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NASA NTRS2026

Methods for Validation of Random Uncertainty Estimates and Their Applications to Ozone Profiles From Limb-Viewing Satellite Instruments

For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or the presence of unknown error sources. This paper presents an overview of the methods used for the validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed. This overview evaluates these methods in application to ozone profile measurements from limb and occultation satellite instruments and provides practical illustrations of random uncertainty validation.

Viktoria F Sofieva↗

DOE OSTI2026

Theory of the thermionic current beyond the traditional space charge limit enabled by trapped ions in the virtual cathode

We show that trapped ions in virtual cathode potential wells can raise the transmitted current of emitted electrons into a plasma much closer to the full emission than is predicted by cathode sheath theories without trapped ions. The transmitted current is controlled by the well barrier voltage, which must adjust to balance the creation of low-energy ions within the well, and their loss. Our model considers the case of a plasma-facing cathode where trapped ions are created passively via charge-exchange collisions and lost passively via thermal leakage over the well. We quantify these rates and estimate the current in terms of system parameters for thermionic emission into a plasma with several cathode geometries. A general prediction is that the current as a function of emitted flux does not saturate at the traditional space charge limit (the onset of a well) but can reach far higher values until the trapped ion balance breaks down, causing instability. The maximum stable current depends on parameters but in principle can be arbitrarily high if active techniques are used to manipulate the trapped ion balance. We conclude that major improvements in plasma technologies with hot cathodes might be achieved by optimizing the current enhancement enabled by trapped ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1993

Estimating the Size of Huffman Code Preambles

Data compression via block-adaptive Huffman coding is considered. The compressor consecutively processes blocks of N data symbols, estimates source statistics by computing the relative frequencies of each source symbol in the block, and then synthesizes a Huffman code based on these estimates. In order to let the decompressor know which Huffman code is being used, the compressor must begin the transmission of each compressed block with a short preamble or header file. This file is an encoding of the list n = (n 1 , n 2 ....,n m ), where n i is the length of the Hufffman codeword associated with the ith source symbol. A simple method of doing this encoding is to individually encode each n i into a fixed-length binary word of length log 2 l, where l is an a priori upper bound on the codeword length. This method produces a maximum preamble length of mlog 2 l bits. The object is to show that, in most cases, no substantially shorter header of any kind is possible.

R J McEliece↗

DOE OSTI2026

Dark Energy Survey year 6 results: Magnification modeling and its impact on galaxy clustering and galaxy-galaxy lensing cosmology

Gravitational lensing magnification alters the observed spatial distribution of galaxies and must be accounted for to prevent biases in cosmological probes of the large-scale structure. We investigate its effects on the Dark Energy Survey Year 6 galaxy clustering and galaxy-galaxy lensing analyses using the fiducial lens (position tracer) sample M ag L im++. Magnification bias is parameterized by a coefficient that describes the response of the number of selected objects per unlensed area element to a change in the lensing convergence. We quantify this coefficient using the BALROG synthetic source injection catalog to account for the complexity of the selection function, and compare these results with simplified estimates. The resulting values of the magnification coefficients for each redshift bin are [3.16 ± 0.08, 2.76 ± 0.21, 4.09 ± 0.15, 4.42 ± 0.16, 4.90 ± 0.29, 4.83 ± 0.25]. Relative to Year 3, this analysis provides more precise and accurate magnification bias estimates through a larger BALROG area and reweighting to better match the data properties. Here, the cosmological results are robust when tested against various magnification parameter prior choices and also when adding cross-clustering between lens redshift bins. Neglecting magnification, however, introduces significant systematic shifts: relative to the fiducial analysis with Gaussian priors centered on the BALROG -derived estimates, we observe shifts of 1.37σ in S 8 and -0.84σ in Ω m (with cosmic shear included: -0.61σ in S 8 and -0.71σ in Ω m ), in agreement with findings from simulated data, demonstrating that magnification must be modeled to avoid biases. Freeing the magnification bias in lens bin 2 leads to unphysical negative values, further justifying its exclusion from the fiducial Year 6 analysis.

