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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
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 OSTI2013

Thermal and hydrometallurgical recovery methods of heavy metals from municipal solid waste fly ash

Highlights: • MSW fly ash was thermally and hydrometallurgically treated to remove heavy metals. • More than 90% of easy volatile heavy metals (Cd and Pb) were removed thermally. • More than 90% of Cd, Cr, Cu an Zn were removed by alkaline – acid leaching. • The best results were obtained for the solution of 3 M NaOH and 2 M H{sub 2}SO{sub 4}. - Abstract: Heavy metals in fly ash from municipal solid waste incinerators are present in high concentrations. Therefore fly ash must be treated as a hazardous material. On the other hand, it may be a potential source of heavy metals. Zinc, lead, cadmium, and copper can be relatively easily removed during the thermal treatment of fly ash, e.g. in the form of chlorides. In return, wet extraction methods could provide promising results for these elements including chromium and nickel. The aim of this study was to investigate and compare thermal and hydrometallurgical treatment of municipal solid waste fly ash. Thermal treatment of fly ash was performed in a rotary reactor at temperatures between 950 and 1050 °C and in a muffle oven at temperatures from 500 to 1200 °C. The removal more than 90% was reached by easy volatile heavy metals such as cadmium and lead and also by copper, however at higher temperature in the muffle oven. The alkaline (sodium hydroxide) and acid (sulphuric acid) leaching of the fly ash was carried out while the influence of temperature, time, concentration, and liquid/solid ratio were investigated. The combination of alkaline-acidic leaching enhanced the removal of, namely, zinc, chromium and nickel.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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)↗
DOE OSTI2018

Strain effects on domain structures in ferroelectric thin films from phase‐field simulations

Abstract Strain and applied external electric fields are known to influence domain evolution and associated ferroelectric responses in ferroelectric thin films. Here, phase‐field simulations are used to predict equilibrium domain structures and polarization‐field ( P ‐ E ) hysteresis loops of lead zirconate titanate (PZT) thin films under a series of mismatch strains, ranging from strongly tensile to strongly compressive. In particular, the evolution of domains and the P ‐ E curves under different applied strains reveal the mesoscale mechanism, the appearance of in‐plane polarization during domain switching, that is responsible for a relatively small coercive field and remnant polarization. A Landau energy distribution is analyzed to better understand the domain evolution under various strain conditions. The results provide guidance for choice of mismatched strains to yield the desired P ‐ E hysteresis loops and the domain structures.

Lin, Fang‐Yin↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

3D Ferroelectric Phase Field Simulations of Polycrystalline Multi‐Phase Hafnia and Zirconia Based Ultra‐Thin Films

Abstract HfO 2 – and ZrO 2 –based ferroelectric thin films have emerged as promising candidates for the gate oxides of next‐generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o‐) phase mixture with partially in‐plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi‐phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here, 3D phase field simulations of HfO 2 – and ZrO 2 –based mixed‐phase ultra‐thin films on silicon are applied to understand the necessary and beneficial conditions for NC stabilization. It is found that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out‐of‐plane direction enhances the NC effect. Furthermore, it is shown that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, it is concluded that topological domain walls play a critical role in experimentally observed NC phenomena in HfO 2 – and ZrO 2 –based ferroelectrics.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

A Scandium‐Stabilized Diisophosphaethynolate Ligand: [OCPPCO] 4−

Abstract The first example of the OCPPCO ligand, diisophosphaethynolate, is reported via reductive coupling of a Sc−OCP precursor. Upon reduction with KC 8 , isolation of the dinuclear complex, namely [K(OEt 2 )] 2 [(nacnac)Sc(OAr)] 2 (OCPPCO), is observed, leading to a unique motif [OCPPCO] 4− , stabilized by two scandium centers. Detailed NMR spectra of all complexes as well as IR and single crystal X‐ray studies were obtained to fully elucidate the nature of these complexes in solution as well as in the solid state. Theory is combined to probe the electronic structure and orbitals responsible for the bonding interactions in the Sc−OCPPCO−Sc skeleton but also to compare to the linear mode observed in the precursor.

Grant, Lauren N.↗
DOE OSTI2018

Reversible Magnetic Agglomeration: A Mechanism for Thermodynamic Control over Nanoparticle Size

Abstract We present a method for the synthesis and precise size control of magnetic nanoparticles in a reversible magnetic agglomeration mechanism. In this approach, nanoparticles nucleate and grow until a critical susceptibility is reached, in which magnetic attraction overcomes dispersive forces, leading to agglomeration and precipitation. This phase change in the system arrests nanoparticle growth and gives true thermodynamic control over the size of nanoparticles. We then show that increasing the alkyl chain length of the surfactant, and hence increasing steric stabilization, allows nanoparticles to grow to larger sizes before agglomeration occurs. Therefore, simply by choosing the correct surfactant, the size and magnetic properties of iron nanoparticles can be tailored for a particular application. With the continuous addition of the precursor solution, we can repeat the steps of nucleation, growth, and magnetic agglomeration indefinitely, making the approach suitable for large scale syntheses.

Bleier, Grant C.↗