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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

One Million Open-source Cislunar Orbits

Cislunar space, encompassing the region from geosynchronous orbit to beyond the Moon, is poised to become a cornerstone for future exploration, scientific discovery, and national security. Missions in this region, spanning durations from weeks to decades, require robust infrastructure and reliable transit capabilities. The complex gravitational influences of the Moon, Sun, and planets, along with thermal radiation from Earth and the Sun, lead to significant trajectory deviations, resulting in kilometer-scale errors within days. Leveraging the high-performance computing resources at Lawrence Livermore National Laboratory (LLNL), we have simulated one million high-fidelity cislunar trajectories, now publicly available via LLNL’s Green Data Oasis and the Unified Data Library. Generated using the open-source Space Situational Awareness Python package, these trajectories match the precision of commercial tools such as AGI’s Systems Tool Kit and NASA’s General Mission Analysis Tool. This data set is a valuable resource for reference, statistical analysis of cislunar orbit populations, and training machine learning models for rapid orbit classification with minimal observational input. Preliminary analysis reveals stable bands in Keplerian element space, particularly around five geosynchronous radii across a range of inclinations and eccentricities. Beyond this threshold, the Moon’s influence disrupts most unassisted orbits, though co-orbiting L4/L5 Lunar Trojans persist throughout the six-year simulation.

Astronomy and AstroPhysics↗
DOE OSTI2024

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture↗
DOE OSTI2026

Revisiting the Supernova Engines in the 3C 397 and W49B Supernova Remnants

The nature of the supernova remnants (SNRs) 3C 397 and W49B has long been a subject of debate, with prior studies offering conflicting interpretations between thermonuclear and core-collapse scenarios. To help settle this debate, we present a systematic, spatially resolved, spectroscopic analysis of both remnants using XMM-Newton. By applying multicomponent thermal models, we derive key physical properties including elemental abundances, ejecta temperatures, ambient densities, and explosion energetics. We compare the inferred metal abundance ratios to a wide range of core-collapse and thermonuclear nucleosynthesis models, including new models whose explosion energies differ from the canonical value of 10 51 erg. We find that the observed Fe/Si and Ca/Si ratios in both SNRs are best matched by certain thermonuclear models. However, no model fully reproduces the complete set of observed abundance patterns. In 3C 397, high Fe enrichment and spatial abundance variations suggest interaction with a dense progenitor environment, and W49B’s composition is overall consistent with a thermonuclear origin; however, both require a low-energy (∼10 50 erg) supernova explosion. We additionally map the Fe Kα line centroid energies and find a spread, with W49B falling within the core-collapse region—highlighting both environmental complexity and the limitations of this diagnostic for supernova classification. Our results highlight the need for caution in relying on any single diagnostic or nucleosynthesis model for supernova typing, underscore the need for improved nucleosynthesis models, and motivate future high-resolution, high-throughput observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quantum simulations of nuclear resonances with variational methods

Background: The many-body nature of nuclear physics problems poses significant computational challenges. These challenges become even more pronounced when studying the resonance states of nuclear systems, which are governed by the non-Hermitian Hamiltonian. Quantum computing, particularly for quantum many-body systems, offers a promising alternative, especially within the constraints of current noisy intermediate-scale quantum (NISQ) devices. Purpose: This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians and implementing it fully on a quantum simulator. Methods: We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm. First, we transform the non-Hermitian Hamiltonian into a Hermitian form by using the energy variance as a cost function within a variational framework. Second, we perform 𝜃-trajectory calculations to determine optimal resonance positions in the complex energy plane. To address resource constraints on NISQ devices, we utilize Gray code (GC) encoding to reduce qubit requirements. Results: We first validate our approach using a schematic potential model that mimics a nuclear potential, successfully reproducing known resonance energies with high fidelity. We then extend the method to a more realistic 𝛼−𝛼 nuclear potential and compute the 𝐷- and 𝐺-wave resonance energies with a basis size of 𝑁=16, using only four qubits. The quantum simulation results closely match the classical values, demonstrating the feasibility of our approach. Conclusions: This study demonstrates, for the first time, that the complete 𝜃-trajectory method can be implemented on a quantum computer without relying on any classical input beyond the Hamiltonian. The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems. This work represents a significant step toward quantum simulations of open quantum systems and lays the foundation for future investigations into resonance structures in nuclear, atomic, and molecular physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Transactional Knowledge Graph Generation To Model Adversarial Activities

