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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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DOE OSTI2021

Electrification of Aircraft: Challenges, Barriers, and Potential Impacts

Small-scale aircraft are being electrified with small e-aircraft already certified to fly and with test flights underway for retrofits of existing aircraft focused on near-term deployment. Internationally, from 2018 to 2020, the number of electric aircraft projects underway increased 50%. These technologies are projected to increase operational efficiency and reduce emissions and noise from a growing aviation sector. This emerging interest in aviation electrification includes aircraft, energy supply equipment, and battery storage manufacturers. Federal agencies are currently funding a variety of efforts including technology research as well as forecasting demand and environmental impacts and state governments have also begun pushing for further investigation in this area. This paper provides an overview of the current state and potential future development of aviation electrification. To understand the types of research questions around current and future challenges for this emerging sector, we consider a case study of the potential deployment of small commercial electric aircraft for existing, under-served markets and consider operational requirements and technical challenges as well as the range of questions that will need to be addressed around charging infrastructure, grid integration, policy and regulatory requirements, and emissions impacts. This case study also outlines potential nearer-term solutions and research areas of interest that will need to be addressed in the longer term at much larger scales and highlights NREL capabilities for addressing these challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Mechanical and durability properties of ultra-high-performance concrete of spent nuclear fuel dry storage systems: a review

Dry storage systems are used for interim storage of spent nuclear fuel (SNF). However, with the growing need to extend the operational periods of these systems, there are concerns about the degradation of their concrete overpacks, which could compromise the system's structural integrity and safety during hazardous events. Traditional concrete mixtures used in SNF dry storage systems have remained largely unchanged since their inception and often use conventional ingredients. These materials are susceptible to degradation mechanisms such as chemical attacks, alkali-silica reactions (ASR), and freeze–thaw cycles, which can lead to a loss of strength and durability over time. To address these challenges, this paper reviews the application of ultra-high-performance concrete (UHPC) as a promising alternative for spent nuclear fuel dry storage system overpacks. UHPC offers superior mechanical properties, exceptional durability, and reduced susceptibility to degradation mechanisms compared to conventional concrete. This paper focuses on the role of supplementary cementitious materials (SCMs) such as silica fume, fly ash, and metakaolin in enhancing UHPC performance for SNF storage applications. These SCMs have been shown to significantly improve the material’s microstructure, strength, and resistance to environmental stressors typically encountered in SNF storage environments. Moreover, incorporating SCMs supports sustainable construction by reducing cement consumption and associated carbon emissions. The review brings together existing research and experimental data, providing insights for engineers and researchers on developing UHPC mixtures that meet the rigorous demands of spent nuclear fuel dry storage systems, extending their service life and minimizing inspection intervals.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

The evolution of HCO + in molecular clouds using a novel chemical post-processing algorithm

Modelling the chemistry of molecular clouds is critical to accurately simulating their evolution. To reduce computational cost, 3D simulations generally restrict their chemistry to species with strong heating and cooling effects. Time-dependent information about the evolution of other species is therefore often neglected. We address this gap by post-processing tracer particles in the SILCC-Zoom molecular cloud simulations. Using a chemical network of 39 species and 301 reactions (including freeze-out of CO and H 2 O) and a novel algorithm to reconstruct a density grid from sparse tracer particle data, we produce time-dependent density distributions for various species. We focus upon the evolution of HCO + , which is a critical formation reactant of CO but is not typically modelled on the fly. We find that ∼ 90 per cent of the HCO + content of the cold molecular gas forms in situ around n HCO + ∼ 10 3 –10 4 cm −3 , over a time-scale of approximately 1 Myr. The remaining ∼ 10 per cent forms at high extinction sites, with minimal turbulent mixing out into the less dense gas. We further show that the dominant HCO + formation pathway is dependent on the visual extinction, with the reaction H 3 + + CO contributing 90 per cent of the total HCO + production above A V, 3D = 3. We produce the very first maps of the HCO + column density, N(HCO + ), and show that it reaches values as high as 10 15 cm −2 . We find that 50 per cent of the HCO + mass is located within AV ∼ 10–30 in a density range of 10 3.5 –10 4.5 cm −3 . Our maps of N(HCO + ) are shown to be in good agreement with recent observations of the W49A star-forming region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Economic Extraction and Recovery of REEs and Production of Clean Value-Added Products from Low-Rank Coal Fly Ash

