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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Triboplasma assisted chemical conversion in granular systems: a semi-analytic model

We present a semi-analytic model for a novel plasma-assisted chemical conversion pathway using triboplasmas generated in granular flows. Triboelectric charge relaxation is a well known phenomena where the potential generated from contact charging of particles exceeds the breakdown voltage of the background gas. In this work, we extend the triboelectric charge relaxation theory to include non equilibrium plasma energy and particle balance equations to predict the formation of dissociated and excited species that act as precursors to chemical conversion, for example in plasma-assisted ammonia synthesis. Our example case study with nitrogen background gas and teflon/aluminum tribomaterial system yielded high excited nitrogen species densities per collision that are comparable to current plasma-assisted conversion pathways. We also present a regime diagram for various gases where Paschen breakdown parameters are used to determine whether triboplasmas can be formed for a given effective work-function difference between two materials. Our sensitivity studies indicate particle velocity, particle radius, solids fraction and space charge effects play a critical role in overall plasma densities and excited species production.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Low friction in bcc metals via grain boundary sliding

In this work, low friction is demonstrated with pure polycrystalline tantalum sliding contacts in both molecular dynamics simulations and ultrahigh vacuum experiments. This phenomenon is shown to be correlated with deformation occurring primarily through grain boundary sliding and can be explained using a recently developed predictive model for the shear strength of metals. Specifically, low friction is associated with grain sizes at the interface being smaller than a critical, material-dependent value, where a crossover from dislocation mediated plasticity to grain-boundary sliding occurs. Low friction is therefore associated with inverse Hall-Petch behavior and softening of the interface. Direct quantitative comparisons between experiments and atomistic calculations are used to illustrate the accuracy of the predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than 2% over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Meshfree Process Modeling and Experimental Validation of Friction Riveting of Aluminum 5052 to Aluminum 6061

Friction riveting (Fric-riveting) is an innovative, fast, and energy-efficient process for spot-joining metal-metal structures. Although fric-riveting has been studied experimentally in recent years, its process modeling is rarely found in the literature primarily because of the associated large material deformation, extreme thermomechanical conditions, and complex contact conditions. In this work, a mesh-free smoothed particle hydrodynamics (SPH) framework that can well handle the abovementioned numerical challenges is used to simulate the fric-riveting of AA5052 to AA6061. Predicted material morphology, multi-point temperatures, and plunge force are thoroughly validated by experimental observations. The material severe plastic deformation zone in the vicinity of the riveting zone is further predicted by the SPH model, which indicates the material mixing and potential grain refinement zone. Based on the validated model, process parameters can be optimized which yields better performance over the baseline case.

Friction riveting (fric-riveting), Smoothed partic↗
DOE OSTI2025

Eucalyptus grandis MYB‐Like and RAN‐Like Zinc Finger Proteins Display Dual Roles in Regulating Plant Immunity and Symbiosis Pathways

Plant roots live in constant contact with diverse microbes in the soil. Plant fitness, therefore, relies on signaling pathways that mount an effective immune response against pathogens while fostering mutualistic symbioses. Plant pathways, and specifically immune genes that may act as "switches," discriminating between pathogenic or mutualistic fungi, remain largely unknown. Using Eucalyptus grandis as a model system, we investigate alterations to the root transcriptomic landscape during pre-symbiosis with either the pathogen Armillaria luteobubalina or the mutualistic fungus Pisolithus microcarpus. Comparative analyses identified three strongly counter-regulated genes that may act as immune switches to accommodate or to repress fungal colonization. We characterized two of these, a MYB-like and RAN-like zinc finger protein, using a transgenic approach and demonstrated that they have bifunctional roles in the regulation of cell death and a hypersensitive-like response, depending on the lifestyle of the associated fungus. Using co-expression network analysis, we identified hypothetical pathways correlated to these genes. We functionally validated these predictions using plants with transgenic roots with increased or decreased transcription of these genes, thereby showing the power of co-expression networks as an a priori approach to identify key immune response pathways in plants. Overall, our results demonstrate that prior to physical contact with microbes, MYB-like and RAN-like zinc finger proteins are key regulators of plant immune signaling that respond to fungal signals and enable or repress symbiotic establishment.

