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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cooled Spray Technology for Particulate Reduction in a Heavy-Duty Engine

Cooled spray (CS) technology passively reduces particulate matter (PM) emissions from diesel engines compared to non-CS-equipped diesel engines. CS inserts are mounted near the injector nozzle and control mixing so that the fuel and air can premix while limiting combustion near fuel-rich zones, thereby reducing the formation of particulate matter. CS components contain no moving parts and could be installed as a retrofit or built into new engines. However, CS technology is early in its development, and further investigations are needed to understand the overall performance implications and practicality of the technology. In this paper, we investigate several important aspects of CS, providing a clearer picture of some challenges and potential benefits of CS. Two alignment techniques are used to characterize measurement ease and bias, namely, an optical alignment and spray-plug impact alignment. While the optical technique facilitates alignment more easily, a bias was measured between the optical and spray-plug techniques, suggesting the optical technique may have insufficient accuracy without additional corrections. We also evaluate the engine performance of a well-aligned and poorly aligned CS insert, compared to the baseline configuration. The poorly aligned insert shows slower combustion than the baseline and mixed overall performance. However, the well-aligned insert shows faster combustion than the baseline and PM emission reduction at most operating conditions, with some conditions showing PM reduction up to 80%. Furthermore, the results of this paper highlight the alignment challenges of CS technology as well as the potential PM reduction benefit of the technology.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Assessment of Buffer-IPyC Thermomechanical Debonding Behavior Using New Experiment Strength Data in BISON

TRIstructural ISOtropic (TRISO) single sub-millimeter-diameter nuclear fuel particle consists of a spherical fuel kernel surrounded by four coating layers. During reactor operation, the TRISO coating layers are subjected to irradiation-induced dimensional changes and the associated thermomechanical behavior of each layer. One of the observed behaviors is gap formation between the buffer and IPyC layer due to the porous buffer’s irradiation-induced shrinkage exceeding that of the IPyC layer. To predict TRISO thermomechanical performance, multi-physics models have been built that are continually updated and refined. The BISON code is a finite element-based nuclear fuel performance code that may be used for TRISO particle simulations. One of the recent additions to the BISON code is the ability to model the process of layer debonding. This paper will focus on the simulation results of the improved BISON debonding model that will utilize updated strengths measured from irradiated TRISO fuel particles. The new experimental strength data from micromechanical tests of irradiated TRISO fuel samples were exercised in the BISON simulations and compared to baseline strength data to assess their applicability in the models. This also includes updated buffer-IPyC bond strengths to simulate layer delamination. Based on current experimental observations it is noted that the buffer-IPyC separation occurs not exactly at the junction of these two layers, but more on the side of the buffer layer. This improved modeling approach using experimental strength data to characterize buffer-IPyC debonding and its potential subsequent cracking will be presented in the paper along with comparisons to available experimental observations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Rapid Coal-Ash Characterization using Geophysical Methods & Machine Learning

Coal combustion products (CCP) are challenging to delineate in heterogeneous field settings. Conventional methods (test pits, coring, and laboratory analyses) are labor-intensive, slow, invasive, and provide sparse spatial coverage. This study evaluates whether rapid non-invasive geophysical screening methods—induced polarization (IP), magnetic susceptibility, and nuclear magnetic resonance (NMR) —combined with surface colorimetry (RGB_24), can discriminate CCP-soil mixtures and provide reliable estimates of CCP content. Laboratory measurements were collected on five CCP-soil mixtures (series) and modeled using (i) a linear baseline, (ii) a calibrated non-linear (power-mean) model, and (iii) a machine-learning (ML) Random Forest approach, with validation via leave-one-series-out and site-specific tests. Across the five series, individual signals—particularly IP and magnetic susceptibility—were strongly predictive of ash content but were consistently outperformed by combined models. The pooled calibrated non-linear and ML models captured the observed non-linearity and achieved high accuracy and precision, improving on linear fits. Colorimetry showed the weakest direct relationship with ash content for the tested samples but improved performance when included in multi-signal models. At pre-selected 3.5% decision threshold, calibrated and ML approaches yielded near-perfect classification (Matthews correlation coefficient ˜ 1), suggesting strong practical operability for field screening. Additionally, field-analog tests highlighted the role of endmembers—accuracy declined without access to end-member measurements but was largely recovered by collecting a minimal labeled pair for local recalibration. With end members, accuracy remained high. Globally trained models performed well on three operational unknowns; however, series-specific refits provided the most accurate predictions. Overall, these results highlight the potential of combining rapid geophysics and minimal local calibration for improved coal-ash delineation.

