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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Advancing Geophysical Techniques to Image a Stratigraphic Hydrothermal Resource

Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this resource and analogous ones. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data products, in conjunction with new and preexisting geoscientific measurements and observations, are being integrated and evaluated for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings, and whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs, will also be evaluated.

Geothermal, Sedimentary Heat, Geophysics, Seismic,↗
DOE OSTI2024

Simultaneous Development of Antiferromagnetism and Local Symmetry Breaking in a Kagome Magnet (Co 0.45 Fe 0.55 )Sn

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to knowledge of the average crystal structure. Here, we report the Fe-doping induced coemergence of the antiferromagentic (AFM) order and local symmetry breaking in (Co 0.45 Fe 0.55 )Sn. Rietveld analysis on the neutron and synchrotron X-ray diffraction data indicates A-type antiferromagnetic order with the moment pointing perpendicular to the kagome layers, associated with the anomaly in the MSn(1) 2 Sn(2) 4 (M = Co/Fe) octahedral distortion and the lattice constant c. Reverse Monte Carlo (RMC) modeling of the synchrotron X-ray total scattering results captured the subtle local orthorhombic distortion involving off-axis displacements of Sn(2). Our results indicate that the stable hexagonal lattice above T N becomes unstable once the A-type AFM order is formed below T N . Here we argue that the local symmetry breaking has a magnetic origin, since the spatially varied M–Sn(2) bond lengths arise from out-of-plane magnetic exchange coupling J c via the exchange pathway M–Sn(2)–M. Our study provides comprehensive information on the crystal structure in both long-range scale and local scale, unveiling unique coupling between AFM order, octahedral distortion, and hidden local symmetry breaking.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bipolar Membranes for Ion Management in (Photo)Electrochemical Energy Conversion

Conspectus (Photo)electrochemical energy conversion is important in the development of a carbon-neutral energy economy because it can provide a pathway for mitigating the intermittency of renewable energy sources such as wind and solar. In order to operate efficiently, these technologies, which include photoelectrochemical cells, water and CO 2 electrolyzers, fuel cells, and redox flow batteries, require fast charge transfer kinetics at the electrode/electrolyte interface as well as robust ion management in the electrolyte. In conventional electrolyzers and fuel cells, the electrolyte is strongly acidic or basic and ionic current is carried by H + or OH – ions. In contrast, photoelectrodes and electrocatalysts for water splitting are often studied in buffered solutions. The question of ion balance in these systems led us to analyze the polarization losses due to ion concentration gradients in cells that employed various buffer–membrane combinations. Continuously driving the buffer ions across an ionomer membrane not only lowers the buffer capacity of an aqueous electrolyte but also introduces pH gradients that result in significant energy losses. To address the problem, we and other groups have studied the use of reverse-biased bipolar membranes (BPMs) in (photo)electrolytic cells. BPMs consist of an anion exchange layer (AEL) laminated with a cation exchange layer (CEL) and are usually equipped with a catalytic layer in between to accelerate the water dissociation reaction. At the AEL/CEL interface, water dissociates into protons and hydroxide ions, which replenish those consumed at the cathode and anode. Compared to conventional water electrolyzers with proton/anion exchange membranes (PEM/AEM), BPM electrolyzers provide the unique advantage of continuously operating the cathode and anode under different pH conditions, which is desirable when the two electrode reactions have different pH requirements. BPMs also enable the use of buffered electrolytes at pH values that are optimized for electrode stability and product selectivity in applications such as CO 2 electrolysis. Product crossover losses and CO 2 pumping can be dramatically reduced in BPM-based CO 2 electrolyzers, relative to conventional alkaline membranes, by electrostatic repulsion (of anionic products) and electroosmotic drag (for neutral products). BPM-based gas fed CO 2 electrolyzers can achieve high current density, but they suffer from low Faradaic efficiency (FE) due to the acidic local environment of the CEL. This problem can be mitigated by adding an aqueous buffering layer or by creating a weak acid cation exchange film on the CEL face of the membrane. The use of BPMs in fuel cells and redox flow batteries offers some interesting advantages. Configurations with both reverse and forward bias have been studied, but forward bias has been favored due to material compatibility, reaction kinetics, and thermodynamic considerations. The net reaction at the AEL/CEL interface is the acid–base neutralization reaction, which has a high inherent reaction rate constant, but in the BPM is limited to a nanometric space-charge layer and requires efficient catalysis to achieve high current density. Understanding the mechanism of the acid–base neutralization and the opposite process, the water dissociation reaction, will be essential for improving the performance of forward-biased BPMs. In conclusion, this Account reviews our current understanding of the working mechanisms of BPMs and discusses how we can use them to effectively manage ions for various (photo)electrochemical applications.

