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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mapping the Outcomes of Stellar Evolution in the Disks of Active Galactic Nuclei

The disks of active galactic nuclei (AGNs) are expected to be populated by numerous stars, either formed in the outer regions of the disk via gravitational instability or captured from the nearby nuclear star cluster. Regardless of their formation mechanism, these stars experience altered evolutionary paths, mostly shaped by the accretion of dense disk material. In this study, through the comparison of different timescales, we chart the evolutionary outcomes of these AGN stars as a function of disk radius and across a range of supermassive black hole masses, spanning from 10 6 to 10 9 M ⊙ , for two popular AGN disk models. We find that in the outer regions of the disk, stars evolve similarly to those in the interstellar medium, but in the inner and denser regions, accretion quickly turns low-mass stars into massive stars, and their fate depends on just how quickly they accrete. If accretion occurs at a faster rate than nuclear burning, they can reach a quasi-steady "immortal" state. If stars accrete faster than they can thermally adjust, runaway accretion occurs, potentially preventing a quasi-steady state and altering the disk structure. During the AGN lifetime, in the regions of the disk that produce massive stars, supernovae (SNe) and gamma-ray bursts (GRBs) may occur within the disk over a wide range of optical depths and ambient densities. Subsequently, in the final phase of the AGN, as the disk becomes depleted, formerly immortal stars will be unable to replenish their fuel, leading to additional SNe and GRBs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Structural mechanism for modulation of functional amyloid and biofilm formation by Staphylococcal Bap protein switch

The Staphylococcal Bap proteins sense environmental signals (such as pH, [Ca 2+ ]) to build amyloid scaffold biofilm matrices via unknown mechanisms. We here report the crystal structure of the aggregation-prone region of Staphylococcus aureus Bap which adopts a dumbbell-shaped fold. The middle module (MM) connecting the N-terminal and C-terminal lobes consists of a tandem of novel double-Ca 2+ -binding motifs involved in cooperative interaction networks, which undergoes Ca 2+ -dependent order-disorder conformational switches. The N-terminal lobe is sufficient to mediate amyloid aggregation through liquid-liquid phase separation and maturation, and subsequent biofilm formation under acidic conditions. Such processes are promoted by disordered MM at low [Ca 2+ ] but inhibited by ordered MM stabilized by Ca 2+ binding, with inhibition efficiency depending on structural integrity of the interaction networks. Furthermore, these studies illustrate a novel protein switch in pathogenic bacteria and provide insights into the mechanistic understanding of Bap proteins in modulation of functional amyloid and biofilm formation, which could be implemented in the anti-biofilm drug design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Shaping dark photon spectral distortions

The cosmic microwave background (CMB) spectrum is an extraordinary tool for exploring physics beyond the Standard Model. The exquisite precision of its measurement makes it particularly sensitive to small effects caused by hidden sector interactions. In particular, CMB spectral distortions can unveil the existence of dark photons which are kinetically coupled to the standard photon. In this work, we use the COBE-FIRAS dataset to derive accurate and robust limits on photon-to-dark-photon oscillations for a large range of dark photon masses, from 10$^{−10}$ to 10$^{−4}$ eV. We consider in detail the redshift dependence of the bounds, computing CMB distortions due to photon injection/removal using a Green’s function method. Our treatment improves on previous results, which had set limits studying energy injection/removal into baryons rather than photon injection/removal, or ignoring the redshift evolution of distortions. The difference between our treatment and previous ones is particularly noticeable in the predicted spectral shape of the distortions, a smoking gun signature for photon-to-dark-photon oscillations. The characterization of the spectral shape is crucial for future CMB missions, which could improve the present sensitivity by orders of magnitude, exploring regions of the dark photon parameter space that are otherwise difficult to access [inline-graphic not available: see fulltext].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Structural and dynamic properties of solvated hydroxide and hydronium ions in water from ab initio modeling

Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water from ab initio molecular dynamics (AIMD) has been a challenging task. The difficulty mainly comes from a lack of accurate and efficient exchange–correlation functional in elucidating the amphiphilic nature and the ubiquitous proton transfer behaviors of the two ions. By adopting the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation functional in AIMD simulations, we systematically examine the amphiphilic properties, the solvation structures, the electronic structures, and the dynamic properties of the two water ions. In particular, we compare these results to those predicted by the PBE0-TS functional, which is an accurate yet computationally more expensive exchange–correlation functional. We demonstrate that the general-purpose SCAN functional provides a reliable choice for describing the two water ions. Specifically, in the SCAN picture of water ions, the appearance of the fourth and fifth hydrogen bonds near hydroxide stabilizes the pot-like shape solvation structure and suppresses the structural diffusion, while the hydronium stably donates three hydrogen bonds to its neighbors. We apply a detailed analysis of the proton transfer mechanism of the two ions and find the two ions exhibit substantially different proton transfer patterns. In conclusion, our AIMD simulations indicate that hydroxide diffuses more slowly than hydronium in water, which is consistent with the experimental results.