mycorrhizal fungi↗
DOE OSTI2024

Progressive Dynamics for Cloth and Shell Animation

We propose Progressive Dynamics, a coarse-to-fine, level-of-detail simulation method for the physics-based animation of complex frictionally contacting thin shell and cloth dynamics. Progressive Dynamics provides tight-matching consistency and progressive improvement across levels, with comparable quality and realism to high-fidelity, IPC-based shell simulations [Li et al. 2021] at finest resolutions. Together these features enable an efficient animation-design pipeline with predictive coarse-resolution previews providing rapid design iterations for a final, to-be-generated, high-resolution animation. In contrast, previously, to design such scenes with comparable dynamics would require prohibitively slow design iterations via repeated direct simulations on high-resolution meshes. We evaluate and demonstrate Progressive Dynamics's features over a wide range of challenging stress-tests, benchmarks, and animation design tasks. Here Progressive Dynamics efficiently computes consistent previews at costs comparable to coarsest-level direct simulations. Its matching progressive refinements across levels then generate rich, high-resolution animations with high-speed dynamics, impacts, and the complex detailing of the dynamic wrinkling, folding, and sliding of frictionally contacting thin shells and fabrics.

Computer Science↗
DOE OSTI2025

Empirical evidence that glucan-interacting amino acid side chains within the transmembrane channel collectively facilitate cellulose synthase function

The fundamental mechanism of cellulose synthesis is widely conserved across Kingdoms and depends on cellulose synthases, which are processive, dual-function, family 2 glycosyltransferases (GT-2). These enzymes polymerize glucose on the cytoplasmic side of the plasma membrane and export the glucan chain to the cell surface through an integral transmembrane (TM) channel. Structural studies of active plant cellulose synthases (CESAs) have revealed interactions between the nascent glucan chain and the side chains of polar, charged, and aromatic amino acid residues that line the TM channel. However, the functional consequences of modifying these side chains have not been tested in vivo in CESAs or other processive GT-2s. To test this, we used an established in vivo assay based on genetic complementation of CESA5 in the moss, Physcomitrium patens. For accurate prediction of glucan-interacting amino acid residues, we generated a complete homotrimeric molecular model of PpCESA5 using a combination of homology and de novo modeling. All-atom molecular dynamics-based analyses of contact metrics and interaction energy identified 23 amino acid residues with high propensity to interact with the nascent glucan chain within the TM channel or on the apoplastic surface of PpCESA5. Mutating any one of 18 of these amino acid residues to alanine, thereby removing their side chains, abolished or impaired CESA function, with the strongest effects observed upon the loss of charged amino acid side chains. This provides direct evidence to support the hypothesis that multiple amino acid residues collectively maintain a smooth energy landscape within the TM channel to facilitate glucan translocation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

58 GEOSCIENCES↗
DOE OSTI2023

Structure of the Ndc80 complex and its interactions at the yeast kinetochore–microtubule interface

The conserved Ndc80 kinetochore complex, Ndc80c, is the principal link between mitotic spindle microtubules and centromere-associated proteins. We used AlphaFold 2 (AF2) to obtain predictions of the Ndc80 ‘loop’ structure and of the Ndc80 : Nuf2 globular head domains that interact with the Dam1 subunit of the heterodecameric DASH/Dam1 complex (Dam1c). The predictions guided design of crystallizable constructs, with structures close to the predicted ones. The Ndc80 ‘loop’ is a stiff, α-helical ‘switchback’ structure; AF2 predictions and positions of preferential cleavage sites indicate that flexibility within the long Ndc80c rod occurs instead at a hinge closer to the globular head. Conserved stretches of the Dam1 C terminus bind Ndc80c such that phosphorylation of Dam1 serine residues 257, 265 and 292 by the mitotic kinase Ipl1/Aurora B can release this contact during error correction of mis-attached kinetochores. We integrate the structural results presented here into our current molecular model of the kinetochore–microtubule interface. The model illustrates how multiple interactions between Ndc80c, DASH/Dam1c and the microtubule lattice stabilize kinetochore attachments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hydrodynamics of countercurrent flow in an additive-manufactured column with triply periodic minimal surfaces for carbon dioxide capture

