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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Mapping structural and dynamic divergence across the MBOAT family

Membrane-bound O-acyltransferases (MBOATs) are membrane-embedded enzymes that catalyze acyl chain transfer to a diverse group of substrates, including lipids, small molecules, and proteins. MBOATs share a conserved structural core, despite wide-ranging functional specificity across both prokaryotes and eukaryotes. The structural basis of catalytic specificity, regulation and interactions with the surrounding environment remain uncertain. Here, we combine comparative molecular dynamics (MD) simulations with bioinformatics to assess molecular and interactional divergence across the family. In simulations, MBOATs differentially distort the bilayer depending on their substrate type. Additionally, we identify lipid binding sites surrounding reactant gates in the surrounding membrane. Complementary bioinformatic analyses reveal a conserved role for re-entrant loop-2 in MBOAT fold stabilization and a key hydrogen bond bridging DGAT1 dimerization. Finally, we predict differences in MBOAT solvation and water gating properties. These data are pertinent to the design of MBOAT-specific inhibitors that encompass dynamic information within cellular mimetic environments.

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

Simulations of a hypersonic turbulent boundary layer over wavy surfaces

Here, we conduct large-eddy simulations of a Mach 5.84 cold wall turbulent boundary layer over one-dimensional wavy walls with varying amplitudes and wavelengths. Across all wall topologies, a series of alternating shock and expansion waves is shown to influence the entire boundary layer, and generate repeating wave patterns in the turbulent stresses, dispersive stresses, and turbulent kinetic energy budget. The series of alternating shocks and expansions imposes repeating adverse and favourable pressure gradients across the wavy wall, and at sufficient wall amplitude, triggers flow separation in the trough of the wave. Flow separation is demonstrated to influence the behaviour of wall pressure fluctuations over the wavy wall. In attached flows, the prominent frequencies are consistent with integral-scale boundary layer turbulence, whereas in separated flows, a two-decade frequency range is present, akin to two-dimensional shock–boundary layer interactions. Counter-rotating streamwise-oriented structures are observed on the windward side of the wave, which diminish over the wave crest. A conditional analysis demonstrates that these structures are present in the upstream boundary layer, and are amplified with increasing wall amplitude. An examination of the Görtler number and probability density function (PDF) of the fluctuating lateral wall shear stress demonstrates the strong correlation between a large Görtler number and growth of the PDF tail density, suggesting that the amplification of the counter-rotating streamwise-oriented structures are linked to centrifugal instabilities in regions of streamline concavity.

boundary layers↗
DOE OSTI2026

A New Nitrogen-Rich Energetic Material with So Many Tautomers

A new nitrogen-rich triazolo-triazole compound, (3-(6-methyl-1H-[1,2,4]triazolo[4,3-b][1,2,4]triazol-3-yl)-1H-1,2,4-triazol-5-amine), TTT1, has been prepared, and its acid–base and tautomeric behavior has been investigated. In the pH range of 0.3–12, TTT1 can accept up to two protons, forming a monocation and a dication, and can deliver one proton, forming a monoanion. The tautomeric behavior is particularly rich for the monocation, for which computational analysis predicts four different tautomers in a narrow energy range of 2 kcal/mol. Two of these tautomers (2H-7H-8H and 3H-7H-8H) have been isolated in salts of the monocation with suitable counterions (chloride, bromide, perchlorate). Surprisingly, the most stable predicted tautomer, 1H-3H-7H, has not been found in the four crystallized salts of the monocation. The energetic perchlorate salt of the monocation (3H-7H-8H tautomer) shows good thermal stability and good stability to impact, friction, and electric discharge. The packing of this compound shows the formation of H-bonded dimers with interactions between N8–H···N1. The crystal structure of this energetic salt was studied experimentally up to 2.8 GPa; no phase change or decomposition was observed.

