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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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DOE OSTI2019

Early Observations from Monitoring a Reintroduction Program: Return of Sockeye Salmon to a Nursery Lake of Historical Importance

Abstract The historical distribution of Sockeye Salmon Oncorhynchus nerka in the Columbia River basin has been constrained by the species’ dependence on nursery lakes for juvenile rearing. Several productive lake systems were impounded by dams during the last century leading to the extirpation of Sockeye Salmon from many nursery lakes in the region. Recent efforts to re‐establish populations in historically natal areas are exemplified by the Cle Elum Lake reintroduction program. The program is founded on outplanting adult fish from two middle Columbia River donor populations with different adaptive potentials. We used genetic stock identification methods to differentiate stock origins between Osoyoos Lake ( OSO ) and Lake Wenatchee ( WEN ) donor stocks and to evaluate the relative productivity from two brood years (2011 and 2012) of natural spawning in the novel environment. Spawning ground surveys revealed assortative mating between earlier‐spawning WEN fish that were more abundant farthest upstream and later‐spawning OSO fish that were concentrated (82%) downstream nearest the lake. Hybrids accounted for only 5% of sampled smolts and 4% of adult returns. Smolts rearing in Cle Elum Lake were significantly larger overall ( OSO , 140 mm; WEN , 129 mm) than smolts in either donor population (84 mm). However, the average size of OSO smolts varied among emigration years, and relative smolt abundances favored the WEN stock (70% overall), indicative of a rearing survival advantage. In relation, the WEN stock exhibited a better average rate of replacement (0.80) in adult‐to‐adult escapement compared with the OSO stock (0.17). Continued monitoring will focus on trends in productivity and potential demographic shifts that may arise in the Cle Elum Lake population and will provide managers with information concerning limiting factors in the environment that might affect similar approaches to reintroductions in other lake systems.

Matala, Andrew P.↗
DOE OSTI2018

Photoluminescence investigations on Sm3+ ions doped borate glasses for tricolor w-LEDs and lasers

Highlights: • Borate glasses with Sm3+ ions were synthesized by melt quenching technique. • Under 402 nm excitation Borate glasses show reddish orange luminescence. • Energy transfer through cross-relaxation leads to fall in decay time of Sm3+ ions. • I-H model confirms energy transfer between Sm3+ ions as dipole-dipole in nature. • 1 mol% Sm3+ ions in these glasses are apt for red photonic device applications. - Abstract: This work describes mainly the optical absorption, photoluminescence (PL) and decay spectral properties of Sm{sup 3+} ions doped borate glasses to understand their possible usage in tricolor w-LEDs and lasers.The XRD spectrum recorded for an un-doped glass confirms glassy nature of the as prepared glass. The Judd-Ofelt (J-O) intensity parameters evaluated from experimental oscillator strengths were used in estimating the various radiative parameters. Under 402 nm excitation, the PL spectra exhibit intense emission at 600 nm for which the decay spectral profiles were recorded.The decay profiles are converting from single exponential to non-exponential with increase in Sm{sup 3+} ion concentration ensuing decrease in experimental lifetimes which may be attributed to the efficient energy transfer through cross-relaxation process. The non-exponential decay curves fitted to I-H model reveals the interaction between the Sm{sup 3+} ions as dipole-dipole in nature. From the evaluated emission cross-sections, quantum efficiency, branching ratios and CIE co-ordinates, it is concluded that 1 mol% Sm{sup 3+} ions in these glasses are aptly suitable to fabricate reddish-orange luminescent devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Primary and secondary battery consumption trends in Sweden 1996–2013: Method development and detailed accounting by battery type

Highlights: • Developed MFA method was validated by the national statistics. • Exponential increase of EEE sales leads to increase in integrated battery consumption. • Digital convergence is likely to be a cause for primary batteries consumption decline. • Factors for estimation of integrated batteries in EE are provided. • Sweden reached the collection rates defined by European Union. - Abstract: In this article, a new method based on Material Flow Accounting is proposed to study detailed material flows in battery consumption that can be replicated for other countries. The method uses regularly available statistics on import, industrial production and export of batteries and battery-containing electric and electronic equipment (EEE). To promote method use by other scholars with no access to such data, several empirically results and their trends over time, for different types of batteries occurrence among the EEE types are provided. The information provided by the method can be used to: identify drivers of battery consumption; study the dynamic behavior of battery flows – due to technology development, policies, consumers behavior and infrastructures. The method is exemplified by the study of battery flows in Sweden for years 1996–2013. The batteries were accounted, both in units and weight, as primary and secondary batteries; loose and integrated; by electrochemical composition and share of battery use between different types of EEE. Results show that, despite a fivefold increase in the consumption of rechargeable batteries, they account for only about 14% of total use of portable batteries. Recent increase in digital convergence has resulted in a sharp decline in the consumption of primary batteries, which has now stabilized at a fairly low level. Conversely, the consumption of integrated batteries has increased sharply. In 2013, 61% of the total weight of batteries sold in Sweden was collected, and for the particular case of alkaline manganese dioxide batteries, the value achieved 74%.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Observational Investigation of the Effect of Turbulence on Microphysics and Precipitation in Warm Marine Boundary Layer Clouds