Cosmological parameters↗

DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗

NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗

DOE OSTI2024

Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols

Abstract Uncertainty in desert dust composition poses a big challenge to understanding Earth’s climate across different epochs. Of particular concern is hematite, an iron-oxide mineral dominating the solar absorption by dust particles, for which current estimates of absorption capacity vary by over two orders of magnitude. Here, we show that laboratory measurements of dust composition, absorption, and scattering provide valuable constraints on the absorption potential of hematite, substantially narrowing its range of plausible values. The success of this constraint is supported by results from an atmospheric transport model compared with station-based measurements. Additionally, we identify substantial bias in simulating hematite abundance in dust aerosols with current soil mineralogy descriptions, underscoring the necessity for improved data sources. Encouragingly, the next-generation imaging spectroscopy remote sensing data hold promise for capturing the spatial variability of hematite. These insights have implications for enhancing dust modeling, thus contributing to efforts in climate change mitigation and adaptation.

Environmental Sciences & Ecology↗

DOE OSTI2025

A quality-agnostic combinatoric cost estimation model for large-format directed energy deposition metal additive manufacturing

Directed energy deposition (DED) additive manufacturing (AM) processes are amenable to synergistic combination into multi-process AM systems due to similar requirements for automation and energy sources. This work analyzes the economic performance of such DED AM systems from a quality-agnostic combinatoric standpoint with a model that calculates lowest-cost system combinations based on part geometry and process performance metrics. Common DED AM systems research focuses on a single process and does not consider the process, system, and application in the context of all possible system combinations (e.g., the combined set of process selection(s), motion system(s), and process hardware), leading to limited applicability of the resulting DED AM systems to cost-sensitive components such as those found in energy generation applications. The model developed herein incorporates the capital, material, and energy costs associated with DED AM system combinations into a predictive tool for estimating part and system cost, the output of which is intended to guide deployment of finite research and development resources towards DED AM system combinations with the lowest costs and greatest likelihood of economic impact. The DED AM systems identified by this framework may enable domestic production of the large conventionally cast and forged components necessary for energy generation.

Shanafield, Alexandra [ORNL]↗

DOE OSTI2025

Photosynthetic capacity is reduced by warming but unaffected by elevated CO2 in seedlings of five boreal tree species

Abstract Increasing atmospheric CO2 concentrations fuel global warming, with boreal regions warming at a faster rate than many other areas. Boreal forests are an important component of the global carbon cycle, yet we have little data on photosynthetic responses of boreal trees to elevated CO2 (EC) and warming. We grew seedlings of 5 widespread North American boreal tree species (from Betula, Larix, Picea, and Pinus) under current (410 ppm) or elevated (750 ppm) CO2 and either ambient (+0 °C) or increased (+4 °C or +8 °C) temperature, then measured photosynthetic traits over a range of leaf temperatures. Our results were generally consistent across species: photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax, and electron transport, Jmax) was unaffected by EC but decreased under +8 °C warming. Accordingly, net photosynthesis measured at the growth CO2 concentration (Agrowth) was reduced under warming and increased under EC. The thermal optimum for Agrowth (ToptA) increased by ∼1.8 °C with EC but increased with warming in only two species. In contrast, the activation energies and thermal optima for Vcmax and Jmax, which are used to estimate photosynthesis in Earth System Models, were unaffected by growth environment. There were a few interactions between growth, CO2, and warming. These results suggest increased photosynthesis of widespread boreal tree species under EC may be offset by future reductions in photosynthetic capacity related to warming. We also show that the temperature sensitivities of parameters used to estimate global photosynthesis in large-scale models are generally unaffected by simulated climate change in these species.

