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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2022

The Fight against Plant-Parasitic Nematodes: Current Status of Bacterial and Fungal Biocontrol Agents

Plant-parasitic nematodes (PPNs) are among the most notorious and underrated threats to food security and plant health worldwide, compromising crop yields and causing billions of dollars of losses annually. Chemical control strategies rely heavily on synthetic chemical nematicides to reduce PPN population densities, but their use is being progressively restricted due to environmental and human health concerns, so alternative control methods are urgently needed. Here, we review the potential of bacterial and fungal agents to suppress the most important PPNs, namely Aphelenchoides besseyi, Bursaphelenchus xylophilus, Ditylenchus dipsaci, Globodera spp., Heterodera spp., Meloidogyne spp., Nacobbus aberrans, Pratylenchus spp., Radopholus similis, Rotylenchulus reniformis, and Xiphinema index.

Pires, David (ORCID:0000000230383319)↗

DOE OSTI2022

Effect of oxidative breakers on organic matter degradation, contaminant mobility and critical mineral release during shale-fracturing fluid interactions in the Marcellus Shale

Oil and gas production from organic-rich shale formations has become viable through advancements in multistage hydraulic fracturing. However, fluctuations in oil and gas prices, coupled with the sharp decline in gas production after the initial days of fracturing operations, drive operators to devise strategies for enhancing hydrocarbon production. The use of highly reactive fracturing fluids that include strong oxidizing agents, also known as breakers, can potentially increase well productivity. The oxidative breakers are used to reduce the viscosity of gel-based fluids after the proppant is transported into fracture zones of the target formation. These breakers can also degrade the organic matter, enhancing the release of hydrocarbons. However, chemical byproducts generated by the interaction of oxidative breakers with the shale matrix have not been extensively studied. Here, this study investigated the fluid-rock interactions between Marcellus Shale and synthetic hydraulic fracturing fluid (HFF) solutions comprising three different oxidative breakers, i.e., ammonium persulfate, sodium bromate, and sodium hypochlorite, commonly used in the Appalachian Basin, USA. Our results demonstrate that the type of oxidizing breakers used in the HFF controlled the type and amount of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) produced. In all HFF reacted effluents, we observed the transformation of VOCs and the presence of organic acids in variable amounts. However, effluents from HFF containing sodium bromate and sodium hypochlorite breakers showed the presence of several halogenated organic compounds. Changes in major ions and mineralogy indicate that carbonate dissolution and barite precipitation were ubiquitous in all shale reacted effluents. Our results also demonstrate that the addition of oxidative breakers increased the concentration of several major and trace elements in the effluents. These elements fall under the critical mineral (CM) or critical element category due to their high demand in emerging technologies and susceptibility to supply chain disruption due to variety of factors. However, before oxidative breakers can be used at a larger field scale to enhance the release and recovery of CM and hydrocarbons, a better understanding is required of the potential environmental impacts associated with the generation and transformation of contaminants during breaker-fluid-rock interaction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Synthesis of Platinum Nanoparticles Supported on Fused Nanosized Carbon Spheres Derived from Sustainable Source for Application in a Hydrogen Generation Reaction

The dwindling supply of fossil fuels has prompted the search for an alternative energy source that could effectively replace them. Potential renewable energy sources such as solar, wind, tidal, and geothermal are all promising but each has its own drawbacks. Hydrogen gas on the other hand can be combusted to produce energy with only water as a byproduct and can be steadily generated via the aqueous media hydrolysis reaction of Sodium Borohydride (NaBH 4 ). This study successfully synthesized fused carbon spheres derived from sugar and decorated them with platinum nanoparticles to form a novel composite material (PtFCS) for catalyzing this reaction. The platinum nanoparticles were produced by reducing chloroplatinic acid in a solution with sodium borohydride and using sodium citrate as a capping agent for the nanoparticles. Transmission electron microscopy (TEM), Energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used to characterize and determine the size and shape of the Pt nanoparticles (PtNPs) and fused carbon spheres. TEM was able to determine the average size of the fused carbon spheres to be 200 nm and the average size for the PtNPs to be 2–3 nm. The PtFCS composite was tested for its ability to catalyze the hydrolysis of NaBH 4 under various reaction conditions including various solution pH, various temperatures, and various dosages of sodium borohydride. The catalyst was found to perform the best under acidic solution conditions (pH 6), producing hydrogen at a rate of 0.0438 mL/mg cat ·min. The catalyst was determined to have an activation energy of 53.0 kJ/mol and could be used multiple times in succession with no loss in the volume of hydrogen produced. This sugar-derived composite catalyst shows promise and could be implemented as a sustainable catalyst for the generation of hydrogen fuel.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Experiment 2: Vapor Transport Crystal Growth of Mercury Cadmium Telluride in Microgravity- USML-2