Computational investigation of the countercurrent flows in an additively manufactured (AM) column packed with triply periodic minimal surface (TPMS) is challenging. Multiphase flow simulations using the volume of fluid (VOF) method can explain the column’s hydrodynamic characteristics, including interfacial area, liquid holdup, pressure drop, flow regime, etc. The interfacial area is a key factor dictating the mass transfer via the absorption process, and thereby the overall efficiency of the column. The impacts of a solvent's physical properties, liquid and gas loads, and contact angle on the hydrodynamics are extensively explored. The solvents include traditional solvent (monoethanolamine [MEA]) used in industry as well as a water-lean solvent (EEMPA) as prospective solvents for carbon capture. As expected, interfacial area and liquid holdup increase with increasing liquid loads (q L ). The EEMPA exhibits higher values for the interfacial area and liquid holdup compared to those for the MEA. The dry and wet pressure drops per unit length in the column at different gas loads (F G ) are calculated and further compared with the empirical correlations for traditional packings. At low gas loads, i.e., in the preloading regime, the predicted values of the wet and dry pressure drops match well with the corresponding values calculated from available correlations. The gas load has a marginal impact on the interfacial area at its lower value, but the higher value of F G (>2.18 $\sqrt{Pa}$) causes flooding in the column. The effect of varying contact angles on the interfacial area and liquid holdup in the preloading regime is also studied. Both interfacial area and liquid holdup decrease with the increase in the value of the contact angle. Altogether, the effect of the contact angle on interfacial area is more pronounced than other parameters influencing the interfacial area.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Micro–macro finite element modeling method for rub response in abradable coating materials

Gas turbine engines experience “rub” when the rotating blades come in contact with a static abradable coating. This results in extreme strain rates and dynamics inside a high-temperature/high-pressure environment. Current rub models are phenomenological and do not reflect the underlying microstructures, thus limiting their prediction accuracy. In this work, a microstructure-informed, reduced order modeling framework is introduced for simulating abradable coating “rub" behavior. This framework comprises a microscale model constructed based on digitized abradable microstructure and explicitly simulates the mechanical behavior of each constituent phases and their interactions. After calibration and validation with experiment data, the calibrated microscale model is used to generate data across a vast range of applied strain rates and temperature with various load paths. Then, the virtually generated data are used to fit the macroscopic-reduced order model, which enables fast component scale rub simulation without compromising the integrity of the complex material behavior. In conclusion, the proposed effort will address the technical challenge of predicting abradable material behavior during rub through the application of multiscale modeling from microstructure to engines behavior, effectively reducing the development costs and time of new abradable material for better “rub” properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessment of MOOSE-Based Tools for Calculating Radial Core Expansion

