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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Post-translational Histidine–Histidine Cross-Link Enhances Enzymatic Oxygen Reduction Activity with Greater pH Adaptability

Cross-linked protein residues exist as enzyme cofactors to enable or enhance catalytic activities. Despite their importance in nature, the chemical identity of the cross-links is limited to certain amino acid combinations, whose function and the formation mechanism remain insufficiently understood due to the difficulty in isolating native enzymes without the cross-links. Herein, we report the formation and characterization of both His-Tyr and His-His cross-links under oxidative enzymatic turnover conditions in L29H/F33Y/F43H Mb, a structural and functional model of heme-copper oxidase (HCO). The connectivity of the cross-link was characterized as Ne ε2 (His29)-Cd δ2 (His43) by mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR). Interestingly, formation of the cross-link significantly enhances the oxygen reduction activity of the enzyme at neutral or basic pH with higher product specificity. X-ray crystallography has identified a novel Tyr-His cross-link through a Tyr-O-His linkage. Our mechanistic studies indicate the involvement of high-valent heme-iron and the neighboring tyrosine in an oxidative self-processing pathway to generate the cross-link. In conclusion, this work serves as a new example while providing insights into the enzyme cross-link formation, allowing the design of artificial biocatalysts containing these novel cross-links with higher activity and pH adaptability.

Liu, Yiwei [Univ. of Texas, Austin, TX (United Sta↗
DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗
DOE OSTI2021

Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)

The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Analysis of SPND Material Candidates for Real-Time Neutron Spectrum Monitoring Poster

The future of the nuclear industry will be shaped by advanced reactor designs requiring innovative instrumentation for safe operation. A key area of innovation is the ability to accurately and reliably monitor the neutron energy spectrum during operations, which would also enhance the experimental capabilities of existing research reactors. To address this need, our research focuses on the potential use of Self-Powered Neutron Detectors (SPNDs) for real-time neutron spectrum monitoring. We investigated various candidate materials for SPNDs by evaluating their energy-dependent reaction rates. Promising materials were further modeled using MCNP to determine the free electron production per neutron across the neutron energy spectrum, approximating their energy-dependent signal sensitivity. Our findings identified Samarium (Sm) and Gadolinium (Gd) as top candidates for thermal neutrons, Hafnium (Hf) and Thulium (Tm) for epithermal neutrons, and Tantalum (Ta) for fast neutrons. These materials will undergo future experimental validation to confirm the modeled responses and assess their feasibility for real-time neutron spectrum measurements.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

An Engineered Multifunctional Composite for Passive Sensing, Power Harvesting, and In Situ Damage Identification with Enhanced Mechanical Performance

A multifunctional fiber-reinforced composite with passive self-sensing, energy-harvesting, and damage detection capabilities is presented. In this work, barium titanate piezoelectric microparticles are deposited on basalt fibers by a scalable, low-cost, environmentally friendly continuous feed-through process. The resulting composite derives a superior interlaminar shear strength from the microparticle-modified fiber–matrix interfaces. The composite also demonstrates passive self-sensing capabilities that produce electrical signals proportional to various dynamic loading events. Vibration and strain-controlled experiments are performed on composite beams to quantify the sensitivity and power output as a function of input acceleration and strain. Furthermore, these composite-generated electrical signals are used to identify in situ damage initiation for structural health monitoring to inform composite damage prior to structural failure. In brief, this truly multifunctional composite simultaneously displays a sensitivity of 0.5–2.6 mV g -1 at a resolution of 0.045–0.20g (g = gravitational acceleration), energy harvesting in the range of nW cc -1 , and prediction of early damage by exhibiting 0.017–1.17 mV peaks in voltage–time history profiles while assuring ≈20% improved interlaminar shear strength.

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

Exploring the Effects of Intersubunit Interface Mutations on Virus-Like Particle Structure and Stability