The University of North Dakota (UND) Energy & Environmental Research Center (EERC) teamed with Pacific Northwest National Laboratory (PNNL) , the North Dakota Industrial Commission Lignite Research Program, and commercial partners Basin Electric Power Cooperative, Great River Energy, and Southern Company to execute a Cooperative Agreement funded by the U.S. Department of Energy’s (DOE’s) National Energy Technology Laboratory (NETL) focused on identifying unique pathways and pretreatments to extract rare-earth elements (REEs) from low-rank coal (LRC) ash in a more economical and environmentally benign manner than current methods. The EERC drew upon the unique and deep knowledge of the team regarding LRC geochemistry, ash transformation mechanisms during combustion, and fly ash chemistry in the approach to understand the portioning of REEs in LRC ash. A comprehensive characterization effort was undertaken to fully establish the form, associations, and partitioning of the REEs and other elements/minerals of interest in fly ash, as well as the ash chemistry, mineralogy, and morphology. Using this information, the team tested methods of REE extraction to take advantage of the unique and advantageous properties of LRC ash. Multiple complementary approaches were evaluated that combined both novel and proven methods to develop an extractive process for REEs and other high-value metals from LRC ash that provide technological and economic progress on the current state of the art. Methodologies were also evaluated for the potential to synergistically remove toxic metals while extracting other high-value metals from the ash, generating a clean, safe ash material as a value-added product. This would eliminate waste and offer significant additional revenue potential. Based on laboratory-scale testing, an initial high-level technical and economic analysis to estimate operating expenses and product revenues to guide future phases of technology development was implemented. By focusing on minimizing process steps and consumptive use of extraction solutions, a tunable, economically viable process for REE extraction from LRC ash was developed. The tunable process has the ability to be adjusted to adapt to the differing ash chemical and physical properties that are characteristic of LRC ash. This is key to deployment as a commercial process. In demonstrating the process, the EERC was able to illustrate mixed REE concentration greater than the goal of 2 wt%. High-level economic analysis of the process with LRC ash of moderate REE levels demonstrated the potential of the process, but recovery of just the REEs was insufficient to cover operational costs. With the additional extraction of other high-value metals found in LRC ash, the process economics approach would break even, and it is anticipated that with process optimization, the developed process could potentially be economically sound. The same economic analysis performed utilizing LRC ash with high levels of REEs showed the recovery of the REEs alone would make the process economically viable even without further optimization.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Fast automated energy changes at synchrotron radiation beamlines equipped with transfocator or focusing mirrors

Algorithms and procedures to fully automate retuning of synchrotron radiation beamlines over wide energy ranges are discussed. The discussion is based on the implementation at the National Institute of General Medical Sciences and the National Cancer Institute Structural Biology Facility at the Advanced Photon Source. When a user selects a new beamline energy, software synchronously controls the beamline monochromator and undulator to maintain the X-ray beam flux after the monochromator, preserves beam attenuation by determining a new set of attenuator foils, changes, as needed, mirror reflecting stripes and the undulator harmonic, preserves beam focal distance of compound refractive lens focusing by changing the In/Out combination of lenses in the transfocator, and, finally, restores beam position at the sample by on-the-fly scanning of either the Kirkpatrick–Baez mirror angles or the transfocator up/down and inboard/outboard positions. The sample is protected from radiation damage by automatically moving it out of the beam during the energy change and optimization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Multireference Electron Correlation Methods: Journeys along Potential Energy Surfaces

