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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Testing the LSST Difference Image Analysis Pipeline Using Synthetic Source Injection Analysis

Abstract We evaluate the performance of the Legacy Survey of Space and Time Science Pipelines Difference Image Analysis (DIA) on simulated images. By adding synthetic sources to galaxies on images, we trace the recovery of injected synthetic sources to evaluate the pipeline on images from the Dark Energy Science Collaboration Data Challenge 2. The pipeline performs well, with efficiency and flux accuracy consistent with the signal-to-noise ratio of the input images. We explore different spatial degrees of freedom for the Alard–Lupton polynomial-Gaussian image subtraction kernel and analyze for trade-offs in efficiency versus artifact rate. Increasing the kernel spatial degrees of freedom reduces the artifact rate without loss of efficiency. The flux measurements with different kernel spatial degrees of freedom are consistent. We also here provide a set of DIA flags that substantially filter out artifacts from the DIA source table. We explore the morphology and possible origins of the observed remaining subtraction artifacts and suggest that given the complexity of these artifact origins, a convolution kernel with a set of flexible bases with spatial variation may be needed to yield further improvements.

Liu, S. (ORCID:0000000244612143)↗
DOE OSTI2026

Bridging semantics, control specifications and assessment: A library for scalable demand flexibility controls

There is growing recognition that Demand Flexibility (DF) can play a major role in enhancing grid reliability, with building control applications emerging as key enablers for DF. However, the traditional approach to deploying new control applications in buildings, including those for DF, remains largely manual and tailored to individual buildings, making it difficult to scale. While research efforts have explored semantics-driven portability, DF controls specification, and assessment approaches, these initiatives are fragmented and limited in scope. This paper proposes a novel methodology, grounded in design science research, to integrate these elements and create a comprehensive DF controls library for both industry and academia. This approach is applied to develop the Demand FLEXibility controls LIBrary using Semantics (DFLEXLIBS), an extensible open-source library that provides DF controls for HVAC systems in Python. DFLEXLIBS enables portable, easy-to-deploy controls that abstract building-specific data points, facilitating assessment across diverse buildings. DFLEXLIBS features nine different control applications, and it is successfully implemented and tested across four virtual and two real buildings, bridging the gap between semantics-driven portability, DF controls specification, and rigorous performance assessment. Its benefits are measured by a reusability ratio greater than 90% and a functional overlap ratio of around 70% for the most common functions used in the library, significantly reducing time for deploying new controls.

Controls library↗
DOE OSTI2025

Enhanced mid-latitude warming due to poleward shift of sea salt aerosols in a warmer future

Natural aerosols provide large uncertainties regarding the quantification of the climate effects of aerosols, especially those related to aerosol-cloud interactions. Advancing research on the interaction of natural aerosols and climate can provide a better assessment of the climate impacts of anthropogenic activities. Furthermore, as one of the most dominant natural aerosols, sea salt has the highest proportion of mass in natural aerosols. Sea salt can be regarded as a purely scattering aerosol, causing a negative radiative effect by scattering shortwave solar radiation and acting as cloud condensation nuclei (CCN) through aerosol-cloud interactions. Future climate change will influence sea salt distribution, thus modifying the radiative effects of sea salt, which will further affect the climate. However, few previous studies have explored the interactions between sea salt changes under warming scenarios and climate change.

Yang, Yang [Nanjing Univ. of Information Science a↗
DOE OSTI2024

Advances in Engineering Nucleotide Sugar Metabolism for Natural Product Glycosylation in Saccharomyces cerevisiae

