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DOE OSTI2023

Near-infrared chemical abundances of stars in the Sculptor dwarf galaxy

Owing to the recent identification of major substructures in our Milky Way (MW), the astronomical community has started to reevaluate the importance of dissolved and existing dwarf galaxies. In this work, we investigate up to 13 elements in 43 giant stars of the Sculptor dwarf galaxy (Scl) using high-signal-to-noise-ratio near-infrared (NIR) APOGEE spectra. Thanks to the strong feature lines in the NIR, we were able to determine high-resolution O, Si, and Al abundances for a large group of sample stars for the first time in Scl. By comparing the [α/Fe] (i.e., O, Mg, Si, Ca, and Ti) of the stars in Scl, Sagittarius, and the MW, we confirm the general trend that less massive galaxies tend to show lower [α/Fe]. The low [Al/Fe] (~ - 0.5) in Scl demonstrates the value of this ratio as a discriminator with which to identify stars born in dwarf galaxies (from MW field stars). A chemical-evolution model suggests that Scl has a top-light initial mass function (IMF), with a high-mass IMF power index of ~ - 2.7, and a minimum Type Ia supernovae delay time of ~100 Myr. Furthermore, a linear regression analysis indicates a negative radial metallicity gradient and positive radial gradients for [Mg/Fe] and [Ca/Fe], in qualitative agreement with the outside-in formation scenario.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

Thermodynamic and Diffusion Model Estimates on Metamorphic Temperatures and Timescales for Basaltic Eucrite GRA 98098