Chemical structure↗
DOE OSTI2024

Anionic Effects on Concentrated Aqueous Lithium Ion Dynamics

In this article, the structural dynamics, chemical reactivity, anisotropy, diffusivity, viscosity, and density were measured for concentrated lithium salt solutions, including lithium chloride (LiCl), lithium bromide (LiBr), lithium nitrite (LiNO 2 ), and lithium nitrate (LiNO 3 ), with methyl thiocyanate as an infrared vibrational probe molecule, using two-dimensional infrared spectroscopy (2D IR), nuclear magnetic resonance (NMR) spectroscopy, and viscometry. 2D IR, NMR, and viscosity results show that LiNO 2 exhibits longer correlation times, lower diffusivity, and nearly four times greater viscosity when compared to the other lithium salt solutions of the same concentration, suggesting that nitrite anions may strongly facilitate structure formation via strengthening water-ion interactions, directly impacting bulk solution properties at sufficiently high concentrations. Additionally, the LiNO 2 and LiNO 3 solutions show significantly reduced chemical reactivity with respect to lithium cations coordinating with the methyl thiocyanate when compared to the lithium halide salts.

2D IR↗
DOE OSTI2026

One Beam, Dual Insights: Simultaneous Chemical and Structural Changes in Nanopatterned Ceria under Reaction Conditions

Ceria’s interaction with hydrogen can proceed through multiple chemical forms (hydride, hydroxyl, and oxyhydroxide-like), with consequences for the oxidation state, density, and morphology that are rarely tracked in the same evolving state. Here, in this work, we show that under mild H 2 (and H 2 and CO 2 ) environments nanopatterned ceria undergoes oxidation-state changes accompanied by hydrogen incorporation that increases the effective electron density, establishing the following order: CeO 2 Hy > CeO 2 > CeO 2–x Hy > CeO 2–x . In parallel, the surface roughens in a chemically specific manner, with the largest changes coinciding with conditions where incorporated hydrogen is driven to react with oxygen supplied either by air exposure between experiments or by added CO 2 . We obtained these insights by using a single X-ray beam to simultaneously perform ambient-pressure X-ray photoelectron spectroscopy and grazing-incidence X-ray scattering on the same sample spot. Single-mode measurements can miss key ceria–H 2 transformations relevant to optimizing ceria-based hydrogenation catalysts and supports.

anions↗
DOE OSTI2025

Defect-Driven Redox Interplay on Anatase TiO 2 : Surface-Structure Dependent Activation for CO 2 Hydrogenation Catalysis

Titanium dioxide (TiO 2 ) is one of the most extensively studied oxides as an active catalyst or catalyst support, particularly in energy and environmental applications, but the atomistic mechanisms governing its dynamic response to reactive environments and their correlation to reactivity remain largely elusive. Using in situ environmental transmission electron microscopy (ETEM), synchrotron X-ray diffraction (XRD), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), temperature-programmed reduction (TPR), reactivity measurements, and theoretical modeling, we reveal the dynamic interplay between oxygen loss and replenishment of anatase TiO 2 under varying reactive conditions. Under H 2 exposure, anatase TiO 2 undergoes surface reduction via lattice oxygen loss, forming Ti 3 O 5 . In contrast, CO 2 exposure induces oxygen replenishment, reversing stoichiometry. In mixed H2/CO 2 environments, the reverse water–gas shift (RWGS) reaction proceeds selectively on stepped and high-indexed TiO 2 surfaces, whereas the thermodynamically stable TiO 2 (101) surface remains inactive and intact. Critically, H 2 pretreatment generates oxygen vacancies on TiO 2 (101), transforming it into an active Ti 3 O 5 or defect-rich surface that catalyzes RWGS. By correlating surface structure, defect dynamics, and gas-phase interactions, this work deciphers the competition between H 2 -driven reduction and CO 2 -driven oxidation pathways at the atomic scale. Furthermore, these insights establish defect engineering as a strategic lever to activate inert TiO 2 facets, advancing the design of adaptive catalysts for sustainable fuel synthesis technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗
DOE OSTI2025

Incubating advances in integrated photonics with emerging sensing and computational capabilities

As photonic technologies grow in multidimensional aspects, integrated photonics holds a unique position and continuously presents enormous possibilities for research communities. Applications include data centers, environmental monitoring, medical diagnosis, and highly compact communication components, with further possibilities continuously growing. Herein, we review state-of-the-art integrated photonic on-chip sensors that operate in the visible to mid-infrared wavelength region on various material platforms. Among the different materials, architectures, and technologies leading the way for on-chip sensors, we discuss the optical sensing principles that are commonly applied to biochemical and gas sensing. Our focus is on passive optical waveguides, including dispersion-engineered metamaterial-based structures, which are essential for enhancing the interaction between light and analytes in chip-scale sensors. We harness a diverse array of cutting-edge sensing technologies, heralding a revolutionary on-chip sensing paradigm. Our arsenal includes refractive-index-based sensing, plasmonics, and spectroscopy, which forge an unparalleled foundation for innovation and precision. Furthermore, we include a brief discussion of recent trends and computational concepts, incorporating Artificial Intelligence & Machine Learning (AI/ML) and deep learning approaches over the past few years to improve the qualitative and quantitative analysis of sensor measurements.

