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DOE OSTI2025

Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide

Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments – including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas – via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes (Creamean et al., 2024, 2020b; https://doi.org/10.5439/1770816).

Creamean, Jessie M. [Colorado State Univ., Fort Co↗

DOE OSTI2019

Spatial and temporal variation in river corridor exchange across a 5th-order mountain stream network

Although most field and modeling studies of river corridor exchange have been conducted at scales ranging from tens to hundreds of meters, results of these studies are used to predict their ecological and hydrological influences at the scale of river networks. Further complicating prediction, exchanges are expected to vary with hydrologic forcing and the local geomorphic setting. While we desire predictive power, we lack a complete spatiotemporal relationship relating discharge to the variation in geologic setting and hydrologic forcing that is expected across a river basin. Indeed, the conceptual model of Wondzell (2011) predicts systematic variation in river corridor exchange as a function of (1) variation in baseflow over time at a fixed location, (2) variation in discharge with location in the river network, and (3) local geomorphic setting. To test this conceptual model we conducted more than 60 solute tracer studies including a synoptic campaign in the 5th-order river network of the H. J. Andrews Experimental Forest (Oregon, USA) and replicate-in-time experiments in four watersheds. We interpret the data using a series of metrics describing river corridor exchange and solute transport, testing for consistent direction and magnitude of relationships relating these metrics to discharge and local geomorphic setting. We confirmed systematic decrease in river corridor exchange space through the river networks, from headwaters to the larger main stem. However, we did not find systematic variation with changes in discharge through time or with local geomorphic setting. While interpretation of our results is complicated by problems with the analytical methods, the results are sufficiently robust for us to conclude that space-for-time and time-for-space substitutions are not appropriate in our study system. Finally, we suggest two strategies that will improve the interpretability of tracer test results and help the hyporheic community develop robust datasets that will enable comparisons across multiple sites and/or discharge conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Physics-Informed Machine Learning-Aided System Space Discretization

Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.

Kim, Junyung↗

DOE OSTI2021

Single-Use Destructive Assay for Uranium Hexafluoride Sampling

Sampling uranium hexafluoride (UF6) for the determination of enrichments by destructive analysis (DA) is a critical component in the International Atomic Energy Agency’s layered safeguards approach for uranium processing facilities. Typically, gram-quantity UF6 samples are collected during inspections and stored under tag-and-seal until transportation to an off-site analytical laboratory. The shipping times can be long, and evolving restrictions on radioactive/corrosive materials shipments may increasingly limit the IAEA’s ability to transport UF6 samples easily. Pacific Northwest National Laboratory has developed a low-cost UF6 sampling technology called Single-Use Destructive Assay (SUDA) that addresses these challenges, as well as provides DA sample geometries that can be tailored for different analytical methods, including potential on-site analyses. The SUDA samplers, along with a unique holder, are designed for direct attachment to existing taps at uranium processing facilities, allowing gaseous UF6 to come into direct contact with a zeolite film. The SUDA technology features the ability to capture uranium in a more easily shipped and handled form as the solid, more stable, and relatively less hazardous hydrated uranyl fluoride (UO2F2•nH2O), which is formed through the controlled hydrolysis of UF6. We have recently simulated uranium collection under enrichment plant sampling conditions to further improve our understanding of SUDA sampling. Presented here is our recent work on measuring the relationship between sampling conditions and uranium collection, which includes control of the uranium-mass-to-zeolite ratio and assessing variable UF6 gas and sampling parameters that can affect collection using the SUDA sampler.

Pope, Timothy R.↗

DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.