Radial core expansion in liquid-metal cooled fast reactor systems is a well-known phenomenon that produces strong reactivity feedback effects. An inherently safe reactor design takes advantage of negative reactivity feedback in accident scenarios by utilizing a core restraint system which produces a bowed shape that allows for radial expansion of the fuel regions. Detailed modeling and simulation of core radial expansion itself as well as subsequent reactivity feedback is a challenging task involving contact of many fuel assembly elements and physics feedback from neutronics, thermal hydraulics, and thermal mechanical response. A variety of physics codes have been developed to model aspects of radial core expansion but in general invoke geometrical or physics approximations. No code system currently exists which tightly and robustly couples these physics with enough detail to fully resolve the complex core radial expansion reactivity feedback effects. The future availability of such a code system is of vital importance to fully understanding the reactivity feedback effects that occur due to radial expansion, and consequently to optimizing the design of the core restraint system. A high-fidelity code will also be used to benchmark existing lower fidelity, faster running models to understand their benefits, limitations, and range of applications. A code development path using MOOSE-based tools is proposed in order to leverage the detailed geometry capabilities and natural tight coupling and robustness of MOOSE-based applications for modeling this complex phenomena. While simulation of the full phenomenon involves several physics, an assessment has been initiated on the capabilities and readiness of the currently available Tensor Mechanics module within MOOSE for calculation of the structural mechanical responses which occur within the reactor core. This report focuses on modeling the force-deformation response which mimics the physics of a duct contact deformation, as well as differential thermal expansion which produces a thermal bowed shaped for the fuel assemblies in a core. Simple examples were initially performed such as simple supported beam bending under load. The complexity of examples was progressively increased to better mimic the duct behavior by including a differential thermal example and inclusion of hexagonal cross-sections in the geometry. Further assessment of the structural mechanical response simulation capability is still required for modeling duct contact interactions and irradiation creep and swelling. Companion thermal hydraulic and neutronics assessments will also be required; these activities are planned for future years. Finally, integration of the multiple physics components through MOOSE is required to predict the core radial expansion and subsequent feedback effects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Geometry, Disorder and Phase Transitions in Topological States of Matter

The quantum Hall effect is the birthplace of topological states of matter, a major theme at the forefront of condensed matter physics in the past two decades. The fractional quantum Hall (FQH) effect revolutionized our understanding of phases of electronic matter. FQH states support exotic fractionally charged excitations that obey Abelian or non-Abelian fractional statistics, which are topological excitations that result from the underlying topological order. During this project, our group discovered a previously unrecognized geometric degree of freedom of incompressible FQH states and studied that for a variety of gapped FQH states. We brought this new concept into direct contact with experiments for the first time by generalizing it to Fermi-liquid states of composite fermions. Using the newly formulated powerful infinite Density Matrix Renormalization Group method, our numerical calculations yielded a parameter free prediction that was found to be in excellent agreement with experimental findings on electron systems in semiconductor heterostructures. In parallel, we performed extensive numerical studies on different, competing phases at various Landau level filling factors, and quantum phase transitions that result from such a competition, e.g. Abelian-non-Abelian phase transitions in bilayer systems. We studied geometrical excitations dubbed “gravitons” (because of their analogy with excitations in the theory of gravitation) and ways to excite and detect them, and explored how they couple with topological excitations. In graphene-based chiral materials, we realized the ability to tune through different incompressible and compressible states in a single Landau level, and found appropriate experimental parameters for the exploration of universal Luttinger liquid behavior not obtained in semiconductor-based electron systems. We showed that topological systems had a very different response from nontopological systems to strong disorder (many-body localization) as well as periodic drives.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Nickel hydroxide–nickel carbonate competitive growth on carbonate surfaces

The thermodynamic and kinetic factors controlling the competitive heterogeneous nucleation and growth of ubiquitous metal carbonate and hydroxide phases are poorly understood. In this work, calcite (CaCO 3 ) and magnesite (MgCO 3 ) powders were reacted with NiCl 2 (0–600 μM) for 7 days at 22 °C and 5 °C. The reacted powders were analyzed with X-ray photoelectron spectroscopy (XPS), scanning electron microscopy, and energy-dispersive X-ray spectroscopy to characterize the Ni surface precipitates formed. Evidence from these techniques pointed to the formation of mixed Ni carbonate-Ni hydroxide amorphous surface precipitates. On calcite, XPS detected primarily Ni(OH) 2 despite the initial solutions being more supersaturated with respect to gaspéite (NiCO 3 ) than to theophrastite (Ni(OH) 2 ) by a factor of 17–18. In contrast, NiCO 3 was the dominant component detected by XPS on magnesite in the same conditions. Decreasing the temperature had the effect of increasing the proportion of NiCO 3 to the detriment of Ni(OH) 2 . The experimental observations were consistent with low lattice/cation size mismatch favoring NiCO 3 nucleation and temperature most influencing Ni(OH) 2 nucleation. Comparison to previous work on Co-reacted powders indicated the differences in lattice/cation size mismatch and/or water exchange rate impacted the composition of the surface precipitates more than the relative thermodynamic stabilities of the competing minerals. This work explored the heterogeneous growth regime of Ni carbonate and hydroxide phases on carbonate surfaces and shed light on the factors that control the competition between surface precipitates when mineral surfaces are in contact with aqueous solutions supersaturated with respect to multiple mineral phases. In conclusion, these results contribute to geochemists’ efforts toward interpreting data from geochemical systems with elevated Ni concentrations, improving Ni environmental remediation and recovery strategies, and predicting the fate and transport of Ni in geochemical systems.