Yan, Zhifei↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2020

Development and validation of a next-generation sequencing-based multigene assay to predict the prognosis of estrogen receptor-positive, HER2-negative breast cancer

Purpose: Multigene assays provide useful prognostic information regarding hormone receptor (HR)-positive breast cancer. Next-generation sequencing (NGS)-based platforms have numerous advantages including reproducibility and adaptability in local laboratories. Here we aimed to develop and validate an NGS-based multigene assay to predict the distant recurrence risk. Experimental Design: In total, 179 genes including 30 reference genes highly correlated with the 21-gene recurrence score (RS) algorithm were selected from public databases. Targeted RNA-sequencing was performed using 250 and 93 archived breast cancer samples with a known RS in the training and verification sets, respectively, to develop the algorithm and NGS–Prognostic Score (NGS-PS). The assay was validated in 413 independent samples with long-term follow-up data on distant metastasis. Results: In the verification set, the NGS-PS and 21-gene RS displayed 91.4% concurrence (85/93 samples). In the validation cohort of 413 samples, area under the receiver operating characteristic curve plotted using NGS-PS values classified for distant recurrence was 0.76. The best NGS-PS cut-off value predicting distant metastasis was 20. Furthermore, 269 and 144 patients were classified as low- and high-risk patients in accordance with the cut-off. Five- and 10-year estimates of distant metastasis–free survival (DMFS) for low- versus high-risk groups were 97.0% versus 77.8% and 93.2% versus 64.4%, respectively. The age-related HR for distant recurrence without chemotherapy was 9.73 (95% CI, 3.59–26.40) and 3.19 (95% CI, 1.40–7.29) for patients aged ≤50 and >50 years, respectively. Conclusions: The newly developed and validated NGS-based multigene assay can predict the distant recurrence risk in ER-positive, HER2-negative breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Novosphingobium aromaticivorans LigR coordinates transcription of genes involved in metabolism of multiple types of aromatics

Aromatic compounds are a ubiquitous and diverse family of chemicals with functions as biomolecules, natural products, industrial chemicals, and pollutants. Novosphingobium aromaticivorans DSM 12444 uses multiple inducible pathways to catabolize H-, G-, and S-type aromatics that contain zero, one, or two methoxy groups, respectively. Here, we obtain a systems-level view of the transcriptional control of its aromatic metabolic pathways. Several in vitro analyses found that a N. aromaticivorans homolog of the Sphingobium lignivorans SYK-6 transcription factor LigR bound genomic DNA upstream of genes involved in metabolism of multiple aromatic types. We found that a ΔLigR mutant had growth defects on all three types of aromatics as sole carbon sources. Transcriptomic analysis revealed that LigR was required to increase expression of gene products that function in metabolism of all three aromatic types. We also found that, in media containing both glucose and an aromatic carbon source, the ΔLigR mutant directed intermediates through alternative aromatic metabolic pathways. Protein-DNA binding assays showed that N. aromaticivorans LigR binds immediately upstream of promoters of genes involved in aromatic metabolism. We found that N. aromaticivorans LigR coordinates the expression of enzymes that function in the catabolism of H-, G-, and S-type aromatics, and that there are differences in the role of LigR in N. aromaticivorans and S. lignivorans. A comparative genomic analysis predicted that LigR homologs and the aromatic-metabolizing genes that it directly regulates are often co-localized in the genomes of Sphingomonadales, but often not found in this arrangement in many other known aromatic metabolizing bacteria.

