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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Materials science↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

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

Field-level reconstruction from foreground-contaminated 21-cm maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the “foreground wedge”. Here, in this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

cosmological perturbation theory↗
DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗
DOE OSTI2025

DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis

SF-25-088 Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Zhou, Tao [Argonne National Laboratory (ANL), Argo↗
DOE OSTI2022

Small-angle X-ray microdiffraction from fibrils embedded in tissue thin sections

Small-angle X-ray scattering (SAXS) from fibrils embedded in a fixed, thin section of tissue includes contributions from the fibrils, the polymeric matrix surrounding the fibrils, other constituents of the tissue, and cross-terms due to the spatial correlation between fibrils and neighboring molecules. This complex mixture severely limits the amount of information that can be extracted from scattering studies. However, availability of micro- and nano-beams has made the measurement of scattering from very small volumes possible, which, in some cases, may be dominated by a single fibrillar constituent. In such cases, information about the predominant species may be accessible. Nevertheless, even in these cases, the correlations between the positions of fibrils and other constituents have a significant impact on the observed scattering. Here, strategies are proposed to extract partial information about fibril structure and tissue organization on the basis of SAXS from samples of this type. It is shown that the spatial correlation function of the fibril in the direction perpendicular to the fibril axis can be computed and contains information about the predominant fibril structure and the organization of the surrounding tissue matrix. This has significant advantages over approaches based on techniques developed for X-ray solution scattering. Examples of correlation calculations in different types of samples are given to demonstrate the information that can be obtained from these measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Programmable Site-Specific Functionalization of DNA Origami with Polynucleotide Brushes

Combining surface-initiated, TdT (terminal deoxynucleotidyl transferase) catalyzed enzymatic polymerization (SI-TcEP) with precisely engineered DNA origami nanostructures (DONs) presents an innovative pathway for the generation of stable, polynucleotide brush-functionalized DNA nanostructures. Here we demonstrate that SI-TcEP can site-specifically pattern DONs with brushes containing both natural and non-natural nucleotides. The brush functionalization can be precisely controlled in terms of the location of initiation sites on the origami core and the brush height and composition. Coarse-grained simulations predict the conformation of the brush-functionalized DONs that agree well with the experimentally observed morphologies. We find that polynucleotide brush-functionalization increases the nuclease resistance of DONs significantly, and that this stability can be spatially programmed through the site-specific growth of polynucleotide brushes. The ability to site-specifically decorate DONs with brushes of natural and non-natural nucleotides provides access to a large range of functionalized DON architectures that would allow for further supramolecular assembly, and for potential applications in smart nanoscale delivery systems.

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

Single-photon emission from two-dimensional perovskites channeled through low-energy edge states

Two-dimensional perovskites, an emerging family of van der Waals materials with an innate quantum-well structure, exhibit remarkable optical characteristics and compositional tunability. However, their potential as a source for quantum emitters remains unexplored. Here, we investigate low-energy edge states unique to two-dimensional perovskites using comprehensive photoluminescence imaging and spectroscopy, to further leverage them towards uncovering quantum emitters. In contrast to above-bandgap excitation, spatially localized single-photon emitters are realized through sub-bandgap energy excitation at the edges in exfoliated single crystals. Specifically, the sub-bandgap excitation facilitates efficient access to deeply confined states within the bandgap, as charge carriers can channel through low-energy edge states while inhibiting competing processes. Furthermore, we demonstrate single-photon emitters from artificially created edges via strain as well as from two-dimensional perovskites with different quantum-well thicknesses. Our findings provide promising opportunities in the development of two-dimensional quantum emitters, where two-dimensional perovskites may provide added functionalities with versatility in material design, fabrication, and scalability.