Glycosylation is a ubiquitous modification present across all of biology, affecting many things such as physicochemical properties, cellular recognition, subcellular localization, and immunogenicity. Nucleotide sugars are important precursors needed to study glycosylation and produce glycosylated products. Saccharomyces cerevisiae is a potentially powerful platform for producing glycosylated biomolecules, but it lacks nucleotide sugar diversity. Nucleotide sugar metabolism is complex, and understanding how to engineer it will be necessary to both access and study heterologous glycosylations found across biology. This review overviews the potential challenges with engineering nucleotide sugar metabolism in yeast from the salvage pathways that convert free sugars to their associated UDP-sugars to de novo synthesis where nucleotide sugars are interconverted through a complex metabolic network with governing feedback mechanisms. Finally, recent examples of engineering complex glycosylation of small molecules in S. cerevisiae are explored and assessed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Electrically writing a magnetic heliknoton in a chiral magnet

A magnetic heliknoton is the three-dimensional counterpart to the two-dimensional magnetic skyrmion, and serves as a pivotal topological soliton for extending topological magnetism into three dimensions. However, its experimental realization remains elusive. Here we report the controlled nucleation of a magnetic heliknoton in the chiral magnet FeGe at zero magnetic field, achieved through nanoscale current-pulse excitation. By combining angle-dependent quantitative electron holography with micromagnetic simulations, we resolve the three-dimensional spin texture of the heliknoton. In particular, the heliknoton exhibits current-driven collinear motion without the Hall effect. Furthermore, our findings establish a readily accessible experimental platform for further exploration of three-dimensional topological solitons and highlight their potential for practical applications.

Magnetic properties and materials↗
DOE OSTI2026

Probing Excited-State Dynamics of Transmon Ionization

The fidelity and quantum nondemolition character of the dispersive readout in circuit QED are limited by unwanted transitions to highly excited states at specific photon numbers in the readout resonator. This observation can be explained by multiphoton resonances between computational states and highly excited states in strongly driven nonlinear systems, analogous to multiphoton ionization in atoms and molecules. In this work, we utilize the multilevel nature of high-𝐸 𝐽 /𝐸 𝐶 transmons to probe the excited-state dynamics induced by strong drives during readout. With up to ten resolvable states, we quantify the critical photon number of ionization, the resulting state after ionization, and the fraction of the population transferred to highly excited states. Moreover, using pulse shaping to control the photon number in the readout resonator in the high-power regime, we tune the adiabaticity of the transition and verify that transmon ionization is a Landau-Zener-type transition. We further extend these methods to a typical transmon with 𝐸 𝐽 /𝐸 𝐶 ≈ 55 and probe the offset-charge dependence of ionization dynamics in a timed-resolved manner. Our experimental results agree well with the theoretical prediction from a semiclassical driven transmon model and may guide future exploration of strongly driven nonlinear oscillators.