Introduction: HED meteorites are thought to rep-resent igneous rocks from Vesta’s basaltic crust and preserve evidence of early crustal metamorphism. Determining the temperatures and timescales of thermal metamorphism is important for reconstructing crustal evolution in the early solar system. Here, we study basaltic eucrite Graves Nunataks (GRA) 98098, which has been identified as a highly metamorphosed eucrite [1]. We present new estimates on metamorphic temperatures determined via thermodynamic modeling as well as the initial results from diffusion models constraining timescales of thermal metamorphism. Sample Description: GRA 98098 is an unbrecciated eucrite with a granoblastic plagioclase and pyroxene mineralogy. Millimeter to cm-long lathes of tridymite cross-cut and poikilitically enclose plagioclase and pyroxene [1,this work]. Pyroxene grains have exsolved into Ca-rich (~Wo38En29Fs33) and Ca-poor (~Wo4.5En36Fs59.5) lamellae. Both unzoned and zoned plagioclase grains are observed. Unzoned plagioclase grains are found solely with tridymite laths. These grains have ~An92 compositions. The cores of the zoned plagioclase grains have the same composition and thin, relatively sodic rims (~An67), (Fig. 1). The bulk sample is unusually enriched in highly in-compatible elements and has one of the most fractionated REE patterns reported [1]. Maximum metamorphic temperatures of 985±78°C have been estimated using two-pyroxene thermometry [2]. Methods: Thermodynamic modeling. Thermodynamic models were constructed using the software Perple_X, which employs a Gibbs free energy minimization in order to determine the most stable phase assemblage for a given bulk rock composition [3]. Bulk composition was calculated using mineral com-positions acquired via EMPA (this study) and the observed abundancies present in the thin section. Two bulk compositions were estimated; 1) includes all phases present in the thin section, (assumes that all phases are present during metamorphism), 2) excludes tridymite from the bulk calculation (assumes that tridymite was not present during metamorphism). In order to determine whether metamorphic equilibria was achieved and estimate temperatures of metamorphism, we compared measured pyroxene compositions with thermodynamically predicted compositions [4, Fig. 2]. Diffusion Modeling. Several time-temperature de-pendent diffusion profiles were calculated in order to determine the best match for XAn chemical profiles observed at the edges of the zoned plagioclase (Fig. 3). We assumed that the start condition was a stepwise gradient at the plagioclase/pyroxene interface. We also assumed an average diffusion coefficient (D) and a constant temperature using the equation in [5]. D was determined for two temperatures (T = 1060ºC; near eucrite solidus [6] and T = 985ºC; metamorphism reported in [4]) and then XAn was calculated as a function of distance from plagioclase core to rim using an error function solution to Fick’s second law. Results: Thermodynamic model results are summarized in Fig. 2. For a bulk composition that includes all phases in the thin section, pyroxene endmember compositions plot in the following temperature ranges: Fs ~660-860ºC, En~1000ºC & 1150ºC, and Wo~760-900ºC (Fig. 2a). For a bulk composition that excludes tridymite from the peak metamorphic assemblage (i.e., the bulk composition minus the contribution from tridymite), a temperature range could not be determined for the Fs component of pyroxene. For Wo, T~760-900ºC and En, T~1000ºC & 1150ºC (Fig. 2b). Fig. 3 summarizes the diffusion model results. For T = 1060°C & 985°C, the most appropriate time interval was estimated based on which diffusion curve most matched (solid lines, Fig. 3) the EMPA data. For T = 1060°C, the best looking match was t = 500 ka. For T = 985°C, the best match was t = 7 Ma. Discussion and future work: Temperature estimates from thermodynamic models are not conclusive because the temperature ranges determined for pyroxene endmember stability do not overlap (colored fields in Fig. 2), thus implying that there is disequilibrium between pyroxene crystals and the bulk composition considered [4]. Thus, additional exploration is needed to define a metamorphically equilibrated do-main that accurately records peak temperature. The utility of defining metamorphically equilibrated do-mains to improve the accuracy and level of detail elucidated regarding the petrogenetic history of metamorphose samples has been demonstrated previously [4,7]. We suggest that in the case of Fig. 2a, the thin section composition is not representative of the length scales over which metamorphic equilibrium was achieve and in the case of Fig. 2b, the assumption that tridymite was not present during metamorphism was incorrect. However, results from thermodynamic models can provide insight into the relative timing of mineral and compositional textures. For example from texture alone, it is unclear whether tridymite was igneous in origin and represents the last bits of melt in a crystallizing magma chamber, or if it formed during (and possibly initiated) open system thermal metamorphism. The latter could be consistent with a partial melt hypothesis [8,9] while the former implies that simple fractional crystallization can yield the textures present in GRA 98098. The lack of coincidence be-tween pyroxene endmember compositions in Fig. 2b suggest that the bulk composition minus tridymite was not the assemblage in equilibrium with the pyroxene, suggesting that tridymite was present during metamorphism and formed during igneous crystallization. We conclude that the development of the Na-rich plagioclase rims likely occurred during or immediately after peak thermal metamorphism, because eucrites of similar metamorphic grade and texture have unzoned plagioclase (~An92) [2,4,8], and Na zoning is only observed in the plagioclase not included in the tridymite. This suggests that the zoning formed after tridymite formation, and therefore after igneous crystallization. Thus, the timescales calculated via diffusion modeling possibly represent the time interval over which thermal metamorphism occurred. Cooling rates approximated for the Vestan crust predict that the crust cooled below 300°C around 35-40 Ma after formation[10]. This is consistent with our modeling results that predict formation of the Na rich plagioclase rims occurring at higher temperatures over a period of 0.5 to 7 Ma years. Future work. Additional thermodynamic modeling work will focus on selecting an equilibrated bulk rock domain in which to elucidate metamorphic conditions. Diffusion models currently provide a minimum time-scale, since diffusion slows down as the system cools. Future work with will focus on integrating cooling into the diffusion models and constraining the depth at which thermal metamorphism occurs because it could be used to determine whether the range of time-scales calculated for thermal metamorphism are consistent with the geologic environment.

J S Gorce↗

DOE OSTI2025

Response of Subsurface Nitrogen-Cycling Microbial Communities to Environmental Fluctuations (Final Technical Report)

