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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated Systems for Solvent Extraction

An automated solvent extraction system for the eventual handling of radioactive materials has been designed and procured for rapid, efficient and safe liquid-liquid extraction processes. This system will be configured to carry out liquid-liquid separations from beginning to end, automating tedious and time-consuming tasks such as organic/aqueous phase prep, pH checks, phase separation, and metal ion analysis. The system features advanced liquid handling capabilities, a robotic arm for precise sample transfer, and sophisticated analytical tools including UV-VIS spectrophotometry for real-time monitoring. Additionally, it incorporates automated capping of vials, vortex mixing, and centrifugation to ensure thorough mixing and phase separation. This report provides an overview of the system and general capabilities as well as initial efforts to develop an automated workflow for liquid-liquid separations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Electrical transport and torque magnetometry studies of the kagome compound LuV 6 ⁢Sn 6 under high magnetic fields

We present a comprehensive investigation of the kagome metal LuV 6 ⁢Sn 6 through magnetotransport and torque magnetometry studies in magnetic fields up to 41 T and temperatures as low as 0.3 K. Magnetoresistance measurements up to 31 T reveal clear Shubnikov–de Haas (SdH) oscillations with two dominant frequency peaks: 𝐹 𝛼 =12 T and 𝐹 𝛽 =155 T. The Berry phase Φ 𝐁 , calculated from Landau level fan diagrams, indicates a nontrivial topology for both the 𝛼- and 𝛽-orbits. To explore the possibility of higher-frequency signals in LuV 6 ⁢Sn 6 , we employed another technique: torque magnetometry. Torque measured with applied fields up to 41 T reveals clear de Haas–van Alphen (dHvA) oscillations, with frequency signals as high as 10 kT. Angular and temperature-dependent quantum oscillation measurements allowed us to extract the effective mass of charge carriers and map the Fermi surface of LuV 6 ⁢Sn 6 . To complement the experimental findings, we performed electronic band structure and Fermi surface calculations. The electronic bands of LuV 6 ⁢Sn 6 reveal intriguing features, including flat bands, van Hove singularities, and Dirac points near the Fermi level. Two bands cross the Fermi level, contributing a deformed cylindrical shape at the Γ-point and small chainlike Fermi surfaces near the Brillouin zone boundaries. Theoretical quantum oscillation frequencies derived from Fermi surface cross-sectional areas align well with experimental SdH and dHvA results. These combined experimental and theoretical insights provide a deeper understanding of the electronic structure of LuV 6 ⁢Sn 6 and establish the foundation for exploring electronic properties in other vanadium- and titanium-based kagome systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Dynamics of heavy quarks in strongly coupled $\mathcal{N}$ = 4 SYM plasma

We calculate the probability distribution P(k) for a heavy quark with velocity v propagating through strongly coupled N = 4 SYM plasma in the ’t Hooft limit (N c → ∞, λ = g 2 N c → ∞) at a temperature T to acquire a momentum k due to interactions with the plasma. This distribution encodes the well-known drag coefficient η D and the transverse and longitudinal momentum diffusion coefficients κ T and κ L . The jet quenching parameter $\hat{q}$ can be extracted from P(k) for v = 1. Going beyond these known Gaussian characteristics of P(k), our calculation determines all of the higher order and mixed moments to leading order in 1/$\sqrt{λ}$ for the first time. These non-Gaussian features of P(k) include qualitatively novel correlations between longitudinal energy loss and transverse momentum broadening at nonzero v. We show that all higher moments scale characteristically with an effective temperature of the boosted plasma in the heavy quark rest frame, and we demonstrate that these non-Gaussian characteristics can be sizable in magnitude and even dominant in physically relevant situations. We use these results to derive a Kolmogorov equation for the evolution of the probability distribution for the total momentum of a heavy quark that propagates through strongly coupled plasma. This evolution equation accounts for all higher order correlations between transverse momentum broadening and longitudinal energy loss, which we have calculated from first principles. It reduces to a Fokker-Planck equation when truncated to only include the effects of η D , κ T and κ L . Remarkably, while heavy quarks do not reach kinetic equilibrium with the plasma if evolved with this Fokker-Planck equation, by showing that the Boltzmann distribution is a static solution of the all-order Kolmogorov equation that we have derived we demonstrate that heavy quarks do reach kinetic equilibrium if evolved with this equation. Our results thus provide a dynamically complete framework for understanding the thermalization of a heavy quark that may be initially far from equilibrium in the strongly coupled N = 4 SYM plasma — as well as new insight into heavy quark transport and equilibration in quark-gluon plasma.