Drgona, Jan↗

DOE OSTI2024

Why the US Needs an Enduring Plutonium Critical Assembly

Jezebel (operated from 1954-1977) has been the primary experiment for fast 239 Pu nuclear data validation for the last 70 years. Validation has included not only k eff , but also spectral indicies, Rossi-α, reactivity coefficients, and neutron leakage spectra. While this has been incredibly valuable to the community, there are three major issues. The first issue is that documentation on many of these experiments were lacking, leading to large uncertainties or (even worse) incorrect assumptions. The second issue is that while there have been many advances in research, there is no way to test those new advances today. The last issue is that since the assembly only operated for 23 years (and at time when 30 other critical assemblies were operating at the same facility and nuclear weapons testing was occurring), there were limited opportunities to observe how any system parameters changed as a function of time. Note that this work is not suggesting that a "Jezebel re placement" used for a limited experiment campaign would have great value. A new enduring (100 year target) plutonium (Pu) assembly with simple geometry and low uncertainties, however, would be extremely valuable. Such a capability would have a transformative impact on many research areas including nuclear data validation, analytical methods validation, dosimetry, reactor kinetics, and materials. The need for a new plutonium assembly is not new: it has been in the DOE Nuclear Criticality Safety Program (NCSP) Mission and Vision for over 10 years and was also the top priority established at the 2022 National Criticality Experiments Research Center (NCERC) Futures meeting. This work will discuss how such a new capability would help ensure that the US retains international leadership in plutonium research. Last, a brief overview of Lilith, a project aimed to design a new enduring plutonium assembly for operation at the NCERC will be given.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Machine Learning based Correlation of the Mechanical Properties of Sub-sized and Standard-sized Specimens

Mechanical testing with sub-sized specimens is essential in the nuclear industry, offering the ability to conduct tests in confined spaces with lower irradiation and expediting material qualification. However, smaller specimens exhibit different material behavior across scales, a phenomenon known as the "specimen size effect". In this study, we compiled over 1,000 tensile testing records, covering 54 parameters such as material type, composition, manufacturing details, irradiation conditions, specimen dimensions, and tensile properties through a comprehensive literature review. We focus on correlating sub-sized and standard specimens’ tensile mechanical properties on SS316 alloy, which has the most extensive dataset available. We explore ML-based models and uncertainty quantification for tensile properties, analyze key factors influencing these properties, and compare the effectiveness of ML models with existing analytical methods in addressing the specimen size effect.

tensile properties↗

DOE OSTI2024

A317_Fast and Thermal Neutron Spectrum Dosimetry Measurements in the Advanced Test Reactor large-B and small-I Positions Following the Sixth Core Internals Change-out

The Advanced Test Reactor (ATR) has a wide variety of irradiation positions that have had experiments that were developed by users from around the world. Most experiment irradiations rely on thoroughly benchmarked numerical models. However, some irradiation positions in ATR are not as well- characterized and are complicated by spectral perturbations from control cylinder orientation. The “small I” and “large-B” irradiation positions are located nearby control cylinders and suffer from these flux perturbations from control cylinder orientations that change during an irradiation cycle to maintain the desired core power distribution. Models predicted that these types of position would exhibit both spectral shifts and amplitude changes in neutron flux, but few measurements have been conducted to benchmark these predictions. Recently, requalification testing was performed to confirm the operational readiness of the ATR following the completion of the Core Internals Change-out (CIC). These tests provided a unique opportunity to validate the analytical methods used to simulate the ATR because nearly all components in the reactor were in a clean as-built state, significantly reducing modelling uncertainties. One subset of the testing included characterization of the fast and thermal neutron flux in the “small-I” and “large-B” positions using silver, cobalt, and nickel neutron dosimetry. In contrast to typical irradiation cycles, the control cylinders were held in position during the post-CIC nuclear testing. The specific activity of these dosimeter wires was measured following two low-power tests with different control cylinder positions.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2026

