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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Three-dimensional neutron far-field tomography of a bulk skyrmion lattice

Abstract Magnetic skyrmions are localized non-collinear spin textures, characterized by an integer topological charge. Commonly observed in thin systems as two-dimensional sheets, in three dimensions skyrmions form tubes that are thought to nucleate and annihilate along their depth on points of vanishing magnetization. However, a lack of techniques that can probe the bulk of the material has made it difficult to perform experimental visualizations of skyrmion lattices and their stabilization through defects. Here we present three-dimensional visualizations of a bulk Co 8 Zn 8 Mn 4 skyrmion lattice through a tomographic algorithm applied to multiprojection small-angle neutron scattering measurements. Reconstructions of the sample show a disordered skyrmion lattice exhibiting three-dimensional topological transitions through emergent (anti)monopole branching and segmentation defect pathways. Our technique provides insights into skyrmion stabilization and topological transition pathways in a bulk skyrmion lattice, guiding the future development and manipulation of skyrmion materials for spintronic applications.

Physics↗
DOE OSTI2023

Study of perovskite CsPbBr 3 detector polarization and its mitigation with ultrahigh x-ray flux

High-flux capable semiconductor x-ray detectors are essential in various applications, but the detrimental effects of detector polarization limit their use in many cases. Here, for this work, we studied the polarization of perovskite CsPbBr 3 semiconductor detectors using ultrahigh flux synchrotron x rays (10 6 –10 12 photons s –1 mm –2 at 58.61 keV). The CsPbBr 3 detectors did not show immediate polarization prominently until a flux higher than 10 10 photons s –1 mm –2 . Using the pump-and-probe technique, we visualized the spatial and temporal effects of polarization. The polarized region, represented by reduced photocurrent, extended beyond the area under direct irradiation, and the reduced photocurrent persisted after potential de-polarization treatments. We found that stronger applied electric fields and fewer carrier traps can mitigate polarization, represented by less photocurrent deficit. By examining the detectors’ current response under controlled ambient light, low, and high-flux x rays, we studied the trap filling and release behavior of CsPbBr 3 . We discovered that the polarization is caused by partial detector damage due to deep defects generated by ultrahigh flux x-ray irradiation. Our work provides insight into CsPbBr 3 polarization under extremely intense x-ray radiation and shows that reducing crystal defects and increasing detector bias voltage are effective solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Solution-Grown Rubrene Crystals as Radiation Detecting Devices

There has been increased interest in organic semiconductors over the last decade because of their unique properties. Of these, 5, 6, 11, 12-tetraphenylnaphthacene (rubrene) has generated the most interest because of its high charge carrier mobility. In this paper, large single crystals with a volume of ~1 cm 3 were grown from solution by a temperature reduction technique. The faceted crystals had flat surfaces and cm-scale, visually defect-free areas suitable for physical characterization. X-ray diffraction analysis indicates that solvent does not incorporate into the crystals and photoluminescence spectra are consistent with pristine, high-crystallinity rubrene. Furthermore, the response curve to pulsed optical illumination indicates that the solution grown crystals are of similar quality to those grown by physical vapor transport, albeit larger. The good quality of these crystals in combination with the improvement of electrical contacts by application of conductive polymer on the graphite electrodes have led to the clear observation of alpha particles with these rubrene detectors. Finally, preliminary results with a 252 Cf source generate a small signal with the rubrene detector and may demonstrate that rubrene can also be used for detecting high-energy neutrons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Color symmetry and confinement as an underlying superconformal structure in holographic QCD