Holography and Hydrodynamics↗
DOE OSTI2026

From Points to Planes: A Workflow for Converting Three‐Dimensional Point Cloud Data Into Discrete Fracture Network Flow and Transport Models

We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks (PLANE-DFN), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE-DFN segments three-dimensional fracture planes from raw point cloud data using RANdom SAmple Consensus coupled with statistical outlier removal and density-based clustering to isolate individual fracture features. Each candidate plane is constrained against site-specific structural constraints based on strike and dip. After segmentation, each fracture is converted into a 2-D convex polygon suitable for meshing and simulation. The PLANE-DFN algorithm is validated by comparing geometric and flow and transport data against data from dfnWorks simulations with ensembles of plane-fit networks. We find that the flow and transport in plane-fit networks are comparable to dfnWorks-generated networks when realistic network geometry is maintained. The PLANE-DFN algorithm provides an automated and streamlined workflow to transform point clouds of data into DFN network geometry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗
DOE OSTI2024

Constituent Data Replacement Tool

The purpose of this tool is to estimate key parameters that may be missing in public wastewater composition datasets. The tool can be applied to develop complete treatment and critical mineral extraction profiles for leachate, produced water and other aqueous waste streams. The tool applies machine learning algorithms to replace missing data in a user’s water data set that are adjusted based on user preferences for options including algorithm type, number of features, and classification variables. The tool can use the user’s data alone or combine user data with the NEWTS USGS Produced Water Database for more robust training. This research was funded by the U.S. Department of Energy’s Office Fossil Energy and Carbon Management (FECM) through National Energy Technology Laboratory’s ongoing research under the Water Management for Power System Field Work Proposal, DE-FECM 1022428 and Critical Minerals Field Work Proposal, DE-FECM 1022420.

Aqueous Chemistry↗
DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗
DOE OSTI2025

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗
DOE OSTI2024

Tractometry of the Human Connectome Project: resources and insights

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Revealing Hidden Quinones Through Diagnostic MS² Fragmentation of Peptide–Quinone Adducts

Quinones are redox-active components of natural organic matter that mediate electron transfer and influence biogeochemical processes, but many quinones in pyrogenic organic matter (PyOM) remain unresolved because they ionize poorly by mass spectrometry. Here, we present a peptide-tagging approach to improve detection of cysteine-reactive electrophiles in PyOM, with quinones expected to be a dominant subset based on reaction chemistry and selectivity experiments. A cysteine-containing peptide was used to form Michael-addition adducts, enhancing electrospray ionization and enabling untargeted screening by high-performance liquid chromatography-high-resolution tandem mass spectrometry. The method was benchmarked with five quinone standards and applied to extracts from charred plant material as a discovery-level screen for cysteine-reactive targets. We identified 98 quinone-candidate adducts (mean neutral mass ~603 Da), of which more than 70% were not detectable in native MS1 data. Among formula-assigned features, hidden quinone candidates had median (O+N)/C of 0.391 and normalized oxidation state of carbon of -0.281, consistent with relatively low polarity and low oxidation state. These results reveal a previously inaccessible pool of hidden redox-active compounds in PyOM and provide a framework for prioritizing quinone-like electrophiles for confirmation and incorporation into models of fire-driven biogeochemical cycling.