Aromatic Compound Degradation↗
DOE OSTI2024

Structure Sensitive Reaction Kinetics of Chiral Molecules on Intrinsically Chiral Surfaces

Enantiospecific heterogeneous catalysis utilizes chiral surfaces to resolve enantiomers via structure sensitive surface chemistry. The catalyst design challenge is the identification of chiral surface structures that maximize enantiospecificity. Herein, we develop data driven models for the enantiospecificity of tartaric acid reactions on chiral Cu(hkl) R&S surfaces. Measurements of enantiospecific rate constants were obtained by using curved Cu(hkl) R&S surfaces that enable kinetic measurements on hundreds of chiral surface orientations. One model uses feature vectors derived from generalized coordination numbers to capture the local structure around Cu atoms exposed by the Cu(hkl) R&S surfaces. The second model introduces the use of chiral cubic harmonic functions to capture the symmetry constraints of the face-centered cubic Cu structure. The model using 58 generalized coordination numbers has a fitting error similar to that of the model using only 5 cubic harmonic functions. The two models predict maxima in the enantiospecificity on surfaces with very similar surface orientations. The models developed in this work are applicable for any enantiospecific reaction happening on any chiral material with a cubic lattice structure, opening the way to understanding the surface structure sensitivity of the enantiospecific reaction kinetics.

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

Resonant Chiral Effects in Nonlinear Dielectric Metasurfaces

We study the resonant enhancement of linear and nonlinear chiroptical effects in planar silicon metasurfaces with an in-plane asymmetry supporting multipolar Mie resonances and quasi-bound states in the continuum (quasi-BICs). We demonstrate theoretically and observe in experiment the pronounced linear circular dichroism at the quasi-BIC resonances originating from the interaction of modes with the substrate. We further find that both local field enhancement and third-harmonic signal are large for Mie resonances and some quasi-BIC modes due to the critical coupling. We demonstrate experimentally a strong nonlinear chiroptical response associated with high efficiency of the third-harmonic generation and large nonlinear circular dichroism varying from +0.918 ± 0.049 to –0.771 ± 0.004 for the samples with different asymmetries. We reveal the nonreciprocal nature of nonlinear chirality governed by the microscopic symmetry of nonlinearities and macroscopic symmetries of the meta-atom and metasurface lattice. Here, we believe our results suggest a general strategy for engineering nonlinear chiroptical response in dielectric resonant metasurfaces.

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

Institutional Framework of Variable Renewable Energy Forecasting in India

The share of variable renewable energy (VRE) in India is growing rapidly, with a national goal of reaching 50% capacity from non-fossil fuel generation by 2030. One implication of this growth is the need for improved VRE forecasting methods. For this reason, the Ministry of New and Renewable Energy (MNRE) in India commissioned this study with support from the United States Agency for International Development (USAID), the National Renewable Energy Laboratory (NREL) in the United States, and the National Institute of Wind Energy (NIWE) in India. The objective of this study was to review the existing institutional framework and suggest changes needed to support the plans for large-scale VRE integration in the country. To achieve that objective, the authors consulted local stakeholders about the status of VRE forecasting in India, reviewed existing studies, and examined VRE forecasting methods around the world to identify best practices. Based on those best practices, the study presents six potential approaches to improve the VRE forecasting framework in India. Approaches include incentivizing VRE forecast improvement and use of the most accurate VRE forecast, creating an institution that will optimize VRE forecasts while maintaining and ensuring access to necessary data for forecasting, implementing a review and certification process for VRE forecast providers, forecasting closer to dispatch time and allowing for more frequent forecast revisions, increasing the frequency of weather forecasts, and aggregating VRE forecasts at the point of interconnection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Simulated Moving Bed Process for CO 2 Capture from Humid Postcombustion Flue Gases Using MUF-16

