimize their process to increase profits. Finally, we wrap up with conclusions summarizing the main takeaways of the presentation.
The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.
Bacterial metabolic interactions with rare earth elements (REEs) can be harnessed for biomining unconventional feedstocks like abandoned coal-mine drainage (AMD). Pennsylvania has ~500 AMD passive remediation systems that can precipitate REE rich solids. REEs include yttrium and the lanthanide series that are used in modern energy and technology. Bacteria that metabolically interact with REEs can be used for biomining in an affordable efficient process that does not require hazardous chemical additives. Currently, the microbial metal mechanisms that contribute to REE biorelease and biorecovery are poorly understood. Our work shows acidogenic bacterial isolates (Bacillus mycoides JR07 and Bacillus pseudomycoides KB7) successfully bioleach a mixed REE solution from AMD solids by their organic acid production and biofilm formation. Further, our work shows the potential for bacterial lanthanide-dependent enzymes to recover lanthanides from a mixed REE solution; here we have bacterial isolate Methylobacterium sp. B3 that can recover soluble lanthanum. Whole genome sequencing of Methylobacterium sp. B3 predict lanthanide-dependent methanol dehydrogenase XoxF. Understanding the microbial metabolism and genes involved in the REE release and recovery is crucial to optimize the biomining of AMD solids. Our work addresses the growing need to develop novel REE mining methods from unconventional feedstocks.
Rare earth elements (REEs) make up integral components in personal electronics, healthcare instrumentation, and modern energy technologies. REE leaching with organic acids is an environmentally friendly alternative to traditional extraction methods. Our previous study demonstrated that batch ultrasound-assisted organic acid leaching of REEs can significantly decrease environmental impacts compared to traditional bioleaching. The batch method is limited to small volumes and is unsuitable for industrial implementation. This study proposes a novel approach to increase reaction volume using a continuous ultrasound-assisted organic acid leaching method. Laboratory experiments showed that continuous ultrasound-assisted leaching increased the leaching rate (µg/h) 11.3–24.5 times compared to our previously reported batch method. Techno-economic analysis estimates the cost of the continuous approach using commercially purchased organic acids is $\$$9465/kg of extracted REEs and $\$$4325/kg of extracted REEs, using gluconic acid and citric acid, respectively. The sensitivity analysis reveals that substituting commercially purchased organic acids with microbially produced biolixiviant can reduce the process cost by approximately 99% while minimally increasing energy consumption. Environmental assessment shows that most of the emissions stemmed from the energy required to power the ultrasound reactor. We concluded that increased leaching capacity using a continuous ultrasound-assisted approach is feasible, but process modifications are needed to reduce the environmental impact.
We extend the Virtual Element Method to a two-dimensional unsteady nonlinear convection-diffusion equation characterized by a fractional-order derivative with respect to the time variable. Our methodology is based on three fundamental technical components: a fractional version of the Grunwald-Letnikov approximation, discrete maximal regularity, and the regularity theory associated with non-linearity. We prove the method's well-posedness, i.e., the approximate solution's existence and uniqueness to the time-fractional convection-diffusion equation with a Lipschitz nonlinear source term. The fully discrete scheme inherently maintains stability and consistency by leveraging the discrete maximal regularity and the energy projection operator. The convergence in the L 2 -norm and H 1 -norm to various mesh configurations is validated by numerical results, underlining the practical effectiveness of the proposed method.
Rare earth elements (REEs) are critical components of modern technology behind renewable energy, transportation, and electronics but have a limited current supply. A substantial portion of global REE production relies on ion adsorption deposits. A high abundance of kaolinite in REE enrichment zones within these deposits suggests that kaolinite controls the subsurface migration of REEs. This study aimed to improve the current understanding of REE binding to kaolinite under varying water chemistry conditions. We conducted batch experiments with kaolinite (KGa-2) and three REEs (Nd, Dy, and Yb) at varying pH, electrolyte concentration, dissolved inorganic carbon (DIC), low molecular weight organic acids (citric and oxalic acids), and total REE concentration conditions. Increasing electrolyte concentration inhibits REE adsorption at pH 7, suggesting that ion exchange contributes to adsorption at these pH values. DIC affects adsorption above pH 7−8 by forming strong aqueous complexes with heavy REEs. Citric acid decreases REE adsorption via aqueous complexation of REEs at pH 5 but does not affect adsorption at pH 5. The surface complexation model captures the main adsorption trends with two mechanisms: ion exchange on basal planes at pH ∼6 and inner-sphere surface complexation to edge sites at pH ∼6. Equilibrium constants for surface complexation increase in the order of Yb Dy Nd, indicating a higher strength of adsorption for heavy REEs. This study demonstrates how water chemistry conditions control the adsorption mechanisms that may determine the mobility of REEs in subsurface environments rich in kaolinite.
