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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 OSTI2026

Direct and rapid uranium isotopic analysis of environmental sample swipes via microextraction – LS-APGD/Orbitrap mass spectrometry

Accurate and precise isotopic analysis of actinides collected on environmental sample swipes from within nuclear facilities is an important safeguarding measure for detecting undeclared materials and activities. Traditional isotope ratio (IR) analysis of actinides using a bulk digestion approach can be laborious and time-consuming. Recently, direct analysis of environmental swipe samples using a microextraction approach has been explored as an alternative to conventional bulk digestion methods. The present study further evaluates this approach for accurate and precise isotopic analysis of uranium (U) in cotton swipe samples using the liquid sampling-atmospheric pressure glow discharge (LS-APGD)/Orbitrap-FTMS Booster detection system. The instrumental parameters were optimized, and quantitative capabilities were demonstrated through excellent linearity (R2 = 0.99) across a deposited mass range of 1 to 500 ng, with a limit of detection of 90 pg (in a 2 × 4 mm region) for total U; well below typical U concentrations (ng–mg range) in environmental sample swipes. The 235U/238U ratio showed excellent accuracy (−0.3% relative difference from the certificate value), with ∼20× improvement over previous microextraction-Orbitrap methods. Precision (∼1% relative standard deviation) was also greatly enhanced by ∼5×. This study also demonstrates that figures of merit achieved with neat U solution were not highly degraded when various external interfering elements/matrices were introduced. Finally, the developed method was successfully applied to analyze practical swipe samples representative of laboratory and outdoor environments. The presented figures of merit and applicability to practical samples further validate the capability of the platform for rapid analysis (<7 minutes) of environmental swipe samples.

Shrestha, Suraj [Clemson University, SC]↗
DOE OSTI2025

High dopant activation in arsenic doped single-crystal CdTe thin films: Insights from MBE growth and rapid thermal processing

Single-crystal model systems are valuable tools to investigate fundamental material properties. In this work, we use molecular beam epitaxy to deposit in situ arsenic (As) doped single-crystal CdTe films on large area Si substrates to better understand As doping for photovoltaic applications. We found that As incorporation is highly temperature dependent: a substrate temperature difference of 50 °C can lead to several orders of magnitude difference in As concentration. Cd overpressure during in situ doping may limit out-diffusion of As but decrease As incorporation, especially at lower growth temperatures. Carrier concentrations greater than 10 16 cm −3 can be achieved with or without Cd overpressure when annealed at temperatures above 500 °C. However, unlike the low (∼1% to 5%) dopant activation commonly observed in polycrystalline CdTe, our films achieve significantly higher activation ratios—exceeding 50%, and in some cases approaching 80%. These values are consistent with or exceed prior reports in single-crystal CdTe systems. In addition to as-deposited arsenic concentrations, we also consider arsenic distribution after different rapid thermal processing temperatures. We propose a detailed definition and description of how arsenic incorporation is considered and calculated. Due to carrier concentration saturation, As incorporation also needs to be controlled to average levels of 10 17 cm −3 to achieve high activation. These findings suggest that higher annealing temperature regimes may be beneficial to polycrystalline CdTe based PV devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning models map rapid plant species changes from citizen science and remote sensing data

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Gillespie, Lauren E.↗
DOE OSTI2026

Beyond the eutectic paradigm: nanolamellar patterns in a rapidly solidified peritectic alloy

Peritectic transformations are central to many structural alloys, yet pattern formation remains poorly understood due to complex growth dynamics and limited three-dimensional data. Here, we report an unusual two-phase microstructure in a Zn–Ag peritectic alloy subjected to rapid solidification by laser surface remelting. Synchrotron X-ray nanotomography reveals a lamellar structure of primary 𝜀-AgZn 3 and peritectic Zn with ∼700 nm spacing. Although resembling a eutectic morphology, this pattern forms without a eutectic reaction through non-steady coupled growth from the liquid. SEM and TEM-EDS confirm interface shapes and phase compositions. These findings expand the design space of peritectics for refined microstructural control.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring rapidity-even dipolar flow in isobaric collisions at RHIC

Abstract Employing the AMPT transport model, we investigate the response of the rapidity-even dipolar flow ( v 1 even ) and its associated Global Momentum Conservation (GMC) parameterKto structural disparities within 96 Ru and 96 Zr nuclei. We analyze Ru + Ru and Zr + Zr collisions at a center-of-mass energy of s NN = 200 GeV. Our analysis demonstrates that the eccentricityε 1 , v 1 even andKexhibit subtle yet discernible sensitivity to the input nuclear structure distinctions between 96 Ru and 96 Zr isobars. This observation suggests that measuring v 1 even and the GMC parameter in these isobaric collisions could serve as a means to fine-tune the comprehension of their nuclear structure disparities and offer insights to enhance the initial condition assumptions of theoretical models.