Determining the Solubility Behavior of Kogarkoite in Simulated Nuclear Waste

Kogarkoite (Na 3 FSO 4 ) is a sparingly soluble fluoride–sulfate double salt that has been identified in high level nuclear waste sludge at the Hanford Site and, more recently, in sludge batch compilation samples at the Savannah River Site (SRS). Due to its complex dissolution behavior, which exhibits an inverse dependence on sodium ion activity, the presence of this mineral poses significant challenges to waste retrieval and processing. Incomplete dissolution during sludge washing can lead to the retention of fluoride and sulfate in the high-level waste feed, potentially causing the formation of corrosive, immiscible molten salt layers, known as "glass gall,” in vitrification melters. Current efforts to optimize flowsheet parameters and wash-water volumes are hindered by the absence of a commercially available, certified reference material, which prevents the accurate calibration of analytical methods and the verification of dissolution kinetics. To address this critical gap, this research focuses on the laboratory synthesis of pure Kogarkoite to serve as a standard for comprehensive solubility and washing performance testing. A coupled synthesis and simulant campaign was executed using an evaporative crystallization protocol designed to replicate the dynamic concentration effects observed in tank farm operations. Thirteen simulant matrices were prepared by dissolving systematically varied ratios of sodium fluoride (NaF) and sodium sulfate (Na 2 SO 4 ) in deionized water under three distinct caustic regimes: 0.0 g (control), 4.0 g (~1 M), and 12.0 g (~3 M) sodium hydroxide (NaOH). While thermodynamic equilibrium models suggest that high-caustic environments should favor the stability of the double salt7, results from this evaporative study at 25 0 C revealed a distinct kinetic divergence. Simulants with high hydroxide loading predominantly yielded large, blocky crystals of sodium sulfate decahydrate (Na 2 SO 4 .10H 2 O). Successful synthesis of pure Kogarkoite was achieved exclusively in specific NaOH-free compositional windows, where the precipitate manifested as fine, opaque granular aggregates. Ion chromatography (IC) analysis confirmed phase purity through the simultaneous stoichiometric depletion of both fluoride and sulfate from the supernatant. This successful synthesis establishes a reproducible route to generate bulk Kogarkoite, enabling the subsequent phase of quantitative dissolution testing using inhibited water to optimize sludge-batch assembly.

Sarker, Md Sharif [Florida International Univ. (FI↗

DOE OSTI2020

Device and system for selective ionization and analyte detection and method of using the same

Disclosed herein are embodiments of a system for selectively ionizing samples that may comprise a plurality of different analytes that are not normally detectable using the same ionization technique. The disclosed system comprises a unique split flow tube that can be coupled with a plurality of ionization sources to facilitate using different ionization techniques for the same sample. Also disclosed herein are embodiments of a method for determining the presence of analytes in a sample, wherein the number and type of detectable analytes that can be identified is increased and sensitivity and selectivity are not sacrificed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Device and system for selective ionization and analyte detection and method of using the same

Denis, Elizabeth↗

DOE OSTI2020

Rossi-alpha Uncertainty Quantification by Analytic, Bootstrap, and Sample Methods to Inform Fitting Best Practices

The prompt neutron period (the negative reciprocal of the prompt neutron decay constant) can be estimated using the Rossi-alpha technique that is predicated on fitting Rossi alpha histograms and of interest in nuclear criticality safety and nonproliferation. The histograms are traditionally fit with a one-exponential model; however, recent work has proposed a two-exponential model to account for reflector induced phenomenon. Until recently, the uncertainty quantification for either model was inadequate (inaccurate and demanded large measurement times). Measurement uncertainty quantification by sample and analytic methods was developed and validated in Ref. The purpose of this transaction is to (i) validate a new bootstrap method by comparing bin-by-bin error bar estimates and (ii) demonstrate how to choose bin widths and reset times to optimize precision and accuracy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Characterizing Reactor Operations from Realistic Simulated Environmental Samples: Combining High-Performance Computing and Data Analytics

Environmental sampling is a common technique employed by inspectors and facility operators in nuclear safeguards, proliferation detection, and process monitoring contexts. Interpreting measurements performed on samples or collections of samples and ensuring the information extracted is accurate and precise is difficult. To date, these analyses have relied on simulated data to enable systematic studies; however, these models are inherently limited by the fidelity of the models and the implicit spatial averaging of isotopic composition or other signatures of interest. To advance this capability, we have refined the spatial discretization and expanded the range of physics in the simulation codes we use to perform reactor simulations and depletion calculations. This allows us to generate data that are more representative of real environmental samples, especially for the length scale of the isotopic composition and associated variation. Accordingly, these new data allow a more realistic assessment of traditional and new data analytic analysis methods. Here we present motivation for developing reactor simulations using high-performance computing methods and resources, impacts of these new simulations on our assessment of data analysis and interpretation methods, and initial results of developing and systematically testing data analytic methods designed to overcome the challenges expected of real-world samples. We also quantify the performance of these analyses using defensible statistical methods.

