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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 OSTI2026

Toward the origins of binding energy shifts and “satellites” formation during plasma-XPS measurements

In-plasma x-ray photoelectron spectroscopy (plasma-XPS) emerges as a powerful platform for real-time, in situ chemical analysis under conditions relevant to semiconductor processing and other plasma-enabled technologies. This study investigates the origins of binding energy (BE) shifts and the formation of “satellite” peaks observed during plasma-XPS measurements across conductive, dielectric, and gas-phase systems. Using a standard laboratory-based ambient pressure XPS apparatus coupled with an alternating current (AC)-driven capacitively coupled plasma source, we demonstrate that metastable surface species, such as transient Au oxides, can be detected during plasma exposure, revealing chemical states that are hardly accessible using conventional ultrahigh vacuum (UHV) XPS. In dielectric samples (e.g., undoped diamond, sapphire), we observe pressure- and plasma-type-dependent BE shifts of more than 50 eV, attributed to x-ray-induced and plasma-mediated surface charging. These shifts are mitigated at higher pressures/plasmas or in electronegative plasmas (e.g., O 2 ), the latter due to enhanced charge compensation mechanisms involving slow negative ions. For gas-phase species, AC-plasma excitation leads to spectral broadening and the emergence of “satellite” peaks with energy separations of a few electron volts, linked to oscillating local plasma potentials in the probing volume. Furthermore, these findings highlight the complex and important interplay between plasma parameters, surface charging, and local electric fields in shaping XPS spectra. Overall, plasma-XPS emerges as a critical metrological tool for probing transient surface chemistry, with implications for semiconductor processing, material synthesis, and plasma diagnostics.

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

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗
DOE OSTI2025

Real Time, In-line Monitoring of Hanford Tank Wastes - Year 1 Report

The team comprised of students, postdocs, early, mid and senior career scientists from Los Alamos National Laboratory, Savanah River National Laboratory, Georgia Tech and Florida International University, with the guidance of H2C, is developing a suite of in-line instruments for the Hanford high level waste (HLW) and low active waste (LAW) processes to provide near-real-time analysis of waste form physical properties and composition. The work builds on results from the recent DOE-ORP, EM Technology Development and other projects that demonstrated promise for the use of real-time in-line monitoring (RTIM) to measure chemical compositions of slurries of up to 20 weight % solids. The goal is for this instrument suite is to substantially reduce the need for sampling for process control. Sample waste, exposure associated with sample analysis, and the demand for an external laboratory facility would be greatly reduced. The throughput of waste treatment systems would be improved by elimination of the downtime caused by waiting for sample results. This translates into reduced process storage as process knowledge will be continuously updated in near-real-time. These breakthrough technologies would significantly reduce the life cycle cost and accelerate the schedule for the Hanford tank waste mission.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2026

Rapid Analytical Methodology for Chloride Molten Salt Reactor Safeguards and Process Monitoring

Technologies that enable near real-time isotopic analysis of advanced molten salt reactor (MSR) fuels are critically needed to safeguard these reactors, increase their operational efficiency, and enable their widespread deployment with confidence. We present a systematic approach to developing near real-time dissolution and chemical isolation of U, Pu, and major fission products from highly radioactive chloride molten salt samples. Chemical yields greater than 95% were observed for both uranium and the lanthanides. Interference reduction enabled the detection and quantification of key diagnostic isotopes (including 112Ag, 147Nd, and 153Sm) that were previously undetectable in the original sample. The results from this initial scoping study lay the foundation for the development of future automated systems that can enable cost-efficient, near-real time chemical separation and analysis of extremely highly radioactive molten salt samples.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2026

Online and Offline Analytical Techniques to Quantify Chloride Salts

This report serves as the deliverable for Milestone- M3FT-26AN080502017: Summary of Accomplishments for Online and Offline Analytical Qualification of Chloride Fuel Salts. The qualification of chloride fuel salts is a critical challenge for the development and deployment of molten salt reactors (MSRs), requiring precise control and verification of chemical and isotopic composition to ensure predictable neutronics, manageable radiological behavior, and safe plant operation. Currently, there is no universally accepted standard for fuel salt qualification, particularly for chloride-based systems, which underscores the need for robust, science-based analytical methodologies. This report presents a comprehensive evaluation of both online and offline techniques for quantifying chloride fuel salts, including multielectrode array voltammetry, differential thermal analysis (DTA) probes, inert gas fusion (IGF) analyzers, and gamma spectrometry using the Mirion NAIS-2x2 NaI(Tl) detector. The integration of these methods enables real-time monitoring of actinide concentrations, redox state, and thermophysical properties, as well as high-precision measurement of impurities and isotopic composition. The acquisition of advanced instruments such as the Bruker Leonardo G6 for IGF and the Mirion NaI detector enhances analytical capabilities, supporting the establishment of operational envelopes and impurity thresholds. These approaches provide essential data for neutronic impact evaluation, feedstock documentation, and compliance with nuclear safety standards. The methodologies developed and validated in this report lay the groundwork for future standardized protocols, bridging the gap between laboratory research and commercial reactor operation, and advancing the safe and efficient deployment of chloride-fueled MSRs.