Simulated moving bed (SMB) designs are increasingly being adapted for the separation of multicomponent gaseous mixtures. In this work, we develop a modified SMB process for capturing CO 2 from humid postcombustion flue gas using MUF-16 (MUF = Massey University Framework). MUF-16 shows excellent selectivity for CO 2 over N 2 , moderate heats of adsorption for CO 2 and H 2 O, and no competitive sorption of CO 2 over water at relative humidities relevant to postcombustion capture. We utilize these characteristics to propose a continuous process that uses N 2 from the feed as the desorbent for the water, eliminating the need for a separate desiccant bed and allowing for localized heating during desorption for H 2 O-saturated MOF beds. Single-component isotherms, single-column breakthrough experiments, and SO 2 stability tests suggest the excellent suitability of MUF-16 to this separation via the proposed SMB design.

CO2 capture from humid flue gas↗
DOE OSTI2021

Nonperturbative dynamics of (2+1)d φ 4 -theory from Hamiltonian truncation

We use Lightcone Conformal Truncation (LCT) - a version of Hamiltonian truncation - to study the nonperturbative, real-time dynamics of φ 4 -theory in 2+1 dimensions. This theory has UV divergences that need to be regulated. We review how, in a Hamiltonian framework with a total energy cutoff, renormalization is necessarily state-dependent, and UV sensitivity cannot be canceled with standard local operator counter-terms. To overcome this problem, we present a prescription for constructing the appropriate state-dependent counterterms for (2+1)d φ 4 -theory in lightcone quantization. We then use LCT with this counterterm prescription to study φ 4 -theory, focusing on the $\mathbb{Z}$ 2 symmetry-preserving phase. Specifically, we compute the spectrum as a function of the coupling and demonstrate the closing of the mass gap at a (scheme-dependent) critical coupling. We also compute Lorentz-invariant two-point functions, both at generic strong coupling and near the critical point, where we demonstrate IR universality and the vanishing of the trace of the stress tensor.

field theories in lower dimensions↗
DOE OSTI2020

Panel Session 116: Waste Management from Remediation of Legacy Sites or Unplanned Releases

Past experiences have shown that there is often a disconnect between Indigenous societies and Western society. This disconnect can be attributed to different value systems between these two group and is not limited to the USA and Canada but also extends into Australia, New Zealand, and the Central Asia Republics (the 'five Stans'). The intent of this Panel Session to give voice to representatives from Indigenous communities, the challenges presented in remediation processes, and potential solutions. Standard risk assessment assumptions developed for Western societies often do not adequately address the cultural and spiritual values that may be unique to indigenous societies. This panel focused on the perspectives of Indigenous societies (e.g., Indian Tribes (US), First Nations (Canada)) related to risk assessment of environmental remediation challenges, including deep geological disposal of HLW and ILW, near-surface disposal of LLW and in-situ entombment of nuclear reactor components, and environmental remediation of abandoned uranium mines and historic wastes. Standard risk assessment assumptions often do not adequately address the cultural and spiritual values that may be unique to indigenous societies. Factoring those into a risk assessment that still meets regulatory requirements and policies such as reasonably anticipated land use at US EPA CERCLA sites can be challenging but has the potential to lead to a site or facility approach that is more accepted. Panelists with presentations: The U.S. Nuclear Regulatory Commission Regulatory Process - A Path for Indigenous Knowledge? (Francis Cameron); Local Community Perceptions and Remediation Projects (Michelle Roberts); Indigenous Perspectives on Risk Assessment (Bob Watts); Navajo Superfund Program: Fundamental Law and Traditional Ecological Knowledge (Dariel Yazzie); Risk Assessment: Two Indigenous Approaches (Whitney Fraser)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Insights into the Rich Polymorphism of the Na + Ion Conductor Na 3 PS 4 from the Perspective of Variable-Temperature Diffraction and Spectroscopy

