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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modernizing the Nuclear Industry and New Ways of Working

Nuclear energy is recognized as the most feasible energy source towards achieving nation-wide net-zero goals (COP 208, MIT). Additionally, the demand for reliable, consistent energy supply is soaring with the construction of energy hungry data centers and AI tools. This means the construction of new nuclear and the continuation of current nuclear are a high national priority. However, this leaves the nuclear industry with a workforce challenge. The rising demand for nuclear power is threatened by an underpopulated workforce. There exist a few contributors to the decreased workforce such as skewed workforce demographics resulting in large-scale workforce retirement. Also, some plants have observed difficulty hiring and retaining skill sets that typically comprise the nuclear power workforce – specifically skilled craftsman. Those who are graduating with desirable skills are seeking employment in sectors that are more modern and culturally more aligned with younger generational values. Lastly, within the nuclear sector, skilled employees will be a competitive commodity as advanced plants come online offering work environments that incorporate modern technologies, skill sets, and opportunities for career advancement. These factors emphasize the need for legacy plants to modernize with a focus on the workplace environment and culture that meets younger generations’ skills, cultural expectations, and desire for advancement opportunities. Nuclear plants are actively engaged in deploying advancements that improve the management of systems, structures, and components as well as process improvements and technologies that can reduce the costs of operation and maintenance. However, the advancements must also be analyzed, reviewed and deployed in a manner that considers the impact on how people within the organization collaborate, communicate, make decisions, and solve problems. Neglecting to consider the cultural impact of modernization risks poor adoption, unrealized opportunities, or insignificant change toward helping attract new employees. The traditional way of working is not necessarily the way new generations want to engage with their employer. For instance, in a survey performed by North American Young Generation in Nuclear the top three reasons for younger nuclear employees to seek other job opportunities was seeking better work-life balance, a lack of advancement opportunities, and work culture and leadership style differences (Smyth et al. 2022). Integrated Operations for Nuclear (ION) is an approach for the nuclear industry to create long-term strategic modernization plans and analyze each advancement for the impact on people and processes and measure how those impacts flow up to support high-level, long-term plant goals and requirements. One principle of ION is to replace the current labor-centric operation style in legacy nuclear plants with data-centric collaborative operation styles. This paper will explain how transitioning operations to leverage centralized skills and responsibility, multi-skilled teams, and collaborative decision making can flatten the hierarchical structure in plants, enable greater individual efficacy while also offering more experiences and advancement opportunities to staff. Adopting the ION way of working shifts the industry mind-set towards more networked, collaborative, and modern way of working that will attract skilled, next-generation workers to operate the legacy nuclear fleet.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

UHT-CAMANCHE: Ultra-High Temperature Ceramic Additively Manufactured Compact Heat Exchangers

The conceptual basis for this project is the convergence of advanced ultra-high temperature ceramic materials and additive manufacturing technologies to produce compact ceramic heat exchangers with complex internal flow path geometries. Task areas were broadly divided into materials and manufacturing development, heat exchanger design, component testing, and techno-economic analysis. Technical challenges included the design and commissioning of new test facilities, improving feature resolution and deposition rate of ceramic additive manufacturing techniques, establishing process-structure-property relationships in additively manufactured ultra-high temperature ceramics, and assessing high temperature materials compatibility in CO 2 environments. The primary candidate material evaluated in this work is a composite comprising zirconium diboride (ZrB2) with 30 vol. % silicon carbide (SiC) which was selected based on its desirable combination of high temperature mechanical properties, high thermal and electrical conductivities, and oxidation resistance. High solids loaded ZrB2-SiC pastes suitable for extrusion-based additive manufacturing were developed for the first time as part of this work. Materials compatibility studies indicate this material oxidizes in CO 2 to form a protective borosilicate scale which transforms to pure silica above 1000°C. Parts made by additive manufacturing displayed enlarged grain sizes produced by pressureless sintering as compared to hot-press sintering. Increases in microstructural coarseness have outsized effect on oxidation performance up to 1400°C due to incomplete oxidation of coarse large diameter SiC particles resulting in lower amounts of silica that apparently inhibit protective scale formation. Additive manufacturing as a forming technique did not appear to significantly affect thermal conductivity, hardness, or elastic modulus, though flexural strength was reduced by half or more as compared to traditionally hot-pressed materials. This effect was attributed to the presence of strength-limiting flaws (ca. 40 microns in size) originating from extrudate inhomogeneities that could potentially be eliminated with further process improvements. Attempts to attain economies of scale for production of multi-kilowatt scale heat exchangers by ceramic additive manufacturing proved difficult. Lack of automation and a modest extrusion rate while retaining fine feature resolution made the overall process labor intensive and limited experimental throughput. A number of full-scale components were taken through post-process heat treatments including drying, binder burnout, and sintering; however, none survived without significant flaws or cracks. Therefore, no operational data from a newly installed heat exchanger test loop were able to be obtained during the performance period. Continued research and development is recommended to improve economic feasibility of ceramic additive manufacturing by standardizing the use of advanced sensors, artificial intelligence, and automation tools to reduce associated labor costs and accelerate production rates. The materials and manufacturing techniques demonstrated in this work are likely to find applications in defense and energy applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Correlating processing variables to material properties in recycled polypropylene: A data‐driven approach

Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.

Estela‐García, John E. [Polymer Engineering Center↗
DOE OSTI2025

10-th order of accuracy for numerical solution of 3-D elasticity equations for heterogeneous materials on unfitted Cartesian meshes

We have developed the Optimal Local Truncation Error Method (OLTEM) with 10-th order of accuracy on unfitted Cartesian meshes for a system of 3-D elasticity equations with smooth irregular interfaces. 5 x 5 x 5 = 125-point stencils (similar to those for quadratic finite elements) for elastic heterogeneous materials are used for OLTEM. There are no unknowns at the interface points between different materials; the structure of the global discrete equations is the same for homogeneous and heterogeneous materials. The calculation of unknown stencil coefficients is based on the minimization of the local truncation error of the stencil equations and yields the optimal 10-th order of accuracy for OLTEM on unfitted Cartesian meshes, i.e., the increase by 7 orders in accuracy compared to quadratic finite elements on conformal meshes. A new post-processing procedure provides the 9-th order of accuracy for stresses in the 3-D case. Similar to basic computations it uses OLTEM with the 125-point stencils, the interface conditions and the elasticity equations. It was shown that the use of the elasticity equations for post-processing improves the accuracy of 0.1% stresses by 6 orders compared to post-processing without the use of PDEs. At an accuracy of for stresses, OLTEM with the new post-processing procedure reduces the number of degrees of freedom by 360 - 8000 times compared to quadratic finite elements with similar stencils. OLTEM with the 125-point stencils yields even more accurate results than high-order finite elements with much wider stencils. OLTEM provides accurate numerical results for compressible and nearly incompressible materials.

elasticity equations↗
DOE OSTI2026

Techno-Economic and Life Cycle Assessment of Chemical Recycling and Upcycling of Mixed Plastics Waste Containing Poly-vinyl-chloride

Developing technologies that completely remove chlorine from plastic waste can allow its chemical recycling and upcycling with catalytic methods. Here, this study compares eight processes involving different dechlorination methods (absorption columns, adsorption in beds of zeolites, catalytic dechlorination, and dissolution in ionic liquids) and chemical conversion technologies (incineration, pyrolysis, hydrogenolysis) to upgrade mixed plastics waste to various products (e.g., electricity, fuels, virgin polymers, and lubricant oil). The analysis determines that the absorption of chlorine in columns with basic aqueous solutions is limited to plastics waste with PVC concentrations below 0.1%. Dissolution in ionic liquids is not cost-competitive. On the contrary, two-step processes with catalytic dechlorination followed by thermochemical catalytic depolymerization, either pyrolysis or hydrogenolysis, significantly improve process economics and emissions. The most economically viable alternative is hydrogenolysis for producing lubricants, while the technology with the lowest global warming potential is chemical recycling via catalytic pyrolysis.