The new epitaxial growth experiments of Hg(l-x)Cd(x)Te on (100) CdTe substrates by chemical vapor transport (CVT), using HgI2 as a transport agent, were performed in the transient growth regime of this ternary, heteroepitaxial system at normal and reduced gravity during the USML-2 flight. The surface and interface morphology, the compositional and structural uniformity, and carrier mobility of the epitaxial layer and islands grown in microgravity are measurably improved relative to ground specimens. These observations demonstrate the effects of convective flow on the transport, deposition, and growth processes of this solid-vapor system even in the transient growth regime. The properties of the Hg(l-x)Cd(x)Te layer grown in a microgravity environment compare quite favorably to those of layers obtained by other techniques.

Wiedemeier, H.↗

NASA NTRS2021

Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

With three complexes spread evenly across the Earth, NASA’s Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network’s capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in between. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft ephemeris data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.

Wilson, Brian↗

DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗

DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

42 ENGINEERING↗

DOE OSTI2022

Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry

PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).

Parris, D. Joshua↗

DOE OSTI2022

The Role of Process-Directing Agents on Enamel Lesion Remineralization: Fluoride Boosters

The aim of this study was to investigate the effects of two process-directing agents (polyaspartic acid and osteopontin) used in a polymer-induced liquid-precursor (PILP) process on the remineralization of bacteria-induced enamel demineralization. Enamel demineralization lesions (depths of about 180–200 µm) were created and exposed to Streptococcus mutans, cultured with a 10% sucrose solution for 21 days, and remineralized using a PILP process (pH = 7.4, 14 days) with a calcium phosphate solution containing either polyaspartic acid or osteopontin in the presence or absence of fluoride (0.5 ppm). The specimens were examined under scanning electron microscopy. The fluoride was successfully incorporated into the PILP remineralization process for both polyaspartic acid and osteopontin. When the fluoride was added to the PILP remineralization solution, there was more uniform remineralization throughout the lesion than with either polyaspartic acid or osteopontin alone. However, in the absence of these process-directing agents, fluoride alone showed less remineralization with the formation of a predominantly surface-only layer. The PILP remineralization process relies on the ability of process-directing agents to stabilize calcium phosphate ions and holds promise for enamel lesion remineralization, and these agents, in the presence of fluoride, seem to play an important role as a booster or supplement in the continuation of remineralization by reducing the mineral gains at the surface layer.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2009

Biomorphic Multi-Agent Architecture for Persistent Computing

A multi-agent software/hardware architecture, inspired by the multicellular nature of living organisms, has been proposed as the basis of design of a robust, reliable, persistent computing system. Just as a multicellular organism can adapt to changing environmental conditions and can survive despite the failure of individual cells, a multi-agent computing system, as envisioned, could adapt to changing hardware, software, and environmental conditions. In particular, the computing system could continue to function (perhaps at a reduced but still reasonable level of performance) if one or more component( s) of the system were to fail. One of the defining characteristics of a multicellular organism is unity of purpose. In biology, the purpose is survival of the organism. The purpose of the proposed multi-agent architecture is to provide a persistent computing environment in harsh conditions in which repair is difficult or impossible. A multi-agent, organism-like computing system would be a single entity built from agents or cells. Each agent or cell would be a discrete hardware processing unit that would include a data processor with local memory, an internal clock, and a suite of communication equipment capable of both local line-of-sight communications and global broadcast communications. Some cells, denoted specialist cells, could contain such additional hardware as sensors and emitters. Each cell would be independent in the sense that there would be no global clock, no global (shared) memory, no pre-assigned cell identifiers, no pre-defined network topology, and no centralized brain or control structure. Like each cell in a living organism, each agent or cell of the computing system would contain a full description of the system encoded as genes, but in this case, the genes would be components of a software genome.

Lodding, Kenneth N.↗

DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Solution structure for an Encephalitozoon cuniculi adrenodoxin-like protein in the oxidized state

