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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Flow annealed importance sampling bootstrap meets differentiable particle physics

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

High-Resolution Sampling of a River Plume Front with Uncrewed Underwater and Aerial Vehicles

Sampling fast-propagating oceanic features is inherently challenging and demands versatile instrumentation and innovative strategies. This paper introduces a novel sampling strategy designed to capture such phenomena, exemplified by a river plume front. Our method revolves around modifying the preprogrammed pathway of an uncrewed underwater vehicle (UUV) to dynamically track and three-dimensionally sample the evolution of the front. To enable the UUV to follow the feature, we adapt the use of a drifting gateway buoy to be positioned and trapped at the front’s convergence zone, allowing underway navigation relative to the buoy. In our demonstration, we showcase the effectiveness of this strategy by successfully conducting over 30 crossings of a river plume front within a 6-h window. The UUV sensors allowed a comprehensive assessment of key front characteristics, including density, velocity, and turbulence. Supplemental drone footage contributed to the overall picture and facilitated the transformation of the dataset into a front-following reference frame. This article provides an in-depth description of the deployment strategy and required postcollection data processing, including frontal crossing detection, the assessment of the frontal orientation from drone footage, and defining the plume bottom boundaries using backscatter intensity contours.

autonomous observations↗
DOE OSTI2024

Sampling Rare Events in Aqueous Systems Using Molecular Simulations

Birth of a new distinct phase is a phenomenon encountered in a myriad of processes, and has wide ranging consequences in material processing, biological self-assembly, separations and several other processes. Several phase transitions are nucleation driven. The nucleation events occur over nanosecond timescales and involve hundreds to thousands of molecules. These length and timescales are difficult to access in experiments, thereby making experimental studies of nucleation challenging. On the other hand, molecular simulations sample the nanosecond and nanometer scales making them ideal to study nucleation. However, nucleation is a rare event, meaning that the waiting time to observe one nucleation event is significant. This makes simulation studies of rare events challenging. The project focused on a multi-pronged approach to address such challenges to develop the next generation rare event sampling methods for molecular simulations. The key outcomes of our work include developing more effective methods for sampling rare events, utilizing machine learning to better elucidate nucleation mechanisms, development of software for easy implementation of the methodologies, and applications of the methods to realistic systems to push the method applicability beyond model systems. Overall, this work has enabled pushing the frontiers of molecular simulations to study rare events with a focus on nucleation in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Underground Test Area (UGTA) Sampling Plan for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nevada National Security Site, Nevada, Revision 2

The purpose of this groundwater sampling plan (referred to herein as “the Plan”) is to describe the approach for collecting and analyzing groundwater samples to meet the objectives of the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program’s Underground Test Area (UGTA) Activity. The Plan is focused on the Central and Western Pahute Mesa corrective action units (CAUs 101 and 102), which are currently in the Corrective Action Decision Document (CADD)/Corrective Action Plan (CAP) stage of the Federal Facility Agreement and Consent Order (FFACO) UGTA Strategy. The remaining UGTA CAUs are in the final stage of the UGTA strategy (Closure Report [CR] stage), and monitoring requirements for these CAUs are described in the Federal Facility Agreement and Consent Order Nevada National Security Site Use Restriction Management Plan (DOE/EMNV, 2024a). The Plan supersedes the Nevada National Security Site Integrated Groundwater Sampling Plan and its ensuing Record of Technical Change (ROTC) (DOE/EMNV, 2019b).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EMP, Attachment 1: Sampling and Analysis Plan (Rev.1)

This Sampling and Analysis Plan (SAP) is written for the Environmental Radiation Task activities related to radioactive air emissions (stack) monitoring and environmental radiological ambient air surveillance of Pacific Northwest National Laboratory (PNNL) operations at the PNNL-Richland campus and PNNL-Sequim campus. PNNL is a U.S. Department of Energy Office of Science laboratory in Richland, Washington. This plan is an attachment to PNNL’s Environmental Radiological Air Monitoring Plan (EMP) (PNNL-20919) and addresses a discrete, vital subject area that is subject to revision independent of the main text of the EMP document. This SAP provides the requirements for planning sampling events and the requirements imposed on the services provided by the analytical laboratory to the PNNL Environmental Radiation Task.