Jain, Sourabh (ORCID:0000000279923275)↗
DOE OSTI2026

Quantum fluctuations in dense plasma simulations

Molecular dynamics (MD) simulations are a powerful tool for modeling warm and hot dense matter. Density functional theory (DFT) MD simulations are often preferred in dense plasmas in order to accurately model quantum electronic structure. However, DFT-MD simulations neglect interaction effects due to fluctuations in excited states. In this work, we present an MD approach that uses excited state method pseudoatoms to run dense plasma simulations with many different core-electron configurations at classical MD speeds. We also allow for transitions between different configurations in our simulations and find that these fluctuations are especially important for highly excited states. Our results suggest that finite configuration lifetimes that are comparable to the inverse ion plasma frequency need to be accounted for in order to accurately model ion distributions in dense plasma simulations. We also demonstrate that excited state fluctuations have a direct impact on ion plasma microfields, generate different plasma microfields for different excitation levels, and thereby induce absorption–emission line shape asymmetries even in steady-state plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visualizing nucleation, condensation and propagation of β-tubulin folding in chaperonin TRiC

AbstractThe folding nucleus (FN) initiates protein folding and enables an efficient folding pathway. Here we directly visualize the tubulin FN consisting of a nonnative, partially assembled Rossmann fold, in the closed chamber of human chaperonin TRiC. Chaperonin TRiC interacts with non-natively folded secondary structural elements, stabilizing the nucleus for transition into its first native domain. Through progressive folding, the unfolded sequence goes through drastic spatial arrangement in the TRiC chamber to sample the conformational space, mediated by the highly dynamic CCT tails. The observed presence of individual nonnative secondary structures first in the nonnative FN and then around the incrementally folded native domains supports the hypothesis that tubulin folding in TRiC is a hierarchical process of nucleation, condensation and propagation in cooperation with TRiC subunits.

Zhao, Yanyan (ORCID:0000000323021763)↗
DOE OSTI2024

Spatiotemporal Bragg gratings forming inside a nonlinear dispersive medium

We show that a spatiotemporal Bragg grating can be created inside a nonlinear dispersive medium (such as silica fibers) by launching a periodic train of pump pulses that travel as fundamental solitons. We develop a theoretical model and use it to find the band structure of such gratings. We study the interaction of a probe pulse with the Bragg grating, both within and outside of momentum gaps. We also show that a photonic analog of the Anderson localization is possible when a disorder is introduced into a spatiotemporal Bragg grating.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗
DOE OSTI2025

AI in Astrophysics: Tackling Domain Shift, Model Robustness and Uncertainty

Artificial Intelligence (AI) is revolutionizing physics research from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This talk will introduce common challenges in applying AI across domains and present solutions based on domain adaptation a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery.

Ciprijanvoic, Aleksandra [Fermilab] (ORCID:0000000↗
DOE OSTI2026

Geographic Distribution of Populus trichocarpa Genotypes by ADMIXTURE Ancestry

An interactive map showing Populus trichocarpa GWAS population structure estimated by ADMIXTURE (k=3, selected as optimal from k=2-11). Sampling locations are colored by their predominant ancestry proportion among the three inferred populations and geographic origins are searchable by genotype or river system using the search bar.