Polke, Amber↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Correlation between infrared spectra features and coverage of different adsorption sites for the NO/Pd(111) system

This study investigates the adsorption of nitric oxide (NO) on Pd(111) surfaces at 200 K using infrared reflection absorption spectroscopy (IRRAS). Peak positions and areas are used as proxies for tracking coverage of different species, serving as groundwork for in-situ experiments that need real-time tracking of surface species. Here, we derived a mathematical model correlating NO coverage with dose, consistent with the Molecular Langmuir Model. Distinct correlations are observed between coverage and spectral features (peak position and peak area for the NO vibration): at lower coverages (θ<0.6), the total integrated peak areas are linearly corelated to coverage; at higher coverages (θ>0.6), the peak center for a compressed-hollow site becomes the primary coverage indicator, showing a linear relationship between wavenumber and coverage. Note that this study addresses coverages below saturation. These findings refine our understanding of NO adsorption and establish a foundation for real-time in situ transient kinetics studies of chemical reactions involving NO on Pd(111).

25 ENERGY STORAGE↗
DOE OSTI2026

Coupling of high-resolution mass spectrometer and photosynthesis system for comprehensive leaf volatile metabolite profiling

Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100–350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.

Biogenic volatile organic compounds↗
DOE OSTI2025

Advancing AI-Driven Analysis in X-ray Absorption Spectroscopy: Spectral Domain Mapping and Universal Models

In recent years, rapid progress has been made in developing artificial intelligence (AI) and machine learning (ML) methods for X-ray absorption spectroscopy (XAS) analysis. Compared to traditional XAS analysis methods, AI/ML approaches offer dramatic improvements in efficiency and help eliminate human bias. To advance this field, we advocate an AI-driven XAS analysis pipeline that features several interconnected key building blocks: benchmarks, workflows, databases, and AI/ML models. Specifically, we present two case studies for XAS ML. In the first study, we demonstrate the importance of reconciling the discrepancies between simulation and experiment using spectral domain mapping (SDM). Our ML model, which is trained solely on simulated spectra, predicts an incorrect oxidation state trend for Ti atoms in a combinatorial zinc titanate film. After transforming the experimental spectra into a simulation-like representation using SDM, the same model successfully recovers the correct oxidation state trend. In the second study, we explore the development of universal XAS ML models that are trained on the entire periodic table, which enables them to leverage common trends across elements. Looking ahead, we envision that an AI-driven pipeline can unlock the potential of real-time XAS analysis to accelerate scientific discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Review and Outlook on Experimental Advances and Innovations in Geological CO 2 Storage: Insights from Depleted Gas Reservoirs and Saline Aquifers

Geological storage of carbon dioxide (CO 2 ) in depleted gas reservoirs and deep saline aquifers is a key part of global decarbonization efforts. As carbon capture and storage advances toward commercial-scale deployment, the credibility and scalability of laboratory experiments are increasingly vital for guiding safe and effective field implementation. This review offers a comprehensive, cross-scale evaluation of experimental methodologies, including core flooding, high-pressure, high-temperature systems, microfluidic visualization, and emerging systems such as multilayer commingled/compartmentalized core flooding, 3D-printed micromodels, and AI-powered digital twins. These innovations are demonstrated to enhance representativeness, reproducibility, and real-time insight, thereby addressing the limitations of conventional workflows. A critical analysis of methodological gaps, such as inconsistent pressure–temperature conditions, oversimplified brine chemistry, and a lack of standardization, reveals experimental sources of scale translation errors and performance uncertainty. By comparing the unique challenges of depleted gas reservoirs (such as low water saturation and legacy well leakage) to those of saline aquifers (including pressure buildup and caprock integrity), this review identifies formation-specific priorities for experimental design. Novel contributions include a synthesis of best practices, integration strategies for model calibration, and recommendations for standardizing core handling, saturation procedures, and reporting protocols. Furthermore, this work serves as a guide for developing robust, field-relevant experimental strategies that can increase the deployment and regulatory acceptance of CO 2 storage technologies at scale.

58 GEOSCIENCES↗
DOE OSTI2025

Using Best Basis Inventory Data to Direct Strategies for Real-Time Monitoring of Hanford High Level Waste

The proposed Direct Feed High Level Waste (DFHLW) approach for processing high-level tank waste at Hanford is intended to reduce processing time by bypassing the Pretreatment Facility and transferring waste directly from the tank farm to the WTP HLW vitrification facility. This processing strategy could reduce or eliminate the washing and leaching steps that would have occurred in the Pretreatment facility. Operation of the vitrification facility is subject to chemical and radiological limits protecting safety (e.g. Waste Acceptance Criteria, or WACs) and process quality (e.g. Process Control Limits, or PCLs). Without washing and leaching, there is a greater risk of exceeding the WACs and PCLs. Hanford process engineers have devised blending strategies based on known chemical and radiological composition, volumes, and solids loadings of individual layers within each waste tank. These blending campaigns succeed in predicting a processing strategy that does not exceed the WACs and PCLs. However, the calculations do not ascribe uncertainties to the tank analysis data, quantities of material taken from the tanks to make the blend, or potential for mixing of layers within tanks. In order to confirm that a process strategy is working, it would be advantageous to have inline or at-line analytical instrumentation installed in the processing facilities that deliver measurement results in real time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Using Best Basis Inventory Data to Direct Strategies for Real Time Monitoring of Hanford High Level Waste – 26226

The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.

