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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fluid Mechanics Challenges in Direct-Ink-Writing Additive Manufacturing

Direct-ink writing (DIW) has rapidly become a versatile 3D fabrication method due to its ability to deposit a wide range of complex fluids into customizable 3D geometries. This review highlights key fundamental fluid mechanics and soft matter challenges across the different stages of the DIW printing process. The rheology of fluids and suspensions governs the flow behavior through narrow nozzles, posing questions about extrudability, confined flow dynamics, and clogging mechanisms. Downstream, the formation and deposition of extruded filaments involve extensional flows and potential instabilities, while postdeposition dynamics introduces complexities related to yield stress and structural stability. These stages are inherently interdependent, as optimizing material composition without considering filament stability risks compromising the final structure. As DIW applications expand through advanced ink formulations, developing fundamental fluid mechanics frameworks is essential to replace trial-and-error approaches with predictive design methodologies to enable more precise control and improved reliability of the printing process.

3D printing↗
DOE OSTI2025

Common occupational classification system amendments for Accelerator Science and Engineering workforce

A common system to classify occupations and skills is essential to support accelerator workforce planning between the Department of Energy (DOE) National Laboratories. In 2025, ten DOE laboratories conducted a census of their accelerator science and engineering workforce and projected their accelerator workforce needs for the next ten years. To support this, revision 3 of the “Common Occupational Classification System” (COCS) was used as a common taxonomy. Modifications were made to include accelerator-specific skills and specialisms. COCS was originally developed for DOE Office of Environmental Restoration and Waste Management in 1996. The framework provides a high-level functional structure that can be expanded with further occupations and specialisms and remains well aligned to DOE laboratory roles nearly 30 years later. Accelerator occupations and specialisms were added to the framework for the 2025 effort to quantify the accelerator workforce. This document is a companion document to COCS and provides a definition for these new occupations and specialisms that do not appear in COCS. Proceeding with a stable taxonomy is seen as essential such that its use becomes easier each year; the taxonomy as used for the 2025 effort is recommended to be continued.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Structural and functional analyses of SARS-CoV-2 Nsp3 and its specific interactions with the 5’ UTR of the viral genome

ABSTRACT Non-structural protein 3 (Nsp3) is the largest open reading frame encoded in the SARS-CoV-2 genome, essential for the formation of double-membrane vesicles (DMV) wherein viral RNA replication occurs. We conducted an extensive structure-function analysis of Nsp3 and determined the crystal structures of the ubiquitin-like 1 (Ubl1), nucleic acid binding (NAB), β-coronavirus-specific marker (βSM) domains, and a sub-region of the Y domain of this protein. We show that the Ubl1, ADP-ribose phosphatase (ADRP), human SARS Unique (HSUD), NAB, and Y domains of Nsp3 bind the 5’ UTR of the viral genome and that the Ubl1 and Y domains possess affinity for recognition of this region, suggesting high specificity. The Ubl1-Nucleocapsid (N) protein complex binds the 5’ UTR with greater affinity than the individual proteins alone. Our results suggest that multiple domains of Nsp3, particularly Ubl1 and Y, shepherd the 5’ UTR of the viral genome during translocation through the DMV membrane, priming the Ubl1 domain to load the genome onto N protein. IMPORTANCE The largest protein encoded by the SARS-CoV-2 genome is Nsp3. In infected cells, this multi-domain protein forms a pore structure in the virus-induced double-membrane vesicles (DMV). We have incomplete data on Nsp3 molecular structure, and here, we describe crystal structures for multiple domains of Nsp3. It is thought that newly replicated viral RNA transits through the DMV pore; however, we possess incomplete data on which regions of Nsp3 actually interact with RNA. Here, we present data showing that five domains of Nsp3 interact with the 5’ UTR of the SARS-CoV-2 RNA, including the Y domain for which no function has ever been discovered. These data suggest that the pore structure plays an active role in recognizing the terminal end of the genome, transiting and loading the viral RNA onto the cytoplasmic nucleocapsid protein. These data help expand our knowledge of Nsp3 structure and function and the SARS-CoV-2 replication cycle.