Peshtani, Klaudio↗
DOE OSTI2025

Influence of Electrolyte Additives on Interfacial Stability of Manganese-Rich Lithium-Ion Battery Cathodes

Affordable, long-lasting energy storage has become critical to support increased electricity demand in recent years. Cobalt-free, lithium- and manganese-rich lithium nickel manganese oxide (LMR-NM) cathodes stand to reduce cost and supply-chain concerns associated with traditional cobalt-containing cathodes for lithium-ion batteries by leveraging more earth-abundant materials; however, they have shown issues with long-term cycling stability. Here, we investigate lithium difluoro(oxalate)borate (LiDFOB), tris(trimethylsilyl) phosphite (TMSPi), and vinylene carbonate (VC) electrolyte additives for their ability to improve cycling performance of LMR-NM (0.3 Li 2 MnO 3 + 0.7 LiMn 0.5 Ni 0.5 0 2 ) cells. Cryogenic scanning transmission electron microscopy (cryo-STEM) with electron energy loss spectroscopy enables the construction of a structure–function relationship between cathode electrolyte interphase (CEI) characteristics and the electrochemical performance of cells aged with these additives. We find the combination of 2 wt % TMSPi + 1 wt % LiDFOB performs better than any single additive, achieving a 28% improvement in specific capacity over the baseline electrolyte after long-term cycling. We attribute this to LiDFOB mitigating Mn ion dissolution, with cryo-STEM showing Mn stabilized up to the CEI surface, coupled with improved CEI structure and chemistry enabled by TMSPi, evidenced by a moderately thick (∼7–15 nm) CEI that appears to protect against further electrolyte reactions with the particle. These results, achieved through site-specific nanoscale characterization, directly reveal mechanisms through which electrolyte engineering can improve the performance of earth-abundant cathodes, enabling informed development of more affordable and reliable batteries to meet future energy storage needs.

25 ENERGY STORAGE↗
DOE OSTI2025

Novel Preprocessing Techniques for Enhancing Flowability of Miscanthus

Purpose-grown energy crops possess significant potential as promising feedstocks for biofuel and biochemical production. However, similar to other biomass feedstocks, their utilization has been hindered by handling and feeding challenges such as clogging and segregation, which prevent biorefineries from operating at full capacity. These challenges arise primarily due to particle attributes including irregular shape, high flexibility, and high compressibility, leading to interlocking, high compaction, and significant friction under stress consolidation. To enhance operational reliability, it is crucial to focus on preprocessing techniques to address these issues, alongside accurately characterizing the mechanical and physical properties and flow performance of these feedstocks. This study investigates novel preprocessing techniques, including pelletization and torrefaction, to fundamentally alter these particle attributes and improve flowability. Miscanthus, an herbaceous energy crop, was the primary focus of this study. Samples were processed using a Forest Concepts Crumbler rotary shear system and an oscillating multi-stack screen bed to achieve two nominal (baseline) particle sizes: 6mm and 2mm. The study evaluated the efficiency and energy consumption of size reduction, revealing that while 2mm particles required more energy, they exhibited better flowability and higher yield strength compared to 6mm particles. Shear and compression tests assessed the material's shear strength, internal friction, and elastic modulus, while wedge-shaped hopper tests measured the critical arching distance and mass flow rate. The results provide valuable insights into the mechanical properties and processing efficiency of Miscanthus, contributing to the development of a preprocessing framework that enhances energy efficiency and material flowability in bioenergy production. However, further preprocessing and development are required to optimize this framework fully.