Multireference electron correlation methods describe static and dynamical electron correlation in a balanced way and, therefore, can yield accurate and predictive results even when single-reference methods or multiconfigurational self-consistent field theory fails. One of their most prominent applications in quantum chemistry is the exploration of potential energy surfaces. This includes the optimization of molecular geometries, such as equilibrium geometries and conical intersections and on-the-fly photodynamics simulations, both of which depend heavily on the ability of the method to properly explore the potential energy surface. Because such applications require nuclear gradients and derivative couplings, the availability of analytical nuclear gradients greatly enhances the scope of quantum chemical methods. This review focuses on the developments and advances made in the past two decades. A detailed account of the analytical nuclear gradient and derivative coupling theories is presented. Emphasis is given to the software infrastructure that allows one to make use of these methods. Notable applications of multireference electron correlation methods to chemistry, including geometry optimizations and on-the-fly dynamics, are summarized at the end followed by a discussion of future prospects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The large scale polarization explorer (LSPE) for CMB measurements: performance forecast

The measurement of the polarization of the Cosmic Microwave Background (CMB) radiation is one of the current frontiers in cosmology. In particular, the detection of the divergence-free component of the polarization field, the B-mode component, reveals the presence of gravitational waves in the early Universe. The detection of such component is at the moment the most promising technique to probe the inflationary theory describing the very early evolution of the Universe. The measurement of the polarization of the Cosmic Microwave Background (CMB) radiation is one of the current frontiers in cosmology. In particular, the detection of the primordial divergence-free component of the polarization field, the B-mode, could reveal the presence of gravitational waves in the early Universe. The detection of such a component is at the moment the most promising technique to probe the inflationary theory describing the very early evolution of the Universe. We present the updated performance forecast of the Large Scale Polarization Explorer (LSPE), a program dedicated to the measurement of the CMB polarization. LSPE is composed of two instruments: LSPE-Strip, a radiometer-based telescope on the ground in Tenerife-Teide observatory, and LSPE-SWIPE (Short-Wavelength Instrument for the Polarization Explorer) a bolometer-based instrument designed to fly on a winter arctic stratospheric long-duration balloon. The program is among the few dedicated to observation of the Northern Hemisphere, while most of the international effort is focused into ground-based observation in the Southern Hemisphere. Measurements are currently scheduled in Winter 2022/23 for LSPE-SWIPE, with a flight duration up to 15 days, and in Summer 2022 with two years observations for LSPE-Strip. In this work, we describe the main features of the two instruments, identifying the most critical aspects of the design, in terms of impact on the performance forecast. We estimate the expected sensitivity of each instrument and propagate their combined observing power to the sensitivity to cosmological parameters, including the effect of scanning strategy, component separation, residual foregrounds and partial sky coverage. We also set requirements on the control of the most critical systematic effects and describe techniques to mitigate their impact. LSPE will reach a sensitivity in tensor-to-scalar ratio of σr < 0.01, set an upper limit r < 0.015 at 95% confidence level, and improve constraints on other cosmological parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

42 ENGINEERING↗
DOE OSTI2022

Capturing Carbonation: Understanding Kinetic Complexities through a New Era of Electron Microscopy

Cryogenic plasma focused ion beam (PFIB) electron microscopy analysis is applied to visualizing ex situ (surface industrial) and in situ (subsurface geologic) carbonation products, to advance understanding of carbonation kinetics. Ex situ carbonation is investigated using NIST fly ash standard #2689 exposed to aqueous sodium bicarbonate solutions for brief periods of time. In situ carbonation pathways are investigated using volcanic flood basalt samples from Schaef et al. (2010) exposed to aqueous CO 2 solutions by them. The fly ash reaction products at room temperature show small amounts of incipient carbonation, with calcite apparently forming via surface nucleation. Reaction products at 75° C show beginning stages of an iron carbonate phase, e.g., siderite or ankerite, common phases in subsurface carbon sequestration environments. This may suggest an alternative to calcite in carbonation low calcium-bearing fly ashes. Flood basalt carbonation reactions show distinct zonation with high calcium and calcium-magnesium bearing zones alternating with high iron-bearing zones. The calcium-magnesium zones are notable with occurrence of localized pore space. Oscillatory zoning in carbonate minerals is distinctly associated with far-from-equilibrium conditions where local chemical environments fluctuate via a coupling of reaction with transport. The high porosity zones may reflect a precursor phase (e.g., aragonite) with higher molar volume that then “ripens” to the high-Mg calcite phase-plus-porosity. These observations reveal that carbonation can proceed with evolving local chemical environments, formation and disappearance of metastable phases, and evolving reactive surface areas. Together this work shows that future application of cryo-PFIB in carbonation studies would provide advanced understanding of kinetic mechanisms for optimizing industrial-scale and commercial-scale applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improved Cross Section Generation Capability of Griffin in FY22

