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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Spatial gradients in the characteristics of soil-carbon fractions are associated with abiotic features but not microbial communities

Coastal terrestrial-aquatic interfaces (TAIs) are dynamic zones of carbon biogeochemical cycling influenced by landscape-scale salinity gradients. However, there is significant cross-system heterogeneity in salinity influences over TAI soil biogeochemical function, such as gas fluxes and dissolved organic carbon concentrations. One potential explanation for the lack of consistency is unrecognized variation in soil organic carbon chemistry and microbial communities. To provide a foundation for understanding the role of C chemistry and microbial communities on biogeochemical cycling in coastal soils, we evaluated associations among a landscape scale soil salinity gradient, molecular-level soil carbon chemistry, and microbial community assembly processes. Based on observations in related systems we hypothesized systematic shifts along the salinity gradient in organic carbon thermodynamics, biochemical transformation profiles, and heteroatom content. While the thermodynamic favorability of water soluble organic compounds in shallow soils did decrease with increasing salinity (R2=0.78, p<0.00001), this was opposite the expected direction. The number of inferred biochemical transformations and total heteroatom content also decreased with increasing salinity. These patterns indicate lower microbial activity at higher salinity that is potentially constrained by less favorable organic carbon. Furthermore, organic compound classes appeared to be primarily marine/algal derived in the floodplains with greater terrestrial signatures in upland soil. Based on a recent simulation-based study we further hypothesized a relationship between microbial community assembly processes and carbon chemistry. Null modelling revealed particularly strong influences of dispersal limitation over microbial composition, which may be due limited hydrologic connectivity within the clay-rich soils. Signatures of dispersal limitation indicate stochastically assembled microbial communities, which was further reflected in the lack of an association between community assembly processes and carbon chemistry. This suggests a disconnect between microbial composition and carbon biogeochemistry, thereby indicating that the salinity-associated gradient in carbon chemistry was driven by a combination of spatially-structured inputs and salinity-associated metabolic responses of microbial communities that are independent of community composition. Our results collectively indicate that soil carbon chemistry varies systematically with salinity at the landscape scale. This variation has not been accounted for in studies examining impacts of salinity to biogeochemical rates and pools. We propose that impacts of salinity in coastal soil biogeochemistry need to be understood in context of carbon chemistry and microbial physiology, while microbial composition may have less influence.

soil carbon, Coastal Biogeochemistry, microbial co↗
DOE OSTI2019

DETAILED DESIGN OF AN EPITHERMAL/INTERMEDIATE CRITICAL EXPERIMENT USING THE SANDIA NATIONAL LABORATORIES CRITICAL FACILITY

Nuclear criticality safety evaluations commonly take credit for neutron-absorbing materials. The normal practice for validating the calculational process to develop safety limits and margins of safety is to compare the safety case models with calculations of various critical experiments of a similar composition. Because there is a lack of critical experiments with various relevant neutron-absorbing materials mixed with fissile material, a new approach is being developed. This new approach uses small amounts of these credited materials in a critical assembly and assesses their worth and neutron energy spectral characteristics using absorption reaction rates. This approach is proposed as a new method for nuclear data validation. The assessment will involve measured central reactivity worth in a critical or near-critical assembly and subsequent comparison with calculated values from Monte Carlo computer analyses. Additionally, four configurations are proposed which replace fuel rods in the driver region with absorber rods. These configurations will allow for examination of the effects of the material in a more thermalized spectrum experiment selection in these scenarios. This paper presents some case studies examining the impact of these changes on some hypothetical safety analysis systems.This paper documents the detailed design portion of the critical experiment design process, focusing on development of a capability to test epithermal/ intermediate energy cross sections for materials using the 7uPCX critical experiment facility. Tantalum is specified as the test material to be examined.