A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. Traditionally, KGs are used to describe metadata about entities and to provide additional context to target application results. Many real-world domains also involve temporal interactions between entities in addition to the metadata data. Modeling these attributed transactions is a critical requirement when using KGs in complex real-world applications. Modeling adversarial activities is one such application that develops methodology and tools to produce realistic large-scale background activity graphs that include embedded Weapons of Mass Destruction (WMD) activity patterns. We present a novel platform for constructing a transactional knowledge graph from a diverse set of sources. We present the core components and architecture of the framework, and a use case for generating a background knowledge graph and WMD activity template to evaluate network alignment and subgraph matching algorithms.

Purohit, Sumit↗
DOE OSTI2023

High-resolution rovibrational spectroscopy of trans -formic acid in the v 1 OH stretching fundamental: Dark state coupling and evidence for charge delocalization dynamics

High-resolution infrared (IR) reduced-Doppler absorption spectra of jet-cooled gas phase trans-formic acid in the v 1 OH stretching fundamental region are reported for the first time, obtained by supersonically expanding trans- formic acid/Ar mixtures through a slit jet nozzle source and rotationally cooling to T rot ≈ 10.9(5) K, with ab- sorption signals recorded by high-resolution difference-frequency IR absorption spectroscopy. Two a/b-type rovibrational bands of comparable intensity, one ~10-fold weaker b-type band, and one ~6-fold weaker a-type band are observed, with vibrational band origins at 3570.493(5), 3566.793(5), 3560.032(9), and 3534.6869(2) cm –1 , respectively. Based on previous Raman jet spectroscopic work by Nejad and Sibert [A. Nejad, E.L. Sibert III, The Raman jet spectrum of trans-formic acid and its deuterated isotopologs: Combining theory and experi- ment to extend the vibrational database, J. Chem. Phys. 154(6) (2021) 064301.], these four rovibrational bands have been assigned to v 1 , (v 2 + v 7 ), (v 6 + 2v 7 + 2v 9 ), and 2v 3 , respectively. Specifically, two of the three upper dark states (2 1 7 1 (a') and 6 1 7 2 9 2 (a')) are close enough to the “bright” 1 1 (a') state to facilitate strong anharmonic resonance interactions, which results in intensity mixing into the two zero-order bands that would otherwise be “dark”. Furthermore, our high-resolution spectral analysis reveals that there are local rotational crossings be- tween these zero-order 1 1 and 2 1 7 1 states resulting in extra lines (i.e., some upper levels in the nominally v 1 band have majority zero-order 2 1 7 1 state character). This motivates development of a 3 coupled state (1 1 , 2 1 7 1 , and 6 1 7 2 9 2 ) picture to aid in the spectral analysis, which is able to match all 3 observed band origins and relative band intensities, as well as indicate the necessity of multistate (> 2) coupling. Though limited by range of J and Ka levels (J’ ≤ 9 and K a ’ ≤ 3) populated at supersonic jet temperatures, this work offers first precision spec- troscopic analysis of trans-formic acid in the v 1 OH stretch region, which should aid in assignment of the more complete yet highly congested room temperature FTIR spectra. Lastly, and in sharp contrast to the spectral complexity in the three predominantly b-type bands, the lone a-type 2v 3 rovibrational band at 3534.6869(2) cm –1 is well described by a simple, rigid asymmetric top Hamiltonian.

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

DNA-mediated assembly of Au bipyramids into anisotropic light emitting kagome superlattices

Colloidal crystal engineering with DNA allows one to design diverse superlattices with tunable lattice symmetry, composition, and spacing. Most of these structures follow the complementary contact model, maximizing DNA hybridization on building blocks and producing relatively close-packed lattices. Here, low-symmetry kagome superlattices are assembled from DNA-modified gold bipyramids that can engage only in partial DNA surface matching. The bipyramid dimensions and DNA length can be engineered for two different superlattices with rhombohedral unit cells, including one composed of a periodic stacking of kagome lattices. Enabled by the partial facet alignment, the kagome lattices exhibit lattice distortion, bipyramid twisting, and planar chirality. When conjugated with Cy-5 dyes, the kagome lattices serve as cavities with high-density optical states and large Purcell factors along lateral directions, leading to strong dipole radiation along thezaxis and facet-dependent light emission. Such complex optical properties make these materials attractive for lasers, displays, and quantum sensing constructs.