Riparian floodplains are dynamic ecosystems linking terrestrial and riverine systems. These floodplains experience hydrological shifts such as changes in water table height, flooding, and drought and can be ‘hotspots’ of biogeochemical cycling due to shifting sediment moisture (and saturation) and subsurface exchanges of water, nutrients, and other compounds across different sediment layers. Subsurface microbial communities are the primary drivers of biogeochemical processes in floodplains, and thus their structure and function can directly influence both surface and groundwater quality. The microbial nitrogen (N) cycle is particularly important in floodplains as it affects nutrient availability and removal. Two functional guilds of chemoautotrophic (i.e. CO2-fixing) microorganisms are responsible for the first oxidative step of the N cycle, nitrification: ammonia-oxidizing archaea (AOA) and bacteria (AOB) catalyze the oxidation of ammonia to nitrite, while nitrite-oxidizing bacteria (NOB) oxidize nitrite to nitrate. Despite the critical role nitrification plays in N-cycling in both terrestrial and aquatic ecosystems, our understanding of the diversity, ecophysiology, and activity of nitrifying organisms in subsurface floodplain soils/sediments is extremely limited. To help address this critical knowledge gap, the overarching goal of this project was to determine how shifts in key environmental parameters and gradients impact microbial N-cycling communities/processes, with particular emphasis on nitrification, within hydrologically-variable floodplain sediments in the Wind River Basin near Riverton, Wyoming. The three specific objectives of this project were to: (1) to associate in situ environmental drivers of N cycling with distinct functional guilds; (2) determine the guild response to variation in key ecosystem drivers; and (3) develop a dynamic ecosystem model of the microbial N cycle with the Riverton subsurface using community genomic and biogeochemical data collected in the first two objectives. Over the course of this project, we employed both 16S rRNA gene amplicon sequencing and genome-resolved metagenomics to examine the phylogenetic diversity and metabolic potential of subsurface nitrifier communities within 68 samples collected across multiple sites, depths, and time points within the Riverton floodplain, allowing for both spatial and temporal investigations at different scales. This project benefitted tremendously from recent advances in high-throughput sequencing technologies coupled with dramatic improvements in the computational tools and algorithms available for analyzing such large, complex genomic datasets. By pairing these cutting-edge genomic approaches with depth-resolved sampling and detailed geochemical analyses of the Riverton floodplain, we have gained novel insights into the structure and function of subsurface nitrifier communities in relation to both hydrology and biogeochemistry. This project resulted in the most detailed and comprehensive characterization of N-cycling floodplain microbial communities to date and will hopefully inspire and pave the way for future studies using similar approaches in other floodplains. Indeed, such information is critical for understanding subsurface biogeochemical cycling and how elemental stores are altered from perturbations initiated by the water cycle within floodplains. Finally, because of the terrestrial-aquatic nature of the Riverton floodplain, results from this project are also of relevance to disciplines such as soil science, estuarine science, limnology & oceanography, biogeochemistry, geobiology, environmental engineering, as well as genomics and data science.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Insights into Rotational and Translational Dynamics in Mixtures of Ethylene Glycol and Choline Chloride Using Nuclear Magnetic Resonance Techniques

This work examines molecular dynamics and interactions in ethylene glycol–choline chloride (EG–ChCl) mixtures across 0–33 mol % ChCl, spanning the true eutectic region near 17–20 mol % and the commonly used 1:2 formulation. We combine pulsed-field-gradient (PFG) diffusion, fast-field-cycling (FFC) relaxometry, temperature-dependent 13 C T 1 , and nuclear Overhauser effect spectroscopy (NOESY) to disentangle local from macroscopic dynamics. PFG and FFC show that both translational and average rotational motions largely track the strong increase in viscosity with ChCl content, with ethylene glycol consistently diffusing faster than the choline cation and no global dynamical anomaly at the eutectic composition. More subtle, site-specific composition effects nevertheless emerge. The ratio of the diffusion coefficient of the hydroxyl group of choline to the diffusion coefficient of the methyl group of choline displays a shallow minimum in the 17–25 mol % region, indicating a modest change in how the hydroxyl-bearing end of choline samples the underlying translational motion relative to the methyl groups. 13 C T 1 analysis shows that rotational correlation times at 25 °C generally increase with ChCl, reflecting viscosity-coupled slowing, while the CH 2 –N α site exhibits a small but reproducible deviation from this monotonic trend near the eutectic. NOESY spectra at similar compositions reveal enhanced cross-relaxation between EG and choline protons, consistent with increased headgroup–solvent contact density rather than a wholesale structural rearrangement. Overall, our multitechnique study demonstrates that EG–ChCl dynamics are predominantly viscosity-dominated, with the eutectic region acting as a subtle dynamical crossover where specific choline segments become maximally coupled to the hydrogen-bond network. These insights refine the structure–dynamics picture of choline-chloride DESs and provide practical guidance for tuning composition in electrochemical, separation, and catalytic applications.