Dedicated to the memory of our colleague, Harald Fritzsch, who, together with Murray Gell-Mann, introduced the color quantum number as the exact symmetry responsible for the strong interaction, thus establishing quantum chromodynamics (QCD) as a fundamental non-Abelian gauge theory. A basic understanding of hadron properties, however, such as confinement and the emergence of a mass scale, from first principles QCD has remained elusive: Hadronic characteristics are not explicit properties of the QCD Lagrangian and perturbative QCD, so successful in the large transverse momentum domain, is not applicable at large distances. In this article, we shall examine how this daunting obstacle is overcome in holographic QCD with the introduction of a superconformal symmetry in anti de Sitter (AdS) space which is responsible for confinement and the introduction of a mass scale within the superconformal group. When mapped to light-front coordinates in physical spacetime, this approach incorporates supersymmetric relations between the Regge trajectories of meson, baryon and tetraquark states which can be visualized in terms of specific SU(3) C color representations of quarks. Finally, we will also briefly discuss here the implications of holographic models for QCD color transparency in view of the present experimental interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Visualizing dispersive features in 2D image via minimum gradient method

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative application to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

The Role of Sensitivity Analysis in the Building Performance Analysis: A Critical Review

A building system is highly nonlinear and commonly includes a variety of parameters ranging from the architectural design to the building mechanical and energy system. Serving as a powerful tool to understand the complicated building system, sensitivity analysis (SA) has been receiving increasing attention in the recent decade. This review paper focused on the application of SA in the building performance analysis (BPA). First, the existing review papers on the application of SA for BPA were briefly reviewed and summarized. Then, a large amount of recent SA case studies in BPA were reviewed. The critical information regarding the implementation of a SA, such as the sampling method, the SA method, etc., were extracted and summarized. Next, an extensive analysis was performed to evaluate the role of the SA in the BPA. This includes: 1) The typical selection of the input and output parameters for the SA were visualized; 2) The uncertainty level and sampling strategies for the input parameters were investigated; 3) The principles, benefits, and drawbacks of the SA methods were analyzed and summarized; and 4) The various tools used in the SA, including the simulation software, sampling tools, and SA tools were introduced to facilitate the practitioners. Lastly, several conclusions were summarized on the role of the SA in the BPA. Suggestions were also provided to offer practical solutions to the current challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Unimodal Imaging of Monovalent Metal-Chelator Complexes and Lipids by MALDI Imaging Mass Spectrometry

Careful regulation of monovalent metal ions (M + ) is necessary to maintain a functional cellular system. Of these ions, appropriate sodium (Na + ) and potassium (K + ) concentrations are particularly integral for electrochemical signaling, as well as the secondary transport of nutrients and waste. Dysregulation of M + homeostasis can disrupt these mechanisms, potentially influencing the metabolism of downstream biomolecules such as lipids. Thus, the relationship between M + abundances and related biomolecular distributions must be elucidated to better understand the physiology of healthy and disordered tissues. Traditional techniques for imaging biological metal distributions include SIMS, LA-ICP-MS, and XRF; however, these capabilities are limited to elemental analysis or the analysis of molecular fragments and must be paired with other modalities to visualize distributions of more complex biomolecules within the same or similar samples. Conversely, matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool often used for mapping such biomolecular distributions, but current methods are unable to detect metals within tissue. This study illustrates a novel methodology that adds metal detection to the MALDI IMS repertoire through which the simultaneous detection of M + metals and lipids is achievable. Using a robotic sprayer for homogeneous application, on-tissue deposition of the chelator deferiprone (DEF) enables subsequent detection of the ionizable metal-chelator complex by MALDI without hindering lipid detection. Our work provides proof-of-concept data for the simultaneous detection of K + , Na + , and intact lipids using MALDI IMS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Imaging Quantum Vortices in Superfluid Helium Droplets