Clarity, Justin↗
DOE OSTI2019

EFFICIENT SURROGATE FOR LOW ENERGY DAMAGE EFFECTS IN POLYATOMIC MATERIALS

We present an efficient surrogate for binary-collision Monte-Carlo (BCMC) calculations in polyatomic materials for low ion energies based on Parkin and Coulter’s net displace- ment function. Most of BCMC’s execution time is spent on the low energy tail of cascades when recoils’ residual travel distance is small compared to the desired resolution of the damage calculation. We propose to curtail an ions’ random walk if its energy reduces below a threshold Et and locally deposit an estimate of the expected number of defects it creates; the estimate of the number of defects is obtained from Parkin and Coulter’s net displacement functions. We find that results obtained with the BCMC code MyTRIM with and without truncation of the random walks compare well for large energy thresholds Et > 5 keV. For Et = 5 keV larger discrepancies are observed because of large differ- ences between the net displacement function and MyTRIM for very low energy recoils. Future work will investigate and resolve this discrepancy. The energy threshold should be chosen so that ions’ average distance to next nuclear scattering is small compared to the desired resolution. A reduction of execution time of damage cascades by over 90 % is observed for an energy threshold of Et = 5 keV and a 1 MeV Xe-135 primary impinging on UO2. The desired application of this surrogate model are coupled FEM-BCMC cal- culations with varying compositions where net displacement functions are tabulated by composition. For reducing the number of required net displacement functions, derivatives of the net displacement function with respect to number fraction are computed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

New Materials and an Efficient Processing Approach for Materials for Harsh Environments – Continuous Electric Field Assisted Sintering

Electric-field assisted sintering (EFAS) is an advanced manufacturing method for the consolidation and processing of ceramic and metal materials. It is a particularly excellent candidate for efficient materials processing as it has greater than 90% energy cost savings and improved CO2 emissions compared to traditional sintering in a furnace. This is due to direct rapid heating of the materials and molds leveraging an electrification phenomenon called Joule heating where electrical current is passed through the sample/mold assemblies causing direct heating. The technique does have limitations though as it is traditionally a batch-process and suffers from scale up difficulties. To overcome this, researchers at Idaho National Laboratory changed the form factor of traditional EFAS instruments to develop and construct a continuous rolling electric-field assisted sintering (CEFAS) device. Its unique design creates highly localized electrical, thermal, and mechanical process parameters for continuous samples of limitless length. Advanced core shell powder materials were also created using fluidized powder bed atomic layer deposition of core/shell solid oxide electrochemical electrode/electrolyte BaCe0.7Zr0.1Y0.1Yb0.1O3 (BCZYYb) and yttrium doped BaZrO3 (BZY) to be a feedstock for the new CEFAS instrument. The innovative core/shell powder is hypothesized to have high protonic conductivity with enhanced environmental resistance. The completed project covers innovations in process control and design enabled by control theory, finite element modeling and mechanical design, three dimensionally printed carbon-carbon composite manufacturing and utilization, and green materials processing techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effective Defect Detection Using Instance Segmentation for NDI

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask- RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.

computer vision techniques↗
DOE OSTI2019

Corrosion Tests with Developmental Iron Phosphate Glass Waste Forms

Corrosion tests are in progress to assess the dissolution behavior of developmental iron phosphate waste forms for salt waste from electrochemical separation operations. Specimens cut from materials made at PNNL to represent a range of waste salt loadings in prototype composition DPF-5 and a range of iron oxide loadings in prototype composition DPF-3 were subjected to modified ASTM C1308 tests to assess the compositional effects on the relative durabilities and retention of immobilized salt cations. The materials were composed of different amounts of glass and crystalline phases. The test results show both the intrinsic durability of the material and the salt cation retention decrease as the total amount of salt cations added to the material is increased. The results of ASTM C1220 and ASTM C1285 tests conducted with materials DPF5-280 and DPF5-336, which contained about 28% and 34% salt cations (on a mass oxide basis) and bounded the durabilities of the other materials, show that solution feedback does not attenuate the dissolution rate. This suggests the degradation of iron phosphate waste forms can be modelled by using a constant rate that probably depends on the composition and volume fraction of the glass phase. A reference composition should be formulated and used in tests to determine those dependencies. The ASTM C1220 and ASTM C1285 tests with DPF5-280 and DPF5-336 remain in progress to corrode the specimens enough to distinguish the relative extents of glass and crystalline phase corrosion by microscopic examinations. Those results are expected to provide insights regarding the relative dissolution rates of crystalline and glass phases and impact on durability to inform the formulation of the reference composition.