Microbiology↗
DOE OSTI2022

Exciton-vibrational dynamics induces efficient self-trapping in a substituted nanoring

Cycloparaphenylenes, being the smallest segments of carbon nanotubes, have emerged as prototypes of the simplest carbon nanohoops. Their unique structure–dynamics–optical properties relationships have motivated a wide variety of synthesis of new related nanohoop species. Studies of how chemical changes, introduced in these new materials, lead to systems with new structural, dynamics and optical properties, expand their functionalities for optoelectronics applications. Herein, we study the effect that conjugation extension of a cycloparaphenylene through the introduction of a satellite tetraphenyl substitution has on its structural and dynamical properties. Our non-adiabatic excited state molecular dynamics simulations suggest that this substitution accelerates the electronic relaxation from the high-energy band to the lowest excited state. This is partially due to efficient conjugation achieved between specific phenyl units as introduced by the tetraphenyl substitution. We observe a particular exciton redistribution during relaxation, in which the tetraphenyl substitution plays a significant role. As a result, an efficient inter-band energy transfer takes place. Besides, the observed phonon-exciton interplay induces a significant exciton self-trapping. Finally, our results encourage and guide the future studies of new phenyl substitutions in carbon nanorings with desired optoelectronic properties.

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

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗
DOE OSTI2022

Leaf microscopy applications in photosynthesis research: identifying the gaps

Abstract Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure–function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

Plant Sciences↗
DOE OSTI2022

Embedded acoustic sensing and monitoring techniques for small modular reactors

The goal of this project was to study the applicability of specific innovative instrumentation techniques for assessing parameters needed for safe small modular reactor operation and safeguarding of nuclear material. Small modular reactor core designs are currently being developed to provide energy more effectively and efficiently than in the past because they can be built as modules at fabrication sites and then transported to a power-producing facility. However, these modules and/or the final core will often be sealed and not accessible again until disposal. Instead of instruments that access the core directly, during and after operation, to monitor flux/dose and structural integrity, as current power plants use, new sensors need to be designed that can be built into the reactor initially to determine operating history, structural integrity through operation of the system, and nuclear material accountancy after shutdown and before disposition of the core. Embedded sensors already exist that can provide neutron flux, gamma dose, and temperatures, but techniques to expand upon these for assessing structural health and material inventory in the system over time need to be developed. Structural health assessments include the detection and imaging of cracks in the components that could eventually cause radioactive fission products to be released.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Three-dimensional atomic packing in amorphous solids with liquid-like structure

Liquids and solids are two fundamental states of matter. However, our understanding of their three-dimensional atomic structure is mostly based on physical models. Here we use atomic electron tomography to experimentally determine the three-dimensional atomic positions of monatomic amorphous solids, namely a Ta thin film and two Pd nanoparticles. We observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Instead of forming icosahedra, the majority of pentagonal bipyramids arrange into pentagonal bipyramid networks with medium-range order. Molecular dynamics simulations further reveal that pentagonal bipyramid networks are prevalent in monatomic metallic liquids, which rapidly grow in size and form more icosahedra during the quench from the liquid to the glass state. Furthermore, these results expand our understanding of the atomic structures of amorphous solids and will encourage future studies on amorphous–crystalline phase and glass transitions in non-crystalline materials with three-dimensional atomic resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗
DOE OSTI2021

Revealing High-Temperature Reduction Dynamics of High-Entropy Alloy Nanoparticles via In Situ Transmission Electron Microscopy

Understanding the behavior of high-entropy alloy (HEA) materials under hydrogen (H 2 ) environment is of utmost importance for their promising applications in structural materials, catalysis, and energy-related reactions. Herein, the reduction behavior of oxidized FeCoNiCuPt HEA nanoparticles (NPs) in atmospheric pressure H-2 environment was investigated by in situ gas-cell transmission electron microscopy (TEM). The reduction reaction front was maintained at the external surface of the oxide. During reduction, the oxide layer expanded and transformed into porous structures where oxidized Cu was fully reduced to Cu NPs while Fe, Co, and Ni remained in the oxidized form. In situ chemical analysis showed that the expansion of the oxide layer resulted from the outward diffusion flux of all transition metals (Fe, Co, Ni, Cu). Revealing the H-2 reduction behavior of HEA NPs facilitates the development of advanced multicomponent alloys for applications targeting H 2 formation and storage, catalytic hydrogenation, and corrosion removal.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structural transitions in the GTP cap visualized by cryo-electron microscopy of catalytically inactive microtubules

Microtubules (MTs) are nonequilibrium polymers that switch between states of growth and shrinkage. This property is critical for their function and is a consequence of GTP hydrolysis in the MT. The structure of the stable GTP part of the MT (the GTP cap) has previously been inferred from MTs polymerized with nonhydrolyzable GTP analogs. Here, we report high-resolution structures of MTs truly containing GTP, polymerized from mutated, hydrolysis-deficient tubulins. We find that GTP-MTs have an “expanded lattice” and a “closed seam,” structural characteristics possibly responsible for stabilizing the GTP cap. These results provide insight into the structural transitions at growing MT ends, furthering our understanding of the bistable nature of MTs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Statistics on magnetic properties of Co compounds: A database-driven method for discovering Co-based ferromagnets

