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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Utilizing the unique charge extraction properties of antimony tin oxide nanoparticles for efficient and stable organic photovoltaics

Simultaneously enhancing device performance and longevity, as well as balancing the requirements on cost, scalability, and simplification of processing, is the goal of interface engineering of organic solar cells (OSCs). In our work, we strategically introduce antimony (Sb 3+ ) cations into an efficient and generic n-type SnO 2 nanoparticles (NPs) host during the scalable flame spray pyrolysis synthesis. Accordingly, a significant switch of conduction property from an n-type character to a p-type character is observed, with a corresponding shift in the work function (WF) from 4.01 ± 0.02 eV for pristine SnO 2 NPs to 5.28 ± 0.02 eV for SnO 2 NPs with 20 mol. % Sb content (ATO). Both pristine SnO 2 and ATO NPs with fine-tuned optoelectronic properties exhibit remarkable charge carrier extraction properties, excellent UV resistance and photo-stability being compatible with various state-of-the-art OSCs systems. The reliable and scalable pristine SnO 2 and ATO NPs processed by doctor-blading in air demand no complex post-treatment. Our work offers a simple but unique approach to accelerate the development of advanced interfacial materials, which could circumvent the major existing interfacial problems in solution-processed OSCs.

14 SOLAR ENERGY↗
DOE OSTI2023

Accelerating the development of a sustainable bioenergy portfolio through stable isotopes

Bioenergy could help limit global warming to 2°C above pre-industrial levels while supplying almost a fourth of the world's renewable energy needs by 2050. However, the deployment of bioenergy raises concerns that adoption at meaningful scales may lead to unintended negative environmental consequences. Meanwhile, the full consolidation of a bioenergy industry is currently challenged by a sufficient, resilient, and resource-efficient biomass supply and an effective conversion process. Here, we provide a comprehensive analysis of how stable isotope approaches have accelerated the development of a robust bioeconomy by advancing knowledge about environmental sustainability, feedstock development, and biological conversion. We show that advances in stable isotope research have generated crucial information to (1) gain mechanistic insight into the potential of bioenergy crops to mitigate climate change as well as their impact on water and nutrient cycling; (2) develop high-yielding, resilient feedstocks that produce high-value bioproducts in planta; and (3) engineer microbes to enhance feedstock conversion to bioenergy products. Further, we highlight knowledge gaps that could benefit from future research facilitated by stable isotope approaches. We conclude that advances in mechanistic knowledge and innovations within the field of stable isotopes in cross-disciplinary research actions will greatly contribute to breaking down the barriers to establishing a robust bioeconomy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A systems-level analysis of drought and density response in the model C4 grass Setaria viridis

The need for novel crops in an emerging bioenergy economy presents many challenges and opportunities for plant scientists. Plant breeding has been successful at transforming weedy species into some of the highest yielding crops on the planet. The plant domestication has led, in part, to an anticipated delivery of up to 1 billion dry tons of lignocellulosic feedstocks into the energy economy by 2030. However, The success of modern food crop breeding suggests that even greater advances can be made by developing the next generation of bioenergy feedstocks from C4 grass species that have been to date subjected to little or no breeding. The challenge we face is to exponentially accelerate the crop improvement process so that we can meet this level of bioenergy feedstock production. To achieve this unprecedented goal, it will be necessary to rapidly advance the process of plant breeding through large-scale biology, computational biology and systems approaches. Therefore, the goal of this proposal was to understand how productivity, drought tolerance and water use efficiency (WUE) are regulated at the genomic level in order to inform metabolic engineering and molecular-assisted breeding efforts.This necessitates the use of a model species that is closely related to less genetically tractable feedstock grasses such as Sorghum, switchgrass and Miscanthus. To achieve this we have significantly enhanced the utility of Setaria viridis as a model C4 species for system biology. This was achieved through field, greenhouse and laboratory experiments to survey a breadth of environments and explore the potential of gene networks to predict physiological responses across environments. We challenged plants under abiotic stresses to examine the plasticity of the network and identify targets for future manipulation. Finally, we have identified promising new gene engineering technologies to modify genetic networks in this model C4 species to understand and enhance abiotic stress tolerance. The outcomes of this project led to 1) identification of gene regulatory and metabolic networks important for adaptation to low water availability and high-density plantings, which lead to a greater understanding of the physiology underlying these adaptations and 2) we developed technologies to measure important water use efficiency traits and to precisely control gene insertion and replacements events for large scale engineering of pathways in model and target feedstocks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Quantum Computing for Quantum Simulation for NNSA Mission