Heterogeneous nucleation↗
DOE OSTI2025

Colloidal Behavior of Plutonium Oxide in Concentrated Electrolyte Solutions

The Hanford Site in Washington State manages legacy high-level radioactive waste streams that display major chemistry and engineering challenges, including the high salt levels and pH values that correspond to conditions under which many classical concepts describing chemical reactivity cannot be applied. One particular challenge that needs to be tackled at Hanford is that Pu concentrations, [Pu], in the soluble phases of the tank wastes are higher than expected based on the solubility of crystalline PuO2, which is widely accepted to be caused by the formation of PuO2 colloid, consisting of nano- to submicron-sized particles (PuO2 NPs). Fundamental research underpinning the behavior of PuO2 NPs under conditions not only relevant to the Hanford tank waste but at high ionic strength in general is needed to reliably predict the chemical reactivity of PuO2 NPs and develop engineering solutions to safely and efficiently process high-level radioactive waste into forms suitable for long-term storage. In this work, we study the behavior of PuO2 NPs (particle size ~100 nm) under high ionic strength conditions by reacting it with highly concentrated (up to 5 M) salt solutions. We explore different electrolyte compositions to elucidate the impact of different anions (NO3-, Cl-, ClO4-, SO42-, C2O42-, CO32-) on the stability of PuO2 NP in the acidic and alkaline pH regime. PuO2 NP aggregation and precipitation as function electrolyte concentration is tracked by a combination of liquid scintillation counting, dynamic light scattering for determination of particle size distributions, and zeta potentials as a proxy for particle charge. At acidic pH, electrolytes containing non-coordinating anions, such as NaNO3, NaCl, and NaClO4 mostly stabilize PuO2 NPs over a large electrolyte concentration range, showing only subtle differences in their reactivity. Other electrolyte anions show a more pronounced effect on the PuO2 NP stability: SO42-, binds directly to the particles’ surface, reverses the particle charge, and precipitates the PuO2 NPs efficiently even at intermediate sulfate concentrations (>0.1 M). In contrast, C2O42- is found to lead to high [Pu] in solution, in the milli-molar range, even at mildly acidic pH (~4). Thermodynamic modeling of the dissolved Pu concentrations using PHREEQC is unable to predict the observed [Pu] in the acidic pH regime, supporting the influence of colloids in maintaining elevated [Pu]. It is noteworthy that the current thermodynamic databases do not include constants for colloidal Pu phases and cannot accurately predict many of the high ionic strength solutions relevant to this work. The mechanisms and models responsible for these observations will need further investigation in the future. At high pH values (~12), PuO2 NPs exhibits classical sol-gel chemistry, meaning that upon destabilization of the colloidal sol, for example by addition of concentrated NaOH, highly porous and viscid PuO2 coagulates are formed that consist of a three-dimensional network likely held together by physical interactions. The PuO2 NP coagulate shows no significant reversibility of the aggregation when contacted with concentrated brines; however, PuO2 NPs can be efficiently resuspended in solution by addition of diluted electrolytes, alkaline solutions containing high amounts of carbonate, or simple addition of water. Especially carbonate is shown to stabilize PuO2 NPs in solution at high pH, characterized by stable colloidal suspensions that are resistant against sedimentation during centrifugation. Thermodynamic modeling of the carbonate system was able to predict an increasing dissolved Pu concentration with increasing carbonate concentration. However, the model was profoundly sensitive to the fixed redox potential and does not include any thermodynamic constants for colloidal Pu species.

