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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Substrate availability and not thermal acclimation controls microbial temperature sensitivity response to long-term warming

Microbes are responsible for cycling carbon (C) through soils, and predicted changes in soil C stocks under climate change are highly sensitive to shifts in the mechanisms assumed to control the microbial physiological response to warming. Two mechanisms have been suggested to explain the long-term warming impact on microbial physiology: microbial thermal acclimation and changes in the quantity and quality of substrates available for microbial metabolism. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year-old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationships between temperature sensitivity of physiology (growth, respiration, carbon use efficiency, and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation of microbial respiration, but only in summer, when warming had exacerbated the seasonally-induced, already small dissolved organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon increased the extracellular enzymatic pool and its temperature sensitivity. We propose that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes to decadal warming. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming effects on these soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Shifts in bacterial traits under chronic nitrogen deposition align with soil processes in arbuscular, but not ectomycorrhizal-associated trees

Nitrogen (N) deposition increases soil carbon (C) storage by reducing microbial activity. These effects vary in soil beneath trees that associate with arbuscular (AM) and ectomycorrhizal (ECM) fungi. Variation in carbon C and N uptake traits among microbes may explain differences in soil nutrient cycling between mycorrhizal associations in response to high N loads, a mechanism not previously examined due to methodological limitations. Here, we used quantitative Stable Isotope Probing (qSIP) to measure bacterial C and N assimilation rates from an added organic compound, which we conceptualize as functional traits. As such, we applied a trait-based approach to explore whether variation in assimilation rates of bacterial taxa can inform shifts in soil function under chronic N deposition. We show taxon-specific and community-wide declines of bacterial C and N uptake under chronic N deposition in both AM and ECM soils. N deposition-induced reductions in microbial activity were mirrored by declines in soil organic matter mineralization rates in AM but not ECM soils. Our findings suggest C and N uptake traits of bacterial communities can predict C cycling feedbacks to N deposition in AM soils, but additional data, for instance on the traits of fungi, may be needed to connect microbial traits with soil C and N cycling in ECM systems. In conclusion, our study also highlights the potential of employing qSIP in conjunction with trait-based analytical approaches to inform how ecological processes of microbial communities influence soil functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing Using Latent Stochastic Differential Equations

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.

Fagin, Joshua [City Univ. of New York (CUNY), NY (↗
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Particle Dark Matter Across Scales

This is the final technical report of the DOE Early Career award for PI Safdi. The award was active from 09/01/2018 - 05/01/2021. The Early Career award funded theoretical work to investigate the particle nature of dark matter across a range of astrophysical and particle physics scales.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

BASS. XXXVI. Constraining the Local Supermassive Black Hole–Halo Connection with BASS DR2 AGNs

We investigate the connection between supermassive black holes (SMBHs) and their host dark matter halos in the local universe using the clustering statistics and luminosity function of active galactic nuclei (AGNs) from the Swift/BAT AGN Spectroscopic Survey (BASS DR2). By forward-modeling AGN activity into snapshot halo catalogs from N -body simulations, we test a scenario in which SMBH mass correlates with dark matter (sub)halo mass for fixed stellar mass. We compare this to a model absent of this correlation, where stellar mass alone determines the SMBH mass. We find that while both simple models are able to largely reproduce the abundance and overall clustering of AGNs, the model in which black hole mass is tightly correlated with halo mass is preferred by the data by 1.8 σ . When including an independent measurement on the black hole mass–halo mass correlation, this model is preferred by 4.6 σ . We show that the clustering trends with black hole mass can further break the degeneracies between the two scenarios and that our preferred model reproduces the measured clustering differences on one-halo scales between large and small black hole masses. These results indicate that the halo binding energy is fundamentally connected to the growth of SMBHs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exotic Dark Matter Search with the Majorana Demonstrator

With excellent energy resolution and ultralow-level radiogenic backgrounds, the high-purity germanium detectors in the enable searches for several classes of exotic dark matter (DM) models. In this work, we report new experimental limits on keV-scale sterile neutrino DM via the transition magnetic moment from conversion to active neutrinos ν s → ν a . We report new limits on fermionic dark matter absorption ( χ + A → ν + A ) and sub-GeV DM-nucleus 3 → 2 scattering ( χ + χ + A → ϕ + A ), and new exclusion limits for bosonic dark matter (axionlike particles and dark photons). These searches utilize the (1–100)-keV low-energy region of a 37.5-kg y exposure collected by the between May 2016 and November 2019 using a set of Ge 76 -enriched detectors whose surface exposure time was carefully controlled, resulting in extremely low levels of cosmogenic activation. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Path integral approach to quantum anomalies in interacting models