Beginning with Feynman in 1982, it was proposed that highly controllable quantum devices, later to become known as quantum computers, would be especially good at the simulation of other quantum systems. Around the turn of the 21st century, this notion was later formalized (by Lloyd and coworkers at MIT and Laflamme and coworkers at Los Alamos) to show how in particular instances, we expect an exponential speedup in the solution of the Schroedinger equation for chemical systems. Subsequently, quantum-accelerated quantum simulation for chemistry techniques have been developed by IBM, Google, Harvard, etc. This effort has led to the OpenFermion (openfermion.org, OF) community, an open source effort for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, the current version of OF features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. In October, 2017, the cover of Chemical and Engineering News posited “Quantum computers: Chemistry could be the machine’s killer app” for first principles understanding of biological systems and complex materials. Prof. Aspuru-Guzik (Harvard) predicts “I think that before 2035 QC will be beating classical computers at chemistry.” The algorithms at the heart of quantum chemistry codes (eigensolvers, fast fourier transforms) are the computation bottleneck.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Accelerometer Modeling in the State-Space

Generically, accelerometers are sensors that measure the accelerating force applied to an object. Their operation is based on a simple mechanical system and D’Alembert’s force principle along with Newton’s laws of motion. This device is typically mounted to the body of the test object under investigation that is subjected to an accelerating force. The displacement of the seismic or “proof” mass due to the acceleration is sensed by an embedded component from which the acceleration is determined. Interestingly enough, the resulting the resulting mathematical relations governing the dynamical motion is based on the underlying physics, sensor materials, geometry and fabrication. Mathematically, the various designs satisfy the underlying Newtonian equations of motion, but their type and performance differ significantly.

42 ENGINEERING↗
DOE OSTI2026

Multitarget Rydberg gates via spatial blockade engineering

Multi-target gates offer the potential to reduce gate depth in syndrome extraction for quantum error correction. Although neutral-atom quantum computers have demonstrated native multi-qubit gates, existing approaches that avoid additional control or multiple atomic species have been limited to single-target gates. We propose single-control-multi-target CZ^n gates on a single-species neutral-atom platform that require no extra control and have gate durations comparable to standard CZ gates. Our approach leverages tailored interatomic distances to create an asymmetric blockade between the control and target atoms. Using a GPU-accelerated pulse synthesis protocol, we design smooth control pulses for CZZ and CZZZ gates, achieving fidelities of up to 99.55% and $99.24\%$, respectively, even in the presence of simulated atom placement errors and Rydberg-state decay. Our approach is most effective for N=2 (CZZ) and N=3 targets (CZZZ); for larger N, increasing spatial crowding of the targets introduces significant challenges for maintaining the required blockade asymmetry. This work presents a practical path to implementing low-overhead multi-target gates in single-species neutral-atom systems, significantly reducing the resource overhead for syndrome extraction. To motivate the impact of these gates, we apply a greedy scheduling algorithm and we demonstrate that our proposed gates can reduce the number of atom reconfiguration costs by up to 50% for color code syndrome extraction of code distances greater than 5.