Lascola, Robert [Savannah River National Laborator↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2026

A microfluidic spore chamber for long-term imaging of single-spore hyphal development.

Understanding the life cycle of fungal spores is essential for elucidating their roles in pathogenesis, dispersal, and survival. However, studying spore development under controlled, spatially defined conditions remains challenging. Here, we present the Spore Chamber, a custom-built microfluidic platform engineered for parallel trapping and long-term imaging of individual spores under defined media conditions, enabling real-time visualization of hyphal development. Using Aspergillus fumigatus as a model organism, we demonstrate that sparse trapping of individual spores within size-matched trap geometries enables long-term time-lapse imaging of key developmental stages, including germination, polarized hyphal elongation, branching, and conidiophore formation. To assess the device's capacity to resolve morphogenetic responses to exogenous signals, we introduced lipochitooligosaccharides (LCOs) and short-chain chitooligosaccharides (COs). Rhizobium-derived, non-sulfated LCO (nsLCO) mixtures induced enhanced secondary branching (hyperbranching), a response not previously reported in A. fumigatus under these signal conditions, to our knowledge, whereas sulfated LCOs and CO4 did not significantly alter branching patterns. In addition, long-term confinement and imaging revealed rare developmental morphologies previously described primarily in mutant strains, including split conidiophore formation, elongated phialides, and stress-associated phenomena such as microcyclic conidiation, and chlamydospore development. Together, these results establish the Spore Chamber as a targeted microfluidic platform for single-spore phenotyping and long-term developmental analysis, with applications in fungal biology, chemical signaling studies, and host–microbe interaction research.

Antifungal screening↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Application of Raman Spectroscopy to Determine Uranium Content in ADUN Solution

The work presented in this report is part of the ongoing efforts to address the nuclear material control and accounting needs for advanced reactor fuel fabrication facilities. This work was supported by the Materials Protection, Accounting, and Control Technologies (MPACT) program under the US Department of Energy Office of Nuclear Energy‘s Nuclear Fuel Cycle and Supply Chain program. The activities and engagements under the MPACT program are designed to support a robust US civilian nuclear energy enterprise. In the work described in this report, we supported MPACT objectives by developing measurement techniques that could be used for material accounting and process monitoring and by working with industry partners to identify existing gaps and areas for improvement. Oak Ridge National Laboratory has been working with commercial tristructural isotropic (TRISO) fuel fabricators such as Standard Nuclear to develop technology for rapid and cost-effective uranium content assessment. This work has focused on demonstrating advanced measurement techniques (e.g., Raman spectroscopy) that can be used for rapid, reliable, and cost-effective routine measurements of uranium content in feed solutions and liquid waste streams as well as for monitoring in-line process measurements and product streams. Specifically, this report explores techniques for accurately determining uranium content in acid-deficient uranyl nitrate (ADUN) solutions and detecting low uranium concentrations in ammonia solutions. Developing such measurement techniques will benefit TRISO fuel fabrication facilities, facilities involved in other parts of the fuel cycle that require online monitoring of aqueous solutions, and potentially molten salt fuel reactors. This work supports developing Raman spectroscopy procedures to determine uranium concentrations in ADUN solutions, which are used as feedstock in the sol–gel process for creating TRISO fuel. Some additional benefits of using Raman spectroscopy for uranium quantification in fabrication facilities include enabling online monitoring of the chemical process, which would provide near real-time feedback; eliminating the need for sample transfers, preparation, or dilution; providing nondestructive measurements; and user friendliness. In this fiscal year, FY25, we determined the identity of the unknown Raman band at approximately 853 cm−1 that was discovered in ADUN Raman spectra in FY24, created calibration curves and determined uranium concentrations of two ADUN solutions, and compared the Raman results to results obtained from inductively coupled plasma mass spectrometry and Davies–Gray titration. Furthermore, we have identified focus areas for experimentation in future fiscal years. A key result is that the accuracy of using Raman can provide accuracy comparable to destructive analysist techniques, With a well-developed calibration curve, using standards and a large number of samples (more than five samples), uncertainty on the order of 1%–3% is achievable. Given that the uncertainties achieved by Raman spectroscopy were on the order of the uncertainties achieved using ICP-MS, we conclude that with a well-developed procedure Raman spectroscopy can be used to determine uranium concentrations in ADUN solutions for NMC&A applications. The benefits of such an approach are that the time and effort will be less than that of comparable destructive analysis techniques, with approximately the same level of technical expertise. This will be attractive to operators of fuel fabrication facilities as it will lower costs and improve efficiencies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