40 CFR 61 Subpart H↗
DOE OSTI2025

147 Nd Quantification Using HSCCC-Purified Samples

Quantifying the fission product 147 Nd in nuclear debris samples is an important component of post-detonation nuclear forensics. The most accurate quantifications are obtained when Nd is purified from all other fission products, actinides, activation products, and environmental matrix contained within the debris. In this study, a recently developed method for Nd purification was tested, purifying 147 Nd from solutions of mixed fission products using high-speed counter-current chromatography (HSCCC). Importantly, the new method allowed for faster elution of Nd from the column as compared with established high performance liquid chromatography (HPLC) methods, and resulted in accurate/precise 147 Nd quantification by gamma-ray spectrometry. While the up-front equipment costs associated with HSCCC may be higher, its operational costs are on par with those of HPLC (solvents, extractants, power). Gas-flow proportional beta decay counting revealed contamination from the nearest neighbor lanthanide 143 Pr (a gamma-silent radioisotope) in the HSCCC-purified samples, but the activity contribution from 147 Nd could still be quantified. Remarkably consistent elution profiles were observed for the HSCCC method, spanning rare earth element (REE) loadings of more than 10 orders of magnitude (tracer to mmol quantities). In conclusion, the reliability and speed of the new method suggest utility for the rapid separation and quantification of 147 Nd in unknown samples.

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

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling

Here, we investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sublinear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of π-system delocalization, we observe the anticipated linear scaling and an explicit cost crossover with standard variational Monte Carlo.

Bumann, Sonja [University of California, Berkeley,↗
DOE OSTI2024

A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning

A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a dataset constructed via ab initio molecular dynamics (AIMD) simulations, supplemented by adaptive biasing forces, to enrich the sampling of catalytically relevant configurations. Our results reveal that by incorporating collective variables that capture the behavior of the reactant molecule, as well as additional frames that describe the dynamic response of the catalytic surface, it is possible to enhance considerably the accuracy of predicted energies and forces. By employing enhanced sampling schemes in the refinement of the MLIP, we systematically explore the potential energy surface, leading to a refined MLIP capable of predicting DFT-level energies and forces and replicating key geometric characteristics of the catalytic system. The resulting free energy landscapes at several temperatures provide a detailed view of the thermodynamics and dynamics of methane activation. Specifically, as methane approaches and dissociates on the catalytic surface, the process involves the dynamic interplay of CH 4 and the Ni catalyst that includes both enthalpic and entropic contributions. The progression towards the transition state involves an CH 4 moiety that is increasingly restrained in its ability to rotate or translate, while the stage following the transition state is characterized by a notable rise of the Ni atom that interacts with the cleaved C–H bond. Furthermore, this leads to an increase in the mobility of the adsorbed species, a feature that becomes more pronounced at higher temperatures.

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

Computing Reaction Kinetics with MC-PDFT–OPESf: Combining Multireference Electronic Structure Theory and Enhanced Sampling

Accurate rate constants are crucial for understanding and optimizing catalytic reactions mediated by enzymes, metalloproteins, and heterogeneous catalysts. These systems frequently present a dual computational challenge. Multiconfigurational reaction sites require multireference techniques for the accurate treatment of the electronic structure, and high activation barriers prevent efficient sampling of unbiased reactive transitions. In this work, we combine multiconfiguration pair-density functional theory (MC-PDFT) as an accurate and efficient multireference electronic structure method with on-the-fly probability-enhanced sampling flooding (OPESf) as an enhanced sampling method capable of accelerating reactive transitions. We demonstrate the approach on the Diels–Alder [4+2] cycloaddition between cis-butadiene and ethene as a reaction characterized by a large activation barrier and multireference character. MC-PDFT–OPESf provides reaction rates in agreement with experiments at a fraction of the computational cost required by conventional unbiased ab initio calculations. Here, we propose MC-PDFT–OPESf as an efficient approach for computing kinetics in strongly correlated molecular systems.

Chemical calculations↗
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
DOE OSTI2025

Abundant ammonia and nitrogen-rich soluble organic matter in samples from asteroid (101955) Bennu

Organic matter in meteorites reveals clues about early Solar System chemistry and the origin of molecules important to life, but terrestrial exposure complicates interpretation. Samples returned from the B-type asteroid Bennu by the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer mission enabled us to study pristine carbonaceous astromaterial without uncontrolled exposure to Earth’s biosphere. Here we show that Bennu samples are volatile rich, with more carbon, nitrogen and ammonia than samples from asteroid Ryugu and most meteorites. Nitrogen-15 isotopic enrichments indicate that ammonia and other N-containing soluble molecules formed in a cold molecular cloud or the outer protoplanetary disk. We detected amino acids (including 14 of the 20 used in terrestrial biology), amines, formaldehyde, carboxylic acids, polycyclic aromatic hydrocarbons and N-heterocycles (including all five nucleobases found in DNA and RNA), along with ~10,000 N-bearing chemical species. All chiral non-protein amino acids were racemic or nearly so, implying that terrestrial life’s left-handed chirality may not be due to bias in prebiotic molecules delivered by impacts. The relative abundances of amino acids and other soluble organics suggest formation and alteration by low-temperature reactions, possibly in NH 3 -rich fluids. Bennu’s parent asteroid developed in or accreted ices from a reservoir in the outer Solar System where ammonia ice was stable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Mineralogical evidence for hydrothermal alteration of Bennu samples