09 - BIOMASS FUELS↗
DOE OSTI2022

Report on Evolution of Inconel 718 Following HFIR Irradiation

The report presents the microstructure and mechanical properties of 3D printed Inconel 718 after irradiation in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) to assess its potential use as a structural material. The structural components near the outlets of several proposed reactor cores will experience significant neutron fluxes and outlet coolant temperatures ranging from the hot standby temperature of 300 °C to nearly 550 °C at the center of the part. These components must support the core in appropriate loading conditions and require structural analysis at relevant temperatures. In FY21, three heat treatments were designed and conducted to simplify the microstructure and to determine how each precipitating phase contributed to the overall strength. In FY21, baseline mechanical properties were measured from uniaxial tensile tests on subsize SS-J2 specimens at room temperature and at elevated temperatures of 300, 450, and 600 °C to serve a comparison to the irradiated properties. Four capsules containing 3D printed Inconel 718 were inserted into HFIR in FY21 for a matrix of two temperatures and two doses. The lower of the two doses was available for characterization in FY22. Multiple heat treatments of Inconel 718 irradiated to nominal conditions of 2 displacements per atom (dpa) at either 300 or 600 °C were strained with uniaxial tensile tests at the Irradiated Material Examination and Testing (IMET) Facility to discern the mechanical properties. Transmission electron microscopy was performed to correlate the observed mechanical properties with nanoscale features. The initially homogenous AM718-HM increased in strength at both irradiation temperatures based on a high density of nanometer-scale radiation-induced cavities at lower temperature and nucleation and growth of γ" precipitates at higher temperatures. The precipitate-hardened AM718-HT2 showed very small differences in strength before and after irradiation: the contribution to strength from γ" precipitates was replaced with dislocation loops. Because the radioactivity of the nickel superalloys from neutron activation limited the scope of the analysis, a feasibility study examined the possibility of an ultra-miniature specimen geometry, colloquially SS-Tiny (SS-T), for mechanical property determination using the nonirradiated Inconel 718. This study found an overestimation of ductility from the SS-T geometry with yield strength and ultimate tensile strength slightly above the SS-J2 geometry: this could be contributed to a reduction in specimen thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2021

Radiation exposure induces cross-species temporal metabolic changes that are mitigated in mice by amifostine

Exposure to acute, damaging radiation may occur through a variety of events from cancer therapy and industrial accidents to terrorist attacks and military actions. Our understanding of how to protect individuals and mitigate the effects of radiation injury or Acute Radiation Syndrome (ARS) is still limited. There are only a few Food and Drug Administration-approved therapies for ARS; whereas, amifostine is limited to treating low dose (0.7–6 Gy) radiation poisoning arising from cancer radiotherapy. An early intervention is critical to treat ARS, which necessitates identifying diagnostic biomarkers to quickly characterize radiation exposure. Towards this end, a multiplatform metabolomics study was performed to comprehensively characterize the temporal changes in metabolite levels from mice and non-human primate serum samples following γ-irradiation. The metabolomic signature of amifostine was also evaluated in mice as a model for radioprotection. The NMR and mass spectrometry metabolomics analysis identified 23 dysregulated pathways resulting from the radiation exposure. These metabolomic alterations exhibited distinct trajectories within glucose metabolism, phospholipid biosynthesis, and nucleotide metabolism. A return to baseline levels with amifostine treatment occurred for these pathways within a week of radiation exposure. Together, our data suggests a unique physiological change that is independent of radiation dose or species. Furthermore, a metabolic signature of radioprotection was observed through the use of amifostine prophylaxis of ARS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars

In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ~ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sound at Scale: Characterizing Impacts of Noise Ordinances on the Onshore Wind Energy Technical Potential for the United States