Neumann, Julia↗
DOE OSTI2020

Modified Isotherm Modeling to Predict Cs Exchange with Crystalline Silicotitanate in Tank Waste Simulants

The U.S. Department of Energy is working to expedite processing of Hanford tank waste supernate at the Hanford Waste Treatment and Immobilization Plant. To support this goal, Washington River Protection Solutions is designing a Tank Side Cesium Removal (TSCR) system for suspended solids and cesium (Cs/ 137 Cs) removal from Hanford tank waste supernate. The ion exchange media selected for Cs removal at TSCR is crystalline silicotitanate (CST) that is manufactured in a nearly spherical form by Honeywell UOP LLC (UOP; Des Plaines, IL) as product IONSIV® R9140-B (Na form). The Zheng Anthony Miller (ZAM) isotherm model (Zheng et al. 1997) is a multicomponent ion exchange model used to predict the exchange of Group I metals onto CST. The ZAM isotherm has historically been used to predict Cs distribution values from Hanford and Savannah River Site (SRS) tank waste simulants. Figure S.1 summarizes model predictions from the ZAM isotherm that indicate poor prediction of Cs distribution values for simple and complex simulants with the engineered form of CST, IONSIV® R9140-B, and IONSIV® R9120-B where the solid line indicates a perfect fit by the model. The dotted lines indicate ±20% error. Batch contact testing with Hanford tank waste complex and simple simulants was used in conjunction with SRS simulants to experimentally determine Cs distribution values using a modification to the original isotherm model. The experimentally determined maximum Cs capacity for IONSIV® R9140-B CST in both the simple and complex matrices was found to be 0.53±0.3 mmoles Cs/g of CST. This value is not drastically different from the maximum Cs capacity of 0.58 mmoles Cs/g TAM-5 reported by Zheng et al. (1997). However, it is important to note that TAM-5 (commercially IONSIV® IE-910) is a powder. Hamm et al. (2002) determined that a dilution factor was needed to account for the Zr(OH)2 binder in the engineered form of CST. Hamm et al. determined that a dilution factor of 0.68 was appropriate to account for binder contribution and correct overprediction of Cs exchange on the engineered form of CST in ZAM calculations. This reduced the total capacity from 0.58 mmol/g with TAM-5 to 0.39 mmol/g for the engineered form of CST (Hamm et al. 2002). Despite substituting the experimentally determined maximum Cs capacity of 0.55 mmoles Cs/g for the literature-reported capacity of 0.39 mmoles Cs/g, it was determined that additional modifications to the model’s equilibrium rate constants were necessary in refining the isotherm model. The modified model overpredicted K+ uptake by the CST when compared to digested CST results described by Campbell et al. (2019). The modified model was further revised to omit three of the five K+ exchange equilibrium reactions described by ZAM to reduce the additional K+ loading seen by the model. Figure S.2 summarizes the revised model isotherm predictions plotted against measured Kd values.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Low barrier height in a ZnO nanorods/NbSe 2 heterostructure prepared by van der Waals epitaxy

Two-dimensional (2D) materials as contacts for semiconductor devices have attracted much attention due to minimizing Fermi level pinning. Schottky–Mott physics has been widely employed to design 2D material-based electrodes and to elucidate their contact behavior. In this study, we revealed that charge transfer across a 2D/semiconductor heterointerface and materials characteristics besides work function should be accounted for in fabrication of electrodes based on 2D materials. Our density functional theory (DFT) calculations predicted that charge transfer between ZnO and NbSe 2 lowers the barrier height at the heterojunction and that conductive surface states of ZnO provide an additional conduction channel in the ZnO/NbSe 2 heterostructures. Crystalline ZnO/NbSe 2 heterostructures were prepared by the hydrothermal method. Electrical characterizations of the ZnO/NbSe 2 heterostructures showed Ohmic-like behavior as predicted by the DFT calculations, opposed to the prediction based on the Schottky–Mott model.

2D materials↗