Plant Sciences↗

NASA NTRSDate not supplied

Lunar Nuclear Reactor Neutron Fluence and Gamma Dose Estimates

NASA aims to deploy a fission reactor on the lunar surface by 2030 to generate 100 kWe of power for at least 10 years without maintenance or refueling. To assess system and subsystem survivability according to those requirements, a model of the reactor pallet was created in the radiation transport code MCNP to determine the neutron fluences and gamma doses that the materials and systems would be exposed to. The model was based on the 2025 government design. The model indicated that the control drum motors, Brayton cycle engines, and pipes and valves would be exposed to neutron fluences of 1×10 15 – 1×10 16 n/cm 2 per year and gamma doses ranging from 10 to 500 Mrad per year. The electronics box housing the instrumentation and control elements is expected to receive 1×10 14 – 1×10 15 n/cm 2 per year and gamma doses in the range of 1–5 Mrad per year. These doses necessitate that the control drum motors, Brayton cycle engines, valve components, control electronics, and other components be radiation-hardened (>10 16 n/cm 2 and >10 Mrad gamma dose) or additionally shielded to survive the 10-year lunar mission.

Radiation-Hardened Materials↗

NASA NTRS2026

Global Model Estimates of Atmospheric Al, Ca, Fe, Si, and Ti from Dust and Non-Dust Aerosols Informed by EMIT Surface Mineralogy and Evaluated Against Observations

Atmospheric deposition of micro-nutrients like Fe has been shown to be important for ocean biogeochemistry. The largest source of atmospheric Fe and other elements (e.g., Ca, Al, Si, and Ti) is desert dust, although there are significant non-dust sources in some regions. However, past estimates of these elements have been substantially uncertain due to limited information about the composition of the desert source regions. Here we use elemental distributions estimated from new Earth Surface Mineral Dust Source Investigation (EMIT) observations, which provide mineralogical composition at the surface of the Earth based on imaging spectroscopy measurements from the International Space Station. We add in other sources of these elements (anthropogenic and natural) and compare to a compilation of available surface concentration data from stations over land and from shipborne observations. Our results suggest that the modeled distribution is similar to available observations, but discrepancies still exist in both natural desert dust regions as well as regions dominated by anthropogenic sources. Global budgets for the elements Ca, Al, Fe, Si, and Ti suggest that desert dust remains the dominant source for these elements but anthropogenic or volcanic sources are also important for these elements. Changes in elemental distributions since preindustrial times were also estimated.

aerosols↗

DOE OSTI2026

Estimating Return on Investment for Energy Technical Assistance Programs

The U.S. Department of Energy's Office of State and Community Energy Programs engaged the National Laboratory of the Rockies to assess the return on investment (ROI) of technical assistance (TA) programs that support state, local, and Tribal energy planning. Although TA delivers value through capacity building, stakeholder engagement, and knowledge transfer, these benefits are often intangible and challenging to monetize. This study reviews existing ROI frameworks and synthesizes the most relevant elements into a hybrid approach tailored to energy TA programs. The proposed framework integrates monetary and non-monetary outcomes through early logic model development, baseline data collection, and the use of proxies for intangible benefits. As a case study, this paper applies this approach to the Communities Local Energy Action Program (Communities LEAP), demonstrating how ROI can inform program design, data strategy, and performance assessment. Findings underscore that ROI should be applied selectively and planned from the outset to ensure data alignment and attribution accuracy. The framework offers TA practitioners a structured approach that can be leveraged for future programs to evaluate and communicate the multifaceted value of TA investments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRSDate not supplied

