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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Atomically Revealing Bulk Point Defect Dynamics in Hydrogen‐Driven γ‐Fe 2 O 3 → Fe 3 O 4 → FeO Transformation

Understanding how point defects in the bulk govern redox transformations is essential for advancing hydrogen-based metal production and designing high-performance oxide materials. This study reveals the atomic-scale mechanisms driving hydrogen-induced reduction of γ-Fe 2 O 3 to Fe 3 O 4 , focusing on how bulk vacancy dynamics dictate structural evolution and reaction kinetics. A key finding is the pronounced contrast in defect behavior between the two oxides: in γ-Fe 2 O 3 , intrinsic Fe vacancies promote oxygen vacancy clustering, destabilizing the local lattice and driving nanopore formation. In contrast, Fe 3 O 4 exhibits a higher oxygen vacancy formation energy and lacks intrinsic Fe vacancies, suppressing vacancy aggregation and maintaining a dense, pore-free structure. This divergence governs distinct reduction pathways—γ-Fe 2 O 3 undergoes an interface-reaction-limited transformation confined to the γ-Fe 2 O 3 /Fe 3 O 4 boundary, while Fe 3 O 4 supports a uniform increase in oxygen vacancy concentration, enabling bulk-phase reduction to lower-oxide FeO. Integrated in situ electron microscopy and density functional theory modeling uncover a vacancy-mediated mechanism, where synergistic cation-anion vacancy dynamics steer microstructure evolution and phase progression. These insights highlight the critical role of vacancy dynamics in controlling oxide reactivity and offer a pathway toward vacancy engineering to enhance reduction kinetics in hydrogen metallurgy and to tailor porosity, reactivity, and structural resilience in oxide-based catalysts and energy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microbially mediated nitrification improves modeled temperate forest responses to declining nitrogen deposition

As nitrogen deposition declines across the US, uncertainty remains in whether temperate forests will continue to sequester carbon. This uncertainty is amplified by ecosystem models that inaccurately capture the microbial mechanisms that drive soil carbon sequestration and nitrogen loss. Further, even soil process models that represent decomposer microbes and better capture soil carbon retention under nitrogen deposition broadly underrepresent microbial nitrogen transformations. To address this limitation, we leveraged three decades of biogeochemical cycling data from a whole-watershed nitrogen fertilization experiment to incorporate microbially-driven nitrification in the FUN-CORPSE (Fixation and Uptake of Nitrogen-Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment) model. Our objectives include: 1) Reproducing key ecosystem responses to fertilization, 2) Integrating microbially-explicit nitrification in FUN-CORPSE, and 3) Assessing modeled soil C and N under projected N deposition shifts. FUN-CORPSE accurately represented soil C and streamwater N losses under ambient N deposition and captured how fertilization induced a 25 % decline in plant C cost of N acquisition, reducing decomposition and increasing soil C. Furthermore, with microbially-explicit nitrification, FUN-CORPSE captured the 100 % increase in nitrification rates and the 50 % increase in streamwater nitrate loss under N fertilization. Specifically, incorporating microbial nitrification improved modeled streamwater N leaching from R 2 = 0.01 to R 2 = 0.57. Under declining N deposition, FUN-CORPSE simulates that N losses recover more quickly than soil C pools. However, the predicted return of soil C to pre-fertilized levels suggests that additional C sequestered due to N deposition may be vulnerable to loss over the next century.

Microbial nitrification model↗
DOE OSTI2024

Accelerating the identification of novel secondary metabolites in bioenergy plant root exudates using MicroED