Virus-like particles (VLPs) from bacteriophage MS2 provide a platform to study protein self-assembly and create engineered systems for drug delivery. Here, we aim to understand the impact of intersubunit interface mutations on the local and global structure and function of MS2-based VLPs. In previous work, our lab identified locally supercharged double mutants [T71K/G73R] that concentrate positive charge at capsid pores, enhancing uptake into mammalian cells. To study the effects of particle size on cellular internalization, we combined these double mutants with a single point mutation [S37P] that was previously reported to switch particle geometry from T = 3 to T = 1 icosahedral symmetry. These new variants retained their enhanced cellular uptake activity and could deliver small-molecule drugs with efficacy levels similar to our first-generation capsids. Surprisingly, these engineered triple mutants exhibit increased thermostability and unexpected geometry, producing T = 3 particles instead of the anticipated T = 1 assemblies. Transmission electron microscopy revealed various capsid assembly states, including wild-type (T = 3), T = 1, and rod-like particles, that could be accessed using different combinations of these point mutations. Molecular dynamics experiments recapitulated the structural rationale in silico for the single point mutation [S37P] forming a T = 1 virus-like particle and showed that this assembly state was not favored when combined with mutations that favor rod-like architectures. Through this work, we investigated how interdimer interface dynamics influence VLP size and morphology and how these properties affect particle function in applications such as drug delivery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bio-inspired strategies for next-generation perovskite solar mobile power sources

Smart electronic devices are becoming ubiquitous due to many appealing attributes including portability, long operational time, rechargeability and compatibility with the user-desired form factor. Integration of mobile power sources (MPS) based on photovoltaic technologies with smart electronics will continue to drive improved sustainability and independence. With high efficiency, low cost, flexibility and lightweight features, halide perovskite photovoltaics have become promising candidates for MPS. Realization of these photovoltaic MPS (PV-MPS) with unconventionally extraordinary attributes requires new ‘out-of-box’ designs. Natural materials have provided promising designing solutions to engineer properties under a broad range of boundary conditions, ranging from molecules, proteins, cells, tissues, apparatus to systems in animals, plants, and humans optimized through billions of years of evolution. Applying bio-inspired strategies in PV-MPS could be biomolecular modification on crystallization at the atomic/meso-scale, bio-structural duplication at the device/system level and bio-mimicking at the functional level to render efficient charge delivery, energy transport/utilization, as well as stronger resistance against environmental stimuli (e.g., self-healing and self-cleaning). In this paper, we discuss the bio-inspired/-mimetic structures, experimental models, and working principles, with the goal of revealing physics and bio-microstructures relevant for PV-MPS. Here the emphasis is on identifying the strategies and material designs towards improvement of the performance of emerging halide perovskite PVs and strategizing their bridge to future MPS.

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

Solid-Density Ion Temperature from Redshifted and Double-Peaked Stark Line Shapes

Heβ spectral line shapes are important for diagnosing temperature and density in many dense plasmas. This work presents Heβ line shapes measured with high spectral resolution from solid-density plasmas with minimized gradients. The line shapes show hallmark features of Stark broadening, including quantifiable redshifts and double-peaked structure with a significant dip between the peaks; these features are compared to models through a Markov chain Monte Carlo framework. Line shape theory using the dipole approximation can fit the width and peak separation of measured line shapes, but it cannot resolve an ambiguity between electron density n e and ion temperature T i , since both parameters influence the strength of quasistatic ion microfields. Here a line shape model employing a full Coulomb interaction for the electron broadening computes self-consistent line widths and redshifts through the monopole term; redshifts have different dependence on plasma parameters and thus resolve the n e – T i ambiguity. The measured line shapes indicate densities that are 80–100% of solid, identifying a regime of highly ionized but well-tamped plasma. This analysis also provides the first strong evidence that dense ions and electrons are not in thermal equilibrium, despite equilibration times much shorter than the duration of x-ray emission; cooler ions may arise from nonclassical thermalization rates or anomalous energy transport. Furthermore, the experimental platform and diagnostic technique constitute a promising new approach for studying ion-electron equilibration in dense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Origins of high ductility exhibited by an extruded magnesium alloy Mg-1.8Zn-0.2Ca: Experiments and crystal plasticity modeling

Low ductility and strength are major bottlenecks against Mg alloys' wide applications. In this work, we systematically design the composition and fabrication process for a low-alloyed Mg-Zn-Ca alloy, showing that it can be extruded at low temperatures (~ 250 degrees C) and high speeds (~2 mm/s). After the extrusion, this alloy exhibits a substantially weakened basal texture, relatively small grain size, very high tensile elongation (~ 30%), and good strength. The origin of the considerably improved ductility was studied using a combination of three-dimensional atom probe tomography (3D-APT), transmission electron microscopy (TEM), electron backscattered diffraction (EBSD) in conjunction with surface slip trace analysis, in-situ synchrotron X-ray diffraction, and elasto-plastic self-consistent (EPSC) modeling. Co-segregation of Zn and Ca atoms at a grain boundary is observed and associated with texture weakening and grain boundary mediated plasticity, both improving the ductility. While basal slip and prismatic slip are identified as the dominant deformation systems in the alloy, the ratio between their slip resistances is substantially reduced relative to pure Mg and most other Mg alloys, significantly contributing to the improved ductility of the alloy. This Mg-Zn-Ca alloy exhibiting excellent mechanical properties and low fabrication cost is a promising candidate for industrial productions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Late-time dark matter oscillations and the core-cusp problem