Samples of asteroid (101955) Bennu delivered by the OSIRIS-REx mission offer the opportunity to study pristine planetary materials unchanged by exposure to the terrestrial environment. Here we use a combination of X-ray diffraction and various electron microscopy techniques to explore the detailed mineralogy of Bennu samples and determine the alteration history of the planetesimal protolith from which they originated. The samples consist largely of hydrated sheet-silicate minerals, namely nanoscale serpentine and saponite of varied grain size, which are decorated with micro- to nanoscale Fe-sulfides, magnetite and carbonates. We observe sheet silicates parallel and normal to sulfide surfaces and as inclusions in sulfides; sulfur-rich veins transecting the sheet-silicate matrix; zoned carbonates and phosphates and sulfide and magnetite grains exhibiting embayment. The mineralogical evidence indicates alteration of accreted minerals by a fluid that evolved with time, leading to etching, dissolution and reprecipitation. Sulfide compositions indicate alteration at ~25 °C, similar to conditions inferred for asteroid (162173) Ryugu and Ivuna-type (CI) chondrite meteorites. The fluid probably evolved from neutral to alkaline, culminating with the precipitation of highly soluble salts. We conclude that Bennu’s protolith comprised mainly nanometre to micrometre silicates, with fewer chondrules and calcium–aluminium-rich inclusions than those of most chondrite groups.

Zega, T J↗
DOE OSTI2025

CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS

We present CAMELSH (Catchment Attributes and Hourly HydroMeteorology for Large-Sample Studies), the first large-sample hydrometeorological dataset at the hourly scale for the contiguous United States. CAMELSH intergrates hourly meteorological time series, catchment attributes and boundaries from GAGES-II and HydroATLAS for 9,008 catchments across diverse climatic, hydrological, and anthropogenic conditions. In addition, hourly streamflow time series is provided for 3,166 catchments. The dataset spans 45 years (1980–2024) with 11 meteorological variables from the NLDAS-2 forcing dataset, from which we compute nine climate indices related to precipitation, evapotranspiration, seasonality, and snow fraction. Additionally, CAMELSH includes two sets of catchment attributes: 439 from GAGES-II and 195 derived from HydroATLAS. These attributes include factors related to climate, geology, hydrology, river/stream morphology, landscape, nutrient, soil, topography, and anthropogenic influences. Developed in accordance with FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, CAMELSH is the first large-sample dataset at an hourly timescale, supporting machine learning applications for short-term streamflow (flood) prediction and advancing data-driven hydrological research across multiple timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exploring toroidal anvil profiles for larger sample volumes above 4 Mbar

Abstract With the advent of toroidal and double-stage diamond anvil cells (DACs), pressures between 4 and 10 Mbar can be achieved under static compression, however, the ability to explore diverse sample assemblies is limited on these micron-scale anvils. Adapting the toroidal DAC to support larger sample volumes offers expanded capabilities in physics, chemistry, and planetary science: including, characterizing materials in soft pressure media to multi-megabar pressures, synthesizing novel phases, and probing planetary assemblages at the interior pressures and temperatures of super-Earths and sub-Neptunes. Here we have continued the exploration of larger toroidal DAC profiles by iteratively testing various torus and shoulder depths with central culet diameters in the 30–50 µm range. We present a 30 µm culet profile that reached a maximum pressure of 414(1) GPa based on a Pt scale. The 300 K equations of state fit to our P–V data collected on gold and rhenium are compatible with extrapolated hydrostatic equations of state within 1% up to 4 Mbar. This work validates the performance of these large-culet toroidal anvils to > 4 Mbar and provides a promising foundation to develop toroidal DACs for diverse sample loading and laser heating.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Real-time elemental analysis of liquids for process monitoring using laser-induced breakdown spectroscopy with a liquid wheel sampling approach