Free superfluid helium droplets constitute a versatile medium for a diverse range of experiments in physics and chemistry that extend from studies of the fundamental laws of superfluid motion to the synthesis of novel nanomaterials. In particular, the emergence of quantum vortices in rotating helium droplets is one of the most dramatic hallmarks of superfluidity and gives detailed access to the wave function describing the quantum liquid. This review provides an introduction to quantum vorticity in helium droplets, followed by a historical account of experiments on vortex visualization in bulk superfluid helium and a more detailed discussion of recent advances in the study of the rotational motion of isolated, nano- to micrometer-scale superfluid helium droplets. Ultrafast X-ray and extreme ultraviolet scattering techniques enabled by X-ray free-electron lasers and high-order harmonic generation in particular have facilitated the in situ detection of droplet shapes and the imaging of vortex structures inside individual, isolated droplets. Here, new applications of helium droplets ranging from studies of quantum phase separations to mechanisms of low-temperature aggregation are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bacterial diversity dynamics in microbial consortia selected for lignin utilization

Lignin is nature’s largest source of phenolic compounds. Its recalcitrance to enzymatic conversion is still a limiting step to increase the value of lignin. Although bacteria are able to degrade lignin in nature, most studies have focused on lignin degradation by fungi. To understand which bacteria are able to use lignin as the sole carbon source, natural selection over time was used to obtain enriched microbial consortia over a 12-week period. The source of microorganisms to establish these microbial consortia were commercial and backyard compost soils. Cultivation occurred at two different temperatures, 30°C and 37°C, in defined culture media containing either Kraft lignin or alkaline-extracted lignin as carbon source. iTag DNA sequencing of bacterial 16S rDNA gene was performed for each of the consortia at six timepoints (passages). The initial bacterial richness and diversity of backyard compost soil consortia was greater than that of commercial soil consortia, and both parameters decreased after the enrichment protocol, corroborating that selection was occurring. Bacterial consortia composition tended to stabilize from the fourth passage on. After the enrichment protocol, Firmicutes phylum bacteria were predominant when lignin extracted by alkaline method was used as a carbon source, whereas Proteobacteria were predominant when Kraft lignin was used. Bray-Curtis dissimilarity calculations at genus level, visualized using NMDS plots, showed that the type of lignin used as a carbon source contributed more to differentiate the bacterial consortia than the variable temperature. The main known bacterial genera selected to use lignin as a carbon source were Altererythrobacter , Aminobacter , Bacillus , Burkholderia , Lysinibacillus , Microvirga , Mycobacterium , Ochrobactrum , Paenibacillus , Pseudomonas , Pseudoxanthomonas , Rhizobiales and Sphingobium . These selected bacterial genera can be of particular interest for studying lignin degradation and utilization, as well as for lignin-related biotechnology applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗
DOE OSTI2025

Advancing \textit{otsdaq}: Enhancements for Usability, Accuracy, and Robustness

High-energy physics (HEP) experiments require data acquisition (DAQ) systems that can orchestrate complex detector operations, high data throughput, and responsive, real-time feedback to operators. Traditional DAQ stacks, which are often bespoke, command-line driven and highly specific, impose large learning curves on users. The Off-The-Shelf Data Acquisition (\textit{otsdaq}) framework was created to address these issues by offering a highly customizable and scalable browser-based ’desktop’ environment, in which experiment-specific control and monitoring applications can be easily deployed and integrated. Although the initial development of the \textit{otsdaq} software was aimed at the Fermilab Test Beam Facility, \textit{otsdaq} is now being leveraged for broader deployment, including the upcoming Mu2e experiment, where real-time monitoring of field-programmable gate array (FPGA)-based Data Transfer Controllers (DTCs), Clock and Fanout (CFO) boards, and several other subsystems are critical. We contribute a set of targeted improvements to \textit{otsdaq}: bitmap visualization functionality for configured data, improved and corrected delta-based DTC throughput metrics, version control (VC)-backed source navigation for console messages, custom navigation hooks to eliminate disruptive user interface glitches, and copy-to-clipboard support for macro execution history. These changes improve usability, reduce debugging time, and increase accuracy in performance data as Mu2e moves toward commissioning.