The search for new ferromagnetic compounds is often targeted at known structure families, particularly those containing iron, cobalt, and manganese. Here, we propose a method to expand this search to lesser-known structure types, using a database of experimental Curie and Néel temperatures. This study, in particular, illustrates the case of compounds containing the element cobalt, and we demonstrate how the use of such a database can lead to the discovery of ferromagnetic materials that had previously been overlooked. We report statistics from a literature survey of the magnetic properties of Co-based compounds with more than 33 at. % Co. We classify more than 13 000 compounds by structure type, cobalt content, and magnetic ground state. From these data, compounds TaCo 2 Ga, La 6 Co 13 Bi, and Nd 2 Co 3 were identified as potential ferromagnets, and we confirm their ferromagnetic ordering theoretically via first-principles calculations and experimentally via synthesis and characterization measurements. In addition, the analysis is focused on the collection of data trends and discovery of ferromagnetic materials with easy-axis magnetic anisotropy. Both known ferromagnetic materials with unknown magnetic anisotropy, and unstudied compounds were considered. From the subset of known ferromagnets with unknown anisotropy, the compound Co 2 Mg was synthesized, characterized, and determined to have easy-axis magnetic anisotropy at room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Vortex rings from high energy central 𝑝 + 𝐴 collisions

Relativistic p + A collisions may produce droplets of quark gluon plasma (QGP) that quickly develop a toroidal vortex structure similar to that of an expanding smoke ring. We present viscous relativistic hydrodynamic calculations of ultra-central p + A collisions and develop an experimental observable to probe the structure, correlating the polarization and momentum of hyperons emitted from the collision. This effect is robust against changes in the definition of vorticity used to calculate the polarization. Experiments at RHIC and LHC may test the existence and strength of the vortex toroids, bringing new evidence to bear on the question of collectivity in the smallest QGP droplets.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Structure of Nd 155 and Gd 163 from Cf 252 spontaneous fission

Background: A puzzle has arisen recently caused by the apparent shift in maximum deformation from the expected 66 Dy isotopic chain to the Nd 60 isotopic chain in the 82 155 Nd and 163 Gd, useful for constraining parameters in models that seek to answer the six proton shift in maximum deformation. Method: Data from the spontaneous fission of 252 Cf were taken by the Gammasphere detector array at Lawrence Berkeley National Laboratory to observe the excited states of 155 Nd and 163 Gd. Results: The structure of 163 Gd has been expanded with the addition of two new levels and three new γ rays, which are found to be consistent with previously published calculations and the structure of 165 Dy. In 155 Nd, nine new levels and 12 new γ rays are observed. The spins and parities of the previously known levels in 155 Nd have been reassigned from a ν3/2 - [521] ground state configuration to a ν5/2 + [642] isomeric configuration by comparison of these newly observed levels with levels in 153 Nd and 155 Sm. Conclusion: Further experimentation is required to determine the energy of the newly reassigned ν5/2+[642] level in 155 Nd with respect to the suspected ν3/2-[521] ground state. Additionally, more experiments should be conducted to further determine the structure of neutron rich nuclei, rarely produced in the spontaneous fission of 252 Cf, such as 163 Gd.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Biochemical and structural characterization of enzymes in the 4-hydroxybenzoate catabolic pathway of lignin-degrading white-rot fungi

White-rot fungi (WRF) are the most efficient lignin-degrading organisms in nature. However, their capacity to use lignin-related aromatic compounds, such as 4-hydroxybenzoate, as carbon sources has only been described recently. Previously, the hydroxyquinol pathway was proposed for the bioconversion of these compounds in fungi, but gene- and structure-function relationships of the full enzymatic pathway remain uncharacterized in any single fungal species. Here, we characterize seven enzymes from two WRF, Trametes versicolor and Gelatoporia subvermispora, which constitute a four-enzyme cascade from 4-hydroxybenzoate to β-ketoadipate via the hydroxyquinol pathway. Furthermore, we solve the crystal structure of four of these enzymes and identify mechanistic differences with the closest bacterial and fungal structural homologs. Overall, this research expands our understanding of aromatic catabolism by WRF and establishes an alternative strategy for the conversion of lignin-related compounds to the valuable molecule β-ketoadipate, contributing to the development of biological processes for lignin valorization.

59 BASIC BIOLOGICAL SCIENCES↗