Recent surveys have documented the rapid rise of sound ordinances across state and county jurisdictions, which has become crucial for wind energy siting. However, the lack of information on ordinances and computational challenges in turbine sound modeling create uncertainties regarding how evolving policies may affect resource potential and clean energy objectives. Therefore, we develop an approach to evaluate wind turbine sound profiles at millions of locations across the U.S. and translate them into setback distances for every residential structure. Compared to a baseline reference scenario, we find a 7% reduction in the national wind energy capacity potential when accounting for counties with existing sound ordinances. Additionally, when expanding the surveyed sound ordinances nationwide, we observe a potential loss of 53% of the national wind capacity under the most stringent ordinances, with a disproportionate share of this lost capacity coming from high-quality and low-cost wind resource. This work reveals that neglecting sound ordinances results in a significant overestimation of wind resource potential and highlights the important trade-offs between increased wind energy deployment to meet target decarbonization goals and the social/environmental impacts of this deployment that must be considered.

aeroacoustics↗
DOE OSTI2022

On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability

Artificial intelligence technologies have emerged as a game changer not only in specific applications such as image recognition and machine translation but also in many scientific domains. In particular, as deep reinforcement learning (DRL) has shown great success in complex control problems, DRL-based control has been considered as a potential solution to efficiently control and manage building systems. However, broad assessment of DRL-based building control is still required to characterize their pros and cons in comparison with conventional building control methods (e.g., rule-based feedback controls). In this paper, we assessed DRL-based controls with on-policy learning-based algorithms and continuous control actions for cooling control of large office buildings in the summer season to minimize whole-building energy use and occupant discomfort. We compared DRL-based control methods with two baseline control methods: (1) a pre-determined schedule with supply temperature and static pressure setpoints, and (2) advanced reset method that adjusts setpoints based on heuristic rules, i.e., ASHRAE Guideline 36. We also tested the DRL algorithms to evaluate their performances in multiple climate locations. We found that DRL-based control methods outperformed the baseline control methods in terms of energy savings while maintaining a thermal comfort. DRL reduced energy use between ~4%–22% on average compared to the baseline methods, depending on climate location. We also evaluated DRL-based control in terms of control stability and showed that DRL-based methods should address the span of hardware lifetimes in practical operations.

control stability↗
DOE OSTI2024

Distribution of centrality measures on undirected random networks via the cavity method

The Katz centrality of a node in a complex network is a measure of the node’s importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its “global” character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability P ( K ) that a random node in the ensemble of undirected random graphs has centrality K satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdős-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank- 1 projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Implications of Non-Bingham Rheology

One of the key challenges now facing the US Department of Energy (DOE) is the fate of radioactive waste remaining from World War II and the Cold War which is stored underground in tanks some 75 feet in diameter and over 30 feet tall. Over time, the waste has segmented into multiple layers with sludges and slurries at the bottom with salt crust layers often at the top and liquid in between. DOE’s current official baseline plans call for remaining sludges and slurries to be removed from the tanks and converted into a more stable glass waste form. Minimizing worker exposure to radiation drives DOE to use slurry processing techniques to suspend, mobilize, transport, mix, and process the waste. Therefore, a clear and quantitative understanding of Hanford waste rheology is essential for the success of the DOE mission. Historically much of the waste has been characterized using Eugene Bingham’s model more than a century old that provides a straight line fit to higher shear rate data with the intercept suggesting a yield stress and the slope providing the consistency. Yet, Bingham fits overestimate the shear stress at a given shear rate for low to intermediate shear rates, exactly the range of shear rates typically encountered in pipe flow, where shear rates peak along the pipe wall and vanish in the center. This model produces a fictitious yield stress for some of the wastes that do not exhibit yield phenomena. While over estimating the yield stress may be prudent, safe, and conservative for some applications (e.g., pump sizing to ensure that pumps can handle yield stresses), over estimating the rheology may be inaccurate and non-conservative for other applications (e.g., eroding settled particle beds). Therefore, this paper evaluates the slurry rheology of Hanford and Savannah River wastes using a more modern rheological model that fits the full range of experimental data. Although a bias has been recognized and alternative models proposed, the magnitude of this bias and the implications for tank waste have only been qualitatively suggested. The purpose of this paper is to evaluate quantitatively implications of the poor quality of fit between a Bingham model for rheology and experimental data at modest shear rates. We first demonstrate the magnitude of the bias between the data and the Bingham extrapolation. We then evaluate quantitatively the velocity profile under laminar conditions. This analysis shows that the bias may be large (hundreds of percent or more) at modest shear rates and that modest shear rates dominate pipe velocity profiles.

