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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
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)↗
DOE OSTI2026

Relaxing Direct Ptychography Sampling Requirements via Parallax Imaging Insights

Direct ptychography enables the retrieval of information encoded in the phase of an electron wave passing through a thin sample by deconvolving the interference effects of a converged probe with known aberrations. Under the weak phase object approximation, this permits the optimal transfer of information using noniterative techniques. However, the achievable resolution of the technique is traditionally limited by the probe step size—setting stringent Nyquist sampling requirements. At the same time, parallax imaging has emerged as a dose-efficient phase technique which relaxes sampling requirements and enables scan-upsampling. Here, we formulate parallax imaging as a quadratic approximation to part of the direct ptychography kernel and use this insight to enable upsampling in direct ptychography. We validate our analytical results numerically using simulated and experimental reconstructions.

direct ptychography, parallax imaging, scan Nyquis↗
DOE OSTI2025

Exploring the interaction between the MW and LMC with a large sample of blue horizontal branch stars from the DESI survey

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7σ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 deg away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is –24 km s –1 ⁠, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first 2 yr of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW–LMC interaction will improve significantly.

Galaxy: evolution↗
DOE OSTI2025

Study of the connected four-point correlation function of galaxies from the DESI Data Release 1 luminous red galaxy sample

We present a measurement of the non-Gaussian four-point correlation function (4PCF) from the DESI DR1 luminous red galaxy (LRG) sample. For the gravitationally induced parity-even 4PCF, we detect a signal with a significance of 14.7⁢𝜎 using our fiducial setup. We assess the robustness of this detection through a series of validation tests, including auto and cross-correlation analyses, sky partitioning across multiple patch combinations, and variations in radial scale cuts. Due to the low completeness of the sample, we find that differences in fiber assignment implementation schemes can significantly impact estimation of the covariance and introduce biases in the data vector. After correcting for these effects, all tests yield consistent results. This is one of the first measurements of the connected 4PCF on the DESI LRG sample; the good agreement between the simulation and the data implies that the amplitude of the density fluctuation inferred from the connected 4PCF is consistent with the Planck Λ⁢ CDM cosmology. The methodology and diagnostic framework established in this work provide a foundation for interpreting parity-odd 4PCF.

Cosmology↗
DOE OSTI2026

Parity-odd four-point correlation function from the DESI data release 1 luminous red galaxy sample

The parity-odd four-point function provides a unique probe of fundamental symmetries and potential new physics in the large-scale structure of the Universe. We present measurements of the parity-odd four-point function using the Dark Energy Spectroscopic Instrument (DESI) DR1 luminous red galaxy (LRG) sample and assess its detection significance. Our analysis considers both auto- and cross-correlations, using two complementary approaches to the covariance: (i) the full analytic covariance matrix applied to the uncompressed data vector, and (ii) a compressed data vector combined with a hybrid covariance matrix constructed from simulations and analytic estimates. When using the full analytic covariance matrix without corrections, we observe apparent auto-correlation signals with significance up to 4⁢𝜎. However, this excess is also consistent with a mismatch between the statistical fluctuations estimated from the simulations and those present in the real data. Our findings therefore suggest that the parity-odd signal in the current DESI DR1 LRG sample is consistent with zero. We note, however, that the low completeness of this sample may have a non-negligible impact on the detection sensitivity. Future data releases with improved completeness will be crucial for further investigation.

Hou, Jiamin [Ludwig-Maximilians-Universität; Unive↗
DOE OSTI2026

Probing cosmic velocities with the pairwise kinematic Sunyaev-Zel’dovich signal in DESI Bright Galaxy Sample DR1 and ACT DR6

We present a measurement of the pairwise kinematic Sunyaev-Zel’dovich (kSZ) signal using the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Sample (BGS) Data Release 1 (DR1) galaxy sample overlapping with the Atacama Cosmology Telescope (ACT) CMB temperature map. Our analysis makes use of 1.6 million galaxies with stellar masses log⁡ 𝑀 ⋆ /𝑀 ⊙ >10, and we explore measurements across a range of aperture sizes (2.1′ <𝜃 ap <3.5′) and stellar mass selections. This statistic directly probes the velocity field of the large-scale structure, a unique observable of cosmic dynamics and modified gravity. In particular, at low redshifts, this quantity is especially interesting, as deviations from General Relativity are expected to be largest. Notably, our result represents the highest-significance low-redshift (𝑧 ∼ 0.3) detection of the kSZ pairwise effect yet. In our most optimal configuration (𝜃 ap =3.3′, log⁡ 𝑀 ⋆ >11), we achieve a 5⁢𝜎 detection. Assuming that an estimate of the optical depth and galaxy bias of the sample exists via e.g., external observables, this measurement constrains the fundamental cosmological combination 𝐻 0 ⁡𝑓⁡𝜎$^2_8$. A key challenge is the degeneracy with the galaxy optical depth. We address this by combining CMB lensing, which allows us to infer the halo mass and galaxy population properties, with hydrodynamical simulation estimates of the mean optical depth, $\bar{𝜏}$ . We stress that this is a proof-of-concept analysis; with BGS DR2 data we expect to improve the statistical precision by roughly a factor of two, paving the way toward robust tests of modified gravity with kSZ-informed velocity-field measurements at low redshift.

Hadzhiyska, Boryana [Institute of Astronomy; Kavli↗
DOE OSTI2025

Importance Sampling Model-Based Diffusion for Trajectory Optimization

Trajectory optimization for robotic systems remains a challenging problem. This is especially true for robotic systems featuring nonlinear dynamics and many degrees of freedom. Data-based or model-free diffusion has recently been popularized in the fields of artificial intelligence and trajectory optimization. Model-Based Diffusion provides a data-free method of trajectory optimization, trained at runtime on a system dynamics model, suitable for high-dimensional models. This paper examines how importance sampling can enhance the performance of Model-Based Diffusion for trajectory optimization. Here, we quantify the benefits of importance sampling across three long horizon planning tasks. These results show as much as a 13x improvement in sample efficiency depending on environment and optimization parameters.

Golembeski, Seth [Georgia Institute of Technology,↗
DOE OSTI2025

Reagent-free Hyperspectral Diagnosis of SARS-CoV-2 Infection in Saliva Samples

Rapid, reagent-free pathogen-agnostic diagnostics that can be performed at the point of need are vital for preparedness against future outbreaks. Yet, many current strategies are pathogen-specific and require several reagents. We present hyperspectral sensing, using light to non-invasively measure the composition of several molecules to form a spectral signature, to overcome these barriers. To generate these spectral signatures, we present the ProSpectral TM V1, a novel, miniaturized hyperspectral platform with high spectral resolution with two mini-spectrometers. Furthermore, we developed state-of-the-art ML pipelines for near real-time analysis of spectral signatures in saliva samples. We found that we could accurately identify SARS-CoV-2 infection status in double-blinded saliva samples and demonstrate 100% accuracy on a hold out test dataset. To our knowledge, this establishes the fastest hyperspectral diagnostic platform and in a small form factor, and executable with liquid samples, without ligands or reagents, all while maintaining PCR level specificity and sensitivity.

59 BASIC BIOLOGICAL SCIENCES↗