Solid electrolytes are crucial for next-generation solid-state batteries, and Na 3 PS 4 is one of the most promising Na + conductors for such applications, despite outstanding questions regarding its structural polymorphs. In this contribution, we present a detailed investigation of the evolution in structure and dynamics of Na 3 PS 4 over a wide temperature range 30 < T T < 500 °C, which we attribute to dynamic local tetragonal distortions. The first-order phase transition to the mesophasic high-temperature polymorph (γ, Fddd ) is associated with a sharp volume increase and the onset of liquid-like dynamics for sodium-cations (translational) and thiophosphate-polyanions (rotational) evident by inelastic neutron and Raman spectroscopies, as well as pair-distribution function and molecular dynamics analyses. Overall, these results shed light on the rich polymorphism of Na 3 PS 4 and are relevant for a range host of high-performance materials deriving from the Na 3 PS 4 structural archetype.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nonunitary Variational Quantum Eigensolver with the Localized Active Space Method and Cost Mitigation

Accurately describing strongly correlated systems with affordable quantum resources remains a central challenge for quantum chemistry applications on near and intermediate term quantum computers. The localized active space self-consistent field (LASSCF) approximates the complete active space self-consistent field (CASSCF) by generating active space-based wave functions within specific fragments while treating interfragment correlation with mean-field approach, hence is computationally less expensive. Hardware-efficient ansatzes (HEA) offer affordable and shallower circuits, yet they often fail to capture the necessary correlation. Previously, Jastrow-factor-inspired nonunitary qubit operators were proposed to use with HEA for variational quantum eigensolver (VQE) calculations (so-called nuVQE), as they do not increase circuit depths and recover correlation beyond the mean-field level for Hartree–Fock initial states. Here, in this study, we explore running nuVQE with LASSCF as the initial state. The method, named LAS-nuVQE, is shown to recover interfragment correlations, reach chemical accuracy with a small number of gates (<70) in both H 4 and square cyclobutadiene (C 4 H 4 ), and produces more accurate energetics than its HEA counterparts at all circuit depths. To further address the inherent symmetry-breaking in HEA, we implemented spin-constrained LAS-nuVQE to extend the capabilities of HEA further and show spin-pure results for square cyclobutadiene. We also mitigate the increased measurement overhead of nuVQE via Pauli grouping and shot-frugal sampling, reducing measurement costs by up to 2 orders of magnitude compared to ungrouped operator, and show that one can achieve better accuracy with a small number of shots (10 3–4 ) per one expectation value calculation compared to noiseless simulations with one or two orders of magnitude more shots. Finally, wall clock time estimates show that, with our measurement mitigation protocols, nuVQE becomes a cheaper and more accurate alternative than vanilla VQE with HEA. Taken together, these developments illustrate a practical pathway toward performing multireference chemical simulations with accuracy and affordable resources on today’s quantum hardware, achieving both accuracy and affordability in challenging correlated systems.

Wang, Qiaohong [Univ. of Chicago, IL (United State↗
DOE OSTI2026

A Mesopore-Confined and Graphene Oxide-Localized Ruthenium Catalyst Increases Rates of Mid-Chain Polyolefin Hydrogenolysis