diffusion↗

DOE OSTI2023

Effect of heat treatment paths on the aging and rejuvenation of metallic glasses

Here, this study explores the influence of heat treatment paths on the structural relaxation of metallic glasses (MGs) in the regime of fast dynamics. We create MG samples using various quenching rates, from 10 9 to 10 11 K/s, and expose these samples to near-T g heat treatments with assorted combinations of heating/cooling rates, from 5×10 10 to 5×10 12 K/s, and annealing durations, from 10 ps to 10 ns. Results show that the effect of the heating rate is intricately tied to the initial structure of the MG, while a decrease in the cooling rate invariably intensifies the aging process. Extending the annealing duration may induce either aging or rejuvenation, subject to the specific thermal history. To interpret these findings, we hypothesize that the memory effect, governed by the activation and annihilation of reversible and irreversible flow units, underpins the rejuvenation-aging competition in MGs. Moreover, we suggest viewing the cooling stage as an annealing phase controlled by gradient temperature and fine-tuning its rate to achieve the targeted active flow unit distributions. This study illuminates the role of fast dynamics during the glass relaxation process and offers practical strategies for tailoring heat treatments to optimize MG structures and mechanical performance.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Stable water isotopes and tritium data from porewater at Elkhorn Slough

Salt marshes are dynamic hydrologic systems where terrestrial groundwater, terrestrial surface water, and seawater mix due to bi-directional flows and pressure gradients. Due to the counteracting terrestrial and marine forcings that control these environments, we do not comprehensively understand water fluxes in these complex coastal systems. To understand the water sources, flow directions, and velocities in salt marsh porewater, we collected geochemical tracers across a hillslope-to-salt marsh continuum in a salt marsh experiencing daily inundation of estuarine surface water (SW) from tides and mixing of fresh seasonal groundwater. The experimental transect is located at the Elkhorn Slough National Estuarine Research Reserve.Here we present a data table (.csv) with the results of electrical conductivity, stable water isotopes, and tritium in porewater across several elevations in the hillslope-to-salt marsh continuum (information in the data set). We analyzed the stable water isotope samples by cavity ring-down spectroscopy in a Picarro L2130-i at the University of California Santa Cruz (accuracy of 0.025‰ and 0.1‰ for δ18O and δ2H, respectively). We measured the electric conductivity of the samples with an Orion Star™ A329 multiparameter meter (0.5% accuracy, Thermo Fisher Scientific, Massachusetts, USA). We analyzed 3H samples at Lawrence Livermore National Laboratory by helium-3 accumulation (Clarke et al., 1976; Surano et al., 1992).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

The distribution of early- and late-type galaxies in the Coma cluster

The spatial distribution and the morohology-density relation of Coma cluster galaxies are studied using a new homogeneous photmetric sample of 450 galaxies down to B = 16.0 mag with quantitative morphology classification. The sample covers a wide area (10 deg X 10 deg), extending well beyond the Coma cluster. Morphological classifications into early- (E+SO) and late-(S) type galaxies are made by an automated algorithm using simple photometric parameters, with which the misclassification rate is expected to be approximately 10% with respect to early and late types given in the Third Reference Catalogue of Bright Galaxies. The flattened distribution of Coma cluster galaxies, as noted in previous studies, is most conspicuously seen if the early-type galaxies are selected. Early-type galaxies are distributed in a thick filament extended from the NE to the WSW direction that delineates a part of large-scale structure. Spiral galaxies show a distribution with a modest density gradient toward the cluster center; at least bright spiral galaxies are present close to the center of the Coma cluster. We also examine the morphology-density relation for the Coma cluster including its surrounding regions.

Doi, M.↗

DOE OSTI2021

Structural transitions of 4:1 methanol–ethanol mixture and silicone oil under high pressure

A 4:1 (volume ratio) methanol–ethanol (ME) mixture and silicone oil are two of the most widely used liquid pressure-transmitting media (PTM) in high-pressure studies. Their hydrostatic limits have been extensively studied using various methods; however, the evolution of the atomic structures associated with their emerging nonhydrostaticity remains unclear. Here, we monitor their structures as functions of pressure up to ∼30 GPa at room temperature using in situ high-pressure synchrotron x-ray diffraction (XRD), optical micro-Raman spectroscopy, and ruby fluorescence spectroscopy in a diamond anvil cell. No crystallization is observed for either PTM. The pressure dependence of the principal diffraction peak position and width indicates the existence of a glass transition in the 4:1 ME mixture at ∼12 GPa and in the silicone oil at ∼3 GPa, beyond which a pressure gradient emerges and grows quickly with pressure. There may be another liquid-to-liquid transition in the 4:1 ME mixture at ∼5 GPa and two more glass-to-glass transitions in the silicone oil at ∼10 GPa and ∼16 GPa. By contrast, Raman signals only show peak weakening and broadening for typical structural disordering, and Raman spectroscopy seems to be less sensitive than XRD in catching these structural transitions related to hydrostaticity variations in both PTM. These results uncover rich pressure-induced transitions in the two PTM and clarify their effects on hydrostaticity with direct structural evidence. The high-pressure XRD and Raman data on the two PTM obtained in this work could also be helpful in distinguishing between signals from samples and those from PTM in future high-pressure experiments.