Ebert, W. L.↗
DOE OSTI2022

Microstructural pattern formation during liquid metal dealloying: Phase-field simulations and theoretical analyses

In recent years, liquid metal dealloying has emerged as a promising material processing method to generate micro- and nanoscale bicontinuous or porous structures. Most previous studies focused on the experimental characterization of the dealloying process and on the properties of the dealloyed materials, leaving the theoretical study incomplete to fully understand the fundamental mechanisms of the liquid metal dealloying process. In this paper, we use theoretical models and phase-field simulations to clarify the kinetics and pattern formation during liquid metal dealloying. Our investigation starts from a theoretical analysis of the 1D dissolution of a binary precursor alloy, which reveals that the 1D dissolution process involves two regimes. In the first regime, due to the low solubility of one of the elements in the melt, it accumulates at the solid-liquid interface, which reduces the dissolution kinetics. In the second regime, the interface kinetics reaches a stationary regime where both elements of the precursor alloy dissolve into the melt. Previous works revealed that in the early dealloying stage, the dealloying front is destabilized by an interfacial spinodal decomposition, which triggers the formation of interconnected ligaments. Here we extend this line of work by proposing a linear stability analysis able to predict the initial length scale of the ligaments formed in the initial stage of the dealloying. Combining this analysis with the 1D dissolution model proposed here enables us to better understand the initial conditions (composition of the precursor alloy and the melt) leading to a planar dissolution without interface destabilization. Finally, we report a strong influence of solid-state diffusion on dealloying. Although the solid-state diffusivity is four to five orders of magnitude smaller than in the liquid phase, it is found to affect both dissolution kinetics and ligament morphologies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Remote Sensing Technique to Upscale Methane Emission Flux in a Subtropical Peatland

Abstract Quantification of methane (CH 4 ) gas emission from peat is critical to understand CH 4 budget from natural wetlands under a climate warming scenario. Previous studies have focused on prediction and mapping of CH 4 emission flux using process‐based models, while application of statistical‐empirical models for upscaling spatially sparse in situ measurements is scarce. In this study, we developed an empirical remote sensing upscaling approach to estimate CH 4 emission flux in the Everglades using limited in situ point‐based CH 4 emission flux measurements and Landsat data during 2013–2018. We spatially and temporally linked in situ data with Landsat surface reflectance based on temporally composite data sets and developed an object‐based machine learning framework to model and map CH 4 emission flux. An ensemble analysis of two machine learning models, k ‐Nearest Neighbor ( k ‐NN) and Support Vector Machine (SVM), shows that the upscaling approach is promising for predicting CH 4 emission flux with a R 2 of 0.65 and 0.87 based on a fivefold cross‐validation for a dry season and wet season estimation, respectively. We generated emission flux map products that successfully revealed the spatial and temporal heterogeneity of CH 4 emission within the dominant freshwater marsh ecosystem in the Everglades. We conclude that Landsat is promising for upscaling and monitoring CH 4 emission flux and reducing the uncertainty in emission estimates from wetlands.

Zhang, Caiyun↗
DOE OSTI2021

An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties

This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.

Aleatory uncertainty↗
DOE OSTI2025

Quantifying dislocation-type defects in post irradiation examination via transfer learning

The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solid Waste Release Calculations for the Composite Analysis Baseline Assessment

The purpose of the Hanford Site Composite Analysis (CA) is to provide an estimate of the cumulative radiological impacts from active and planned low-level radioactive waste disposals and other potentially interacting radioactive waste disposal sources that will remain following Hanford Site closure. The need for a waste form release model addressing multiple solid waste forms is documented in CP-60410, Hanford Site Composite Analysis Technical Approach Description: Waste Form Release.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Focused Ion Beam Tomography of Alloy 617 Corroded in Molten Chloride Salt

Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials.