Stein, Samuel A.↗
DOE OSTI2019

Lightwave-driven gapless superconductivity and forbidden quantum beats by terahertz symmetry breaking

Light-induced supercurrents chart a path forward for the electromagnetic design of emergent materials phases and collective modes for quantum engineering applications. However, controlled spatial–temporal modulation of the complex order parameter characterizing such non-equilibrium macroscopic quantum states remains elusive. Such ultrafast phase-amplitude modulation can manifest via high harmonic modes beyond those allowed by equilibrium symmetries. Here, we drive moving condensate states via subcycle dynamical symmetry breaking achieved with nonlinear oscillating terahertz photocurrents. These non-equilibrium macroscopic quantum states with broken inversion symmetry are controlled via Cooper pair acceleration by asymmetric and multi-cycle terahertz photoexcitations. Furthermore, the observed supercurrent-carrying states evolve during a lightwave cycle and exhibit three distinguishing features: Anderson pseudo-spin precessions forbidden by equilibrium symmetry, strong high harmonic coherent oscillations assisted by pairing and long-lived gapless superfluidity with minimal condensate quench. Lightwave tuning of persistent photocurrents can be extended for quantum control of unconventional superconductors and topological matter, with implications on quantum gate and sensing functionalities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

On the synthesis of heavy nuclei in protomagnetar outflows and implications for ultra-high energy cosmic rays

ABSTRACT It has been suggested that strongly magnetized and rapidly rotating protoneutron stars (PNSs) may produce long duration gamma-ray bursts (GRBs) originating from stellar core collapse. We explore the steady-state properties and heavy element nucleosynthesis in neutrino-driven winds from such PNSs whose magnetic axis is generally misaligned with the axis of rotation. We consider a wide variety of central engine properties such as surface dipole field strength, initial rotation period, and magnetic obliquity to show that heavy element nuclei can be synthesized in the radially expanding wind. This process is facilitated provided the outflow is Poynting-flux dominated such that its low entropy and fast expansion time-scale enables heavy nuclei to form in a more efficient manner as compared to the equivalent thermal GRB outflows. We also examine the acceleration and survival of these heavy nuclei and show that they can reach sufficiently high energies ≳ 1020 eV within the same physical regions that are also responsible for powering gamma-ray emission, primarily through magnetic dissipation processes. Although these magnetized outflows generally fail to achieve the production of elements heavier than lanthanides for our explored electron fraction range 0.4–0.6, we show that they are more than capable of synthesizing nuclei near and beyond iron peak elements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cyberbiosecurity and Public Health in the Age of COVID-19

Cyberbiosecurity, the aspect of biosecurity involving the digital representation of biological data, had already been emerging as a matter of public concern even prior to the onset of the COVID-19 pandemic. Key issues of concern include, among others, the privacy of patient data, the security of public health databases, the integrity of diagnostic test data, the integrity of public biological databases, the security implications of automated laboratory systems and the security of proprietary biological engineering advances. With the onset of the COVID-19 pandemic, and the importance of digital resources in combatting it, concern about the potential for cyber attacks by state-based or non-state actors has been elevated. To illuminate the challenges, we focus on the cyber vulnerabilities that need to be addressed in public health activities such as disease surveillance and outbreak management. In particular, we examine cyber issues raised by the accelerated pace of development for COVID mitigations, treatments, and vaccines.

cybersecurity, biosecurity↗
DOE OSTI2024

Surrogate Model Guided Optimization of Expensive Black-Box Multi-Objective Problems: A Posteriori Methods

Many engineering applications require the simultaneous optimization of multiple conflicting objective functions. Often, these objective functions are evaluated using highly accurate computer simulations that are computationally too expensive to be evaluated hundreds or thousands of times during optimization. Thus, the goal is to find good approximations of the Pareto front using as few of these expensive simulations as possible. Here, we describe an optimization approach based on surrogate models and diverse sampling strategies to accelerate the search for the Pareto solutions. We use a separate surrogate model for approximating each objective function and then we use the surrogate models to inform where additional expensive simulations should be run. The surrogate models are updated in an active learning framework whenever new information from the expensive simulations becomes available. The sampling strategies aim at balancing local improvements of the approximate Pareto front and global exploration to identify the extrema and fill in large gaps of the approximate Pareto front. We demonstrate on a large set of benchmark problems the effectiveness of the method for finding good approximations of the Pareto front.

MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Deciphering flux adjustments of engineered E. coli cells during fermentation with changing growth conditions

Microbial fermentation conditions are dynamic, due to transcriptional induction, nutrient consumption, or changes to incubation conditions. In this paper, 13 C-metabolic flux analysis was used to characterize two violacein-producing E. coli strains with vastly different productivities, and to profile their metabolic adjustments resulting from external perturbations during fermentation. The two strains were first grown at 37 °C in stage 1, and then the temperature was transitioned to 20 °C in stage 2 for the optimal expression of the violacein synthesis pathway. After induction, violacein production was minimal in stage 3, but accelerated in stage 4 (early production phase) and 5 (late production phase) in the high producing strain, reaching a final concentration of 1.5 mmol/L. On the contrary, ~0.02 mmol/L of violacein was obtained from the low producing strain. To have a snapshot of the temporal metabolic changes in each stage, we performed 13 C-MFA via isotopomer analysis of fast-turnover free metabolites. The results indicate strikingly stable flux ratios in the central metabolism throughout the early growth stages. In the late stages, however, the high producer rewired its flux distribution significantly, which featured an upregulated pentose phosphate pathway and TCA cycle, reflux from acetate utilization, negligible anabolic fluxes, and elevated maintenance loss, to compensate for nutrient depletion and drainage of some building blocks due to violacein overproduction. The low producer with stronger promoters shifted its relative fluxes in stage 5 by enhancing the flux through the TCA cycle and acetate overflow, while exhibiting a reduced biomass growth and a minimal flux towards violacein synthesis. Finally, interestingly, the addition of the violacein precursor (tryptophan) in the medium inhibited high producer but enhanced low producer's productivity, leading to hypotheses of unknown pathway regulations (such as metabolite channeling).

13C-MFA↗
DOE OSTI2026

Surrogates for Valve-Controlled Pipe Flow: Accelerating Nuclear Reactor Design

Neural surrogate models are developed to replace expensive steady-state RANS CFD simulations for valve-controlled pipe flow in nuclear reactor design. Using parametric CFD data generated with MOOSE Pronghorn across a range of valve geometry and flow conditions, three approaches are compared: a POD-based reduced-order model, a structured UNet on a cylindrical grid, and unstructured models (DeepONet and BiStride MeshGraphNet) on nondimensionalized point clouds. POD achieves the highest accuracy (99%) with fast inference but requires storing all solution snapshots, while the DeepONet and BSMS-GNN both achieve ~89% accuracy at sub-second inference, with the BSMS-GNN offering superior geometric generalizability. These surrogates enable rapid ranking of candidate valve designs and can warm-start CFD solvers to accelerate convergence, supporting agentic design iteration on the Prometheus platform.

42 - ENGINEERING↗
DOE OSTI2023

Optimal decision-making in high-throughput virtual screening pipelines

Screening large pools of molecular candidates to identify those with specific design criteria or targeted properties is demanding in various science and engineering domains. While a high-throughput virtual screening (HTVS) pipeline can provide efficient means to achieving this goal, its design and operation often rely on experts' intuition, potentially resulting in suboptimal performance. In this paper, we fill this critical gap by presenting a systematic framework that can maximize the return on computational investment (ROCI) of such HTVS campaigns. Based on various scenarios, we empirically validate the proposed framework and demonstrate its potential to accelerate scientific discoveries through optimal computational campaigns, especially in the context of virtual screening.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On a Possibility of the Overgrowth of Diamonds in the Hydrocarbon Atmosphere

The possibility to increase sizes of diamond crystals at least 25 μm by means of their annealing at a temperature of 1450 K in a hydrocarbon atmosphere has been studied. The initial diamond crystals are incorporated in the polyvinyl acetate layer on the surface of a silicon monocrystal and subjected to the annealing in methane atmosphere with low pressure in the presence of an external electric field with intensity up to 0.04 V/μm. Under these conditions the charged ions of methane dissociation products are accelerated and acquire the kinetic energy that is comparable with energy sufficient to form sp{sup 3}-hybride bonds, which can lead to an increase in the sizes of initial seed crystallites. The obtained substrates of a composite with a typical thickness not higher than 1.2 mm, which contain a joined diamond crystal in the carbon matrix, can be used as thermal conductive and electrical insulating spacers in devices for cooling items of electronic engineering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Innovative Waste Packaging and Associated Venting/Hydrogen Management - 16594