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

Mechanically activated and deactivated ion transport across nanopores with heterogeneous surface charge distributions

To mimic the intricate and adaptive functionalities of biological ion channels, electrohydrodynamic ion transport has been studied extensively, albeit mostly, across uniformly charged nanochannels. Here, we analyze the ion transport under coupled electric field and pressure across heterogeneously charged nanopores with oppositely charged sections on their lateral surface. We only consider such pores with symmetric hourglass-like and cylindrical shapes to focus on the effects of the non-uniform surface charge distribution. Finite-element simulations of a continuum model demonstrate that a pressure applied in either direction of the pore-axis equally suppresses or amplifies the ionic conductance, depending on the electric field polarity, by distorting the quasi-static distribution of ions in the pore. The resulting anomalous mechanical deactivation and activation of ionic current under opposite voltage biases exhibit the functional modularity of our setup, while their intensities are highly tunable, substantially greater than those of analogous behaviors in other nanochannels, and fundamentally correlated to ionic current rectification (ICR) in our pores. A detailed study of ICR subsequently reveals its counterintuitive non-monotonous variations, in the pores, with the magnitude of applied voltage and the pore length, that can help optimize their diode-like behavior. We further illustrate that while the hourglass-shaped nanopores yield the more efficient mechanical suppressors of ion transport, their cylindrical analogs are the superior rectifiers and mechanical amplifiers of ion conduction. Therefore, this article provides a blueprint for the strategic design of nanofluidic circuits to attain a robust, modular, and tunable control of ion transport under external electrical and mechanical stimuli.

Physics↗
DOE OSTI2022

Smart material based multilayered microbeam structures for spatial self-deployment and reconfiguration: A residual stress approach

Alleviation of the potentially damaging effects induced by residual stresses was comprehensively investigated in previous research. Here, this paper, however, presents a spatially self-deployable and reconfigurable multilayered microbeam which takes advantage of residual stresses and shape memory effects. Reconfigurable mechanism of a typical four-layered microbeam composed of Pt\Ni 50 Ti 50 \Ni 50 Ti 50 \Pt is introduced, followed by analytical modeling of the maximum distance of the self-deployed gap as functions of variable structural and material parameters, including compressive residual stress in Pt layers and tensile residual stress in Ni 50 Ti 50 layers. Analytical solutions given by the static model agree well with the results obtained via finite element models (FEMs). Fabrication, characterization, and in-situ experiments were carried out to validate the feasibility of deployment of the as-released four-layered microbeam. The maximum distance of the gap was measured to be 41.39 μm at 20 °C, which could be increased to 51.73 μm thanks to controllable reconfiguration driven by shape memory effects. Theoretical analysis of such self-deployment and reconfiguration suggested a tensile residual stress increase by 52 MPa in Ni 50 Ti 50 layers. The multilayered microbeam structure with capabilities of self-deployment and reconfiguration offers great potential for various emerging applications, such as micro robotics, medical drug delivery devices, and intelligent chip scale spacecraft.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Novel Functional Thermal Energy Storage Materials for Buildings Applications

The leakage of solid-liquid phase change materials (PCMs) tremendously limits their long-term application in thermal energy storage (TES). In this work, we present durable and form-stable shape-stabilized PCMs (ss-PCMs) for TES in building envelopes. These ss-PCMs are fabricated by encapsulating polyethylene glycol (PEG) consisting of different molecular weights within mesoporous magnesium oxide and silica dioxide. For the first time, the phase transition temperature (Tt) of ss-PCMs has been fine-tuned synthetically to be comfortable to building occupants by utilizing PEG blends with molecular weights of 600 and 800 g/mol. Several parameters, including surface hydrophilicity/hydrophobicity, surface area, and PCM loading percentage, have been studied to maximize the latent heat enthalpy for high energy efficiency and maintain form stability. The best ss-PCM candidate with suitable Tt and appreciable latent heat enthalpy exhibits a repeatable phase change behavior for up to 1,000 thermal cycles without leakage, which provides a promising solution for durable TES in buildings. The Tt tunability extends its application over a wider temperature range beyond buildings.

building envelope↗
DOE OSTI2021

Magnetic charge and geometry confluence for ultra-low forward voltage diode in artificial honeycomb lattice