circular economy↗
DOE OSTI2025

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves↗
DOE OSTI2025

Microwave Debinding of Ceramics Produced by Additive Manufacturing

Microwave-assisted debinding offers an innovative approach to processing 3D-printed engineering ceramics. Ceramic parts produced with photopolymer-based additive manufacturing techniques require thermal debinding to remove polymers that bind ceramic particles during the printing process. Microwave-assisted debinding significantly reduces processing times and offers energy savings over conventional thermal treatments due to higher heating rates and more uniform heating. Finite-element modeling of the debinding process allows for the optimization and prediction of temperature distribution, stress development, and potential defects, leading to improved process control and material quality.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework

Owing to uncertainties in convective microphysics processes, improving parameterizations in Earth system models (ESMs) can benefit from observationally constrained cases suitable for scaling between cloud-resolving models and ESMs. We propose a benchmark large-eddy simulation (LES) cumulus congestus case study from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP 2 Ex) for evaluating and improving ESMs in single-column model (SCM) mode. We seek observational constraints using novel polarimetric retrievals and in situ cloud microphysics measurements. Simulations using bulk and bin microphysics initialized with observed aerosol profiles are compared to cloud-top retrievals of cloud droplet effective radius (R eff ), effective variance (ν eff ), and number concentration (N d ) from the airborne Research Scanning Polarimeter (RSP). Both schemes reproduce characteristics of cloud-top N d and R eff that increase and decrease with altitude, respectively. Cloud-top N d is low-biased relative to RSP retrievals in both schemes, potentially due to limitations in both simulations and retrieval assumptions. Cloud-top R eff is low-biased in the bulk scheme but reasonably reproduced by the bin scheme. Profiles of N d and R eff are sensitive to the collision–coalescence process and the vertical variation in aerosol size distribution. Comparison of simulated and in situ droplet size distributions (DSDs) shows that, to first order, integrated moments are always sensitive to sizes < ~ 30 µm and can also be sensitive to larger sizes if the DSDs are sufficiently broad, with implications for the assumed maximum observed size retrieved by the RSP. The bin scheme captures the observed extended tail of the DSD, while the bulk scheme is unable to due to parametric constraints. Differences in expected relationships between in situ measurements of cloud cores and cloud-top retrievals by RSP demonstrate difficulty in constraining well the case presented herein. Finally, a thermal-tracking framework demonstrates that the dilution of N d throughout a thermal's lifetime is heavily determined by collision–coalescence and the height-varying aerosol distribution and that, in the absence of these, the impact of entrainment on diluting N d is largely offset by secondary activation. Implications for evaluating warm-phase convective microphysics schemes in ESMs and translating results for use on global, space-based polarimetry platforms are discussed.

Stanford, McKenna Wallace [Columbia Univ., New Yor↗
DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Utilization of Hemp Processing Waste for 3D Printing of Biocomposites

Unlike stem biomass, the residues after the extraction of cannabidiol (CBD) oil from hemp flower are challenging to utilize because of their high extractive content (~ 40%, mainly lipids) and are typically considered waste and landfilled. This study presented a novel approach to effectively valorize this underutilized hemp processing waste via chemical processing for three-dimensional (3D) printing applications. Hemp processing waste was processed with sodium hydroxide (NaOH) to control extractives for solving nozzle clogging and then applied as a biofiller in polylactic acid (PLA) composites to improve the mechanical strength. The novelty of this work lies in demonstrating that controlled extractive removal via NaOH treatment not only improves processability but also enhances mechanical performance in 3D-printed biocomposites. We systematically investigated the effect of the processed biofiller content (2.5–10 wt%) on the mechanical and thermal properties of the biocomposites. The decrease in the content of extractives reduced the non-structural components and improved the surface compatibility of the hemp waste with the PLA matrix, thereby enhancing the polymer-biofiller interactions. The best performance was achieved at 2.5 wt% loading, where Young’s modulus increased from 2.3 GPa to 2.6 GPa and tensile strength from 42.7 MPa to 48.8 MPa. Interestingly, the complete removal of extractives also reduced the mechanical strength of their biocomposites, indicating the interfacial adhesion effects of extractives. Furthermore, this study provides new insights into balancing extractive content for optimal mechanical properties, offering a sustainable solution for waste valorization in additive manufacturing.