cavity quantum electrodynamics↗
DOE OSTI2024

Advanced Fuels Campaign Execution Plan

The Advanced Fuels Campaign (AFC) Execution Plan outlines the strategy, mission, scope, near-term and long-term goals, structure, and organization associated with nuclear fuels and materials research, development, and demonstration activities within the Department of Energy’s (DOE) Nuclear Fuel Cycle and Supply Chain (NFCSC) program. NFCSC has been given responsibility to identify and mature advanced fuel technologies for the DOE using a science-based approach, focused on developing a fundamental understanding of nuclear fuels and materials to drive development of integrated nuclear fuel and materials technology. This science-based approach combines theory, experiments, and multiscale modeling and simulation to achieve a predictive understanding of relevant behaviors ranging from fuel fabrication processes (and their resulting fuel microstructures) through fuel/cladding performance under irradiation (in contrast to more empirical, observation-based approaches frequently used in fuel performance modeling and fuel qualification). The traditional scope of AFC includes the evaluation and development of multiple fuel forms to support two fuel cycle options: once-through and full recycle. The word “fuel” is used generically to include conventional fuels, transmutation targets, and any associated cladding or duct materials. The once-through fuel cycle addresses advanced light water reactor fuels with enhanced performance, extended burnup, and reduced waste generation. In fiscal year (FY) 2012, AFC’s scope expanded to include research, development, and demonstration (RD&D) for light water reactor (LWR) fuels with enhanced accident tolerance. Fuel fabrication activities include the development of innovative methods to enhance process efficiencies, reduce waste, and improve control over as-fabricated fuel microstructural properties to achieve desired in-reactor performance. Using modern modeling and simulation approaches, the objective is to predict fresh fuel properties given the feedstock characteristics and fabrication process parameters. The performance-related activities include small-scale, in-reactor, and out-of-reactor phenomenological testing (distinct from, but synergistic with, integral prototypic testing) and extensive, quantitative characterization (focusing on characterization of fuel and cladding materials at the scale of microstructure) both before and after testing. Larger-scale, prototypic experiments are conducted in concert with phenomenological testing to drive a Fuel Development and Qualification program, incorporating a fundamental understanding of fuel behavior performance characteristics. Then, using the tools developed under the productive science-based approach, fuels will be optimized to meet specific performance requirements, thereby minimizing the need to repeatedly perform large-scale, integral experiments over a wide parametric range as a means of experimental exploration. Two significant initiatives are underway within AFC. First, a gap analysis completed in early FY 2019 identified critical irradiation testing needs that are lacking within the national light water reactor (LWR) fuels testbed since the shutdown of the Halden Reactor in 2018. The identified gaps are for instrumented, prototypic testing of LWR fuels, especially under boiling water reactor conditions, ramp conditions, and conditions leading to fuel failure; these needs exist for supporting current LWR fuels and their possible extension to higher burnups, but are especially urgent relative to near-term development and qualification of accident-tolerant fuels. Recommendations that resulted from the Halden Gap Analysis focused on enhancements at Advanced Test Reactor (ATR) and Transient Reactor Test Facility (TREAT) to fill gaps in testing capabilities relative to these needs. Second, a concerted effort to develop and demonstrate a systematic approach to accelerating the development, testing, and qualification of new fuel systems has been initiated. This is highlighted by a test strategy that combines the considerable advances in multiscale, mechanistic fuel modeling of recent years with a MiniFuel separate effects test program in the High Flux Isotope Reactor (HFIR) and a Fission Accelerated Steady-state Testing (FAST) semi-integral accelerated test program in ATR. This approach is being tested/demonstrated using the metallic fuel system, but if successful it is expected to be extensible to multiple fuel types and diverse applications. This document includes an overview of the NFCSC program, a definition of science-based development of nuclear fuels, near-term goals for Advanced LWR fuels (ALFs), and longer-term goals for Advanced Reactor Fuels (ARFs) RD&D. This includes the activities that will be conducted to achieve success toward the grand challenge, as well as the goals and milestones to be achieved over the next few decades of research and development. Long-term goals are based on the DOE Office of Nuclear Energ

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Macroscopic Monochalcogenide van der Waals Ferroics: Growth, Domain Structures, and Curie Temperature

Two-dimensional and layered van der Waals materials promise to overcome the limitations of conventional ferroelectrics in terms of miniaturization and material integration, but synthesis has produced only small (up to few micrometer-sized) ferroic crystals. Here, we report the realization of in-plane ferroelectric few-layer crystals of the monochalcogenides tin(II) sulfide and selenide (SnS, SnSe) whose linear dimensions exceed the current state of the art by up to 1 order of magnitude. Such large crystals allow the investigation of ferroic domain patterns that are unaffected by edges and finite-size effects. Analysis of the abundant stripe domains by electron microscopy and nanobeam electron diffraction shows two distinct domain types: twin domains separated by positively charged walls with alternating head-to-head and tail-to-tail polarization as well as not previously observed purely rotational domains connected by neutral domain walls with head-to-tail dipoles. Access to large crystals allowed the determination of the Curie temperature of few-layer SnSe van der Waals ferroelectrics, and it enables the investigation of this class of ferroelectrics by widely available methods such as polarized optical microscopy. Furthermore, the combination with layer transfer protocols promises uniform materials for exploring fundamentals and for implementing devices for information processing and energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Containers on Switches: A Cluster School Experience