We report spin diode is important prerequisite to practical manifestation of spin electronics. Yet, a functioning magnetic diode at room temperature is still illusive. Here, we reveal diode-type phenomena due to magnetic charge mediated conduction in artificial honeycomb geometry, made of concave shape single domain permalloy element. We find that honeycomb lattice defies symmetry by populating vertices with low and high multiplicity magnetic charges, causing asymmetric magnetization, in applied current of opposite polarity. High multiplicity units create highly resistive network, thereby inhibiting magnetic charge dynamics propelled electrical conduction. However, practical realization of this effect requires modest demagnetization factor in constituting element. Concave structure fulfills the condition. Subsequently, magnetic diode behavior emerges across broad thermal range of T = 40 K–300 K. The finding is a departure from the prevailing notion of spin-charge interaction as the sole guiding principle behind spintronics. Consequently, a new vista, mediated by magnetic charge interaction, is envisaged for spintronic research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pathways for Electron Transfer at MgO–Water Interfaces from Ab Initio Molecular Dynamics

The nature of electron transfer across metal oxide–water interfaces depends significantly on the band gap of the oxide and its band edge energies relative to the potentials of relevant aqueous redox couples. Here we focus on the water interface with MgO, a prototypical wide band gap oxide whose conduction band edge is close in energy to that of water. We investigate the behavior of an excess electron at and out of equilibrium near the interface using ab initio molecular dynamics based on hybrid density functional theory. Our simulations show that under equilibrium conditions the excess electron (donated by an Al impurity in MgO) localizes to a midgap defect state comparable in energy and shape to a hydrated electron in bulk water. To characterize the electron transfer from the conduction band of MgO to interfacial product states, we dope near-equilibrium configurations of the pristine MgO–water system with Al and run short trajectories of these instantaneously out-of-equilibrium systems. Here we observe two distinct products associated with the excess electron: a surface-localized electron (e surf –) and an aqueous hydrogen radical (H • ). The H • pathway exhibits a much higher activation barrier despite being more exoergic, making e surf – the kinetic product. Our characterization of the pathways on the basis of Marcus theory is consistent with the poor observed utility of MgO for water radiolysis. Moreover, we anticipate that the computational framework employed here will be broadly applicable to assessing electron transfer mechanisms at aqueous, photocatalytic interfaces.

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

Alternating Conditional Expectations: Introducing a Non‐Parametric Statistical Method to Interpret Long‐Term Greenhouse Gas Flux Measurements Over Semi‐Arid and Wetland Ecosystems

Abstract We explore the potential of using a non‐parametric statistical method called Alternating Conditional Expectations, ACE, to quantify functional relationships in biogeosciences. Here, ACE is used to quantify the non‐linear and multi‐faceted responses of greenhouse gas fluxes to a set of biophysical forcings, when the shapes of those response surfaces are unknown. We evaluated the statistical method over two contrasting ecosystems and two contrasting time steps. One case involved quantifying the biophysical controls of water vapor and carbon dioxide (CO 2 ) fluxes over a semi‐arid oak savanna using daily integrated fluxes. The other case evaluated the responses of CO 2 and methane (CH 4 ) flux measurements to a set of biophysical forcings at a restored tidal wetland using thirty‐minute averages. The statistical model, based on 4 independent variables, explained up over 90% of the variation in daily integrated flux densities of water vapor and net carbon dioxide exchange at the savanna site. This fit was defined by distinct non‐linear responses to such drivers as gross primary production, photosynthetically active radiation, air temperature, vapor pressure deficit and soil moisture. At the tidal wetland site, we evaluated net carbon dioxide and methane fluxes with short‐term measurements to capture the influence of rising and falling tides and seasonality in biological activity. The statistical model defined the shape of the forcing of fluxes due to the roles of carbon exudates, water table depth, oxygen level in the water column, temperature and vegetation status. The statistical fits of the greenhouse gas fluxes were less precise than the savanna case. The fetch varies on a run‐to‐run basis as it is comprised of a heterogeneous mosaic of open water and vegetation. Furthermore, it is difficult to monitor the environmental conditions of the archaea and bacteria in the sediments that produce methane and carbon dioxide.

Environmental Sciences & Ecology↗
DOE OSTI2022

Templated synthesis enhances the cobalt adsorption capacity of a porous organic polymer

Divalent transition metals such as Co(II) are important targets for removal from water sources, due to their potential toxicity as well as their high value. In this study, we found that a series of porous organic polymers based on amide-linked tetraphenylmethane units are effective Co(II) ion adsorbents in aqueous solution. To increase the density of Co(II) binding sites, we then developed a templated synthesis in which the branched, rigid monomers are pre-assembled around Co(II) ions prior to polymerization. Furthermore, after polymer formation, the Co(II) template ions are removed to yield a material rich in Co(II) binding sites. Ion adsorption isotherms show that the Co(II)-templated material has an ion adsorption capacity significantly greater than those of the non-templated materials, highlighting the utility of a templated synthetic route. SEM and TEM images show the morphology of the templated polymer to be dramatically different from the non-templated polymers and to be similar in size and shape to the Co(II)–monomer precursors, emphasizing the role of the template ions in directing the formation of the resulting polymer. This guest-templated approach requires no functionalization of the generic monomer and represents a promising synthetic route to high-capacity ion adsorbents for water purification and aqueous separations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Muon-induced fission as a probe of the underlying dynamics in nuclear fission