The core-cusp problem persists as an unresolved tension between the pre- dictions of ΛCDM cosmology and observations of dark matter (DM) profiles in dwarf spheroidal and other galaxies. We present a novel scenario for converting cusps into cores through reactivation of DM annihilation in galaxies at late times. This can happen in asymmetric DM models when there is a very small DM-number violating mass term that causes oscillations between DM and its antiparticle. Using analytic methods as well as gravitational N-body simulations, we show that this mechanism can robustly eliminate cusps from galactic DM profiles for light fermionic DM of mass $m_χ$ ~ (0.1 – 1) GeV and a lighter mediator into which the DM can annihilate. We identify regions of parameter space where annihilation of DM particles is more efficient than elastic scattering at reducing the inner density of the DM profile. Dark matter annihilation is therefore a qualitatively distinct alternative to the mechanism of elastic self-interacting dark matter for addressing the cusp-core problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Islands in non-minimal dilaton gravity: exploring effective theories for black hole evaporation

We start from (3 + 1)-dimensional Einstein gravity with minimally coupled massless scalar matter, through spherical dimensional reduction, the matter theory is non-minimally coupled with the dilaton in (1 + 1)-dimensions. Despite its simplicity, constructing a self-consistent one-loop effective theory for this model remains a challenge, partially due to a Weyl-invariant ambiguity in the effective action. With a universal splitting property for the one-loop action, the ambiguity can be identified with the state-dependent part of the covariant quantum stress tensor. By introducing on-shell equivalent auxiliary fields to construct minimal candidates of Weyl-invariant terms, we derive a one-parameter family of one-loop actions with unique, regular, and physical stress tensors corresponding to the Boulware, Hartle-Hawking and Unruh states. We further study the back-reacted geometry and the corresponding quantum extremal islands that were inaccessible without a consistent one-loop theory. Along the way, we elaborate on the implications of our construction for the non-minimal dilaton gravity model.

2D Gravity↗
DOE OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

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

A novel semi-structured kinetic model of methanotroph-photoautotroph cocultures for biogas conversion

Through metabolic coupling of methane oxidation and oxygenic photosynthesis, methanotroph-photoautotroph (M-P) cocultures offer a highly promising technology platform for biogas conversion. However, there has not been any quantitative modeling of the coculture growth kinetics. This is mainly due to the inherent difficulty associated with real time characterization of the M-P cocultures and the complex interactions such as the cross-feeding mechanism within the coculture. To address this challenge, we recently developed a novel experimental-computational (E-C) protocol to accurately characterize the M-P coculture in real-time, and validated its accuracy through cell counting. Enabled by the E-C protocol, this work presents the very first kinetic model for M-P cocultures. By explicitly modeling the exchange of in situ produced O 2 /CO 2 within the M-P coculture and coupling the individual biomass growth with mass transfer between the gas and liquid phases, the semi-structured kinetic model accurately predicts the growth dynamics of the M-P coculture under a wide range of growth conditions. The proposed model is validated by a series of wet-lab experiments using Methylomicrobium buryatense 5GB1 - Arthrospira platensis as the model coculture. Although it has been speculated that there may exist other emergent metabolic interactions within the M-P coculture, in addition to the exchange of in situ produced O 2 /CO 2 , there has not been any experimental validation prior to this study. By integrating designed experiments with the semi-structured kinetic model, this study is the first to confirm the existence of the additional emergent metabolic exchanges within the coculture. Furthermore, this study further quantifies the effect of these unknown metabolic interactions on the growth of both species in the model coculture, supporting further research to identify these exchanged metabolites for metabolic engineering.