Dayman, Ken J.↗

DOE OSTI2020

Results of the International Energy Agency Bioenergy Round Robin on the Analysis of Heteroatoms in Biomass Liquefaction Oils

A round robin study evaluating the analysis of biomass liquefaction oils (BLOs) from fast pyrolysis and hydrothermal liquefaction (HTL) was performed, involving fifteen laboratories in seven countries in order to assess the current status of analytical techniques for the determination of nitrogen, sulfur, and chlorine content in BLOs and evaluate potential differences in origin (i.e. fast pyrolysis vs HTL). The BLOs were produced from a range of feedstocks including pine, mixed softwoods, forest residues, micro-algae, miscanthus, and wheat straw to cover a variety in nitrogen, sulfur, and chlorine content and speciation. Nine samples were distributed, comprised of eight separate BLOs and one blind duplicate produced by five producers. The samples were analyzed for water, carbon, hydrogen, nitrogen, sulfur, and chlorine content. No analytical test method was mandated; laboratories were encouraged to utilize whichever method they determined would be most applicable, relying on the existing body of BLO literature as a guide. The results of this round robin study are presented in this paper. The results of the carbon, hydrogen, and water measurements as reference analyses had relative standard deviations (2.9%, 3.5, and 5.6%, respectively) that were comparable to those found in past round robin studies on fast pyrolysis bio-oil. The analysis of nitrogen, sulfur, and chlorine showed higher levels of variability. Laboratories mostly chose the same method for water, carbon, hydrogen and nitrogen determination whereas there were a variety of methods chosen for sulfur and chlorine determination. The results suggest that specific analytical methods for the determination of nitrogen, sulfur, and chlorine should be further refined to ensure reproducible and accurate results for BLO analysis due to their importance on emissions, material selection, and catalyst activity.

Biomass, IEA Bioenergy, pyrolysis, hydrothermal li↗

DOE OSTI2022

Systems and methods for data analytics for virtual energy audits and value capture assessment of buildings

A system may provide virtual energy audits of one or more target buildings. The system may retrieve weather data and energy usage data specific to a given target building from a weather server and a utility server, respectively. The system may store predefined building characteristics corresponding to the given target building in local memory. Based on the weather data, energy usage data, and/or predefined building characteristics, the system may generate one or more building markers that characterize the energy usage and efficiency of the given target building. Building efficiency diagnostics and energy conservation prognostics may be generated based on the building markers and may be sent by the system to be displayed via a user interface of a client device. The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

2.3.4.100 - Lignin Utilization

Lignin depolymerization to aromatic monomers is a primary route for myriad lignin valorization strategies. To date, there are many strategies able to cleave aryl-ether linkages in lignin, but the lignin polymer, in both its native and processed forms, contains a substantial fraction of refractory carbon-carbon linkages between aromatic units, which typically limits aromatic monomer yields to -30-40 wt% or lower, depending on the feedstock. To that end, the Lignin Utilization (LigU) project addresses the critical challenge of lignin depolymerization catalysis with emphasis on C-C bond cleavage. Being able to achieve cost-effective C-C bond catalysis in lignin depolymerization would enable a substantial increase in accessible aromatic monomer yields from lignin. Among the catalysis strategies that have been investigated in the LigU project, we have made substantial progress in the use of autoxidation catalysis, inspired by the industrial conversion of p-xylene to terephthalic acid, for C-C bond cleavage in lignin. Using multiple substrates, we have demonstrated that autoxidation catalysis can produce mixtures of bio-available aromatic monomers for conversion to exemplary bioproducts, such as cis,cis-muconic acid, in collaboration with the Biological Lignin Valorization project. Prior to FY23, the LigU project also included lignin analytical chemistry method development, lignin analytics for BETO-funded projects, and model compound syntheses, which will also be presented.