Battery Evaluation Profiles for X-57 and Future Urban Electric Aircraft

Battery energy density is one of the most critical design parameters for sizing all-electric aircraft, however it’s easily overestimated. Establishing the effective, usable energy density is confused by varying degrees of margin needed to account for structural and thermal management between different cell chemistry and pack designs. Therefore, a better methodology is needed to fairly compare emerging battery technologies for electric aircraft. Currently, there is a loss of critical information when vehicle trade studies are performed using “nominal” published cell-level performance metrics. Aircraft power demands rarely match these nominal power profiles, and aircraft designers lack the ability to accurately simulate the battery performance and temperature off-nominally unless the battery chemistry is well established. Conversely, battery suppliers have no generalized reference cases to publish more realistic performance metrics. This can lead to poor assumptions, such as aircraft studies assuming a fixed discharge efficiency of a battery, when in reality the usable energy in a pack is dependent on the power and thermal profile. Information needed to properly assess weight penalties for thermal management is also typically poorly characterized when assessing candidate batteries. This paper serves to better inform battery development, and similarly, provide aircraft designers with more realistic assumptions for applying knockdown margins in their designs. Detailed power and thermal performance estimates are provided, which provide a starting point for sizing power and thermal budgets using experimentally derived battery models. Results show that the X-57 battery-to-shaft efficiency is 77.3% for a particular optimized mission. Considering a 25% reserve on the battery capacity, this means that only roughly half of the original 55.3kWh ‘nominal’ pack energy can be converted to useful work during a mission. Further estimates on a clean-sheet VTOL optimization show an average 82.7% battery-to-shaft efficiency, using 98% peak efficiency inverters and 97.4% peak efficiency motors. Although higher battery efficiencies are possible, the resulting weight penalty negates improvement in vehicle performance. These trade-offs and resulting power profiles are provided as a starting point to better assess future battery designs.

Battery↗

DOE OSTI2026

Proteome-wide analysis of protein stability in Escherichia coli under acid stress

Knowledge of protein acid sensitivity remains sparse and is largely derived from low-throughput, enzyme-specific assays. We used a scalable framework to map acid stability across the Escherichia coli proteome to assess the acid stability of 1,675 unique proteins, estimating pH 50 values for over 90% of them. The parameter pH50 was defined as the pH value at which only 50% of the initial protein remains in solution following acid treatment. Proteome-wide pH 50 values ranged from 2.28 to 6.33 (median 5.11). Approximately 9% of detected proteins remained stable across all tested pH conditions. Our results align with published data and the assay of citrate synthase (GltA) performed here. Protein acid stability differed significantly by subcellular localization: periplasmic proteins were relatively more abundant in the acid-stable group, cytoplasmic proteins were abundant at pH 50 values 4.5–5.5, and inner membrane proteins at higher pH 50 between 5.5 and 6.0. Outer membrane proteins were too few to draw strong conclusions regarding enrichment within specific pH 50 groups. Notably, the periplasmic binding protein of the molybdate ABC transporter (ModA), was enriched after incubation at low pH. Estimated pH 50 values showed no correlation with protein isoelectric point and molecular weight. Together, this work provides the first proteome-wide map of protein acid stability and establishes a general framework for studying different chemical stressors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI1969

Application of Nuclear Technology to Art Identification Problems: First Annual Report

Throughout the centuries, as man has become increasingly affluent, his interest in antiquities and objects of art, and the price he has been willing to pay for them, has increased. Inevitably, one result has been to attract the unscrupulous to the production and sale of counterfeit paintings, sculptures, and other works of art. The skill of forgers varies but often is highly sophisticated. However, dealers, museum curators, and other experts have also become more skillful in the detection of forgeries. Scientific tools of increasing sensitivity and sophistication have gradually been applied to the examination of the materials of art and archaeology. Such tools frequently support and render more reliable the judgment of the experts. The quality of forgeries may improve further as their makers become acquainted with the new methods of detection and in turn learn to circumvent or confound the methods. Therefore, the development of still more advanced methods of examination has great utility in the art world. This is particularly true if the ultimate effect is to make circumvention of the methods of examination so costly that the economic incentives for producing forgeries may be substantially reduced, if not eliminated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