Additive manufacturing↗
DOE OSTI2025

Geometric origin of the energy-momentum tensor improvement terms

In a flat background, the canonical energy momentum tensor of Lorentz and conformally invariant matter field theories can be improved to a symmetric and traceless tensor that gives the same conserved charges. We argue that the geometric origin of this improvement process is unveiled when the matter theory is coupled to metric-affine gravity. In particular, we show that the Belinfante-Rosenfeld improvement terms correspond to the matter theory’s hypermomentum. The improvement terms in conformally invariant matter theories are also related to the hypermomentum; however, a general proof would require an extended investigation. We demonstrate our results through various examples, such as the free massless scalar, the Maxwell field, Abelian p-forms, the Dirac field, and a nonunitary massless scalar field. Possible applications of our method for theories that break Lorentz or special conformal invariance are briefly discussed.

classical solutions in field theory↗
DOE OSTI2026

Effects of B 2 O 3 on the Growth, Structural, and Magneto-Optical Properties of Yttrium Iron Garnet Single-Crystal Fibers

This study explores the fabrication of yttrium iron garnet (YIG) single crystal fibers using the laser heated pedestal growth (LHPG) method with the experimental addition of B 2 O 3 . The incorporation of B 2 O 3 facilitates the fiber fabrication process by lowering the required growth temperatures and likely modifying melt viscosity behavior, consistent with the established fluxing behavior of B2O3 and the comparative viscosity trend observed in the TMA−VFT analysis, thereby improving process efficiency while maintaining fiber quality. Structural characterization using EBSD and SC-XRD reveals a transition from polycrystalline to single-crystal behavior, with improved alignment along the [111] direction without altering the garnet structure. Magnetic measurements show increases in saturation magnetization in B 2 O 3 -assisted fibers. Three-dimensional anisotropy energy modeling, based on EBSD-derived Euler angles, indicates that the enhanced crystallinity and orientation contribute to reorientation of MCA energy distribution due to improved crystallographic alignment. Faraday rotation measurements show that the B 2 O 3 -assisted sample exhibits a rotation angle closer to reported values for high-quality YIG, suggesting improved phase purity and crystallographic quality. These findings demonstrate that B 2 O 3 -assisted LHPG growth is a scalable and nontoxic approach to producing high-performance YIG fibers for integrated photonic and magnetic field sensing applications.

Crystal structure↗
DOE OSTI2025

Scale-Up of Friction Self-piercing Riveting Process for Multi-material Joints

A single-class joining process known as “friction self-piercing riveting (F-SPR)” has been developed for joining various low-ductility lightweight materials on a laboratory scale. The frictional heat generated during the F-SPR process improved local ductility, resulting in crack-free joints and robust mechanical performance. This innovative joining technology was further advanced through the scale-up of the process using a new system with several key features (e.g., automatic rivet feeding and clamping system, vacuum system) toward industry readiness. The new integrated F-SPR systems were effectively demonstrated for joining different material combinations (e.g., carbon fiber composite to 7075 Al alloy, 7075 Al alloy to 7075 Al alloy, and 7075 Al alloy to casting Al Aural 5) with a unified technique. Crack-free joint with adequate mechanical interlocking resulted in good mechanical joint strength for each material combination. Then, the process was successfully scaled up by producing multiple joints without any cracks on larger CFC-Al and Al-Al components by the new integrated system, bringing it closer to industrial application.

Lim, Yong Chae [ORNL] (ORCID:0000000321773988)↗
DOE OSTI2026

Quality Control of Silicon Sensor Modules for Particle Detectors

The High-Luminosity Large Hadron Collider (HL-LHC) will produce a higher rate of particle collisions than the current Large Hadron Collider (LHC), requiring important upgrades to the Compact Muon Solenoid (CMS) to handle an increased amount of data. An important upgrade is the Phase-2 Outer Tracker Upgrade, which consists of 13,000 silicon sensor modules made of two parallel silicon sensors and readout electronics. These modules undergo careful quality control checks both during and after module assembly to ensure precise and reliable detector performance. This project focuses on precision testing for quality control of silicon sensor modules at Fermilab. Hands-on work includes visual inspection, current-voltage testing, module testing, and ultraviolet (UV) light exposure of modules showing abnormal current-voltage behavior. The ultraviolet exposure process improves the abnormal sensor readout data by placing the selected sensor side of the module directly under the UV light inside a controlled box. In addition to laboratory testing and ultraviolet experiments, I developed a Python-based data tool that connects to a module database and allows selected testing conditions and module information to be retrieved and displayed efficiently. These different testing procedures, experimental processes, and computational tools support the broader goal of identifying module issues and improving modules that will be used in the CMS Outer Tracker Phase-2 Upgrade.