Admixture↗
DOE OSTI2026

In Vitro Selection of Antibodies Targeting Yersinia pestis Membrane Lipids Using Nanodisc-Based Antigen Presentation

Proteins are the most common targets for antibody discovery and vaccine development, but their sequence variability can limit the breadth of resulting antigens. Lipids represent an alternative class of antigens due to their structural conservation and roles in host–pathogen interactions. Here, we describe the development and optimization of an in vitro antibody selection workflow using lipid-containing nanodiscs as antigen presentation platforms to enable phage and yeast display selections under conditions adapted for these non-protein targets. Lipopolysaccharide (LPS) nanodiscs were first used as a model system to evaluate selection strategies, including competitive and subtractive approaches to reduce non-specific binders, yielding peptide and single-chain variable fragment (scFv) binders that were affinity matured to improve binding signals. The same approach was subsequently used to select scFv antibodies that recognize lipid nanodiscs prepared from Yersinia pestis membrane lipid extracts. These antibodies show binding to lipid nanodiscs derived from Y. pestis, with evidence of selectivity relative to control nanodiscs. Overall, this work establishes a workflow for antibody selection against lipid-containing nanodisc antigens and highlights practical considerations associated with these targets. The approach may be useful for generating affinity reagents to membrane-associated lipids, although further characterization is required to define antigen specificity and functional activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Particle Acceleration in Relativistic Alfvénic Turbulence

Abstract Strong magnetically dominated Alfvénic turbulence is an efficient engine of nonthermal particle acceleration in a relativistic collisionless plasma. We argue that in the limit of strong magnetization, the type of energy distribution attained by accelerated particles depends on the relative strengths of turbulent fluctuations δ B 0 and the guide field B 0 . If δ B 0 ≪ B 0 , the particle magnetic moments are conserved, and the acceleration is provided by magnetic curvature drifts. Curvature acceleration energizes particles in the direction parallel to the magnetic field lines, resulting in log-normal tails of particle energy distribution functions. Conversely, if δ B 0 ≳ B 0 , interactions of energetic particles with intense turbulent structures can scatter particles, creating a population with large pitch angles. In this case, magnetic mirror effects become important, and turbulent acceleration leads to power-law tails of the energy distribution functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Using Gamification to Enhance Mastery of Network Security Concepts

Gamification has proven to be effective in engaging and encouraging people to work towards and achieve goals. Many students struggle to focus on schoolwork, due to a lack of interest, lack of understanding, or other factors unique to the student. Applying gamification elements to education can help engage these students in learning their course material and help them excel academically. This study examines the effectiveness of using gamification techniques to enhance the learning experience in college Computer Science courses. A video game application is utilized to review and reinforce cybersecurity concepts that students have already been taught in class. Previous work has been made on a prototype game build that teaches about ARP (Address Resolution Protocol) components. The focus of this study is to refine and develop the structure of the prototype into a more interactive and enjoyable format with non-competitive and captivating activities that allow students to study at their own pace. An updated version of the game was created that focused on reaching a balance between education and entertainment. The game was used by students enrolled in a cybersecurity class, where pre-survey, post-survey and a focus group interview were conducted to determine how effective the updated version is compared to the current build, in addition to how effective the gamification method is regarding student retention of taught material. The pre-survey and post-survey results revealed an increase in interest and mastery of cybersecurity concepts as a result of playing the game. Students found value in the game as both a method of reviewing material taught in class and an entertaining and engaging game. These results show potential in using gamification in cybersecurity and education.

Hilliard, Kevin↗
DOE OSTI2024

Effects of N-Doping on Silicon Carbide

Silicon carbide (SiC) is a widely preferred material within many industries due to its favorable properties, most notably its low electrical resistivity at high temperatures, excellent thermal conductivity, and sturdy mechanical properties. Doping, particularly n-type doping, is shown to extremely reduce electrical resistivity, but due to structural changes within the lattice caused by interaction with thermal neutrons, it is possible that other attributes of SiC may also be affected. In addition to review of past literature and data, four-point probe testing, scanning electron microscopy, nanoindentation, strength tests, differential scanning calorimetry and laser flash analysis were used to investigate the effects of n-doping 3C ß-phase SiC. According to temperature dependent measurements, electric resistivity and thermal conductivity both declined as dopant levels increases. Dopant levels are shown to have a significant effect on the mechanical performance of SiC, with the highest dopant levels (4 x 1018 cm-3) providing a 40% decrease in elastic modulus from 420 GPa to 258 GPa and a 30% decrease in hardness from 40 GPa to 27.7 GPa. These values are still above average and doped SiC may prove valuable for nuclear applications.

36 MATERIALS SCIENCE↗