Copeland-Johnson, Trishelle↗
DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗
DOE OSTI2024

Constraining the Dense Matter Equation of State with New NICER Mass–Radius Measurements and New Chiral Effective Field Theory Inputs

Pulse profile modeling of X-ray data from the Neutron Star Interior Composition Explorer is now enabling precision inference of neutron star mass and radius. Combined with nuclear physics constraints from chiral effective field theory (χEFT), and masses and tidal deformabilities inferred from gravitational-wave detections of binary neutron star mergers, this has led to a steady improvement in our understanding of the dense matter equation of state (EOS). Here, we consider the impact of several new results: the radius measurement for the 1.42 M ⊙ pulsar PSR J0437−4715 presented by Choudhury et al., updates to the masses and radii of PSR J0740+6620 and PSR J0030+0451, and new χEFT results for neutron star matter up to 1.5 times nuclear saturation density. Using two different high-density EOS extensions—a piecewise-polytropic (PP) model and a model based on the speed of sound in a neutron star (CS)—we find the radius of a 1.4 M ⊙ (2.0 M ⊙ ) neutron star to be constrained to the 95% credible ranges ${12.28}_{-0.76}^{+0.50}$ km (${12.33}_{-1.34}^{+0.70}$ km) for the PP model and ${12.01}_{-0.75}^{+0.56}$ km (${11.55}_{-1.09}^{+0.94}$ km) for the CS model. The maximum neutron star mass is predicted to be ${2.15}_{-0.16}^{+0.14}$M ⊙ and ${2.08}_{-0.16}^{+0.28}$M ⊙ for the PP and CS models, respectively. We explore the sensitivity of our results to different orders and different densities up to which χEFT is used, and show how the astrophysical observations provide constraints for the pressure at intermediate densities. Moreover, we investigate the difference R 2.0 − R 1.4 of the radius of 2 M ⊙ and 1.4 M ⊙ neutron stars within our EOS inference.

gravitational wave sources↗
DOE OSTI2023

Electronic transport in copper–graphene composites

In this work, we investigate electronic transport properties of copper–graphene (Cu–G) composites using a density-functional theory (DFT) framework. Conduction in composites is studied by varying the interfacial distance of copper/graphene/copper (Cu/G/Cu) interface models. Electronic conductivity of the models computed using the Kubo–Greenwood formula shows that the conductivity increases with decreasing Cu–G distance and saturates below a threshold Cu–G distance. The DFT-based Bader charge analysis indicates increasing charge transfer between Cu atoms at the interfacial layers and the graphene with decreasing Cu–G distance. The electronic density of states reveals increasing contributions from both copper and carbon atoms near the Fermi level with decreasing Cu–G interfacial distance. By computing the space-projected conductivity of the Cu/G/Cu models, we show that the graphene forms a bridge to the electronic conduction at small Cu–G distances, thereby enhancing the conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling of Reactor Design and Optimization for Scale-Up of the Catalyxx Process for Ethanol Conversion to Higher Alcohol Biofuels

This report summarizes the results of a collaborative efforts between Oak Ridge National Laboratory (ORNL) and Catalyxx Inc. to investigate scale-up of Catalyxx’s Ethanol upgrading to higher alcohols process. The study was funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) under CRADA (Cooperative Research and Development Agreement) No: NFE-20-08396. The project is part of the Direct Funding Opportunity (DFO) for Computational Science to Enable Bioenergy program which utilized computational toolsets developed by the Consortium for Computational Physics and Chemistry, a multi-laboratory consortium in BETO. The report here summarizes a packed-bed reactor modeling effort spanning the range from lab to industrial scale (from 4 gram to 5-ton catalyst beds), and examining reactor design, process optimization strategies, and suggested design and operating conditions for Catalyxx’s ethanol upgrading plants. The results in this report have been shared in monthly steering meetings and presentations are available in the shared data house owned by Catalyxx. The modeling effort helped to define optimum operation conditions for maximum alcohol selectivity and yield: i.e., temperature control scenarios ranging from adiabatic to isothermal, feed rate, pressure, and inlet H 2 /Ethanol ratio. The modeling results were verified at lab-(4 gram) and pre-pilot (4 kg) scales and has been used to evaluate a 5-ton packed-bed reactor and identify operating conditions to maximize the butanol yield. Special focus was given to understanding mass-transfer effects in the pre-pilot and pilot-scale reactors, over the domain of flow rate, pressure, feed composition, pellet size, shape, porosity, bed voidage, and reactor dimensions (i.e., length/diameter). Modeling was also used to evaluate innovative reactor design concepts such as water removal to improve alcohol selectivity and yield, and a reactor with an additional side inlet to facilitate quenching. These concepts were thoroughly explored, and potential benefits were disclosed. The results in this report are summarized and described qualitatively to protect the IP rights of Catalyxx. The details have been shared with the Catalyxx team in the regular steering meetings. At the end of the project, Catalyxx Inc. announced a successful demonstration of pilot scale operation in Seville, Spain.