Abstract Turbulence is omnipresent in the marine boundary layer (MBL) and several mechanisms have been proposed regarding its role on MBL cloud microphysics. In particular, past modeling studies suggested that increasing turbulence broaden the droplet size distribution (DSD) and leads to precipitation initiation, this suggested relationship has not been evaluated yet using long‐term observations. Here, comprehensive retrievals from long‐term ground‐based observations are used to explore the impact of turbulence on microphysics and precipitation in drizzling MBL clouds. Our analysis supports that broader DSD and larger droplets are observed with strong turbulence, but interestingly, the precipitation is reduced instead of being enhanced. This suppression mechanism is mainly due to the entrainment and mixing process in which the entrained dry air evaporates cloud water. This study provides observational evidence to show the complex effect of turbulence on MBL cloud properties and implies the potential influence of turbulence on the clouds lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate

Abstract Plant growth and distribution in high-latitude tundra ecosystems is strongly limited by nutrient availability and is critical for quantifying centennial-scale carbon-climate interactions. However, land model representations of plant–nutrient interactions are uncertain, leading to poor comparisons with high-latitude observations. Although it has been recognized for decades in the observational community that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process. Here we address the role tundra plant nutrient acquisition during the non-growing season (NGS) has on centennial-scale vegetation growth and dynamics, with a focus on shrub expansion. We apply a well-tested mechanistic model of coupled plant, microbial, hydrological, and thermal dynamics that explicitly represents nutrient acquisition based on plant and microbial traits, thereby allowing a prognostic assessment of NGS nutrient uptake. We first show that the model accurately represents observed seasonality of NGS plant nutrient uptake in a northern Alaskan tundra site. Applying the model across the North America tundra indicates that NGS nutrient uptake is consistent with observations and ranges between ∼5% and 50% of annual uptake, with large spatial variability and dependence on plant functional type. We show that NGS plant nutrient acquisition strongly enhances modeled 21 st century tundra shrub growth and expansion rates. Our results suggest that without NGS nutrient uptake, total shrub aboveground dominance would be ∼50% lower, limited primarily by their inability to grow tall enough to maximize their inherent capacity for light competition. Evergreen shrubs would be more strongly affected because of their relatively lower capacity for nutrient remobilization and acquisition compared to deciduous shrubs. Our results highlight the importance of NGS plant and soil processes on high-latitude biogeochemistry and vegetation dynamics and motivate new observations and model structures to represent these dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Abstract Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Computer Science↗
DOE OSTI2023

Boosting new physics searches in $$t{\bar{t}}Z$$ and tZj production with angular moments

Abstract The angular moments of the Z boson can be used as analyzers for the underlying production dynamics for the $$t{{\bar{t}}} Z$$ t t ¯ Z and tZj processes. In this manuscript, we derive these angular moments at leading and next-to-leading order in QCD at the LHC. We show that these observables work as efficient probes to beyond the Standard Model effects, considering the Standard Model Effective Field theory framework. Remarkably, we observe that these probes unveil blind directions to CP-odd operators, providing sizable new physics sensitivity at the 14 TeV LHC with 3 ab $$^{-1}$$ - 1 of data.

Physics↗
DOE OSTI2023

Catalytic Graphitization of Biocarbon for Lithium‐Ion Anodes: A Minireview

Abstract The demand for electrochemical energy storage is increasing rapidly due to a combination of decreasing costs in renewable electricity, governmental policies promoting electrification, and a desire by the public to decrease CO 2 emissions. Lithium‐ion batteries are the leading form of electrochemical energy storage for electric vehicles and the electrical grid. Lithium‐ion cell anodes are mostly made of graphite, which is derived from geographically constrained, non‐renewable resources using energy‐intensive and highly polluting processes. Thus, there is a desire to innovate technologies that utilize abundant, affordable, and renewable carbonaceous materials for the sustainable production of graphite anodes under relatively mild process conditions. This review highlights novel attempts to realize the aforementioned benefits through innovative technologies that convert biocarbon resources, including lignocellulose, into high quality graphite for use in lithium‐ion anodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data

ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well ( R 2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

Ogle, Kiona [School of Informatics, Computing, and↗
DOE OSTI2019

Gravastars in (3+1) dimensions admitting Karmarkar condition

Highlights: • Model of Gravastar in (3+1) dimensions is studied under GR. • Karmarkar condition of Embedding class 1 has been incorporated. • The class 1 condition provides an exact solution of three regions of gravastar. • Surface redshift confirms the stability of the model which is non-singular. - Abstract: The development of gravastar model by Mazur and Mottola (2004) is one of the most acceptable alternatives of conventional classical black hole. They have studied phase transition of the collapsing stellar objects at low temperature near event horizon which leads to the condensate phase, i.e. Bose–Einstein condensation. In the present study we have obtained a unique solution of this gravastar model under Einstein’s GR in (3+1) dimensions by incorporating the Karmarkar condition for embedding class 1. The most important motive of this work is to find an exact solution of the shell without taking the thin shell approximation. We have obtained singularity free finite solutions for the interior of the gravastar.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Electron‐Scale Reconnection in Three‐Dimensional Shock Turbulence

Abstract Magnetic reconnection has been observed in the transition region of quasi‐parallel shocks. In this work, the particle‐in‐cell method is used to simulate three‐dimensional reconnection in a quasi‐parallel shock. The shock transition region is turbulent, leading to the formation of reconnecting current sheets with various orientations. Two reconnection sites with weak and strong guide fields are studied, and it is shown that reconnection is fast and transient. Reconnection sites are characterized using diagnostics including electron flows and magnetic flux transport. In contrast to two‐dimensional simulations, weak guide field reconnection is realized. Furthermore, the current sheets in these events form in a direction almost perpendicular to those found in two‐dimensional simulations, where the reconnection geometry is constrained.

58 GEOSCIENCES↗
DOE OSTI2023

Scaling of Turbulence and Microphysics in a Convection–Cloud Chamber of Varying Height

Abstract The convection–cloud chamber enables measurement of aerosol and cloud microphysics, as well as their interactions, within a turbulent environment under steady‐state conditions. Increasing the size of a convection–cloud chamber, while holding the imposed temperature difference constant, leads to increased Rayleigh, Reynolds and Nusselt numbers. Large–eddy simulation coupled with a bin microphysics model allows the influence of increased velocity, time, and spatial scales on cloud microphysical properties to be explored. Simulations of a convection–cloud chamber, with fixed aspect ratio and increasing heights of H = 1, 2, 4, and (for dry conditions only) 8 m are performed. The key findings are: Velocity fluctuations scale as H 1/3 , consistent with the Deardorff expression for convective velocity, and implying that the turbulence correlation time scales as H 2/3 . Temperature and other scalar fluctuations scale as H −3/7 . Droplet size distributions from chambers of different sizes can be matched by adjusting the total aerosol injection rate as the horizontal cross‐sectional area (i.e., as H 2 for constant aspect ratio). Injection of aerosols at a point versus distributed throughout the volume makes no difference for polluted conditions, but can lead to cloud droplet size distribution broadening in clean conditions. Cloud droplet growth by collision and coalescence leads to a broader right tail of the distribution compared to condensation growth alone, and this tail increases in magnitude and extent monotonically as the increase of chamber height. These results also have implications for scaling within turbulent, cloudy mixed‐layers in the atmosphere, such as fog layers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Unique Temperature Trend Pattern Associated With Internally Driven Global Cooling and Arctic Warming During 1980–2022

Abstract Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unconventional non-local relaxation dynamics in a twisted trilayer graphene moiré superlattice

Abstract The electronic and structural properties of atomically thin materials can be controllably tuned by assembling them with an interlayer twist. During this process, constituent layers spontaneously rearrange themselves in search of a lowest energy configuration. Such relaxation phenomena can lead to unexpected and novel material properties. Here, we study twisted double trilayer graphene (TDTG) using nano-optical and tunneling spectroscopy tools. We reveal a surprising optical and electronic contrast, as well as a stacking energy imbalance emerging between the moiré domains. We attribute this contrast to an unconventional form of lattice relaxation in which an entire graphene layer spontaneously shifts position during assembly, resulting in domains of ABABAB and BCBACA stacking. We analyze the energetics of this transition and demonstrate that it is the result of a non-local relaxation process, in which an energy gain in one domain of the moiré lattice is paid for by a relaxation that occurs in the other.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

ABSTRACT Weak lensing convergence maps – upon which higher order statistics can be calculated – can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic – the peak count as a function of signal-to-noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large-scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well-defined confidence levels.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