LC-MS/MS↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Visualizing the impact of quenched disorder on 2D electron Wigner solids

Electron Wigner solids (WSs) provide an ideal system for understanding the competing effects of electron–electron and electron–disorder interactions, a central unsolved problem in condensed matter physics. Progress in this topic has been limited by a lack of single-defect-resolved experimental measurements as well as accurate theoretical tools to enable realistic experiment/theory comparison. Here, in this study, we overcome these limitations by combining atomically resolved scanning tunnelling microscopy (STM) with neural-quantum-state quantum Monte Carlo (NQS-QMC) simulation of disordered 2D electron WSs to discover new disorder-induced physical regimes of correlated electron behaviour. STM was used to image the electron density (n e )-dependent evolution of electron WSs in gate-tunable bilayer MoSe 2 (BL-MoSe 2 ) devices with varying long-range (n LR ) and short-range (n SR ) disorder densities. These images were compared with NQS-QMC simulations using realistic disorder maps extracted from experiment, thus allowing the roles of different disorder types to be disentangled. We identify two distinct physical regimes for disordered electron WSs that depend on n SR . For n SR ≲ n e , the WS behaviour is dominated by long-range disorder and features extensive mixed solid–liquid phases, a new type of local re-entrant melting/crystallization and prominent Friedel oscillations. By contrast, when n SR ≫ n e , these features are suppressed and a more robust amorphous WS phase emerges that persists to higher n e , highlighting the importance of short-range disorder in this regime. Our work establishes a powerful framework for studying disordered quantum solids through a combined experimental–theoretical approach.

Ge, Zhehao [University of California, Berkeley, CA↗
DOE OSTI2026

The XPS of Azines: A Comparative Study

A detailed analysis is presented of the X-ray Photoelectron Spectroscopy, XPS, of thin films of three Azines: Pyrazine, Pyridine and Pyrimidine. This includes not only the binding energies of the various core ionizations but also their intensities. A major focus is to compare our theoretical predictions with our measured XPS for N(1s) and C(1s) as a basis for assigning the features and for justifying the broadening parameters that must be applied to the theoretical results. The C(1s) XPS of Pyridine and Pyrimidine are significantly broadened because of unresolved XPS for their inequivalent C atoms. The extent of the binding energy, BE, shifts and the XPS intensities for the unique C atoms, which are responsible for this broadening, are obtained from the theory. The additional broadening parameters to be applied to the theory to enable comparison with measured XPS are discussed in terms of lifetime broadening, experimental resolution, and BE shifts in different layers of the thin film. A novel feature of our analysis is that it is necessary to invoke surface core level shifts to explain and to justify the broadenings needed to apply to the theoretical results to have a complete comparison with the measured XPS. The results presented have general value for extracting chemical and physical information from XPS.

Bagus, Paul↗
DOE OSTI2024

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A search for R-parity-violating supersymmetry in final states containing many jets in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for R-parity-violating supersymmetry in final states with high jet multiplicity is presented. The search uses 140 fb –1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature prompt gluino-pair production decaying directly to three jets each or decaying to two jets and a neutralino which subsequently decays promptly to three jets. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted. Gluinos with masses up to 1800 GeV are excluded when decaying directly to three jets. In the cascade scenario, gluinos with masses up to 2340 GeV are excluded for a neutralino with mass up to 1250 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2025

A high repetition rate millimeter wavelength accelerator for an X-ray free-electron laser

A compact collinear wakefield accelerator has been designed for an X-ray free-electron laser capable of operating at a pulse repetition rate in the tens of kilohertz. The maximum achievable accelerating gradient has been determined, with its limitation linked to beam breakup instability. The fabrication techniques for the principal components of the accelerator including wakefield generation, couplers for excess power extraction and diagnostics, focusing quadrupoles, and a novel undulator have been discussed. Results from various laboratory and beam-based tests on these components have been compared to their original design specifications and demonstrated very good agreement. A preliminary design for the XFEL has been presented, featuring a novel small-period, force-neutral, adjustable-phase undulator.

26 undulator↗
DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