BIOMASS FUELS↗

DOE OSTI2025

Chelation ion chromatography as an automated, and cost-effective analytical technique for REE determination: method development and applications

Rare earth elements (REEs), as critical minerals, have important uses in modern energy and technologies, yet are vulnerable to potential supply chain disruptions. To establish domestic REE supply chain, efficient REE detection methods for resource characterization and mineral processing will be needed to accelerate innovations for domestic REE recovery. This study developed a rapid, novel, and cost-effective for REE detection method using ion chromatography (IC) for aqueous samples. Various REE-targeted eluent gradients and post-column agent compositions were tested on the chelation ion chromatography (CIC) with UV-vis detector for optimal separation and quantification of REEs within approximately 20 min. The single-channel pump to deliver the post-column solution to UV-vis detector was replaced with a 4-channel gradient pump, to increase operation and maintenance efficiencies. After method optimization, resulting calibration curves for more than ten REEs achieved high coefficients of determination (R2>0.999) and low relatively standard deviations (below 3.24%), demonstrating sub-ppm level detection limits (0.0897 to 0.1149 mg/L). The reliability of the CIC method was validated through comparison with inductively coupled plasma mass spectrometry (ICP-MS), showing strong agreement in REE recovery from certified standards. The impact of metal ions and salts on REE recovery using CIC was also systematically investigated. CIC consistently exhibited reliable performance in the presence of salt solutions such as NaCl and Na₂SO₄ (up to 10,000 mg/L). Our study also found the presence of high concentrations of Al ions (at 10,000 mg/L) significantly influenced REE determination, and elevated concentrations of Ca ions affected the recovery of specific REEs, including La, Ce, and Pr. The CIC method was further tested on REE-containing eluents from solvent extraction tests out of fly ash leachates. REE detection from these real processing fluids were reported to achieve 90% to 100% recovery rate from our IC method, compared to ICP-MS results. This study underscores the potential of CIC as a reliable and efficient alternative for REE determination in complex matrices. It also highlights the importance of minimizing select interfering metal ions in solutions to ensure accurate results. The REE CIC method presents a promising, low-maintenance, salt-tolerant, and cost-effective alternative to traditional analytical methods for REE analysis.

detection of rare earth elements (REE)↗

DOE OSTI2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: exploring the halo occupation distribution model for emission line galaxies

We study the modelling of the halo occupation distribution (HOD) for the eBOSS DR16 emission line galaxies (ELGs). Motivated by previous theoretical and observational studies, we consider different physical effects that can change how ELGs populate haloes. We explore the shape of the average HOD, the fraction of satellite galaxies, their probability distribution function (PDF), and their density and velocity profiles. Our baseline HOD shape was fitted to a semi-analytical model of galaxy formation and evolution, with a decaying occupation of central ELGs at high halo masses. We consider Poisson and sub/super-Poissonian PDFs for satellite assignment. We model both Navarro–Frenk–White and particle profiles for satellite positions, also allowing for decreased concentrations. We model velocities with the virial theorem and particle velocity distributions. Additionally, we introduce a velocity bias and a net infall velocity. We study how these choices impact the clustering statistics while keeping the number density and bias fixed to that from eBOSS ELGs. The projected correlation function, w p , captures most of the effects from the PDF and satellites profile. The quadrupole, ξ 2 , captures most of the effects coming from the velocity profile. We find that the impact of the mean HOD shape is subdominant relative to the rest of choices. Here, we fit the clustering of the eBOSS DR16 ELG data under different combinations of the above assumptions. The catalogues presented here have been analysed in companion papers, showing that eBOSS RSD+BAO measurements are insensitive to the details of galaxy physics considered here. These catalogues are made publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