A catalyst architecture with mesoporous silica coating Ru nanoparticles on reduced graphene oxide (mSiO 2 /Ru/rGO) localizes the 2 nm Ru solely at the closed bottoms of 2.9 nm diameter mesopores. mSiO 2 /Ru/rGO catalyzes the rapid, selective hydrogenolysis of polyolefins at wax formation rates (ν wax ) up to 1700 g wax ·g Ru –1 ·h –1 and a turnover frequency (TOF) for C–C bond cleavage of 130 ± 8 min –1 . The ν wax and TOF for mSiO 2 /Ru/rGO are 23× and 16× those of nonporous Ru/rGO, respectively, indicating that faster chain cleavage is also more selective inside mesopores. The methane yield from mSiO 2 /Ru/rGO is ca. 30% of the value obtained from Ru/rGO. Methane decreases, and wax selectivity improves with narrow-pore (2.3 nm) mSiO 2 /Ru/rGO. The reaction rate decreases at lower and higher H 2 pressures from its maximum at 37 bar. Log(TOF)-log(P H2 ) plots reveal rates ∝ [P H2 ] 2.8 or [P H2 ] −2.0 in the lower or higher pressure ranges, respectively. Corresponding plots for Ru/C give rates ∝ [P H2 ] 1 or [P H2 ] −2.4 . Ru is hydrocarbon-covered at low pressure; thus, the higher H 2 order for mSiO 2 /Ru/rGO indicates that mesopores increase the density of cleavable unsaturated moieties on the Ru surface. The lower H 2 order on Ru/C implies a lower density of cleavable groups because saturated segments occupy a larger fraction of the Ru surface. Higher surface occupancy by the hydrocarbon reactant correlates with more methane formation. Therefore, pore confinement, narrower pore diameter in mSiO 2 /Ru/rGO, or higher H 2 pressure leads to lower methane yields. Ru nanoparticles in mSiO 2 /Ru/rGO maintain equivalent activity and selectivity over five recycling tests.

Catalysts↗
DOE OSTI2023

Dissect two-halo galactic conformity effect for central galaxies: the dependence of star formation activities on the large-scale environment

We investigate the two-halo galactic conformity effect for central galaxies, which is the spatial correlation of the star formation activities for central galaxies to several Mpcs, by studying the dependence of the star formation activities of central galaxies on their large-scale structure in our local Universe using the SDSS data. Here we adopt a novel environment metric using only central galaxies quantified by the distance to the nth nearest central galaxy. This metric measures the environment within an aperture from ∼1 to ≳ 10 Mpc, with a median value of ∼4 Mpc. We found that two kinds of conformity effects in our local Universe. The first one is that low-mass central galaxies are more quenched in high-density regions, and we found that this effect mainly comes from low-mass centrals that are close to a more massive halo. A similar trend is also found in the IllustrisTNG simulation, which can be entirely explained by backsplash galaxies. The second conformity effect is that massive central galaxies in low-density regions are more star-forming. This population of galaxies also possesses a higher fraction of spiral morphology and lower central stellar velocity dispersion, suggesting that their low quiescent fraction is due to less-frequent major merger events experienced in the low-density regions and, as a consequence, less-massive bulges and central black holes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Estimating Electron Temperature and Density Using Van Allen Probe Data: Typical Behavior of Energetic Electrons in the Inner Magnetosphere

Abstract The Earth's inner magnetosphere contains multiple electron populations influenced by different factors. The cold electrons of the plasmasphere, warm plasma that contributes to the ring current, and the relativistic plasma of the radiation belts often seem to behave independently. Using omni‐directional flux and energy measurements from the HOPE and Magnetic Electron Ion Spectrometer instruments aboard the Van Allen Probes, we provide a detailed density and temperature description of the inner magnetosphere, offering a comprehensive statistical analysis of the entire Van Allen Probe era. While number density and temperature data at geosynchronous orbit are available, this study focuses on the warm plasma in the inner magnetosphere . Values of density and temperature are extracted by fitting energy and phase space density to obtain the distribution function. The fitted distributions are related to the zeroth and second moments to estimate the number density and temperature. Analysis has indicated that a two Maxwellian fit is sufficient over a wide range of and that there are two independent plasma populations. The more energetic population has a median number density of approximately and a temperature of around 130 keV, with a temperature peak observed between L * = 4 and L * = 4.5. This population is relatively uniform in magnetic local time (MLT). In contrast, the less energetic warm electron population has a median number density of about and a temperature of 7.4 keV. Strong statistical trends in density and temperature across both L * and MLT are presented, along with potential sources driving these variations.

58 GEOSCIENCES↗
DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