Small molecule metabolites drive inter- and intraspecies communication and dependencies in diverse biological systems, yet a large proportion of these important chemical compounds remain uncharacterized in plants and microbes. Approximately 90% of the metabolites in root exudate profiles are unknown compounds, despite the importance of root exudate composition in plant-microbe interactions. We need advanced analytical capabilities that will support rapid discovery and structural elucidation of metabolites from biological samples that may be limited in quantity and high in complexity. To fill this gap, this project aimed to develop an integrated workflow involving metabolite extraction, separation, and crystallization from plant root exudates followed by characterization using nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and microcrystal electron diffraction (MicroED). Using crude root exudates from sorghum, this project successfully developed higher throughput exudate fractionation strategies to obtain pure compounds for crystallization and identified crystals in multiple fractions that diffracted. Additional efforts to increase the throughput of high-quality crystal generation for MicroED, such as crystallization screening and crystallization chaperone exploration, will be needed to further advance root exudate metabolite identification. The overall optimized sample preparation process can then be integrated with the existing data collection and data analysis pipelines for MicroED at PNNL to facilitate more rapid natural product discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

UNIFI's Grid-Forming (GFM) Inverter Reference Design: A Tutorial on Modeling, Control, and Experimental Implementation of GFM Inverters

The UNIFI Consortium's tutorial on grid-forming (GFM) inverters provides a comprehensive guide to the modeling, control, and experimental implementation of GFM inverters. As the integration of renewable energy accelerates, the transition from traditional grid following (GFL) to GFM inverters is crucial to ensure stable and sustainable power systems. This document outlines a reference design for three-phase and single-phase GFM inverters developed at the University of Texas at Austin. The tutorial also provides step-by-step guidance for accessing and using UNIFI’s GitHub repository, enabling users to design, build, and test GFM inverters efficiently. By fostering collaboration and equipping users with accessible resources, this initiative aims to drive widespread adoption of GFM technology across academia, utilities, and industries.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Chromium segregation-induced oxide evolution in Ni-10Cr alloys during high-temperature oxidation

The oxidation behavior of a Ni-10(wt%)Cr alloy under high-temperature O 2 conditions is investigated using transmission electron microscopy and first-principles calculations. Results reveal that chromium segregation plays a central role in driving the evolution of complex oxide phase structures during oxidation. At low Cr concentrations, Cr preferentially segregates to NiO grain boundaries or internal pores, substituting for Ni atoms and forming Ni(Cr)O solid solutions. As Cr content increases, enhanced diffusion promotes Cr penetration into the NiO lattice, leading to the formation of multiphase oxide structures. First-principles modeling corroborates these findings: at low Cr concentrations, Cr atoms favor surface and grain-boundary segregation, while higher concentrations lead to Cr aggregation within the NiO bulk. Furthermore, the integrated experimental-theoretical approach provides atomistic insights into Cr-mediated mass transport mechanisms during alloy oxidation and offers valuable guidance for controlling oxide growth kinetics and phase stability in Ni-Cr alloys, with implications for improving oxidation resistance in high-temperature structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Morphogenesis of spin cycloids in a noncollinear antiferromagnet

Pattern formation in spin systems with continuous-rotational symmetry (CRS) provides a powerful platform to study emergent complex magnetic phases and topological defects in condensed-matter physics. However, its understanding and correlation with unconventional magnetic order along with high-resolution nanoscale imaging are challenging. Here, we employ scanning nitrogen vacancy (NV) magnetometry to unveil the morphogenesis of spin cycloids at both the local and global scales within a single ferroelectric domain of (111)-oriented BiFeO 3 , which is a noncollinear antiferromagnet, resulting in formation of a glassy labyrinthine pattern. We find that the domains of locally oriented cycloids are interconnected by an array of topological defects and exhibit isotropic energy landscape predicted by first-principles calculations. We propose that the CRS of spin-cycloid propagation directions within the (111) drives the formation of the labyrinthine pattern and the associated topological defects such as antiferromagnetic skyrmions. Unexpectedly, reversing the as-grown ferroelectric polarization from [$\overline{1}$ $\overline{1}$ $\overline{1}$] to [111] produces a noncycloidal NV image contrast which ¯ could be attributed to either the emergence of a uniformly magnetized state or a reversal of the cycloid polarity. These findings highlight that (111)-oriented BiFeO 3 is not only important for studying the fascinating subject of pattern formation but could also be utilized as an ideal platform for integrating novel topological defects in the field of antiferromagnetic spintronics.