Encephalitozoon cuniculi is a unicellular, obligate intracellular eukaryotic parasite in the Microsporidia family and one of the agents responsible for microsporidosis infections in humans. Like most Microsporidia, the genome of E. cuniculi is markedly reduced and the organism contains mitochondria-like organelles called mitosomes instead of mitochondria. Here we report the solution NMR structure for a protein physically associated with mitosome-like organelles in E. cuniculi, the 128-residue, adrenodoxin-like protein Ec-Adx (UniProt ID Q8SV19) in the [2Fe-2S] ferredoxin superfamily. Oxidized Ec-Adx contains a mixed four-strand β-sheet, β2-β1-β4-β3 (↓↑↑↓), loosely encircled by three α-helices and two 310-helices. This fold is similar to the structure observed in other adrenodoxin and adrenodoxin-like proteins except for the absence of a fifth anti-parallel β-strand next to β3 and the position of α3. Cross peaks are missing or cannot be unambiguously assigned for 20 amide resonances in the 1 H- 15 N HSQC spectrum of Ec-Adx. These missing residues are clustered primarily in two regions, G48-V61 and L94-L98, containing the four cysteine residues predicted to ligate the paramagnetic [2Fe-2S] cluster. Missing amide resonances in 1 H- 15 N HSQC spectra are detrimental to NMR-based solution structure calculations because 1 H- 1 H NOE restraints are absent (glass half-empty) and this may account for the absent β-strand (β5) and the position of α3 in oxidized Ec-Adx. On the other hand, the missing amide resonances unambiguously identify the presence, and immediate environment, of the paramagnetic [2Fe-2S] cluster in oxidized Ec-Adx (glass half-full).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Artificial-intelligence-driven shot reduction in quantum measurement

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL)-based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction Ansätze. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A multi-scale cognitive interaction model of instrument operations at the Linac Coherent Light Source

The Linac Coherent Light Source (LCLS) is the world’s first x-ray free electron laser. It is a scientific user facility operated by the SLAC National Accelerator Laboratory, at Stanford, for the U.S. Department of Energy. As beam time at LCLS is extremely valuable and limited, experimental efficiency—getting the most high quality data in the least time—is critical. Our overall project employs cognitive engineering methodologies with the goal of improving experimental efficiency and increasing scientific productivity at LCLS by refining experimental interfaces and workflows, simplifying tasks, reducing errors, and improving operator safety and stress. Here, in this study, we describe a multi-agent, multi-scale computational cognitive interaction model of instrument operations at LCLS. Our model simulates the aspects of human cognition at multiple cognitive and temporal scales, ranging from seconds to hours, and among agents playing multiple roles, including instrument operator, real time data analyst, and experiment manager. The model can roughly predict impacts stemming from proposed changes to operational interfaces and workflows. Example results demonstrate the model’s potential in guiding modifications to improve operational efficiency. We discuss the implications of our effort for cognitive engineering in complex experimental settings and outline future directions for research. The model is open source, and the videos of the supplementary material provide extensive detail.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Low-Power Nuclear Heating Tests of UN Fuel Particles in Hydrogen Environment for Nuclear Thermal Propulsion

Recently, there has been a reemergence of interest within the space-exploration community regarding nuclear thermal propulsion (NTP) for interplanetary travel. NTP, as conceptualized, is a compact propulsion source, is relatively light because there is no need for an oxidizing agent and has a very high specific impulse compared to traditional forms of propulsion. These factors would reduce first-leg orbital-rocketry costs (by the source’s low weight and volume) and decrease overall travel time for astronauts with higher transit speeds. Idaho National Laboratory was provided bare, uncoated, and tungsten-coated uranium nitride fuel particles as part of a preliminary investigation. The fuel was fabricated by BWX Technologies and irradiated at the Massachusetts Institute of Technology research reactor. X-ray computed tomography and scanning electron microscopy and associated chemical analysis techniques were performed on these particles at Idaho National Laboratory. Both coated and uncoated forms were examined in the as-fabricated and as-irradiated conditions. The tungsten coating was found to be intact following the irradiation, indicating positive attributes for performance under these low-power-irradiation conditions. The irradiation of uncoated particles resulted in nitrogen enrichment at the edge of the particle.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Trust in News as Moderated by Known Truth Assessments and Source

Disinformation has become a problem in various spaces, from social media to organizations. Given the lack of evidence and transparency surrounding organizational disinformation, we aim to understand issue of information exposure effect and source trust from the lens of traditional information consumption online. Using an online experiment and multi-level mixed-effects modeling, we find that the exposure effect exists even over only two exposures to a headline, as long as participants are not shown fact-checking. Fact-checking can be effective in reducing the impact of the exposure effect. Additionally, we find that participants trusted sources significantly more before seeing the headlines. The possible presence of disinformation reduces trust in sources, even when considered highly neutral and reputable. These findings can inform simulations of agent interactions with misinformation as well as future experimental designs focused on evaluations of belief and trust based on recognizability of a source.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1976

Graphite-reinforced bone cement

Chopped graphite fibers added to surgical bone cement form bonding agent with mechanical properties closely matched to those of bone. Curing reaction produces less heat, resulting in reduced traumatization of body tissues. Stiffness is increased without affecting flexural strength.

Knoell, A. C.↗