Siddiqui, Hooriya [DuPage Coll.] (ORCID:0009000151↗
DOE OSTI2026

Quality Control of Silicon Sensor Modules for Particle Detectors

The High-Luminosity Large Hadron Collider (HL-LHC) will produce a higher rate of particle collisions than the current Large Hadron Collider (LHC), requiring significant upgrades to the Compact Muon Solenoid (CMS) to handle the increased amount of data. An important upgrade is the Phase-2 Outer Tracker Upgrade, which consists of 13,000 silicon sensor modules made of two parallel silicon sensors and readout electronics. These modules undergo careful quality control checks both during and after module assembly to ensure precise and reliable detector performance. This project focuses on precision testing for quality control of silicon sensor modules at Fermilab. Hands-on work includes visual inspection, current-voltage testing, module testing, and ultraviolet (UV) light exposure of modules showing abnormal current-voltage behavior. The ultraviolet exposure process improves the abnormal sensor readout data by placing the selected sensor side of the module directly under the UV light inside a controlled box. In addition to laboratory testing and ultraviolet experiments, I developed a Python-based data tool that connects to a module database and allows selected testing conditions and module information to be retrieved and displayed efficiently. These different testing procedures, experimental processes, and computational tools support the broader goal of identifying module issues and improving modules that will be used in the CMS Outer Tracker Phase-2 Upgrade.

Siddiqui, Hooriya [DuPage Coll.; Fermilab] (ORCID:↗
DOE OSTI2025

Process intensification in hydrothermal liquefaction of biomass: A review

Hydrothermal liquefaction (HTL) presents a promising pathway for converting wet biomass resources into biofuels, offering significant advantages over conventional methods. However, numerous challenges must be addressed for HTL scale-up, including energy provision for the endothermic process, heat and mass transfer limitations, slurry concentration and pumpability challenges, char and coke formation, and continuous phase separation. This review explores key strategies such as autothermal HTL, which improves process efficiency and reduces external energy requirements by coupling exothermic and endothermic reactions within the same reactor, thereby simplifying reactor design and reducing operational costs. Additionally, multistage HTL processes are highlighted for their ability to optimize biocrude quality and yield by fractionating biomass conversion stages, resulting in higher energy returns on investment and better-quality biocrude. Solvothermal HTL and integration techniques for aqueous phase are also discussed. Furthermore, the HTL patent landscape is discussed to provide insights into current technological advancements. This review aims to offer a comprehensive understanding of process intensification in HTL, highlighting innovative solutions to enhance the efficiency and scalability of the process for sustainable biofuel production.

Biocrude oil↗
DOE OSTI2025

Cloud-atmosphere impacts on the central Arctic surface energy budget (Final Report)

This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chloride-Based Volatility for Waste Reduction and/or Reuse of Metallic-, Oxide- and Salt-Based Reactor Fuels

The objective of the chloride based volatility project (CBV) was to demonstrate the ability to separate uranium from used fuel to enable process improvements resulting in 10x reduction of waste volume, while maintaining safeguards standards and global backend costs at $\$$1/MW-hr. Current industrial practices perform separations of used fuel using solvent media in the form of aqueous and molten salt processes, resulting in contaminated process waste. The CBV approach utilized solid state chemistry with no solvent media and was successful in chlorinating uranium and fission product oxides that sublimed into the gas phase and were collected in targeted condensation zones based on temperature gradients of chlorinated products. Recovery of better than 95% of initial uranium in the form of UCl4 was demonstrated when simulated used nuclear fuel was used. Laser induced breakdown spectroscopy, LIBS, and ultraviolet-visible spectroscopy, UV-Vis, were combined into a high temperature flow cell design and utilized as process monitoring techniques to observed chlorinated products leave the reaction vessel in real time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