Rheology, Radioactive waste↗
DOE OSTI2021

Mechanical behavior of additively manufactured and wrought 316L stainless steels before and after neutron irradiation

Fabrication of nuclear reactor components using additive manufacturing (AM) methods is now a practical option since the AM technologies have advanced to allow for building of complex parts with high quality materials. To assess the mechanical performance of printed components in reactor-relevant conditions and to build a property database for the AM 316L stainless steel (SS), mechanical testing and characterization were performed before and after neutron irradiation. In this work, miniature tensile specimens were irradiated at the High Flux Isotope Reactor (HFIR) to 0.2 and 2 displacements per atom (dpa) at 300 and 600°C. The AM 316L SS was tested in the as-built, stress-relieved, and solution-annealed conditions, and the wrought (WT) 316L SS in solution-annealed condition as a reference alloy. The baseline test result showed that the AM 316L SS, regardless of the post-build heat treatment, had higher strength than the WT 316L SS, but similar ductility. Post-irradiation tensile testing was conducted at RT, 300°C, and 500°C for selected irradiation conditions. Neutron irradiation induced significant changes in the mechanical behavior of the AM stainless steels, including both hardening and softening. Although the as-built 316L steel after 300°C irradiation showed necking just after yielding, the overall property changes of the as-printed alloy became less significant after 600°C irradiation. Irradiation-induced ductilization was also observed after the higher temperature irradiation. In general, the strength change was smaller in the relatively stronger as-built and stress-relieved AM SSs than in the solution-annealed AM and WT SSs. These relatively lower strength 316L SSs overall retained higher ductility in the irradiation conditions tested, but the stronger 316L SSs demonstrated a similar level of ductility after the higher temperature (600°C) irradiation. It is a positive assessment for the AM 316L materials that no embrittlement was observed within the test and irradiation conditions of the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ FTIR Detection of Transition Metal (TM)-Ion Dissolution From Cathodes in Li-Ion Batteries