A Tutorial on Bayesian analysis of linear shock compression data

Gas gun and other shock compression experiments often produce shock wave velocity measurements that are linearly associated with particle velocity. Traditionally, this empirical relationship is quantified with a single Hugoniot curve that is estimated using least squares regression. However, for downstream modeling and simulation tasks, it is often more useful to have multiple Hugoniot curves in the pressure–volume plane that are consistent with the data. We employ Bayesian uncertainty quantification methods as a framework for propagating measurement uncertainty through to model parameters and predictions. Specifically, this Tutorial shows how to sample multiple Hugoniot curves in the pressure–volume plane that are consistent with the shock wave-particle velocity measurements in a two-step Bayesian approach. First, we obtain an analytical expression for the posterior distribution of the linear model parameters using Bayesian linear regression. Second, we propagate samples from the posterior distribution through the Rankine–Hugoniot equations to yield Hugoniot curves in the pressure–volume plane. The procedure is demonstrated with publicly available data on argon, copper, and nickel, and compared against bootstrapping and linear regression. The Bayesian procedure is shown to be interpretable, computationally inexpensive, and less sensitive than an alternative bootstrapping approach to the removal of the point in the copper dataset that has the largest particle velocity. As a Tutorial on Bayesian methodology for the shock compression community, we provide several derivations and explanations that make this paper self-contained, and make all code and data available at github.com/llnl/BALSCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Dielectric Spectroscopy Cable NDE for Unequal Aging Over Different Lengths

This Pacific Northwest National Laboratory milestone report assesses the effect of unequal aging over different lengths of cable on dielectric spectroscopy (DS) bulk impedance measurements from the cable end. DS measurements can indicate the remaining useful life of cables; however, most benchmark tests are based on accelerated aging of the entire cable. If only a portion of the cable is exposed to a harsh environment (as is frequently the case), the DS measurement will indicate a significantly less-aged cable than if the entire cable were exposed to a uniform environmental stress. The DS measurement is primarily related to the intrinsic insulation material relative permittivity—typically between 1.5 and 3.0. Insulation aging increases permittivity and, correspondingly, the cable’s overall capacitance measured from the cable end. To judge the health of the overall cable system, operators are primarily interested in the condition of the most severely aged section since that is where a failure is most likely to occur. This can be expressed as the dimensionless relative permittivity of the severely aged section divided by the permittivity of a pristine cable. This relative permittivity is an intrinsic material parameter independent of cable length and can serve as a quantitative indication of the insulation condition. If the relative percentages of cable exposed to a harsh and benign environment are known, the DS measurement of the entire cable length, including both pristine and aged sections, can be compensated to predict the relative permittivity (relative to pristine permittivity) of the most severly aged segment. A test was performed on three 50 ft (16 m) cables with 10%, 50%, and 90% of the cable inside a thermal aging oven. Predictably, the aging effect on the DS response was greatest in the 90% sample, followed by the 50% sample, and then the 10% sample. The more interesting result is the ratio of the aged insulation permittivity to the pristine insulation permittivity. Based on prior work, an end-of-life cable has a value of approximately 1.35. If the relative percentages of pristine and severely aged cable lengths are known, and the total cable-length capacitance of the initial pristine cable and the combined pristine plus aged cable are known, the relative aged permittivity (ratio of aged section permittivity / pristine section permittivity) can be estimated. The relative cable lengths exposed to a harsh environment may be known or estimated based on plant layout drawings or cable reflectometry tests that can locate the oven entry and exit points. The compensation was verified using a lumped-parameter model to predict the aged-segment permittivity e2/e1, where e2 is the aged-segment permittivity and e1 is the pristine-segment permittivity. For all three percentages of aged cable lengths, the ratio was quite similar, as would be expected since all cable segments were exposed to the same aging environment. This ratio of aged permittivity to pristine permittivity can be compared to other damage indicating tests, including elongation at break, to assess cable condition.

ARENA Test Bed↗