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of deterministic steady-state transport solvers for fixed source, k-eigenvalue, adjoint, and subcritical multiplication as well as transient solvers for spatial dynamics with the improved quasi-static method. Griffin uses cross section data in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. In recent years, the MC 2 -3 modules have been added to Griffin for fast reactor cross section generation, and the self-shielding application programming interface (SSAPI) was implemented in the ISOXML module for thermal reactor cross section generation. The on-the-fly slowing down method and double-heterogeneity treatment have been implemented to SSAPI and verified against particulate fuel-bearing graphite-moderated thermal reactor problems with high accuracy. This year, work has been focused on improving the cross section generation capability of ISOXML and streamlining the cross section generation procedures. In addition, the form function data were added to ISOXML in order to support the pin power reconstruction capability that was newly implemented in Griffin in this fiscal year. To facilitate the cross section generation using MC 2 -3 and SSAPI in Griffin, the cross section generation workflows have been set up for both fast and thermal spectrum reactors. The MOOSE action system tool was devised for fast spectrum problems, and the MOOSE stochastic tool was adopted to the branch calculation procedure for thermal spectrum problems. Meanwhile, to ensure the accuracy of group-constants, the thermal up-scattering kernel calculator accounting for resonance scattering was implemented in ISOXML, demonstrating the accurate computation of a Doppler-broadened scattering kernel of any Legendre order within a reasonable timescale. Other aspects of ISOXML, such as deletion solver and data, documentation, ISOXML file management, and the interface for Mixture, were improved as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A biosorption-based approach for selective extraction of rare earth elements from coal byproducts

Coal byproducts could be a promising feedstock to alleviate the supply risk of critical rare earth elements (REEs) due to their abundance and REE content. Herein, we investigated the economic and environmental potential of producing REEs from coal fly ash and lignite through an integrated process of leaching, biosorption, and oxalic precipitation based on experimental data and modeling results. Two microbe immobilization systems (PEGDA) microbe beads and Si sol-gels) were examined for their efficiency in immobilizing Arthrobacter nicotianae to selectively recover REEs. Techno-economic analysis revealed that North Dakota lignite could be a profitable feedstock when Si sol-gel is used due to its high cell loading and REE adsorption capacity. Life cycle analysis revealed that Si sol-gel based biosorption could be more environmental friendly than the prevailing REE production in China due to use of less toxic chemicals. However, fly ash sourced from Powder River Basin coals was neither profitable or environmentally sustainable, primarily due to low solublity of high-value scandium at an economical pulp density (100 ash/L). To further improve the proposed biotechnology, future research could focus on scandium recovery, leaching efficiency, and microbe carriers.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Abundance of spring‐ and winter‐active arthropods declines with warming

Abstract Because ectotherm activity and metabolism are sensitive to temperature, terrestrial arthropods may be especially responsive to ongoing climatic warming. Here, we quantified responses of arthropod abundance to two years of warming in an outdoor temperature manipulation experiment at Duke Forest, North Carolina, USA. Nine open‐top chambers were individually heated year‐round from 1.5° to 5.5°C above ambient temperature. From two years of monthly pitfall trapping, we collected and identified 4,468 arthropods representing 24 orders. We initially predicted that arthropods would experience the greatest negative effects of experimental warming during the summer months, when temperatures reach their yearly maximum and arthropods may be close to their maximum thermal tolerance limits. Instead, we found that the strongest negative effects on arthropod abundance occurred during the winter and spring, when ambient temperatures are relatively cooler, whereas the effects of experimental warming on abundance were not significant during the summer or fall. During the spring of 2012, the warmest spring on record for the southeastern USA, total arthropod abundance declined 20% per °C of experimental warming. Abundance declines were driven largely by flies (Diptera), which were the most abundant insect order, representing approximately a third of all arthropods collected. The most abundant arthropod family, Mycetophilidae (fungus gnats), declined 64% per °C of warming during the spring of 2012. Although previous research on climatic warming has focused on the impact of maximum yearly temperatures on organismal performance, our results are more consistent with the cool‐season sensitivity hypothesis, which posits that arthropods adapted for cooler conditions are likely to face the strongest negative effects of warming during the cooler seasons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Techno-Economic and Life Cycle Assessments for Sustainable Rare Earth Recovery from Coal Byproducts using Biosorption