The prediction and subsequent discovery of topological semimetal phases of matter in solid state systems has instigated a surge of activity investigating the exotic properties of these unusual materials. Among these are transport signatures which can be attributed to the chiral anomaly; the breaking of classical chiral symmetry in a quantum theory. This remarkable quantum phenomenon, first discovered in the context of particle physics has now found new life in condensed-matter physics, connecting topological quantum matter and band theory with effective field theoretic models. In this paper we investigate the interplay between interactions and the chiral anomaly in field theories inspired by semimetals using Fujikawa's path integral method. Starting from models in one spatial dimension we discuss how the presence of interactions can affect the consequences of the chiral anomaly leading to renormalization of excitations and their transport properties. This is then generalized to the three-dimensional case where we show that the anomalous response of the system, namely, the chiral magnetic and quantum Hall effects, are modified by the presence of interactions. These properties are investigated further through the identification of anomalous modes which exist within interacting Weyl semimetals. These massive excitations are nonperturbative in nature and are a direct consequence of the chiral anomaly. The effects of interactions on mixed axial-gravitational anomalies are then investigated and the conditions required for interaction effects to be observed are discussed.

Anomalies↗
DOE OSTI2020

Complex plant-derived organic aerosol as ice-nucleating particles – more than the sums of their parts?

Quantifying the impact of complex organic particles on the formation of ice crystals in clouds remains challenging, mostly due to the vast number of different sources ranging from sea spray to agricultural areas. In particular, there are many open questions regarding the ice nucleation properties of organic particles released from terrestrial sources such as decaying plant material. In this work, we present results from laboratory studies investigating the immersion freezing properties of individual organic compounds commonly found in plant tissue and complex organic aerosol particles from vegetated environments, without specifically investigating the contribution from biological particles, which may contribute to the overall ice nucleation efficiency observed at high temperatures. To characterize the ice nucleation properties of plant-related aerosol samples for temperatures between 242 and 267 K, we used the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud chamber and the Ice Nucleation SpEctrometer of the Karlsruhe Institute of Technology (INSEKT), which is a droplet freezing assay. Individual plant components (polysaccharides, lignin, soy and rice protein) were mostly less ice active, or similarly ice active, compared to microcrystalline cellulose, which has been suggested by recent studies to be a proxy for quantifying the primary cloud ice formation caused by particles originating from vegetation. In contrast, samples from ambient sources with a complex organic matter composition (agricultural soils and leaf litter) were either similarly ice active or up to 2 orders of magnitude more ice active than cellulose. Of all individual organic plant components, only carnauba wax (i.e., lipids) showed a similarly high ice nucleation activity as that of the samples from vegetated environments over a temperature range between 245 and 252 K. Hence, based on our experimental results, we suggest considering cellulose as being representative for the average ice nucleation activity of plant-derived particles, whereas lignin and plant proteins tend to provide a lower limit. In contrast, complex biogenic particles may exhibit ice nucleation activities which are up to 2 orders of magnitude higher than observed for cellulose, making ambient plant-derived particles a potentially important contributor to the population of ice-nucleating particles in the troposphere, even though major uncertainties regarding their transport to cloud altitude remain.

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

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO 2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

On Single-Cell Enzyme Assays in Marine Microbial Ecology and Biogeochemistry

Extracellular enzyme activity is a well-established parameter for evaluating microbial biogeochemical roles in marine ecosystems. The presence and activity of extracellular enzymes in seawater provide insights into the quality and quantity of organic matter being processed by the present microorganisms. A key challenge in our understanding of these processes is to decode the extracellular enzyme repertoire and activities of natural communities at the single-cell level. Current measurements are carried out on bulk or size-fractionated samples capturing activities of mixed populations. This approach – even with size-fractionation – cannot be used to trace enzymes back to their producers, nor distinguish the active microbial members, leading to a disconnect between measured activities and the producer cells. By targeting extracellular enzymes and resolving their activities at the single-cell level, we can investigate underlying phenotypic heterogeneity among clonal or closely related organisms, characterize enzyme kinetics under varying environmental conditions, and resolve spatio-temporal distribution of individual enzyme producers within natural communities. In this perspective piece, we discuss state-of-the-art technologies in the fields of microfluidic droplets and functional screening of prokaryotic cells for measuring enzyme activity in marine seawater samples, one cell at a time. We further elaborate on how this single-cell approach can be used to address research questions that cannot be answered with current methods, as pertinent to the enzymatic degradation of organic matter by marine microorganisms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Active Plasmonics and Active Chiral Plasmonics through Orientation-Dependent Multipolar Interactions