Science & Technology - Other Topics↗
DOE OSTI2021

Small-angle neutron scattering solution structures of NADPH-dependent sulfite reductase

Sulfite reductase (SiR), a dodecameric complex of flavoprotein reductase subunits (SiRFP) and hemoprotein oxidase subunits (SiRHP), reduces sulfur for biomass incorporation. Electron transfer within SiR requires intra- and inter-subunit interactions that are mediated by the relative position of each protein, governed by flexible domain movements. Using small-angle neutron scattering, we report the first solution structures of SiR heterodimers containing a single copy of each subunit. These structures show how the subunits bind and how both subunit binding and oxidation state impact SiRFP’s conformation. Neutron contrast matching experiments on selectively deuterated heterodimers allow us to define the contribution of each subunit to the solution scattering. SiRHP binding induces a change in the position of SiRFP’s flavodoxin-like domain relative to its ferredoxin-NADP + reductase domain while compacting SiRHP’s N-terminus. Reduction of SiRFP leads to a more open structure relative to its oxidized state, re-positioning SiRFP’s N-terminal flavodoxin-like domain towards the SiRHP binding position. These structures show, for the first time, how both SiRHP binding to, and reduction of, SiRFP positions SiRFP for electron transfer between the subunits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Machine-learning-assisted insight into spin ice Dy 2 Ti 2 O 7

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy 2 Ti 2 O 7 . Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy 2 Ti 2 O 7 . The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantized electronic transitions in electrodeposited copper indium selenide nanocrystalline homojunctions

Pairing semiconductors with electrochemical processing offers an untapped opportunity to create novel nanostructures for practical devices. Here we report the results of one such pairing: the in-situ formation of highly-doped, interface-matched, sharp nanocrystalline homojunctions (NHJs) with single step electrodeposition of two copper-indium-selenide (CISe) compounds on flexible foil. It produces a homogenous film, comprising inherently ordered, 3-dimensional interconnected network of pn -CISe NHJs. These CISe NHJs exhibit surprising non-linear emissions, quantized transitions, large carrier mobility, low trap-state-density, long carrier lifetime and possible up-conversion. They facilitate efficient separation of minority carriers, reduce recombination and essentially function like quantum materials. This approach mitigates the material issues and complex fabrication of incumbent nanoscale heterojunctions; it also overcomes the flexibility and scale-up challenges of conventional planar pn junctions. The self-stabilized CISe NHJ film can be roll-to-roll processed in ambient atmosphere, thus providing a promising platform for a range of optoelectronic technologies. This concept exemplified by CISe compounds can be adapted to create nano-scale pn junctions with other inorganic semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Domain-specific text embedding model for accelerator physics

Accelerator physics presents unique challenges for natural language processing (NLP) due to its specialized terminology and complex concepts. A key component in overcoming these challenges is the development of robust text embedding models that transform textual data into dense vector representations, facilitating efficient information retrieval and semantic understanding. In this work, we introduce AccPhysBERT, a sentence embedding model fine-tuned specifically for accelerator physics. Our model demonstrates superior performance across a range of downstream NLP tasks, surpassing existing models in capturing the domain-specific nuances of the field. We further showcase its practical applications, including semantic paper-reviewer matching and integration into retrieval-augmented generation systems, highlighting its potential to enhance information retrieval and knowledge discovery in accelerator physics. Published by the American Physical Society 2025