Network switches, such as those from Arista and Mellanox, often have underutilized computational resources in the form of built-in processors and memory. By leveraging these untapped resources, we can optimize functionality and efficiency of computational cluster networks. Our research focuses on deploying containers directly onto these switches to execute various auxiliary tasks ranging from metric logging to system-wide management via post-boot configuration. By doing so, we can significantly enchance the capabilities of the cluster without the need for additional dedicated hardware. Our research involved five distinct scenarios where switch utilization could have a profound impact on HPC Clusters: run cloud-init services via link-local connection; configuring a Telegraf container to export metrics; deploying a caching proxy; creating a reconfigurable IPv6 DHCP/DNS provider for VLAN; and implementing a client detection with Magellan discovery. These scenarios were containerized with podman and docker, and tested both physically on the switch virtually on a QEMU VM both running SONiC OS. Testing and findings indicate that network switches can indeed be used for these scenarios. They offer a wide range of possibilities beyond these applications. They run as expected as containers on the switches, and although there were some minor issues, work-arounds were implemented. Overall, this is a positive result that can be further explored with more scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2024

Graduating Sustainable Industrial Decarbonization Solutions from the Laboratory to Real-World Deployments

Accelerating innovation and integration of industrial decarbonization technology solutions is crucial for achieving industry-wide net-zero greenhouse gas emissions targets. This can be accomplished in various ways through increased utilization of low-embodied carbon materials and the adoption of advanced manufacturing techniques in construction practices. At the National Renewable Energy Laboratory (NREL), we facilitate dialogue and action across all sectors to uncover industry needs to inform innovation, and explore ways we can help with implementation of emerging solutions in the real world. As an applied laboratory, NREL sits between academia and industry to fully bridge the gap from foundational science research to examining the feasibility of market applications. Integration of innovative technologies and solutions that are efficient, resilient, and grid-interactive will play a pivotal role in supporting a clean energy future. However, it is imperative that these solutions prioritize equitable outcomes, addressing the challenges faced by all communities. By embracing decarbonization, the construction industry can revolutionize itself, promoting sustainability, reducing carbon emissions, conserving resources, while enhancing performance and durability - all major steps toward a greener and more sustainable future.

cement↗
DOE OSTI2025

Airborne imaging spectroscopy surveys of Arctic and boreal Alaska and northwestern Canada 2017–2023

Since 2015, NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) has investigated how climate change impacts the vulnerability and/or resilience of the permafrost-affected ecosystems of Alaska and northwestern Canada. ABoVE conducted extensive surveys with the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) during 2017, 2018, 2019, and 2022 and with AVIRIS-3 in 2023 to characterize tundra, taiga, peatlands, and wetlands in unprecedented detail. The ABoVE AVIRIS dataset comprises ~1700 individual flight lines covering ~120,000 km 2 with nominal 5 m × 5 m spatial resolution. Data include individual transects to capture important gradients like the tundra-taiga ecotone and maps of up to 10,000 km 2 for key study areas like the Mackenzie Delta. The ABoVE AVIRIS surveys enable diverse ecosystem science, provide crucial benchmark data for validating retrievals from the PACE, PRISMA, and EnMAP satellite sensors and help prepare for the SBG and CHIME missions. This paper guides interested researchers to fully explore the ABoVE AVIRIS spectral imagery and complements our guide to the ABoVE airborne synthetic aperture radar surveys.

Miller, Charles E. [California Institute of Techno↗
DOE OSTI2026

The Hydropower Game: An Interactive Learning Tool for the Future Hydropower Workforce