Physics↗
DOE OSTI2025

Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data

The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forces directly from atomic coordinates. This approach dramatically reduces computation time and minimizes the need for technical knowledge. In this paper, we produce a phonon database comprising nearly 5000 inorganic crystals to benchmark the performance of several leading uMLIPs. We further assess these models in real-world applications by using them to analyze experimental inelastic neutron scattering data collected on a variety of materials. Through detailed comparisons, we identify the strengths and limitations of these uMLIPs, providing insights into their accuracy and suitability for fast calculations of phonons and related properties, as well as the potential for real-time interpretation of neutron scattering spectra. Our findings highlight how the rapid advancement of AI in science is revolutionizing experimental research and data analysis.

inelastic neutron scattering↗
DOE OSTI2025

Prediction of L⁢i 3 ⁢F⁡e 8 ⁢B 8 compound with rapid one-dimensional ion diffusion channels

Using a computational crystal structure search in the Li-Fe-B ternary system, we predict a stable phase of L⁢i 3 ⁢F⁡e 8 ⁢B 8 , featuring 1D channels that enable rapid Li-ion transport. Ab initio molecular dynamics simulations show that the Li-ion diffusion coefficient in L⁢i 3 ⁢F⁡e 8⁢ B 8 surpasses that of common electrode and conductive additive materials by several orders of magnitude. The high diffusion in L⁢i 3 ⁢F⁡e 8 ⁢B 8 can be explained by the Frenkel–Kontorova model, which describes an incommensurate state between the Li diffusion chain and the periodic potential field caused by the FeB backbone structure. The favorable lithium-ion diffusivity and mechanical properties of L⁢i 3 ⁢F⁡e 8 ⁢B 8 make it a promising conductive additive for battery materials. Furthermore, an external magnetic field can further manipulate the properties of this material due to its predicted itinerant ferromagnetism, which also offers a platform for exploring spin-dependent phenomena.

1-dimensional systems↗
DOE OSTI2025

Measurement of elliptic flow of 𝐽/𝜓 in $\sqrt{s_{NN}}$ = 200 GeV ⁢Au + Au collisions at forward rapidity

Here, we report the first measurement of the azimuthal anisotropy of 𝐽/𝜓 at forward rapidity (1.2 < |𝜂| < 2.2) in Au + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV at the BNL Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5 nb −1 . The second Fourier coefficient (𝑣 2 ) of the azimuthal distribution of 𝐽/𝜓 is determined as a function of the transverse momentum (𝑝𝑇) using the event-plane method. The measurements were performed for several selections of collision centrality: 0%–50%, 10%–60%, and 10%–40%. We find that in all cases the values of 𝑣 2 ⁡(𝑝 𝑇 ), which quantify the elliptic flow of 𝐽/𝜓, are consistent with zero. Within uncertainties, the results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the 𝐽/𝜓 within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{NN}}$ = 200 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

First-order structural phase transition at low temperature in GaPt 5 P and its rapid enhancement with pressure

Single crystals of X Pt 5 ⁢P (X = Al, Ga, and In), belonging to the 1-5-1 family of compounds, were grown from a Pt-P solution at high temperatures, and measurements of the ambient pressure, temperature-dependent magnetization, resistivity, and x-ray diffraction were made. Additionally, the ambient-pressure Hall resistivity and temperature-dependent resistance under pressure were measured on GaPt 5 ⁢P. All three compounds have a tetragonal P4/mmm crystal structure at room temperature with metallic transport and weak diamagnetism over the 2–300 K temperature range. Surprisingly, at ambient pressure, both the transport and magnetization measurements on GaPt 5 ⁢P show a steplike feature in the 70–90 K region, suggesting a possible structural phase transition. Neither AlPt 5 ⁢P nor InPt 5 ⁢P have any signatures of a phase transition in their temperature-dependent electrical resistance and magnetization data. Both the hysteretic nature and sharpness of the features in the GaPt 5 ⁢P data suggest that the transition is first-order. Further, single-crystal x-ray diffraction measurements provided further details of the structural transition with a possibility of a crystal symmetry different from P⁢4/mmm below the transition temperature. The transition is characterized by anisotropic changes in the lattice parameters and a volume collapse with respect to the high-temperature tetragonal crystal structure. Furthermore, satellite peaks are observed at two distinct and nonequivalent wave vectors (0, 0, 0.5) and (0.5, 0.5, 0.5), and density functional theory calculations present phonon softening, especially at (0.5, 0.5, 0.5), as a possible driving mechanism. Additionally, we find that the structural transition temperature increases rapidly with increasing pressure, reaching room temperature by ~2.2 GPa, highlighting the high degree of pressure sensitivity of GaPt 5 ⁢P and fragile nature of its room-temperature structure. Even though the volume collapse and extreme pressure sensitivity suggest chemical pressure should drive a similar structural change in AlPt 5⁢ P, where both unit-cell dimensions and volume are smaller, its structure is found to be the same as that of the room-temperature GaPt 5 ⁢P. Overall, GaPt 5 ⁢P stands out as a sole member of the 1-5-1 family of compounds for which a temperature-driven structural change has been observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Covariant formulation of spinodal decomposition in rapidly expanding quark gluon plasma