Hellert, Thorsten (ORCID:0000000227970926)↗
DOE OSTI2023

Development of Scalable Reactive Transport Framework for PFAS

Research on PFAS chemicals is extensive and covers all aspects, including analytical quantification, determination of properties, toxicology, ex situ treatment, and in situ remediation. PFAS chemicals are very stable and thus persistent/recalcitrant in the environment. Although there are many unknowns about PFAS chemicals, degradation pathways, reaction rates, etc., many different sorption, oxidation, reduction, biological, and innovative treatment approaches are being developed. Sorption with activated carbon is currently the only fully available in situ treatment technology for PFAS-impacted groundwater. Given the wide array of PFAS chemicals and transformation products, remediation may need multi-step treatment trains to fully address the PFAS contamination. The work here provides kinetic reaction modules that represent an initial cut at functionality representing PFAS migration and reaction in groundwater aquifer flow and transport models. One reaction kinetics module provides a method to model kinetically limited adsorption using a mass transfer model. The second reaction module represents biological transformation of 8:2 FTOH and daughter species, illustrating how a complex reaction pathway network can be represented. Both reaction modules allow for spatially variable parameter values so that a variety of remediation approaches (e.g., a PRB or volumetric treatment or variations in geochemical conditions) can be simulated. The intent with these PFAS reaction modules is to provide tools for practitioners to aid in the selection, design, and assessment of potential in situ PFAS remediation strategies. It is anticipated that, as PFAS remediation technologies and scientific understanding advances, these modules would be refined or replaced to match new knowledge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Digital Twin: Visualizing the Future of the Power Grid [Slides]

The energy systems are generating data at a scale and complexity that increasingly outpaces our ability to analyze it. This talk argues that rigorous, large-scale visualization is a critical tool for meeting that challenge. Drawing on recent work in the Computational Science Center at the National Laboratory of the Rockies, we trace a progression of visualization research culminating in an immersive digital twin of a real campus power grid. Along the way, we examine why visualization matters at all, where widely-used techniques quietly fail, and what becomes analytically possible when you match the display environment to the scale of the problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Fission-like Neutron Spectrum Shaping Assembly based on D-T Generator

For nearly five decades, 252 Cf sources have been used for testing and calibration over a wide range of neutron detection devices used for nuclear safety and radiation protection. However, the vastly increased cost of 252 Cf sources, its short half-life, and concerns about future shortage have prompted nuclear facilities and radiological calibration laboratories that rely on high-intensity neutron sources to seek alternatives. While there appear to be no other radionuclide neutron sources that would match the 252 Cf performance characteristics, all of them, including 252 Cf, present similar concerns in terms of hazards, safety, and security. As a possible solution to this issue, the Pacific Northwest National Laboratory (PNNL) developed a low-risk, cost-efficient alternative to produce fission-like spectra based on commercially available deuterium-tritium (D-T) neutron generators. The alternative of producing a fission-like neutron spectrum without any fissionable materials or beryllium would enable the nuclear facilities across the entire complex to eliminate their reliance on high intensity 252 Cf sources for nuclear safety and radiation protection applications, substantially reducing risks and associated costs. This report describes efforts of designing the spectrum shaping assembly, its construction and characterization of the neutron field produced. Performance of this newly developed D-T generator based neutron field is demonstrated via calibration of a criticality detector, neutron survey instruments, and personal dosimeters verified against current standardized fields produced by 252 Cf spontaneous fission sources.

42 ENGINEERING↗
DOE OSTI2025

Sparse measurement medical CT reconstruction using multi-fused block matching denoising priors

A major challenge for medical X-ray CT imaging is reducing the number of X-ray projections to lower radiation dosage and reduce scan times without compromising image quality. However these under-determined inverse imaging problems rely on the formulation of an expressive prior model to constrain the solution space while remaining computationally tractable. Traditional analytical reconstruction methods like Filtered Back Projection (FBP) often fail with sparse measurements, producing artifacts due to their reliance on the Shannon-Nyquist Sampling Theorem. Consensus Equilibrium, which is a generalization of Plug and Play, is a recent advancement in Model-Based Iterative Reconstruction (MBIR), has facilitated the use of multiple denoisers are prior models in an optimization free framework to capture complex, non-linear prior information. However, 3D prior modelling in a Plug and Play approach for volumetric image reconstruction requires long processing time due to high computing requirement. Instead of directly using a 3D prior, this work proposes a BM3D Multi Slice Fusion (BM3D-MSF) prior that uses multiple 2D image denoisers fused to act as a fully 3D prior model in Plug and Play reconstruction approach. Our approach does not require training and are thus able to circumvent ethical issues related with patient training data and are readily deployable in varying noise and measurement sparsity levels. In addition, reconstruction with the BM3D-MSF prior achieves similar reconstruction image quality as fully 3D image priors, but with significantly reduced computational complexity. We test our method on clinical CT data and demonstrate that our approach improves reconstructed image quality.

Hossain, Maliha [ORNL]↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