Targeted uranium recovery from complex alloys using fluoride volatility
Not Available
SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Epsilon metal (e-metal) is the metallic phase that forms as inclusions at the grain boundaries in the UO2 fuel during reactor operation. This metal is composed of Pd, Mo, Rh, Ru, and Tc. These metallic inclusions are insoluble in strong acid and remnants of these metallic inclusions have been found in the UO2 matrix that remains from the natural reactors in Gabon that were active 1.8 billion years ago, therefore e-metal should be an excellent waste form for the immobilization of the long-lived isotopes 107Pd (6.5 ? 106 a) and 99Tc (2.13 ? 105 a), with 99Tc being the isotope of interest for repository performance. Therefore, the chemical durability of this potential waste form is assessed in this study. Typically, corrosion rates for metallic materials are measured electrochemically because they are quick, inexpensive, and can reveal the mechanism by which a metal corrodes, at least initially. However, in a repository the waste form would be subjected to slowly flowing water without an applied electrical potential over long time periods. Therefore, the corrosion rates of e-metal specimens were measured with both electrochemical tests and the single-pass flow-through test (SPFT). Potentiodynamic and potentiostatic polarization results suggest that a thin passive film exists on the alloy surface, which seems to be responsible for its high corrosion resistance. Additionally, X-Ray photoelectron spectroscopic results suggest that Pd oxides are significantly enriched in the passive film Results from the SPFT show that the dissolution rates were weakly dependent on pH. Only Mo and Re were found in solution and were used for the calculation of the dissolution rates. In general, the electrochemically determined corrosion rates agree reasonably well with the initial dissolution rate measured with the SPFT test, but they are about one or two orders of magnitude higher than the steady state rates. The causes for this discrepancy are discussed.
Membrane distillation (MD) is a membrane-based thermal desalination process capable of treating hypersaline brines. Standard MD systems rely on preheating the feed to drive the desalination process. However, relying on the feed to carry thermal energy is limited by a decline of the thermal driving force as the water moves across the membrane, and temperature polarization. In contrast, supplying heat directly into the feed channel, either through the membrane or other channel surfaces, has the potential of minimizing temperature polarization, increasing single-pass water recoveries, and decreasing the number of heat exchangers in the system. When solar thermal energy can be utilized, particularly if the solar heat is optimally delivered to enhance water evaporation and process performance, MD processes can potentially be improved in terms of energy efficiency, environmental sustainability, or operating costs. Here we describe an MD process using layered composite membranes that include a high-thermal-conductivity layer for supplying heat directly to the membrane-water interface and the flow channel. Here, the MD system showed stable performance with water flux up to 9 L/m 2 /hr, and salt rejection >99.9% over hours of desalinating hypersaline feed (100 g/L NaCl). In addition to bench-scale system, we developed a computational fluid dynamics model that successfully described the transport phenomena in the system.
G4CMP simulates phonon and charge transport in cryogenic semiconductor crystals using the Geant4 toolkit. The transport code is capable of simulating the propagation of acoustic phonons as well as electron and hole charge carriers. Herein processes for anisotropic phonon propagation, oblique charge-carrier propagation, and phonon emission by accelerated charge carriers are included. The simulation reproduces theoretical predictions and experimental observations such as phonon caustics, heat-pulse propagation times, and mean charge-carrier drift velocities. In addition to presenting the physics and features supported by G4CMP, this report outlines example applications from the dark matter and quantum information science communities. These communities are applying G4CMP to model and design devices for which the energy transported by phonons and charge carriers is germane to the performance of superconducting instruments and circuits placed on silicon and germanium substrates. The G4CMP package is available to download from GitHub: github.com/kelseymh/G4CMP.
This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.
The oscillator strengths of 43 transitions of neutral gadolinium from 530 to 535 nm were determined based on high-resolution absorption spectra measured by probing a laser-produced plasma (LPP) using time-resolved dual-comb spectroscopy (DCS). Absorption spectra were measured at various time delays, ranging from 33 to 252 µs after the onset of the plasma formation, over which the temperature and number density varied as the ablation plume evolved. A Boltzmann analysis was used to determine the excitation temperature and column density of Gd in the ablation plume, based on measured absorption from 20 Gd I spectral lines with known oscillator strengths. Oscillator strengths were then determined for additional dipole-allowed Gd I transitions identified in the absorption spectra, 19 of which were previously unreported. Oscillator strengths of all measured transitions were compared with literature values when available and show good agreement in most cases. Time-resolved measurement of atomic absorption in the cooling plasma provides access to a range of excitation temperatures and optical densities for the atoms in the ablation plume, allowing repeated measurements under different conditions, thereby improving confidence in the results. The high spectral bandwidth and resolution capabilities of DCS make it well-suited to measuring the dense optical spectrum of Gd, and the technique is applicable to other elements, especially lanthanides and actinides.
Characterizing large four-dimensional materials datasets is difficult due to the presence of complex mi- crostructures and time-varying length scales. We showcase the use of two-point statistics as an efficient and un-biased way of extracting materials parameters from an Al-Cu alloy during solidification. The evo- lution of dendrite primary arm thickness, average secondary arm spacing, and average tip-to-tip spacing were tracked using two-point Pearson auto-correlations of scaled mean curvatures. Insights into compet- itive side-branching are also reported. We show both visually and quantitatively that most length scales change rapidly during early stages of dendritic growth, but slow as diffusion fields of dendrites overlap.