While most active plasmonic efforts focus on responsive metamaterials to modulate optical response, we present a simple alternative based on applied orientation control that can likely be implemented for many passive plasmonic materials. Passive plasmonic motifs are simpler to prepare but cannot be altered postfabrication. Here, we show that such systems can be easily manipulated through substrate orientation control to generate both active plasmonic and active chiral plasmonic responses. Using gold nanocrescents as our model platform, we demonstrate tuning of optical extinction from -21% to +36% at oblique incidence relative to normal incidence. Variation of substrate orientation in relation to incident polarization is also demonstrated to controllably switch chiroptical handedness (e.g., Δg = ± 0.55). These active plasmonic responses arise from the multipolar character of resonant modes. In particular, we correlate magnetoelectric and dipole–quadrupole polarizabilities with different light-matter orientation-dependence in both near- and far-field localized surface plasmon activity. Additionally, the attribution of far-field optical response to higher-order multipoles highlights the sensitivity offered by these orientation-dependent characterization techniques to probe the influence of localized electromagnetic field gradients on a plasmonic response. The sensitivity afforded by orientation-dependent optical characterization is further observed by the manifestation in both plasmon and chiral plasmon responses of unpredicted structural nanocrescent variance (e.g., left- and right-tip asymmetry) not physically resolved through topographical imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Molecular Origins of the Barriers to Proton Transport in Acidic Aqueous Solutions

The self-consistent iterative multistate empirical valence bond (SCI-MS-EVB) method is used to analyze the structure, thermodynamics, and dynamics of hydrochloric acid solutions. The reorientation timescales of irreversible proton transport are elucidated by simulating 0.43 M, 0.85 M, 1.68 M, and 3.26 M HCl solutions at 270 K, 285 K, 300 K, 315 K, and 330 K. The results indicate increased counter-ion pairing with increasing concentration, which manifests itself via a reduced hydronium oxygen–chloride (O*-Cl) structuring in the radial distribution functions. Increasing ionic concentration also reduces the diffusion of the hydrated excess protons, principally by reducing the contribution of the Grotthuss proton hopping (shuttling) mechanism to the overall diffusion process. In agreement with prior experimental findings, a decrease in the activation energy of reorientation timescales was also observed, which is explicitly explained using activated rate theory and an energy-entropy decomposition of the state-averaged radial distribution functions. Furthermore, these results provide atomistic verification of suggestions from recent two-dimensional infrared spectroscopy experiments that chloride anions (as opposed to hydrated excess protons) create entropic barriers to proton transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploring regular and turbulent flow states in active nematic channel flow via Exact Coherent Structures and their invariant manifolds

This work is a unified study of stable and unstable steady states of 2D active nematic channel flow using the framework of Exact Coherent Structures (ECS). ECS are stationary, periodic, quasiperiodic, or traveling wave solutions of the governing equations that, together with their invariant manifolds, organize the dynamics of nonlinear continuum systems. We extend our earlier work on ECS in the preturbulent regime by performing a comprehensive study of stable and unstable ECS for a wide range of activity values spanning the preturbulent and turbulent regimes. In the weakly turbulent regime, we compute more than 200 unstable ECS that co-exist at a single set of parameters, and uncover the role of symmetries in organizing the phase space geometry. We provide conclusive numerical evidence that in the preturbulent regime, generic trajectories shadow a series of unstable ECS before settling onto an attractor. Lastly, our studies hint at shadowing of quasiperiodic type ECS in the turbulent regime.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