magnetoelectric↗
DOE OSTI2026

Air Emissions Impacts of Fuel Substitution in the United States Cement Industry

High-heat industrial processes cannot be easily electrified, often rely on comparatively high-emitting solid fuels, and are essential for economic growth. Cement production in particular is critical to supporting construction and the expansion and maintenance of infrastructure systems in countries around the world. Fuels for cement production include petcoke, coal, and waste solvents, and their combustion contributes approximately 40% of the CO 2 emissions from global cement production. Broadening the fuel resources to include agricultural residues and other wastes can reduce emissions and divert waste from landfills. We assess the potential to use 16 different alternative fuels across the 88 integrated cement plants in the United States. The share of alternative fuels in the cement production sector could increase from the current level of 17% to as high as 73%. Fuel substitution can reduce the current fuel-related life-cycle emissions (28.6 ± 5.7 Mton CO 2 /year) by 19–45%. The greatest national-scale human health cobenefits occur when alternative fuels are prioritized at facilities currently using coal and petcoke. NO x emissions outcomes from this strategy are mixed, but SO x and particulate matter emissions reductions drive net human health benefits. These results suggest that targeted fuel substitution in heavy industries can yield outsized emissions benefits.

alternative fuels↗
DOE OSTI2025

Dynamics of nanoscale phase decomposition in laser ablation

Abstract Laser ablation is a process that bears both fundamental physics interest and has wide industrial applications. For decades, the lack of probes on the relevant time and length scales has prevented access to the highly nonequilibrium phase decomposition processes triggered by laser excitation. In this study, a close integration of time-resolved probing by intense femtosecond X-ray pulses with large-scale atomistic modeling has yielded unique insights into the ablation dynamics of thin gold films irradiated by femtosecond laser pulses. The emergence and growth of nanoscale density heterogeneities in the expanding ablation plume, predicted in the simulations, are mapped to the rapid evolution of distinct small angle diffraction features. This mapping enables identification of the characteristic signatures of different phase decomposition processes occurring simultaneously in the plume, which are driven by photomechanical and thermodynamic driving forces. Beyond the specific insights into the ablation phenomenon, this study demonstrates the power of joint X-ray probing and atomistic modeling of material dynamics under extreme conditions of thermal and mechanical nonequilibrium.

Sun, Yanwen (ORCID:0000000159266565)↗
DOE OSTI2025

Inertial Confinement Fusion Design Search Using Bayesian Optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulation codes such as the Lawrence Livermore National Laboratory-developed HYDRA to guide design work. However, these simulations have several dozen tunable parameters and can be computationally expensive. This makes searching the parameter space challenging and time-consuming. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional parameter spaces for optimal designs. The optimization tools run 2D integrated simulations in HYDRA to converge on a design that produces specified scalar or vector outputs. In this paper, we apply the Bayesian optimization tools to two common tuning scenarios. First, we tune simulation inputs to match measurements of a well-characterized experiment at the National Ignition Facility. This type of tuning is commonly performed to compensate for the use of simplified simulation settings (e.g. reduced resolution) or to account for missing physics in the simulations. Second, we search for an ICF simulation design that has a particular radiation drive profile. These optimizations replicate the kinds of tuning researchers routinely perform, but do so with significantly reduced manual effort. This approach demonstrates a powerful and efficient pathway toward autonomous, high-fidelity design optimization for future ICF experiments.

Bayesian optimization↗
DOE OSTI2025

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗
DOE OSTI2024

Nonlinear control of the minimum safety factor in tokamaks by optimal allocation of spatially moving electron cyclotron current drive