This report documents a Seedling project supported by the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO), conducted by Argonne National Laboratory (ANL) and the National Laboratory of the Rockies (NLR). The Seedling and Sapling Program provides small grants over short duration to enable early-stage research at national laboratories. This funding mechanism is intended to cultivate innovative ideas and expand research road maps in hydropower and marine energy technologies. Under this Seedling award, the project team developed an educational outreach tool, or “serious game”, built in Python and powered by the Pygame library, aimed at teaching fundamental hydropower concepts through an engaging, interactive learning experience. The game features multiple lessons covering several hydropower topics while allowing for expansion and customization in the instance of future funding availability: • Hydropower Plant Types: Players explore and compare the mechanics and applications of run-of-river, dam hydropower, and pumped-storage hydropower. • Flow Dynamics and Power Generation: Interactive tasks demonstrate how release rate and hydraulic head combine to determine power output. • Grid Operations and Load Following: Simulations illustrate how hydropower facilities respond to fluctuating electricity demand to balance the grid. • Market Integration: Levels scaffold understanding of how hydropower interfaces with the broader energy market, including operational and economic considerations. The tool was showcased at several science, technology, engineering, and mathematics (STEM) outreach events, where it was demoed to students, educators, and the general public. These events provided valuable opportunities to collect feedback on the gameplay, storyline, and educational objectives from a wide audience. The game was presented at STEMapalooza, Introduce a Girl to Engineering Day, STEMCON, and Clean Currents 2025. Insights gathered during these demonstrations informed refinements to the game’s user experience and strengthened its effectiveness as an educational tool for teaching hydropower concepts. This report outlines the game’s design philosophy, educational objectives, technical implementation, user experience insights, and potential for broader deployment within educational and workforce development contexts. It emphasizes how gamified learning can demystify complex hydropower science and inspire interest in water-power technologies. The project’s success demonstrates the value of Seedling funding in fostering creative, lowcost educational tools that support DOE’s mission to advance energy literacy and innovation. The report concludes with recommendations for expanding the tool by adding new levels, integrating assessment metrics, and exploring commercialization or deployment pathways through future Sapling funding. The official webpage of the Hydropower Game, which includes a link to the educational tool, can be accessed at www.anl.gov/hydropower/hydropower-game.

13 HYDRO ENERGY↗
DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗
DOE OSTI2026

Multi-system analysis of offshore geologic carbon storage: a review of open-source data science solutions

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. Here, we provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

artificial intelligence↗
DOE OSTI2026

The LCLStream Ecosystem for Multi-Institutional Dataset Exploration

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications – AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique contribution to the DOE Integrated Research Infrastructure (IRI) landscape. By creating a flexible, API-driven data request service, we address a significant need for high-speed data streaming sources for the X-ray science data analysis community. With the combination of data request API, mutual authentication web security framework, job queue system, high-rate data buffer, and complementary nature to facility infrastructure, the LCLStreamer framework has prototyped and implemented several new paradigms critical for future generation experiments.

Rogers, David [ORNL] (ORCID:0000000251871768)↗
DOE OSTI2025

De Novo Design of High‐Affinity Miniprotein Binders Targeting Francisella Tularensis Virulence Factor

Abstract Francisella tularensis poses considerable public health risk due to its high infectivity and potential for bioterrorism. Francisella‐like lipoprotein (Flpp3), a key virulence factor unique to Francisella, plays critical roles in infection and immune evasion, making it a promising target for therapeutic development. However, the lack of well‐defined binding pockets and structural information on native interactions has hindered structure‐guided ligand discovery against Flpp3. Here, we used a combination of physics‐based and deep‐learning methods to design high‐affinity miniprotein binders targeting two distinct sites on Flpp3. We identified four binders for site I with binding affinities ranging between 24–110 nM. For the second site, an initial binder showed a dissociation constant ( K D ) of 81 nM, and subsequent site saturation mutagenesis yielded variants with sub‐nanomolar affinities. Circular dichroism confirmed the topology of designed miniproteins. The X‐ray crystal structure of Flpp3 in complex with a site I binder is nearly identical to the design model (Cα root‐mean‐square deviation (RMSD): 0.9 Å). These designed miniproteins provide research tools to explore the roles of Flpp3 in tularemia and should enable the development of new therapeutic candidates.

Gokce‐Alpkilic, Gizem [Molecular Engineering and S↗