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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

Constraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost-affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1-m depth interval were high (52.0 ± 15.1 kg C m −2 and 2.74 ± 0.82 kg N m −2 ) and linearly related ( R 2 = 0.74, p < 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m −2 at 2 m; 117.1 ± 10.4 kg C m −2 at 3 m). The unique small-scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Widespread co‐occurrence of Sebacinales and arbuscular mycorrhizal fungi in switchgrass roots and soils has limited dependence on soil carbon or nutrients

Societal Impact Statement This work addresses a novel group of Sebacinales mycorrhizal fungi being considered for development as inoculants in managed ecosystems because of their potential access to nutrients in soil organic matter. By comparing the diversity, distributions, and abundances of the Sebacinales with the more common arbuscular mycorrhizal fungi in switchgrass, a biofuel crop, we demonstrate that current suggestions for a Sebacinales revolution in agriculture should be tempered by their ecology. In particular, Sebacinales are rare compared to arbuscular mycorrhizal fungi, and are only weakly associated with soil carbon, suggesting that ideas about improved soil carbon cycling associated with Sebacinales need to be sufficiently studied across a range of environmental conditions prior to their consideration for broad‐spectrum soil inoculants. Summary Arbuscular mycorrhizal (AM) fungi are widespread and important root symbionts, but recent work suggests that Sebacinales fungi may play an equally important role in both plant success and ecosystem carbon and nutrient cycling based on their worldwide occurrence and putative access to organic matter. However, the ecological impacts of Sebacinales will depend on their abundance and distribution relative to AM fungi and environmental soil carbon and nutrient patterns, which remain unexplored. We characterized Glomeromycota and Sebacinales fungi in switchgrass ( Panicum virgatum L.) roots and soils across 14 sites with diverse soil conditions. We examined group richness differences, co‐occurrence patterns, and how the relative abundance of these fungi related to soil carbon, nutrient stoichiometry, and host size. Sebacinales were widespread, but less diverse, common, and abundant than Glomeromycota. Moreover, co‐occurrences were predominantly random, suggesting relatively few interactions between these groups. Sebacinales increased relative to Glomeromycota in soils with more carbon, but explanatory power was limited. Based on our findings, we suggest that Sebacinales are likely complementary to AM fungi in roots. Expectations that Sebacinales have large effects on soil carbon and nutrient cycling may need to be reconsidered, at least based on their limited abundances relative to AM fungi in switchgrass. This is an important consideration as Sebacinales are candidates for use as inoculants in managed ecosystems.

Lee, Marissa R.↗
DOE OSTI2020

Preferred Void Orientation in Uniaxially Pressed PBX 9502

Ultra- and small-angle neutron scattering techniques were used to study the morphology of voids in directions parallel and perpendicular to the compaction plane of a uniaxially pressed cylinder of PBX 9502. Two-dimensional scattering patterns from samples cut parallel to the compaction plane had circular equal-intensity contours, implying that along this view the voids are circular or randomly oriented. Identical measurements of samples cut perpendicular to the compaction plane produced elliptical equal-intensity contours, indicating preferred orientation of voids. Analysis of the two dimensional scattering patterns confirmed that the voids are non-spherical with an average aspect ratio of 1.20, flattened so that the larger dimensions of the voids lie within the compaction plane. Model analysis of the combined ultra- and small-angle neutron scattering curves revealed that the PBX 9502 porosity is interconnected, in the form of aggregates comprised of non-spherical, primary voids. The volume dimension was found to be the same in both directions, indicating that the aggregate is randomly oriented throughout the sample, and that it is the primary voids that are preferentially oriented. Furthermore, void interfaces with the surrounding matrix were found to be rough or polydisperse, with a common roughness dimension or polydispersity in all directions.