New constraints on ultraheavy dark matter from the LZ experiment

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV / c 2 to a few TeV / c 2 . Models of dark matter with a mass much heavier than this are well motivated by early production mechanisms different from the standard thermal freeze-out, but they have generally been less explored experimentally. In this work, we present a reanalysis of the first science run of the LZ experiment, with an exposure of 0.9 tonne × yr , to search for ultraheavy particle dark matter. The signal topology consists of multiple energy deposits in the active region of the detector forming a straight line, from which the velocity of the incoming particle can be reconstructed on an event-by-event basis. Zero events with this topology were observed after applying the data selection calibrated on a simulated sample of signal-like events. New experimental constraints are derived, which rule out previously unexplored regions of the dark matter parameter space of spin-independent interactions beyond a mass of 10 17 GeV / c 2 . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Pseudo-Dirac neutrinos and relic neutrino matter effect on the high-energy neutrino flavor composition

We show that if neutrinos are pseudo-Dirac, they can potentially affect the flavor ratio predictions for the high-energy astrophysical neutrino flux observed by IceCube. In this context, we point out a novel matter effect induced by the cosmic neutrino background (CνB) on the flavor ratio composition. Specifically, the active-sterile neutrino oscillations over the astrophysical baseline lead to an energy-dependent flavor ratio at Earth due to the CνB matter effect, which is in principle distinguishable from the vacuum oscillation effect, provided there is an asymmetry between the neutrino and antineutrino number densities, as well as a local CνB overdensity. Considering the projected precision of the 3-neutrino oscillation parameter measurements and improved flavor triangle measurements, we show that the next-generation neutrino telescopes, such as KM3NeT and IceCube-Gen2, can in principle probe the pseudo-Dirac neutrino hypothesis and the CνB matter effect.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Observation and Control of Unidirectional Ballistic Dynamics of Nanoparticles at a Liquid–Gas Interface by 4D Electron Microscopy

Understanding and controlling the dynamics of active Brownian objects far from equilibrium are fundamentally important for emerging technologies such as artificial micro/nanomotors for drug deliveries and noninvasive microsurgery. However, direct observation and control of unidirectional propulsion of individual nanoscale objects are technically challenging due to the required spatiotemporal resolution. Here, we report in situ visualization and manipulation of unidirectional superfast ballistic dynamics of a single-photon-activated gold nanoparticle (NP) along the liquid–gas interface by four-dimensional electron microscopy (4D EM) at nanometer and nanosecond scales. We observed that, upon repetitive femtosecond laser excitation, the NP at the liquid–gas interface exhibits a continuously superfast unidirectional translation with a linear dependence of its root mean squared velocity (νrms) on either the laser fluence or repetition rate. Under a single femtosecond pulse excitation, the NP exhibits a superfast ballistic translation at the nanosecond time scale. Combined experiment and physical modeling reveals that the superfast unidirectional, ballistic translation is driven by unidirectional random impulsive forces arising from the nanobubbles (NBs) induced by enhanced laser heating as a result of plasmonic excitation, which is controllable by tuning the laser characteristics. Furthermore, this directional plasmonic NB-propulsion mechanism sheds light on the design of light-controllable artificially intelligent micro/nanomotor systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Soil properties and biochemical composition of ground-dwelling bee nests in agricultural settings

Soils deliver underrecognized ecosystem services by supplying habitat for ground-dwelling pollinators, such as wild bees and other organisms, that pollinate 80% of insect-pollinated plants and play a critical role in securing resilient pollination provisions. Our objective is to identify soil properties of ground nesting bee nests in agricultural settings of western Oregon, USA. We confirmed ground nesting bee and sand wasp activity in 7 agricultural sites and 1 recreational park. Soils from 17 bee and sand wasp nests were analyzed for pH, particle size distribution, and carbon and nitrogen content. We visually confirmed that 8 of the nesting bees were sweat bees from the Halictidae family and identified a captured bee specimen as Lasioglossum (Dialictus) (Hymenoptera: Halictidae). We located 2 sites with sand wasps where specimens were identified as Cerceris and Bembix (Hymenoptera: Crabronidae). The organic matter composition of three soil samples scraped from the linings of active nests were assessed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The FTICR-MS results identified unknown lipid compounds in the nest soil samples, which we hypothesize are waterproofing lipids secreted to line cell walls. Bee nests occurred in slightly acidic, bare-ground soils with low rock/vegetation coverage, low organic carbon content (<1%), and exhibited significantly higher silt-plus-clay fractions (>75%) versus data published for bee nests in prior work. Our findings present important implications for textural controls on nest site selection in wet, cool environments and demonstrate the importance of integrating soil properties to improve our understanding of ground-dwelling organisms and associated soil habitats.

59 BASIC BIOLOGICAL SCIENCES↗