42 ENGINEERING↗
DOE OSTI2026

Self-interacting sub-GeV dark matter with strong MeV gamma-ray

Sub-GeV dark matter (DM) with s-channel resonant self-scattering provides a promising framework for addressing small-scale structure problems. However, models that also account for the observed relic abundance through the same resonance are strongly constrained by current γ-ray observations, since the associated signals are significantly enhanced. To overcome this limitation, we propose a framework in which the relic abundance and self-scattering are governed independently by two distinct mediators. As a concrete realization, we present a singlet scalar DM model in which self-scattering is mediated by a vector boson associated with a gauged baryon number, while the relic density is determined by forbidden annihilation into dark Higgs bosons that generate the gauge boson mass. By imposing cosmological, experimental, and theoretical constraints, We identify viable parameter regions that reproduce the observed relic density, alleviate small-scale problems, and remain consistent with current bounds. Notably, the model predicts multiple distinctive MeV γ-ray signals, a significant fraction of which will be testable with next-generation MeV γ-ray telescopes, including the Compton Spectrometer and Imager (COSI).

Models for Dark Matter↗
DOE OSTI2024

The RNA-binding protein Modulo promotes neural stem cell maintenance in Drosophila

A small population of stem cells in the developing Drosophila central nervous system generates the large number of different cell types that make up the adult brain. To achieve this, these neural stem cells (neuroblasts, NBs) divide asymmetrically to produce non-identical daughter cells. The balance between stem cell self-renewal and neural differentiation is regulated by various cellular machinery, including transcription factors, chromatin remodelers, and RNA-binding proteins. The list of these components remains incomplete, and the mechanisms regulating their function are not fully understood, however. Here, we identify a role for the RNA-binding protein Modulo (Mod; nucleolin in humans) in NB maintenance. We employ transcriptomic analyses to identify RNA targets of Mod and assess changes in global gene expression following its knockdown, results of which suggest a link with notable proneural genes and those essential for neurogenesis. Mod is expressed in larval brains and its loss leads to a significant decrease in the number of central brain NBs. Stem cells that remain lack expression of key NB identity factors and exhibit cell proliferation defects. Mechanistically, our analysis suggests these deficiencies arise at least in part from altered cell cycle progression, with a proportion of NBs arresting prior to mitosis. Overall, our data show that Mod function is essential for neural stem cell maintenance during neurogenesis.

Parra, Amalia S.↗
DOE OSTI2021

Self-assembled multifunctional nanostructures for surface passivation and photon management in silicon photovoltaics

This work reports the fabrication and characterization of multifunctional, nanostructured passivation layers formed using a self-assembly process that provide both surface passivation and improved light trapping in crystalline silicon photovoltaic (PV) cells. Scalable block copolymer self-assembly and vapor phase infiltration processes are used to form arrays of aluminum oxide nanostructures (Al 2 O 3 ) on crystalline silicon without substrate etching. The Al 2 O 3 nanostructures are characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), and spectroscopic ellipsometry. Injection-level dependent photoconductance measurements are used to determine the effective carrier lifetime of the samples to confirm the nanostructures successfully passivate the Si surface. Finite element method simulations and reflectance measurement show that the nanostructures increase the internal rear reflectance of the PV cell by suppressing the parasitic optical losses in the metal contact. An optimized morphology of the structures is identified for their potential use in PV cells as multifunctional materials providing surface passivation, photon management, and carrier transport pathways.

14 SOLAR ENERGY↗
DOE OSTI2022

Bio-inspired non self-similar hierarchical elastic metamaterials

Hierarchy provides unique opportunities for the design of advanced materials with superior properties that arise from architecture, rather than from constitutive material response. Contrary to the quasi-static regime, where the potential of hierarchy has been largely explored, its role in vibration mitigation and wave manipulation remains elusive. So far, the majority of the studies concerning hierarchical elastic metamaterials have proposed a self-similar repetition of a specific unit cell at multiple scale levels, leading to the activation of the same bandgap mechanism at different frequencies. On the contrary, here, we show that by designing non self-similar hierarchical geometries allows us to create periodic structures supporting multiple, highly attenuative and broadband bandgaps involving (independently or simultaneously) different scattering mechanisms, namely, Bragg scattering, local resonance and/or inertial amplification, at different frequencies. The type of band gap mechanism is identified and discussed by examining the vibrational mode shapes and the imaginary component of the wavenumber in the dispersion diagram of the unit cell. Finally, we also experimentally confirm this by performing measurements in the lowest frequency regime on a 3D printed structure. Hierarchical design strategies may find application in vibration mitigation for civil, aerospace and mechanical engineering.

36 MATERIALS SCIENCE↗