42 ENGINEERING↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Simulation of the Impact of Point Defects and Edge Dislocations on X-Ray Diffraction in Hexagonal (Ni,Co) 1+2 x Ti 1– x O 3 Thin Films

In this work, a computer code for simulating high-resolution X-ray diffraction (XRD) data from disordered crystals with arbitrary spatial composition and local lattice parameters is developed. Simulated patterns are compared with the experimental data collected on a single phase, highly crystalline (Ni 0.42 Co 0.58 ) 2.22 Ti 0.39 O 3 solid-solution thin film exhibiting a large number of subsidiary minima. As a case study, the edge dislocations in hexagonal (Ni,Co) 3 O 3 thin films with Burgers vector parallel and perpendicular to the a-axis and c-axis, respectively, are modeled. No peak profiles are assumed and thus issues related to profile fitting are avoided. Both macroscopic features, such as film thickness, and atomic-scale structure, such as dislocations, are simultaneously modeled. Simulations are run on a desktop machine. Commonly applied database-based phase identification routines can result in wrong phase identification, unless intensities are properly modeled. Modeling tools of diffractometer manufacturers are compared with the present approach. An example of how simulated reciprocal lattice patterns can be used to choose relevant measurement geometries in defected thin films is given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Statistical Correlation Between Quantum Entanglement and Spin–Orbit Coupling in Crossed Beam Molecular Dynamics

Non-classical features like interference are already being harnessed to control the output of chemical reactions. However, quantum entanglement which is an equally enigmatic many-body quantum correlation can also be used as a powerful resource yet has eluded explicit attention. In this report, an experimental scheme under the crossed beam molecular dynamical setup, with the F + HD reaction, is proposed aiming to study the possible influence of entanglement within reactant pairs on the angular features of the product distribution. The aforesaid reaction has garnered interest recently, as an unusual horseshoe shape pattern in the product (HF) distribution was observed, which has been attributed to the coupling of spin and orbital degrees of freedom. An experimental scheme is proposed aiming to study the possible influence of entanglement on the necessity for the inclusion of such spin–orbit characteristics, under circumstances wherein the existence of entanglement and spin–orbit interaction is simultaneously detectable. The attainable results are further numerically simulated highlighting specific patterns corresponding to various possibilities. Finally, such studies if extended can provide unforeseen mechanistic insight into analogous reactions, too, from the lens of quantum information.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Multi-Step Crystallization of Self-Organized Spiral Eutectics

A method for the solidification of metallic alloys involving spiral self-organization is presented as a new strategy for producing large-area chiral patterns with emergent structural and optical properties, with attention to the underlying mechanism and dynamics. This study reports the discovery of a new growth mode for metastable, two-phase spiral patterns from a liquid metal. Crystallization proceeds via a non-classical, two-step pathway consisting of the initial formation of a polytetrahedral seed crystal, followed by ordering of two solid phases that nucleate heterogeneously on the seed and grow in a strongly coupled fashion. Crystallographic defects within the seed provide a template for spiral self-organization. These observations demonstrate the ubiquity of defect-mediated growth in multi-phase materials and establish a pathway toward bottom-up synthesis of chiral materials with an inter-phase spacing comparable to the wavelength of infrared light. Given that liquids often possess polytetrahedral short-range order, our results are applicable to many systems undergoing multi-step crystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing the Atomic Structures of Exposed Lateral Surfaces for Polymorphic Manganese Dioxide Nanowires

Polymorphic 1D MnO 2 nanostructures are widely applied in fields such as catalysis, sensing, and energy storage with the functionality mainly determined by the atomic patterns of their laterally exposed facets, which largely remain unclear so far. Herein, by high‐resolution transmission electron microscopy (HRTEM) imaging directly along their axial directions, the atomic structures of the outmost lateral facets of polymorphic MnO 2 nanowires are disclosed. To generalize the findings, four most commonly seen phases with characteristic tunnel structures are targeted, i.e., β‐, γ‐, α‐, and todorokite(t)‐MnO 2 , which are synthesized conventionally using a hydrothermal method reported in the literature. Axially imaging these MnO 2 nanowires via HRTEM, the {hkl} facets covering the lateral surfaces are accurately indexed, the atomic pattern of each {hkl} facet is revealed, and it is further coupled with the outmost tunnel configuration that can significantly affect the physicochemical property of MnO 2 materials via tunnel‐driven mass adsorption/transport. This work provides a reliable reference for atomic modeling of MnO 2 to benefit the pursuit of its structure–property relationship; in addition, it can benefit surface engineering strategies to better rationalize the facet growth control with optimized functionality.