Materials for optics↗
DOE OSTI2024

Energy-resolved fast-neutron radiography using an event-mode neutron imaging detector

Energy-resolved fast-neutron radiography is a powerful non-destructive technique that can be used to remotely measure the quantity and distribution of elements and isotopes in a sample. This is done by comparing the energy-dependent neutron transmission of a sample with the known cross-sections of individual isotopes. The reconstruction of the composition is possible due to the unique features (e.g. resonances) in the cross-sections of individual isotopes. At short-pulsed (≲ 1 ns) neutron sources, such information is accessible via time-of-flight neutron imaging in principle, but requires a detector with nanosecond temporal resolution. Conventional neutron detectors can meet this requirement only by heavily compromising spatial resolution or efficiency. Here, we present a unique approach on fast neutron resonance radiography using a scintillator-based event-mode imaging detector at a short-pulsed neutron source, including first results on spatially mapped resonance profiles using MeV neutrons. The event mode approach applied in the presented detector allows recording of individual neutron interactions with nanosecond precision in time and sub-mm resolution in space. As a result, the entire available neutron energy spectrum can be measured for each pulse. At the same time, the use of a thick scintillator screen and lenses to focus the produced light results in a highly flexible field of view and a high interaction probability in the sensitive volume of the detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Planning for cooler communities: Vacant lots as components of heat resilience in Mesa, Arizona

Vacant lots are often perceived as contributing to negative socioeconomic and environmental impacts on surrounding communities. However, they also offer opportunities for strategic interventions that promote heat resilience. This study uses a decision-scale congruence analytic approach to examine the correlation between extreme heat and community resilience in the context of vacant lots in the city of Mesa, Arizona. By identifying and analyzing over 1,200 vacant lots, we assessed spatial patterns of Community Resilience Estimates (CRE) for Heat and Body Heat Storage (BHS) to understand their correlation at the unit of analysis of vacant lots, where key decisions are made concerning land use. The results reveal a nonrandom spatial distribution of CRE for Heat and BHS across Mesa’s vacant lots. Vacant lots are disproportionately concentrated in neighborhoods with lower resilience, exacerbating heat exposure. Communities with limited access to cooling infrastructure, tree canopy, and other resources experience lowered heat resilience. A positive correlation between CRE for Heat and BHS shows that areas with higher heat exposure tend to have lower community resilience, reinforcing the need for cooling interventions. This study highlights the potential for converting vacant lots into heat-resilient, community-serving spaces. Using our findings, decision makers can identify priority areas and leverage vacant lots to mitigate heat impacts and foster community resilience.

community resilience↗
DOE OSTI2025

Dynamical Scaling Reveals Topological Defects and Anomalous Evolution of a Photoinduced Phase Transition

Nonequilibrium states of quantum materials can exhibit exotic properties and enable unprecedented functionality and applications. These transient states are inherently inhomogeneous, characterized by the formation of topologically protected structures, requiring nanometer spatial resolution on femtosecond timescales to resolve their evolution. Using ultrafast total x-ray scattering at a free electron laser and a sophisticated scaling analysis, we gain unique access to the dynamics on the relevant mesoscopic length scales. Our results provide direct evidence that ultrafast excitation of LaTe 3 leads to formation of topological vortex strings of the charge density wave. These dislocations of the charge density wave exhibit anomalous, subdiffusive dynamics, slowing the equilibration process, providing rare insight into the nonequilibrium mesoscopic response in a quantum material. Our findings establish a general framework to investigate properties of topological defects, which are expected to be ubiquitous in nonequilibrium phase transitions and may arrest equilibration and enhance competing orders.