Not Available
Combined experimental and theoretical investigations were performed to evaluate the potential of n-type BiFeO 3 as a photoanode. While previous experimental and theoretical studies on BiFeO 3 mainly focused on its ferroelectric properties, several studies have reported the advantages of BiFeO 3 as a photoelectrode for solar water splitting (e.g. bandgap energy and band edge positions relative to water reduction and oxidation potentials). However, the photoelectrochemical properties of n-type BiFeO 3 have not yet been thoroughly investigated. In our experimental investigation, we developed an electrodeposition-based synthesis to prepare uniform n-type BiFeO 3 thin-film electrodes. Furthermore, using a heat treatment under a N 2 environment, we intentionally introduced additional oxygen vacancies into the pristine n-type BiFeO 3 electrodes to increase the majority carrier density. The bandgaps, flatband potentials, photocurrent onset potentials, photocurrent generation, and photoelectrochemical stabilities of the pristine and N 2 -treated BiFeO 3 photoanodes were investigated comparatively to improve our understanding of BiFeO 3 photoanodes and to examine the effect of oxygen vacancies on the photoelectrochemical properties of BiFeO 3 . In our theoretical investigation, we performed first-principles calculations and demonstrated the formation of a small polaron when an extra electron was introduced into the BiFeO 3 lattice. Changes in electronic states cause by the small polaron formation were carefully investigated. We also examined the effects of oxygen vacancies on electron polaron formation and carrier concentration in BiFeO 3 . Using charge formation energy calculations and referencing charge transition levels to the free electron polaron level instead of to the conduction band minimum, we showed that the oxygen vacancy is capable of serving as a donor to enhance the carrier concentration of BiFeO 3 . Furthermore, our theoretical results agree well with our experimental findings. Together, the new experimental and theoretical results and discussion provided in this study have considerably improved our understanding of n-type BiFeO 3 as a photoanode.
Global demand for nuclear energy is expected to rise in the coming decades. To meet these growing needs, new uranium resources must be explored. One of the potential alternatives to traditional uranium mining is oceanic uranium. The capture and recovery of uranium from the ocean has been under investigation for some time, with many recent studies focused on amidoxime based adsorbents. These adsorbents while able to achieve high uranium recovery capacities are, nevertheless, expensive to produce and adsorb a significant amount of hard to remove vanadium. The purpose of this study is to evaluate the adsorption performance of amidoxime based polymer braids synthesized from acrylic fibers which are designed to significantly cut polymer synthesis and conditioning costs. Adsorption experiments were performed in a recirculating raceway flume system at environmental conditions, approximately 10.8 oC, with 40-micron prefiltered seawater over 28 days for small and large sized LCW polymer braids. For both braid sizes, the adsorption of vanadium was far lower on the LCW materials than on Oak Ridge National Laboratory’s amidoxime based AF1 and AI8 small braids, with the LCW small braids outperforming both materials with respect to uranium adsorption under similar conditions. Adsorption modeling was used to simulate the performance at higher temperatures based on 20oC experiments previously performed with similar materials. Simulation results indicated that the small LCW braids would have a less significant advantage with respect to uranium adsorption over both AF1 and AI8 at 20oC and adsorb slightly less uranium than AF1 at 31oC. Vanadium adsorption by LCW small braids was less than one third of vanadium adsorption by either AF1 or AI8 for all temperatures. This behavior indicates that the LCW materials are able to achieve comparable uranium adsorption to other amidoxime adsorbents previously developed, while being cheaper to produce and adsorbing significantly less vanadium.
We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li 2 CO 3 . The model was trained on ab initio electronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li 2 CO 3 into CO 2 residing primarily in the vapor phase and Li 2 O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO 2 partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO 2 in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. Furthermore, the present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.
The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25-100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, such as multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict the high-level excitation energies of a chromophore in solution from only 400 high-level calculations. Here, we show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.
We present ultralow Ir-loaded (ULL) proton exchange membrane water electrolyzer (PEMWE) cells that can produce enough hydrogen to largely decarbonize the global natural gas, transportation, and electrical storage sectors by 2050, using only half of the annual global Ir production for PEMWE deployment. This represents a significant improvement in PEMWE's global potential, enabled by careful control of the anode catalyst layer (CL), including its mesostructure and catalyst dispersion. Using commercially relevant membranes (Nafion 117), cell materials, electrocatalysts, and fabrication techniques, we achieve at peak a 250× improvement in Ir mass activity over commercial PEMWEs. An optimal Ir loading of 0.011 mg Ir cm -2 operated at an Ir-specific power of ~100 MW kg Ir -1 at a cell potential of ~1.66 V versus RHE (85% higher heating value efficiency). Here, we further evaluate the performance limitations within the ULL regime and offer new insights and guidance in CL design relevant to the broader energy conversion field.
Silicon (Si) is the most naturally abundant element possessing 10-fold theoretical capacity than graphitebased anodes. The practicality of implementing Si anodes is, however limited by the unstable solidelectrolyte interphase (SEI) and anode fracturing during continuous lithiation/delithiation. We demonstrate that glyme-based electrolytes (GlyEls) assure a conformal SEI on Si and keep the Si ‘fracture-free’. Benchmarking against the optimal, commonly-used carbonate electrolyte with the fluoroethylene carbonate (FEC) additive, Si anode cycled in a GlyEl exhibits reduced early parasitic current (by 62.5%) and interfacial resistance (by 72.8%), while the cell capacity retention is promoted by >7% over a course of 110 cycles. The mechanistic investigation by X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDX) indicates GlyEl enriches Si SEI with elastic polyether but diminishes its carbonate species. Glyme-based electrolytes prove viable in stabilizing the SEI on silicon for future high energy density lithium-ion batteries.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).