02 PETROLEUM↗
DOE OSTI2021

The Sonora Substellar Atmosphere Models. II. Cholla: A Grid of Cloud-free, Solar Metallicity Models in Chemical Disequilibrium for the JWST Era

Exoplanet and brown dwarf atmospheres commonly show signs of disequilibrium chemistry. In the James Webb Space Telescope (JWST) era, high-resolution spectra of directly imaged exoplanets will allow the characterization of their atmospheres in more detail, and allow systematic tests for the presence of chemical species that deviate from thermochemical equilibrium in these atmospheres. Constraining the presence of disequilibrium chemistry in these atmospheres as a function of parameters such as their effective temperature and surface gravity will allow us to place better constraints on the physics governing these atmospheres. This paper is part of a series of works presenting the Sonora grid of atmosphere models. In this paper, we present a grid of cloud-free, solar metallicity atmospheres for brown dwarfs and wide-separation giant planets with key molecular species such as CH 4 , H 2 O, CO, and NH 3 in disequilibrium. Our grid covers atmospheres with T eff ∈ [500 K, 1300 K], log g ∈ [3.0, 5.5] (cgs) and an eddy diffusion parameter of logK zz = 2,4 and 7 (cgs). We study the effect of different parameters within the grid on the temperature and composition profiles of our atmospheres. We discuss their effect on the near-infrared colors of our model atmospheres and the detectability of CH 4 , H 2 O, CO, and NH 3 using the JWST. We compare our models against existing MKO and Spitzer observations of brown dwarfs and verify the importance of disequilibrium chemistry for T dwarf atmospheres. Finally, we discuss how our models can help constrain the vertical structure and chemical composition of these atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Stellar Abundance Maps of the Milky Way Disk

To understand the formation of the Milky Way's prominent bar it is important to know whether stars in the bar differ in the chemical element composition of their birth material as compared to disk stars. This requires stellar abundance measurements for large samples across the Milky Way's body. Such samples, e.g., luminous red giant stars observed by the Sloan Digital Sky Survey's APOGEE survey, will inevitably span a range of stellar parameters; as a consequence, both modeling imperfections and stellar evolution may preclude consistent and precise estimates of their chemical composition at a level of purported bar signatures, which has left current analyses of a chemically distinct bar inconclusive. Here, we develop a new self-calibration approach to eliminate both modeling and astrophysical abundance systematics among red giant branch (RGB) stars of different luminosities (and hence surface gravity $\mathrm{log} g$). We apply our method to 48,853 luminous APOGEE Data Release 16 RGB stars to construct spatial abundance maps of 20 chemical elements near the Milky Way's mid-plane, covering galactocentric radii of 0 kpc < R GC < 20 kpc. Our results indicate that there are no abundance variations whose geometry matches that of the bar, and that the mean abundance gradients vary smoothly and monotonically with galactocentric radius. We confirm that the high-α disk is chemically homogeneous, without spatial gradients. Furthermore, we present the most precise [Fe/H] versus R GC gradient to date with a slope of – 0.057 ±0.001 dex kpc –1 out to approximately 15 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