The minimum value of the safety factor profile is related to the magnetohydrodynamic (MHD) stability of the plasma confined in a tokamak. Therefore, active control of the minimum safety factor may mitigate MHD instabilities that can degrade or even terminate plasma confinement. Typically, in most tokamak scenarios, the minimum safety factor evolves spatially with time, i.e., the location at which the safety factor achieves the minimum value changes with time. In addition to the inherent nonlinearities in the minimum safety factor evolution, its spatial variation makes the control design challenging. In particular, complexity in control design may arise from the need for time-dependent nonlinear models that account for spatial variation of the minimum safety factor. Furthermore, the minimum safety factor may drift to locations where the actuator authority is low. The problem of minimum safety factor control with target location tracking and moving electron cyclotron current drive (ECCD) is addressed in this work. A nonlinear time-dependent model that incorporates the spatial variation of the minimum safety factor is presented. A nonlinear controller based on optimal feedback linearization is developed to track a target minimum safety factor. The proposed controller treats the ECCD position as a controllable variable. In other words, the controller prescribes the ECCD position (in addition to the non-inductive powers) in real time based on an optimal criterion that is defined a priori. This work also presents the steps necessary to integrate the minimum safety factor controller with a total energy controller to achieve multiple control objectives simultaneously. In conclusion, the proposed integrated control algorithm is tested using nonlinear simulations in the Control Oriented Transport SIMulator (COTSIM) for a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Plasmonic Energetic Electrons Drive CO 2 Reduction on Defective Cu 2 O

Plasmonic photoreduction of CO 2 is valuable for decarbonization and producing value-added chemicals. However, insights into the mechanisms of this reaction remain elusive, particularly regarding the roles of structural defects and their interplay with the nonequilibrium charge carriers. Here, we report density functional theory calculations on Cu 2 O, a prototype photocatalyst, through which we investigate CO 2 reduction over three defected facets to reveal the interfacial charge transfer and bond dynamics under plasmonic excitation. We find that the activation barrier of C–O bond cleavage decreases from 3.2 to about 1 eV, assisted by oxygen vacancies, and that the remaining barrier can be further reduced or eliminated at the plasmon-excited states when Cu 2 O is integrated with plasmonic metals. The regeneration of oxygen vacancies (by H 2 to form water) on Cu 2 O to complete the catalysis cycle is feasible and not affected by the energetic electrons. Furthermore, our calculations thus show the important synergistic effect of energetic electrons and point defects to promote CO 2 reduction.

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

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Advancements in modeling fuel pulverization and cladding behavior during a LOCA

During a loss-of-coolant accident (LOCA), it is possible for nuclear fuel rods to undergo a three-step process known as fuel fragmentation, relocation, and dispersal (FFRD). The chance of FFRD occurring increases as the fuel burnup increases. To support the nuclear industry's desire to increase the discharge burnup of nuclear fuels in light-water reactors (LWRs), it is imperative to understand the mechanisms driving the evolution of FFRD. In this work, a multiscale modeling approach is used to garner insight into underlying mechanisms leading to the fine fragmentation (also known as pulverization) of nuclear fuel during a LOCA. This report includes a summary of the atomistic and phase-field studies to develop a new pulverization criterion for use in the engineering-scale Bison fuel performance code. Details are also provided on cladding model improvements related to hydrogen/hydride embrittlement and damage and anisotropic thermal creep. The new models are used on the existing integral and separate effects LOCA validation cases available in Bison.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Advancements in modeling fuel pulverization and cladding behavior during a LOCA

During a loss of coolant accident (LOCA), there is the possibility of nuclear fuel rods to undergo a three step process known as fuel fragmentation, relocation, and dispersal (FFRD). The chance of FFRD occurring increases as the fuel burnup increases. To support the nuclear industry's desire to increase the discharge burnup of nuclear fuels in light water reactors (LWRs) it is imperative to understand the mechanisms driving the evolution of FFRD. In this work, a multiscale modeling modeling approach is used to garner insight into underlying mechanisms leading to the ne fragmentation (also known as pulverization) of nuclear fuel during a LOCA. This report includes a summary of the atomistic and phase-field studies to develop a new pulverization criterion for use in the engineering scale Bison fuel performance code. Details are also provided on cladding modeling improvements related to hydrogen/hydride embrittlement and damage, and anisotropic thermal creep. The new models are used on the existing integral and separate effects LOCA validation cases available in Bison.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