Chen, Xiehang (ORCID:0000000168541506)↗

NASA NTRS2002

Parallel characterization of anaerobic toluene- and ethylbenzene-degrading microbial consortia by PCR-denaturing gradient gel electrophoresis, RNA-DNA membrane hybridization, and DNA microarray technology

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis.

Non-NASA Center↗

NASA NTRS2012

Advances and Limitations of Atmospheric Boundary Layer Observations with GPS Occultation over Southeast Pacific Ocean

The typical atmospheric boundary layer (ABL) over the southeast (SE) Pacific Ocean is featured with a strong temperature inversion and a sharp moisture gradient across the ABL top. The strong moisture and temperature gradients result in a sharp refractivity gradient that can be precisely detected by the Global Positioning System (GPS) radio occultation (RO) measurements. In this paper, the Constellation Observing System for Meteorology, Ionosphere & Climate (COSMIC) GPS RO soundings, radiosondes and the high-resolution ECMWF analysis over the SE Pacific are analyzed. COSMIC RO is able to detect a wide range of ABL height variations (1-2 kilometer) as observed from the radiosondes. However, the ECMWF analysis systematically underestimates the ABL heights. The sharp refractivity gradient at the ABL top frequently exceeds the critical refraction (e.g., −157 N-unit per kilometer) and becomes the so-called ducting condition, which results in a systematic RO refractivity bias (or called N-bias) inside the ABL. Simulation study based on radiosonde profiles reveals the magnitudes of the N-biases are vertical resolution dependent. The N-bias is also the primary cause of the systematically smaller refractivity gradient (rarely exceeding −110 N-unit per kilometer) at the ABL top from RO measurement. However, the N-bias seems not affect the ABL height detection. Instead, the very large RO bending angle and the sharp refractivity gradient due to ducting allow reliable detection of the ABL height from GPS RO. The seasonal mean climatology of ABL heights derived from a nine-month composite of COSMIC RO soundings over the SE Pacific reveals significant differences from the ECMWF analysis. Both show an increase of ABL height from the shallow stratocumulus near the coast to a much higher trade wind inversion further off the coast. However, COSMIC RO shows an overall deeper ABL and reveals different locations of the minimum and maximum ABL heights as compared to the ECMWF analysis. At low latitudes, despite the decreasing number of COSMIC RO soundings and the lower percentage of soundings that penetrate into the lowest 500-m above the mean-sea-level, there are small sampling errors in the mean ABL height climatology. The difference of ABL height climatology between COSMIC RO and ECMWF analysis over SE Pacific is significant and requires further studies.

COSMIC RO↗

DOE OSTI2022

Unveiling the origins of work-hardening enhancement and mechanical instability in laser shock peened titanium

Laser shock-peening (LSP) produces complex gradients in microstructures and residual stresses, which enables fabrication of metallic engineering components with superior mechanical properties. In this work, we use non-destructive and spatially resolved high-energy X-ray diffraction (HE-XRD) to investigate the influences of LSP on microstructure, surface topography, and residual stress for high purity titanium plates. LSP is found to produce large compressive in-plane residual stresses near the peened surface which monotonically decay to zero about 2.5 mm below the surface. These properties were also tracked during in-situ tensile loading, allowing stress partitioning and work-hardening rates to be measured as a function of depth. The surface region is found to have the highest work-hardening rate and remain mechanically stable up to sample failure. In addition, a crystal rigid rotation along a transversal axis of about 27 & nbsp; was observed at large applied strain near the surface region, which is attributed to the formation of denser dispersed shear bands. Furthermore, a sudden drop in tensile stress was observed at a depth of similar to 160 mu m from the LSP surface, indicating the formation of a localized mechanical instability zone (LMIZ). The origin of this LMIZ could be attributed to the increase in shear stress induced by a load transfer from LSP surface due to the formation of denser dispersed shear bands. These observations indicate a localized transition in deformation behavior has occurred from dispersed shear banding to LMIZ and homogeneous deformation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Spin-Dependent Thermoelectric Power of Nanoislands