Yuan, Yifei↗
DOE OSTI2022

Size and Distribution of Parr Produced from Natural‐ and Hatchery‐Origin Steelhead Spawning Naturally in a Small Pacific Northwest Coastal Stream

Abstract Recent studies suggest that steelhead Oncorhynchus mykiss produced from local hatchery‐origin (HOR) adults have lower lifetime fitness than their natural‐origin (NOR) counterparts. To increase our understanding of this pattern, we compared age‐1 parr size and distribution produced by a local integrated population of HOR and NOR adults spawning naturally across a range of environmental conditions. Across 8 years, we used genetic parentage assignments and field measurements of parr in conjunction with creek temperature and flow data to find small, nonbiologically significant differences in size between parr of different parent origins. This suggests that parr produced by HOR adults are acquiring enough resources to grow at a rate similar to that of parr produced by NOR adults. In contrast to origin, we found strong positive associations between mean size of parr and annual mean water temperature and summer flow. Additionally, the distribution of parr was similar and HOR‐produced parr were not skewed relative to the location of the hatchery. Parr occupying the full extent of accessible and suitable habitat might be a positive outcome for hatchery programs seeking to supplement an existing population or replace an extirpated population. However, these results also imply that HOR fish could be competing with NOR fish for food and space, which might be less desirable if the goal of the hatchery program is harvest and not supplementation. Our results highlight the importance of clearly articulated goals for the hatchery program, as the observed pattern could be deemed a benefit or a risk depending on the hatchery's purpose. Lastly, projections for higher water temperatures and reduced summer flows, when considered in the context of the correlations we observed for size of parr, imply that coastal steelhead populations could start experiencing negative climate impacts with respect to steelhead parr growth metrics during the summer in the next few decades.

Kennedy, Benjamen M.↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
DOE OSTI2021

Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-task and Inductive Transfer Learning

Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Exploring the Spatial Relationship Between Demographic Indicators and the Built Environment of a City

In addition to global and regional drivers of urbanization, neighborhood development in urban areas across the United States has been shown to be influenced by various local socio-economic factors. These factors, despite varying across socio-economic groups, have large implications regarding a population’s vulnerability to extreme climate events, including heat waves resulting in adverse health impacts. Additionally, the demographics of an urban area can shape its infrastructural characteristics, causing different populations groups to face varying levels of risks and benefits. As a result, the urban morphology and socio-economic characteristics of a city are deeply intertwined; however, their interactions on a finer scale are not yet fully understood. This research aims to better understand the relationships between various socio-economic factors and the built environment of a city, considering variability in building types, and temperature patterns. This research focuses on the city of Las Vegas, NV, and uses spatial data analysis to understand the correlation between of socio-economic characteristics, building morphology, building characteristics, and temperature data to understand the correlation between these various factors. Results of these research shows there is a distinct pattern of clustering of socio-economic characteristics with the city and there is a distinct correlation between age and cost, socio-economic characteristics, and locations of high heat distribution within the city.

Singh, Ridhima↗
DOE OSTI2022

Unveiling User Behavior on Summit Login Nodes as a User

We observe and analyze usage of the login nodes of the leadership class Summit supercomputer from the perspective of an ordinary user—not a system administrator—by periodically sampling user activities (job queues, running processes, etc.) for two full years (2020–2021). Our findings unveil key usage patterns that evidence misuse of the system, including gaming the policies, impairing I/O performance, and using login nodes as a sole computing resource. Our analysis highlights observed patterns for the execution of complex computations (workflows), which are key for processing large-scale applications.