The original strategy for managing intermediate level waste (ILW) was developed in the 1980s by the UK Nuclear Industry Radioactive Waste Executive (NIREX), which is now the Radioactive Waste Management Limited (RWM), a wholly owned subsidiary of the Nuclear Decommissioning Authority (NDA). The strategy involved retrieving ILW, sorting and encapsulating it in cement based grout within thin walled stainless steel containers. The containers would then be transferred to a large purpose-built shielded ILW store on site, where they would be stored until the planned final deep geological disposal facility becomes available. In recognition of the high up-front costs of such a strategy, which requires remote facilities for the encapsulation and storage, and coupled with the long programme duration for the design, construction and commissioning of these facilities, waste management organisations responsible for managing the clean-up of legacy facilities within the UK have, in recent years, sought innovative solutions which could potentially both accelerate clean-up and hazard reduction on sites, and with a lower overall programme lifecycle cost. Many of the recent innovative solutions are focused around a new family of packaging designs known as robust shielded containers (RSCs). These are self-shielded containers manufactured from materials such as ductile cast iron or fabricated steel. These containers offer the potential to package wastes for disposal without encapsulation and avoid the need for a highly engineered remote handled shielded store for interim storage. The paper provides a high level overview of the generic concepts of RSCs developed by Croft and focuses on the specific challenges of wasteform evolution on waste package integrity and performance. In particular, the integrity and performance of the filtered vent system required to manage internal pressurisation and to ensure sufficient venting capacity of hydrogen and other evolved gases. The paper summarises the analysis by ARUP undertaken to substantiate the RSC integrity under detonation/deflagration explosion loadings arising from hydrogen accumulation. Analyses of the structural response of the filter assembly were undertaken for both detonation and deflagration explosion scenarios, with the loadings applied to the filter housing rim and to the filter vent cover. The paper also presents evidence of the mechanical performance of the filter medium in terms of pressure transients during deflagration events and the degree to which filtration efficiency is maintained. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scaling patch analysis of turbulent planar plume

Proper scaling in turbulent planar plumes is investigated in this study using a scaling patch approach. Based on the scaled boundary conditions, a proper velocity scale for the mean axial flow is the plume centerline velocity U ref = U ctr , and a proper temperature scale for the temperature excess is Θ ref = T ctr – T ∞ , where T ctr is the plume centerline temperature and T ∞ is the ambient fluid temperature. By seeking an admissible scaling, a key concept in the scaling patch approach, for the mean continuity, mean momentum, and mean energy equations, respectively, the following is found: (1) a proper scale for the mean transverse flow is V ref = ( d δ / d x ) U ctr , where d δ / d x is the growth rate of the plume width. (2) A proper scale for the Reynolds shear stress is R vu , ref = U ctr V ref = ( d δ / d x ) U ctr 2 , a mix of the scales for the mean axial and transverse flows. (3) A proper scale for the turbulent heat flux is R v θ , ref = V ref Θ ctr , a mix of the scales for the mean transverse flow and mean temperature excess. The mean transverse flow thus plays a critical role in the scaling of turbulent planar plumes. Approximate functions are developed for the scaled mean transverse flow, Reynolds shear stress, and turbulent temperature flux, and are found to agree favorably with experimental and numerical simulation data. The integral analysis of the mean momentum equation yields a Richardson number Ri, which remains invariant in the axial direction. The Richardson number is defined as Ri = def g β Θ ctr δ t / ( U ctr V ref ) ≈ 1 / 2 , where g is the gravitational acceleration, β is the thermal expansion coefficient, and δ t is the plume half-width based on the mean temperature profile. This Richardson number arises directly from the scaling patch analysis of the mean momentum equation, including both the streamwise and transverse velocity scales.

42 ENGINEERING↗