Orenstein, Gal [SLAC National Accelerator Laborato↗
DOE OSTI2023

Dynamical Scaling Reveals Topological Defects and Anomalous Evolution of a Photoinduced Phase Transition

Nonequilibrium states of quantum materials can exhibit exotic properties and enable unprecedented functionality and applications. These transient states are inherently inhomogeneous, characterized by the formation of topologically protected structures, requiring nanometer spatial resolution on femtosecond timescales to resolve their evolution. Using ultrafast total x-ray scattering at a free electron laser and a sophisticated scaling analysis, we gain unique access to the dynamics on the relevant mesoscopic lengthscales. Our results provide direct evidence that ultrafast excitation of LaTe$_3$ leads to formation of topological vortex strings of the charge density wave. These dislocations of the charge density wave exhibit anomalous, subdiffusive dynamics, slowing the equilibration process, providing rare insight into the nonequilibrium mesoscopic response in a quantum material. Our findings establish a general framework to investigate properties of topological defects, which are expected to be ubiquitous in nonequilibrium phase transitions and may arrest equilibration and enhance competing orders.

FOS: Physical sciences↗
DOE OSTI2024

rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment

Abstract. Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn, last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis.

Shu, Lele (ORCID:0000000269034466)↗
DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗
DOE OSTI2020

Clostridioides difficile Toxin A Remodels Membranes and Mediates DNA Entry Into Cells to Activate Toll-Like Receptor 9 Signaling

Background & Aims: Clostridioides difficile toxin A (TcdA) activates the innate immune response. TcdA co-purifies with DNA. Toll-like receptor 9 (TLR9) recognizes bacterial DNA to initiate inflammation. We investigated whether DNA bound to TcdA activates an inflammatory response in murine models of C difficile infection via activation of TLR9. Methods: We performed studies with human colonocytes and monocytes and macrophages from wild-type and TLR9 knockout mice incubated with TcdA or its antagonist (ODN TTAGGG) or transduced with vectors encoding TLR9 or small-interfering RNAs. Cytokine production was measured with enzyme-linked immunosorbent assay. We studied a transduction domain of TcdA (TcdA 57-80 ), which was predicted by machine learning to have cell-penetrating activity and confirmed by synchrotron small-angle X-ray scattering. Intestines of CD1 mice, C57BL6J mice, and mice that express a form of TLR9 that is not activated by CpG DNA were injected with TcdA, TLR9 antagonist, or both. Enterotoxicity was estimated based on loop weight to length ratios. A TLR9 antagonist was tested in mice infected with C difficile. We incubated human colon explants with an antagonist of TLR9 and measured TcdA-induced production of cytokines. Results: The TcdA 57-80 protein transduction domain had membrane remodeling activity that allowed TcdA to enter endosomes. TcdA-bound DNA entered human colonocytes. TLR9 was required for production of cytokines by cultured cells and in human colon explants incubated with TcdA. TLR9 was required in TcdA-induced mice intestinal secretions and in the survival of mice infected by C difficile. Even in a protease-rich environment, in which only fragments of TcdA exist, the TcdA 57-80 domain organized DNA into a geometrically ordered structure that activated TLR9. Conclusions: TcdA from C difficile can bind and organize bacterial DNA to activate TLR9. TcdA and TcdA fragments remodel membranes, which allows them to access endosomes and present bacterial DNA to and activate TLR9. Rather than inactivating the ability of DNA to bind TLR9, TcdA appears to chaperone and organize DNA into an inflammatory, spatially periodic structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗
DOE OSTI2026

Lithographic crystallinity regulation in additive fabrication of thermoplastics (CRAFT)

For semicrystalline polyolefin thermoplastics, the balance between interconnected ordered crystalline and disordered amorphous regions is paramount to their performance and processability. However, contemporary manufacturing strategies, from injection molding to three-dimensional (3D) printing, result in monolithic objects, unable to spatially encode crystallinity. We develop a light-based approach for fabricating mechanically robust polyolefin thermoplastics with microscopic control over crystallinity in 3D space. Light dosage governs polymer stereochemistry giving access to a continuum of materials, from strong rigid plastics, such as high-density polyethylene, to more extensible materials akin to low-density polyethylene, all at the flick of a switch. Leveraging this finding in lithographic grayscale 3D printing enables rapid multimaterial fabrication with voxel-level control over optical and mechanical properties, opening avenues in information storage, soft robotics, and energy damping.

36 MATERIALS SCIENCE↗