Quantum chromodynamics (QCD) is expected to have a first order phase transition between the confined hadron gas and the deconfined quark gluon plasma at high baryon densities. This will result in phase boundary effects in the metastable and unstable regions. It is important to include these effects in phenomenological models of heavy ion collisions to identify experimental signatures of a phase transition. This requires building intuition on phase separation in rapidly expanding fluids. In this work we present the covariant equations of relativistic hydrodynamics with a phase boundary, provide prescriptions to extend the equation of state to metastable and unstable regions, and show the effects of spinodal separation in a Bjorken flow. Published by the American Physical Society 2024

Kapusta, Joseph I. (ORCID:0000000259429835)↗
DOE OSTI2025

CuXASNet: Rapid and accurate prediction of copper L-edge x-ray absorption spectra using machine learning

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu -edge x-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the and edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average mean absolute error of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly predict a large number of -edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal edges.

36 MATERIALS SCIENCE↗
DOE OSTI2026

LaueMatching: an approach for rapid and robust indexing of Laue diffraction patterns

Traditional Laue diffraction pattern indexing often struggles with noisy data, weak signals, peak overlap and missing reflections, particularly from complex or deformed microstructures. Here, we introduce LaueMatching, a high-throughput indexing algorithm designed to overcome these limitations. LaueMatching utilizes a fundamentally different approach based on direct pattern correlation: experimentally pre-processed images are compared against a comprehensive pre-computed library of simulated diffraction patterns corresponding to a dense grid of possible orientations. This approach bypasses the need for explicit peak identification and fitting, steps that are often a failure point for traditional methods. The algorithm rapidly and robustly indexes multiple crystallographic orientations and crystal systems simultaneously, even from challenging patterns. LaueMatching's effectiveness and accuracy have been rigorously tested and validated on diverse experimental (Ni, Al, EuAl 2 O 4 ) and simulated diffraction patterns, demonstrating high-fidelity orientation refinement. Code to implement this approach on both CPU and GPU resources can be downloaded from https://github.com/AdvancedPhotonSource/LaueMatching.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transferable Hirshfeld atom model for rapid evaluation of aspherical atomic form factors

Form factors based on aspherical models of atomic electron density have brought great improvement in the accuracies of hydrogen atom parameters derived from X-ray crystal structure refinement. Today, two main groups of such models are available, the banks of transferable atomic densities parametrized using the Hansen–Coppens multipole model which allows for rapid evaluation of atomic form factors and Hirshfeld atom refinement (HAR)-related methods which are usually more accurate but also slower. In this work, a model that combines the ideas utilized in the two approaches is tested. It uses atomic electron densities based on Hirshfeld partitions of electron densities, which are precalculated and stored in a databank. This model was also applied during the refinement of the structures of five small molecules. A comparison of the resulting hydrogen atom parameters with those derived from neutron diffraction data indicates that they are more accurate than those obtained with the Hansen–Coppens based databank, and only slightly less accurate than those obtained with a version of HAR that neglects the crystal environment. The advantage of using HAR becomes more noticeable when the effects of the environment are included. To speed up calculations, atomic densities were represented by multipole expansion with spherical harmonics up to l = 7, which used numerical radial functions (a different approach to that applied in the Hansen–Coppens model). Calculations of atomic form factors for the small protein crambin (at 0.73 Å resolution) took only 68 s using 12 CPU cores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Rapid Characterization and Statistical Analysis of High-Volume Field-Harvested Photovoltaic Connectors

Photovoltaic (PV) installations heavily depend on connectors for efficient module and string interconnections without requiring skilled labor. Yet this seemingly innocuous component of PV systems is a leading cause of module failures, multiple high-profile fires, and lawsuits in the PV industry. This work aims to answer critical questions regarding why connectors fail and the contributing factors to their failure. The study involves collecting and analyzing more than 17,000 field-harvested connectors from various solar installations across the United States. The vast dataset, which includes connector metadata, visual inspections, and resistance measurements, provides unprecedented insight into the state of health of PV connectors across the US, including the geographic locations, connector types, and installation practices most prone to failures. The work presented here describes a novel rapid characterization method for processing large numbers of connectors and is supported by parallel forensic analysis to discern the root causes of failures as well as a levelized cost of lifetime model to determine the economic ramifications of connector failure. Ultimately, the findings may inform PV developers about the best practices to extend connector longevity and lead to more resilient and reliable PV systems.