Mohammed, A. [Unlisted, US]↗
DOE OSTI2023

On the Importance of Chemical Precision in Organic Electronics: Fullerene Intercalation in Perfectly Alternating Conjugated Polymers

Abstract The true structure of alternating conjugated polymers—the state‐of‐the‐art materials for many organic electronics—often deviates from the idealized picture. Homocoupling defects are in fact inherent to the widely used cross‐coupling polymerization methods. Nevertheless, many polymers still perform excellently in the envisaged applications, which raises the question if one should really care about these imperfections. This article looks at the relevance of chemical precision (and lack thereof) in conjugated polymers covering the entire spectrum from the molecular scale, to the micro and mesostructure, up to the device level. The different types of polymerization errors for the alkoxylated variant of the benchmark (semi)crystalline polymer poly[2,5‐bis(3‐tetradecylthiophen‐2‐yl)thieno[3,2‐ b ]thiophene (PBTTT) are identified, visualized, and quantified and a general strategy to avoid homocoupling is introduced. Through a combination of experiments and supported by simulations, it is shown that these coupling defects hinder fullerene intercalation and limit device performance as compared to the homocoupling‐free analog. This clearly demonstrates that structural defects do matter and should be generally avoided, in particular when the geometrical regularity of the polymer is essential. These insights likely go beyond the specific PBTTT derivatives studied here and are of general relevance for the wider organic electronics field.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Predicting strength distributions of MEMS structures using flaw size and spatial density

The populations of flaws in individual layers of microelectromechanical systems (MEMS) structures are determined and verified using a combination of specialized specimen geometry, recent probabilistic analysis, and topographic mapping. Strength distributions of notched and tensile bar specimens are analyzed assuming a single flaw population set by fabrication and common to both specimen geometries. Both the average spatial density of flaws and the flaw size distribution are determined and used to generate quantitative visualizations of specimens. Scanning probe-based topographic measurements are used to verify the flaw spacings determined from strength tests and support the idea that grain boundary grooves on sidewalls control MEMS failure. The findings here suggest that strength controlling features in MEMS devices increase in separation, i.e., become less spatially dense, and decrease in size, i.e., become less potent flaws, as processing proceeds up through the layer stack. The method demonstrated for flaw population determination is directly applicable to strength prediction for MEMS reliability and design.

Instruments & Instrumentation↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Ultra-clear Aerogels for Insulated Glass in Refrigeration Doors (CRADA Final Report)

Display refrigerators commonly employ glass doors to provide product visibility; however, these doors exhibit significantly poorer thermal insulation than solid doors, resulting in increased heat transfer and higher energy consumption. To address this limitation, this project investigated the use of silica aerogel as a transparent insulating medium for refrigerator glass doors, with the objective of enhancing thermal performance without compromising visual clarity. In Phase I of the project, we assessed the feasibility of silica aerogel insulation through laboratory and full-scale studies, including material characterization, durability testing, and heat transfer measurements, along with the development and performance evaluation of prototype glass doors under realistic operating conditions. In Phase II, the project focused on scaling the technology toward practical deployment. This included scaling up the manufacturing process to achieve a surface area roughly 20 times larger for commercial applications, developing full-scale prototypes that integrated silica aerogel into standard display refrigerator doors, and conducting field evaluations to assess thermal performance, energy savings, and operational durability under real-world conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Waveform Simulation Framework: User Manual with Tutorials

This manuscript describes the Waveform Simulation Framework (WSF), a Python-based framework that provides a unified, programmable interface for generating synthetic seismograms for applications such as seismic array design, method development, and special event analysis. WSF standardizes how users define sources, receivers, and velocity models while abstracting simulator-specific configuration details, enabling workflows that are largely independent of the underlying numerical engine. The document provides installation guidance and tutorial-driven examples for three WSF simulator wrappers—WSF PyFK, WSF SW4, and WSF SPECFEM2D—illustrating end-to-end workflows from forward waveform simulation to common post-processing tasks (e.g., visualization and backprojection) using consistent data products (e.g., ObsPy Stream objects and SAC files).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Non-stationary Geostatistical Modeling: A Case Study Comparing LVA Estimation Frameworks

Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.

58 GEOSCIENCES↗