Coal byproducts could be a promising feedstock to alleviate the supply risk of critical rare earth elements (REEs) due to their abundance and REE content. Herein, we investigated the economic and environmental potential of producing REEs from coal fly ash and lignite through an integrated process of leaching, biosorption, and oxalic precipitation on the basis of experimental data and modeling results. Two microbe immobilization systems (polyethylene glycol diacrylate (PEGDA) microbe beads and Si sol–gels) were examined for their efficiency in immobilizing Arthrobacter nicotianae to selectively recover REEs. Techno-economic analysis revealed that North Dakota lignite could be a profitable feedstock when Si sol–gel is used due to its high cell loading and REE adsorption capacity as well as high reuse cycles. Life cycle analysis revealed that Si sol–gel-based biosorption could be more environmental friendly than the prevailing REE production in China due to the use of less toxic chemicals. However, fly ash sourced from Powder River Basin coals was neither profitable nor environmentally sustainable, primarily due to low solubility of high-value scandium at an economical pulp density (100 g ash/L of acid solution). To further improve the proposed biotechnology, future research could focus on scandium recovery, leaching efficiency at a high pulp density, and reuse cycles of the immobilized microbes.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Economic Extraction and Recovery of REEs and Production of Clean Value-Added Products from Low-Rank Coal Fly Ash

TechConnect World Innovation Conference & Expo, Washington, DC, June 13–15, 2022. The University of North Dakota (UND) Energy & Environmental Research Center (EERC) teamed with Pacific Northwest National Laboratory; the North Dakota Industrial Commission Lignite Research Program; and commercial partners Basin Electric Power Cooperative, Great River Energy, and Southern Company to execute a Cooperative Agreement funded by the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) focused on identifying unique pathways and pretreatments to extract rare-earth elements (REEs) from low-rank coal (LRC) ash in a more economical and environmentally benign manner than current methods. By focusing on minimizing process steps and consumptive use of extraction solutions, a tunable, economically viable process for REE extraction from LRC ash was developed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Cross Section Generation Capability in Griffin

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code reads multigroup cross sections in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. The implementation of the cross section generation capability in Griffin was initiated last year by plugging in the cross section application programming interface (CSAPI) and reviewing the methodologies for treating particulate fuels. The focus this year was on improving the CSAPI integration and implementing advanced self-shielding methods for applications to advanced reactor problems with TRISO fuels. First, the process for cross section library generation was updated to accurately and rigorously produce isotopic cross section data. Second, the on-the-fly slowing down method for the resonance treatment was implemented in CSAPI to improve the accuracy of effective multigroup cross sections in the resonance energy range. Among various on-the-fly slowing down methods, the equivalent Dancoff factor cell method was employed. Third, the iterative local spatial self-shielding method was implemented under the calculation framework of the equivalent Dancoff factor cell method to accurately deal with the double heterogeneity effect of particulate fuel. The updated CSAPI with the advanced self-shielding methods, together with the cross section libraries generated based on the improved process, were tested for the very high temperature reactor (VHTR), high temperature test reactor (HTTR), and Empire benchmark problems with various resonance self-shielding conditions, indicating that the updated CSAPI in Griffin is able to produce multigroup cross sections accurately and efficiently. We also show that the methodology works well for pebble bed fuel from HTR-10, but the capability still needs to be fully integrated into CSAPI. In the future, further benchmark tests will be performed for various thermal reactor core problems, including particulate fuel-based pebble bed reactors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