Muon-induced fission could be utilized as a probe to study the underlying dynamics of nuclear fission. Here, the probability of muon attachment to the light asymmetric fission fragment is sensitive to fission dynamics, such as the timescale and friction of the fission event, charge asymmetry, and possibly the shape of the fission fragments. We focus on muonic atoms that are formed with actinide nuclei. A relativistic approach is employed, solving the Dirac equation for the muonic wave function in the presence of a time-dependent electromagnetic field generated by the fissioning nucleus. Computations are carried out on a three-dimensional Cartesian lattice with no symmetry assumptions. The results show a strong dependence of the attachment probability on the fission charge asymmetry and a more modest dependence on friction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Three Artificial Spintronic Leaky Integrate-and-Fire Neurons

We report that due to their non-volatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of an external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient, and the third uses shape variations of the DW track.

neural network crossbar↗
DOE OSTI2024

Adaptive Methods for Radial Basis Functions

Radial basis functions (RBFs) are a powerful tool for constructing high-order accurate reduced representations of scattered data in arbitrary dimension and on manifolds. We present a method of constructing data approximations in which we utilize a functional tail to capture a global background profile and a RBF neural network (NN) to capture the smaller-scale features. In the RBF NN the RBF centers, matrix shape parameters were selected adaptively for each RBF. We also utilized a geodesic notion of distance on the manifold on which the data lies, e.g., the spherical geodesic for data on the sphere. Although each of these ideas have been been investigated separately in previous works, their combination into a single algorithm is novel. We defined a machine learning problem in which these properties are learned to minimize the data reduction error. We demonstrate the algorithm for applications of scattered data reduction in the plane and on the sphere.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Induced nanoscale membrane curvature bypasses the essential endocytic function of clathrin

During clathrin-mediated endocytosis (CME), flat plasma membrane is remodeled to produce nanometer-scale vesicles. The mechanisms underlying this remodeling are not completely understood. The ability of clathrin to bind membranes of distinct geometries casts uncertainty on its specific role in curvature generation/stabilization. Here, we used nanopatterning to produce substrates for live-cell imaging, with U-shaped features that bend the ventral plasma membrane of a cell into shapes resembling energetically unfavorable CME intermediates. This induced membrane curvature recruits CME proteins, promoting endocytosis. Upon AP2, FCHo1/2, or clathrin knockdown, CME on flat substrates is severely diminished. However, induced membrane curvature recruits CME proteins in the absence of FCHo1/2 or clathrin and rescues CME dynamics/cargo uptake after clathrin (but not AP2 or FCHo1/2) knockdown. Induced membrane curvature enhances CME protein recruitment upon branched actin assembly inhibition under elevated membrane tension. These data establish that membrane curvature assists in CME nucleation and that the essential function of clathrin during CME is to facilitate curvature evolution, rather than scaffold protein recruitment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2021

The Switchgrass Microbiome: A Review of Structure, Function, and Taxonomic Distribution

Switchgrass (Panicum virgatum L.) has been championed as a promising bioenergy crop due to its high productivity across a wide environmental range. The switchgrass microbiome—including bacteria, archaea, fungi, and other microbiota inhabiting soil and plant tissues—can influence plant function substantially. We conducted a review of the literature investigating switchgrass microbiome structure, key functional roles, and taxa isolated from field-grown plants. Although site conditions and plant compartment (i.e., location within shoots, roots, or root-influenced soil) appear to be the strongest drivers of switchgrass microbiome structure, the microbiome is also shaped by climate, season, and host genotype. Studies comparing across plant species show that the switchgrass microbiome is more similar to the microbiomes of other perennial plants than to the microbiomes of annual plants. Members of the switchgrass microbiome confer several benefits to their host. Most notably, mycorrhizal fungi can increase plant biomass many-fold, associative nitrogen-fixing bacteria can provide a substantial portion of the plant’s nitrogen demand, and fungal endophytes can improve plant tolerance to drought. Although the fungi and bacteria cultured from switchgrass represent only a portion of the microbiome, these serve as a valuable resource for researchers interested in investigating functional outcomes of the switchgrass microbiome. We highlight areas where additional research is necessary for a more comprehensive understanding of switchgrass microbiome structure, function, and potential to enhance sustainable bioenergy production. Key gaps include the role of understudied organisms (e.g., viruses, microeukaryotes, and nonmycorrhizal fungi), multitrophic relationships, mechanisms underpinning switchgrass–microbiome interactions, and field-scale validation of experimental findings.

54 ENVIRONMENTAL SCIENCES↗