Wilkinson, Sean↗
DOE OSTI2023

A Vision for Coupling Operation of US Fusion Facilities with HPC Systems and the Implications for Workflows and Data Management

The operation of large US Department of Energy (DOE) research facilities, like the DIII-D National Fusion Facility, results in the collection of complex multi-dimensional scientific datasets, both experimental and model-generated. In the future, it is envisioned that integrated data analysis coupled with large-scale high performance computing (HPC) simulations will be used to improve experimental planning and operation. Practically, massive data sets from these simulations provide the physics basis for generation of both reduced semi-analytic and machine-learning-based models. Storage of both HPC simulation datasets (generated from US DOE leadership computing facilities) and experimental datasets presents significant challenges. In this paper, we present a vision for a DOE-wide data management workflow that integrates US DOE fusion facilities with leadership computing facilities. Data persistence and long-term availability beyond the length of allocated projects is essential, particularly for verification and recalibration of artificial intelligence and machine learning (AI/ML) models. Because these data sets are often generated and shared among hundreds of users across multiple leadership computing facility centers, they would benefit from cross-platform accessibility, persistent identifiers (e.g. DOI, or digital object identifier), and provenance tracking. Here, the ability to handle different data access patterns suggests that a combination of low cost, high latency (e.g. for storing ML training sets) and high cost, low latency systems (e.g. for real-time, integrated machine control feedback) may be needed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

The Focus of an Ultraintense Laser Pulse

Back in 1885, von Lommel studied the diffraction of a circular opening. His study was one step further than the Airy diffraction model (describing patterns in the focal plane) because von Lommel’s model was able to describe what the light distribution before and after the focus is. Today’s extreme lasers have a flat-top profile whose diffraction pattern in the vicinity of the focus is very much reminiscent of von Lommel’s description. In the present Chapter, we will compute, based on a Huygens-Fresnel integral method, the laser field distribution around the focus. This modelling is particularly important because the intensity distribution before and after the focal plane (where the Airy disk exists) is very relevant to many contemporary experiments, and is key to the ionization of atoms and the dynamics of liberated electrons. For clarity, we are going to analyze three cases corresponding to three f-numbers: f/10, f/1.8, and f/1.1. Our computed results reveal the present-day significance of von Lommel’s pioneering results. Here, they help us understand the light distribution in the entire focal region of an ultraintense laser pulse including the full vectorial fields, not just the focal plane.

Roso, Luis [Universidad de Salamanca (Spain)]↗
DOE OSTI2021

Nucleation is more than critical: A case study of the electroweak phase transition in the NMSSM

Electroweak baryogenesis is an attractive mechanism to generate the baryon asymmetry of the Universe via a strong first order electroweak phase transition. We compare the phase transition patterns suggested by the vacuum structure at the critical temperatures, at which local minima are degenerate, with those obtained from computing the probability for nucleation via tunneling through the barrier separating local minima. Heuristically, nucleation becomes difficult if the barrier between the local minima is too high, or if the distance (in field space) between the minima is too large. As an example of a model exhibiting such behavior, we study the Next-to-Minimal Supersymmetric Standard Model, whose scalar sector contains two SU(2) doublets and one gauge singlet. We find that the calculation of the nucleation probabilities prefers different regions of parameter space for a strong first order electroweak phase transition than the calculation based solely on the critical temperatures. Our results demonstrate that analyzing only the vacuum structure via the critical temperatures can provide a misleading picture of the phase transition patterns, and, in turn, of the parameter space suitable for electroweak baryogenesis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

An inflationary probe of cosmic Higgs switching

A scalar Higgs field can be repeatedly switched on and off when it couples to a classically oscillating scalar modulus field. The modulus flips the Higgs mass term between stable and tachyonic values. We study a cosmological scenario in which such repeated phase transitions occur during inflation. An irrelevant operator coupling the Higgs field to the inflaton can then imprint the pattern of phase transitions in the correlation functions of the inflaton. Using both numerical and analytic studies, we show that the inflaton 2- point function carries characteristic imprints of the modulus oscillation and its effect on the Higgs boson. We briefly remark on the potential observability of such patterns and how they might be distinguished from other dynamics in the early universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