This article presents an engineered sampling system that used a rotating wheel to form a thin liquid layer, permitting the use of laser-induced breakdown spectroscopy (LIBS) for in situ, real-time elemental impurity quantification during liquid processing. The sampling approach was demonstrated on eight elements from across the periodic table (Na, Al, K, Ca, Ti, Sr, Mo, and Yb). Univariate and multivariate calibrations were presented for each element. The average value for percent root mean square errors of cross-validation for the multivariate models was 3.64%, highlighting the method's strong prediction accuracy. Additionally, the limits of detection for each analyte were estimated from their univariate models: Na = 0.0532, Al = 18.5, K = 0.105, Ca = 0.273, Ti = 67.7, Sr = 0.640, Mo = 22.4, and Yb = 22.9 μg mL –1 . Finally, a test in which multivariate models were used to monitor a liquid system for 80 min was performed to investigate the real-time monitoring capabilities of this liquid LIBS sampling approach. Rigorous measurements were performed to effectively predict the absence and concentrations of multiple analytes as they were spiked and diluted. This demonstration showed the feasibility of using LIBS for real-time liquid quantification models with estimated precision ≤ 8.1%. Finally, the limitations of this approach and potential future improvements are discussed.

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

Chemical imaging of individual stratospheric particles sampled over North America

The increasing size, severity, and frequency of wildfires have led to dramatic increases in particulate matter concentrations in the troposphere. Severe wildfires can generate intense convective systems capable of transporting large quantities of biomass burning organic aerosols (BBOA) to the upper troposphere and lower stratosphere (UTLS). Chemically complex organic matter and light-absorbing carbonaceous material is introduced into stratospheric regions that were historically isolated from direct surface emissions. In this study, stratospheric particles were sampled over North America during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) campaign, an aircraft-based research project designed to characterize convective perturbation in the UTLS. Particle samples collected from six research flights during summer 2022 were analyzed using Computer-Controlled Scanning Electron Microscopy and Scanning Transmission X-ray Microscopy to investigate particle size distributions, morphology, chemical composition, and mixing state of stratospheric particles along transects across the continental United States and adjacent Pacific Ocean airspace. Analysis revealed that all sampled particles contained detectable levels of carbon, with most exhibiting organic volume fractions of 0.37 ± 0.20. Notably, about 5% of the particles also contained soot inclusions, which indicates the presence of refractory black carbon transported to stratospheric altitudes and provides direct evidence of wildfire-derived black carbon reaching the UTLS. Typical particle morphology exhibits organic shells over soot and inorganic cores and suggests secondary processing and aging of BBOA during transport to and within the UTLS. These findings provide compelling evidence that wildfire emissions play a critical role in affecting the long-term composition and radiative properties of stratospheric particles.

Sharpe, Steven [Purdue Univ., West Lafayette, IN (↗
DOE OSTI2025

The role of sample–window coupling in shock temperature experiments

Radiometric studies of opaque materials under shock compression probe a sample–window interface that may be very different than the bulk state. Subtle experimental details, particularly an initial sample–window gap vs an adhesively bonded stack, have tremendous influence on measured temperature. This work experimentally tests gapped vs bonded targets and theoretically investigates observed differences in the measured temperature. The results indicate that perturbations created by a thin adhesive are smaller than those suggested by a previous work, whereas sample–window gaps alter the measured state more significantly than that previously realized.

Adhesives↗
DOE OSTI2024

Grover-QAOA for 3-SAT: quadratic speedup, fair-sampling, and parameter clustering

Abstract The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it has long served as an essential benchmark for classical and quantum algorithms. This study shows numerical evidence for a quadratic speedup of the Grover Quantum Approximate Optimization Algorithm (G-QAOA) over random sampling for finding all solutions to 3-SAT (All-SAT) and Max-SAT problems. G-QAOA is less resource-intensive and more adaptable for these problems than Grover’s algorithm, and it surpasses conventional QAOA in its ability to sample all solutions. We show these benefits by classical simulations of many-round G-QAOA on thousands of random 3-SAT instances. We also observe G-QAOA advantages on the IonQ Aria quantum computer for small instances, finding that current hardware suffices to determine and sample all solutions. Interestingly, a single-angle-pair constraint that uses the same pair of angles at each G-QAOA round greatly reduces the classical computational overhead of optimizing the G-QAOA angles while preserving its quadratic speedup. We also find parameter clustering of the angles. The single-angle-pair protocol and parameter clustering significantly reduce obstacles to classical optimization of the G-QAOA angles.

Zhang, Zewen (ORCID:000000032258613X)↗