connectors↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
DOE OSTI2024

Rapid assessment of beech leaf disease in Fagus sylvatica buds

Abstract The European beech (Fagus sylvatica) is threatened by the foliar nematodeLitylenchus crenataesubsp.mccannii(Lcm), the causal agent of beech leaf disease (BLD). Thus far, the majority of studies regarding BLD have focused on American beech (F. grandifolia). To better determine the impact of Lcm in buds of European beech, a total of 54 buds were collected from naturally symptomatic trees. Here, we characterized for the first time the bud scale morphology of two different cultivars ofF. sylvaticainfected with Lcm. Detailed observations of asymptomatic and symptomatic bud scales provided insight into the physical changes and arrangements of cells in the bud scale, shedding light on the dynamic processes occurring during Lcm infection. In addition, we evaluated the suitability of using the bud scale morphology for the early detection of BLD and Lcm in naturally infected buds. The distinct cellular arrangement of symptomatic bud scales cells (i.e., asymmetric pattern of enlarged cells) provides a rapid and visual, user‐friendly methodology to prematurely diagnose BLD symptoms within the buds, as well as the detection of associated nematodes.

Forestry↗
DOE OSTI2026

Virus-induced gene editing of stomatal regulators in Nicotiana benthamiana enables rapid functional genomics

Virus-induced gene editing (VIGE) holds promise as a rapid and scalable approach for functional genomics in plants. Here, we apply a tobacco rattle virus (TRV)-based single-guide RNA (sgRNA) delivery system to target key regulators of stomatal development in Nicotiana benthamiana using transgenic Cas9-expressing lines. sgRNAs fused to a mobile RNA element and co-delivered with TRV enabled both somatic and heritable genome editing across orthologs of STOMAGEN, EPF2, YODA, and SPEECHLESS. Somatic editing frequencies reached up to 95%, and heritable tetra-allelic mutations were recovered in multiple target genes. Mutants exhibited significant, gene-specific changes in stomatal density, with corresponding effects on leaf temperature indicative of altered evaporative cooling. Additionally, sgRNAs fused to an AmCyan reporter enabled visualization of virus-infected tissues, allowing stomatal phenotyping in edited M0 sectors. This TRV-based platform facilitates functional assessment of genes influencing stomatal patterning and offers a powerful tool for dissecting gene function in a developmentally and physiologically relevant context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Fungal elemental profiling unleashed through rapid laser-induced breakdown spectroscopy (LIBS)

ABSTRACT Elemental profiling of fungal species as a phenotyping tool is an understudied topic and is typically performed to examine plant tissue or non-biological materials. Traditional analytical techniques such as inductively coupled plasma–optical emission spectroscopy (ICP-OES) and inductively coupled plasma–mass spectrometry (ICP-MS) have been used to identify elemental profiles of fungi; however, these techniques can be cumbersome due to the difficulty of preparing samples. Additionally, the instruments used for these techniques can be expensive to procure and operate. Laser-induced breakdown spectroscopy (LIBS) is an alternative elemental analytical technique—one that is sensitive across the periodic table, easy to use on various sample types, and is cost-effective in both procurement and operation. LIBS has not been used on axenic filamentous fungal isolates grown in substrate media. In this work, as a proof of concept, we used LIBS on two genetically distinct fungal species grown on a nutrient-rich and nutrient-poor substrate media to determine whether robust elemental profiles can be detected and whether differences between the fungal isolates can be identified. Our results demonstrate a distinct correlation between fungal species and their elemental profile, regardless of the substrate media, as the same strains shared a similar uptake of carbon, zinc, phosphorus, manganese, and magnesium, which could play a vital role in their survival and propagation. Independently, each fungal species exhibited a unique elemental profile. This work demonstrates a unique and valuable approach to rapidly phenotype fungi through optical spectroscopy, and this approach can be critical in understanding these fungi's behavior and interactions with the environment. IMPORTANCE Historically, ionomics, the elemental profiling of an organism or materials, has been used to understand the elemental composition in waste materials to identify and recycle heavy metals or rare earth elements, identify the soil composition in space exploration on the moon or Mars, or understand human disorders or disease. To our knowledge, ionomic profiling of microbes, particularly fungi, has not been investigated to answer applied and fundamental biological questions. The reason is that current ionomic analytical techniques can be laborious in sample preparation, fail to measure all potential elements accurately, are cost-prohibitive, or provide inconsistent results across replications. In our previous efforts, we explored whether laser-induced breakdown spectroscopy (LIBS) could be used in determining the elemental profiles of poplar tissue, which was successful. In this proof-of-concept endeavor, we undertook a transdisciplinary effort between applied and fundamental mycology and elemental analytical techniques to address the biological question of how LIBS can used for fungi grown axenically in a nutrient-rich and nutrient-poor environment.

59 BASIC BIOLOGICAL SCIENCES↗