The Seebeck effect explains the generation of electric voltage as a result of a temperature gradient. Its efficiency, defined as the ratio of the generated electric voltage to the temperature difference, is sensitive to local inhomogeneities that alter the scattering rate and the density of the conduction electrons. Spin-polarized Seebeck tunneling generates a distinct thermovoltage in spin-up and spin-down charge transport channels, which, as a key to spin caloritronics, focuses on transport phenomena related to spin and heat. Here, we report spatially resolved measurement of the spin-dependent thermovoltage in a tunneling junction formed by ferromagnetic Co nanoislands and a Ni tip using spin-dependent scanning tunneling thermovoltage microscopy (SP-STVthM). We resolve the nanoscale thermoelectric powers with respect to spin polarization, nanoisland size, stacking order of Co layers on a Cu substrate, and local sample heterogeneities. The observed thermally generated spin voltages are supported by first-principles and model calculations.

36 MATERIALS SCIENCE↗

NASA NTRS2003

Structure and Variability of Water Vapor in the Upper Troposphere and Lower Stratosphere

An algorithm has been developed to synoptically-map asynoptic measurements, while rejecting small-scale under-sampled variance that is intrinsic to satellite measurements of water vapor, cloud, and other convective properties. The algorithm has been validated against high-resolution Global Cloud Imagery (GCI) , constructed from 6 satellites simultaneously viewing the global convective pattern. It has been applied to synoptically map Upper-tropospheric humidity (UTH) from UARS/MLS. Mapped distributions of UTH have been used, jointly with the GCI, to study how the upper troposphere is humidified. The time-mean distribution of UTH is spatially correlated to the time-mean distribution of cold cloud fraction eta(sub c) (T less than 230 K). Regions of large UTH coincide with regions of large eta(sub c), which mark deep convection. They also coincide with regions of reduced vertical stability, in which the vertical gradient of Theta is weakened by convective mixing. Coldest cloud cover is attended convective overshoots above the local tropopause, which is simultaneously coldest and highest. Together, these features reflect the upper-troposphere being ventilated by convection, which mixes in air from lower levels.

Salby, Murry L.↗

NASA NTRS2020

Global-scale distribution of ozone in the remote troposphere from ATom and HIPPO airborne field missions

Ozone is a key constituent of the troposphere, where it drives photochemical processes, impacts air quality, and acts as a climate forcer. Large-scale in situ observations of ozone commensurate with the grid resolution of current Earth system models are necessary to validate model outputs and satellite retrievals. In this paper, we examine measurements from the Atmospheric Tomography (ATom; four deployments in 2016–2018) and the HIAPER Pole-to-Pole Observations (HIPPO; five deployments in 2009–2011) experiments, two global-scale airborne campaigns covering the Pacific and Atlantic basins. ATom and HIPPO represent the first global-scale, vertically resolved measurements of O3 distributions throughout the troposphere, with HIPPO sampling the atmosphere over the Pacific and ATom sampling both the Pacific and Atlantic. Given the relatively limited temporal resolution of these two campaigns, we first compare ATom and HIPPO ozone data to longer-term observational records to establish the representativeness of our dataset. We show that these two airborne campaigns captured on average 53 %, 54 %, and 38 % of the ozone variability in the marine boundary layer, free troposphere, and upper troposphere–lower stratosphere (UTLS), respectively, at nine well-established ozonesonde sites. Additionally, ATom captured the most frequent ozone concentrations measured by regular commercial aircraft flights in the northern Atlantic UTLS. We then use the repeated vertical profiles from these two campaigns to confirm and extend the existing knowledge of tropospheric ozone spatial and vertical distributions throughout the remote troposphere. We highlight a clear hemispheric gradient, with greater ozone in the Northern Hemisphere, consistent with greater precursor emissions and consistent with previous modeling and satellite studies. We also show that the ozone distribution below 8 km was similar in the extra-tropics of the Atlantic and Pacific basins, likely due to zonal circulation patterns. However, twice as much ozone was found in the tropical Atlantic as in the tropical Pacific, due to well-documented dynamical patterns transporting continental air masses over the Atlantic. Finally, we show that the seasonal variability of tropospheric ozone over the Pacific and the Atlantic basins is driven year-round by transported continental plumes and photochemistry, and the vertical distribution is driven by photochemistry and mixing with stratospheric air. This new dataset provides additional constraints for global climate and chemistry models to improve our understanding of both ozone production and loss processes in remote regions, as well as the influence of anthropogenic emissions on baseline ozone.

ATom, aircraft profiles, Atlantic pollution, ozone↗