We present the use of discrete element method (DEM) and material point method (MPM) in three relevant green technology applications that include biomass feedstock handling, lithium-ion battery manufacturing, and high-pressure reverse osmosis. Our open-source DEM and MPM solvers are developed using performance portable grid and particle management library, AMReX, thus enabling superior performance on NVIDIA and AMD GPUs with > 100 million particles. Our DEM solver resolves the motion of individual particles in a granular system and includes a bonded sphere method for modeling non-spherical particles along with Hertzian and liquid bridge-based contact models. We simulate highly variable biomass feedstock flows in large-scale hoppers for biofuel production and electrode calendering in battery manufacturing using DEM. Our simulations predict flow blockage in large scale biomass hoppers and electrode microstructure variations, thus providing valuable information for biofuel and battery manufacturers, respectively. The second half of the talk will be on MPM and its application towards pore resolved simulations of reverse osmosis membranes under compressive loads. We present a validation study of our MPM simulations with membrane microscopy imaging thus providing useful insights on membrane stability under high pressure conditions. We also present a spectral stability analysis of using linear hat, quadratic and cubic spline basis in MPM indicating regions of numerical stability.
It is a well-accepted fact that detailed inverter-based resource (IBR) models are needed for electromagnetic transient (EMT) studies, especially for protection studies and protection designs. However, it is not clear which modeling aspects should be included in the EMT model to accurately represent the IBR fault responses. Therefore, this paper addresses this gap by evaluating the impact of key modeling aspects (DC source, inverter model, power control, current/voltage control, and current limiting) on the response of protective relay elements both qualitatively and quantitatively. Both grid-following (GFL) and grid-forming (GFM) IBR models are comprehensively evaluated through a simplified real-world transmission network by varying the IBR modeling aspects, fault locations, and types. The insights and findings from this paper will benefit the power system protection community because this work provides modeling requirements and guidelines for IBR modeling so that correct IBR models will be used for protection scheme settings design.
Acid mine drainage (AMD) has been a challenge for mine operators to address and has had significant environmental impacts when there is a failure to address it. Fortunately, there is a regulation in the US that requires the treatment of AMD before water can be discharged into the environment. AMD is generated when sulfide minerals are exposed to water and air from mining. The acidic water then leaches metals from the surrounding rock, creating the potential for environmental contamination of this acidic water containing dissolved metals. AMD can contain high concentrations of metals like iron, aluminum, and manganese and also have been shown to contain trace concentrations of critical minerals, including rare earth elements (REEs). This environmental waste stream is now being researched as a potential source for REEs. There is a patented process for processing and extracting REEs from AMD which produces rare earth oxide preconcentrate (REOP) from AMD treatment precipitates and a final stage mixed rare earth oxide (MREO) product. This body of research examines a selective leaching process for each of these products and determines the activation energy associated with the leaching processes. Acid leaching with HCl was examined to selectively leach REEs from REOP while contaminant metals remained in the solid residue. The maximum leaching recovery of the REEs from the REOP was approximately 90% and an activation energy of 6.4 kJ/mol at pH 3.0. Ammonium chloride leaching was examined to selectively remove contaminant metals from a MREO product. The ammonium chloride process successfully leached major contaminant metals in excess of 85% recovery and had a maximum activation energy of 40.0 kJ/mol.
Intrinsic silicon (Si) is forbidden for infrared (IR) sensing at the communication wavelength like 1.31 or 1.55 µm due to the well‐known bandgap limitation. In this work, an unexpected physical picture of using argon (Ar) is identified, which is usually inert to the surrounding chemical environment and used as a protective agent in semiconductor processing, to overcome the IR‐sensing‐forbidden problem in Si. Here, in this work, it is shown by an analysis of a dynamic secondary ion mass spectrometer that such a Si, when exposed to laser pulse in Ar gas, can contain a very high dose of Ar up to 10 20 cm −3 even after 1300 days. First‐principles calculations, molecular dynamics, and Hall effect measurements reveal that, due to both steric and dynamic repulsions by Ar orbitals to Si dangling bonds, the Ar‐filled‐vacancy produces a much wider defect band inside the gap, which is not only responsible for strong infrared absorption, but also causes a significant increase in n‐type conductivity, both in line with experiments. The study proves that originally inert elements in fact can act as active impurities in semiconductors for advanced applications, which updates the current knowledge of defect physics.