Transition metal (TM) ions, commonly Ni and Mn, play a crucial role in Li-ion battery cathodes as the reaction centers for rapid redox reactions. A major challenge with TM-based cathodes is capacity degradation, particularly at higher operating voltages. This degradation is closely linked to the dissolution of TMs from the cathode materials and their subsequent deposition on the anode. This process not only modifies the surface structure of the cathode but, more significantly, alters the SEI composition on the anode [1-2]. The dissolution of TMs cations into a liquid electrolyte from cathode materials, such as Mn-ion dissolution from Mn-rich cathode (LMR), is detrimental to the cycling performance of Li-ion batteries [3-4]. Much attention has been paid to this issue but there remains a lack of characterization techniques which can detect the TM-ion dissolution from the cathode during electrochemical measurements. In our study, we use in-situ ATR-FTIR as an effective technique to probe the TM-ion dissolution from the cathode. We have first demonstrated the detrimental effects of TM ions on the electrochemical performance of Li-ion batteries by adding a small amount of TM salt (50 mM Mn(PF6)) to the electrolyte of a Li-ion coin cell with LFP and graphite electrode. We observed a rapid capacity fade after the first delithiation cycle. To investigate TM ion dissolution, we established a baseline IR spectrum for various TM solvation states (such as Mn and Ni) by measuring concentration-dependent IR spectra. This baseline spectrum helps us detect TM ion dissolution during battery cycling. In this work, we discuss in detail the effect of TM ions on the electrochemical performance of Li-ion batteries and the detection of TM ions during battery cycling using in-situ FTIR spectroscopy. We will compare TM dissolution between coated and uncoated cathodes to examine the effect of cathode coatings to mitigate degradation due to TM dissolution and cross-over from cathode to anode. References: (1) Zhan, C.; Wu, T.; Lu, J.; Amine, K. Dissolution, migration, anddeposition of transition metal ions in Li-ion batteries exemplified byMn-based cathodes - a critical review. Energy Environ. Sci. 2018, 11,243-257. (2) Jung, R.; Linsenmann, F.; Thomas, R.; Wandt, J.; Solchenbach,S.; Maglia, F.; Stinner, C.; Tromp, M.; Gasteiger, H. A. Nickel,Manganese, and Cobalt Dissolution from Ni-Rich NMC and TheirEffects on NMC622-Graphite Cells. J. Electrochem. Soc. 2019, 166,A378-A389. (3) Zhao, L.; Chenard, E.; Capraz, O. O.; Sottos, N. R.; White, S.R. Direct Detection of Manganese Ions in Organic Electrolyte by UV-Vis Spectroscopy. J. Electrochem. Soc. 2018, 165, A345-A348 (4) Zhang, Y.; Hu, A.; Xia, D.; Hwang, S.; Sainio, S.; Nordlund, D.;Michel, F. M.; Moore, R. B.; Li, L.; Lin, F. Operando characterization and regulation of metal dissolution and redeposition dynamics nearbattery electrode surface. Nat. Nanotechnol. 2023, 18, 790.

25 ENERGY STORAGE↗
DOE OSTI2023

Material Property Determinations for Rock Valley Legacy Samples in Support of Source Physics Experiment

The Source Physics Experiment (SPE) is a long-term NNSA research and development effort designed to improve nonproliferation verification and monitoring capabilities. The overarching goals of the SPE program are to improve understanding of prompt signals and physical signatures that develop from underground chemical explosions and associated modeling capabilities. Our work focuses on a primary factor controlling chemical explosion induced signals and signatures: the material properties of the rocks in which the chemical explosion takes place. This document reports on material property determinations of legacy core USGS Test Well F and outcrop analogs for the subsurface stratigraphy for the third phase of SPE in the Rock Valley (RV) area of the Nevada National Security Site (NNSS). The objective of this work is to establish a baseline set of lithologic descriptions and material properties expected prior to observatory borehole drilling in support of the SPE-RVDC (Rock Valley Direct Comparison) experiment. We determine for each rock type the compressional failure envelope, elastic properties as a function of stress (bulk modulus versus mean stress, shear modulus versus shear stress, Young’s modulus versus axial stress and Poisson’s ratio versus axial stress), indirect tensile strength, and porosity. Geologic characterization, both at the core-scale and microscale, provides context for using the data in modeling efforts and to inform interpretations for the material properties testing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The K2 Galactic Archaeology Program Data Release 3: Age-abundance Patterns in C1–C8 and C10–C18

We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1–C8 and C10–C18. We provide asteroseismic radius and mass coefficients, κ R and κ M , for ~19,000 red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of ~20%–30% and compare age-abundance relations to Galactic chemical evolution models among both low- and high-α populations for α, light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times as well as significant contributions to r-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting that K2 GAP DR3 is largely unbiased in mass/age, with uncertainties of 2.9% (stat.) ± 0.1% (syst.) and 6.7% (stat.) ± 0.3% (syst.) in κ R and κ M for red giant branch stars and 4.7% (stat.) ± 0.3% (syst.) and 11% (stat.) ± 0.9% (syst.) for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.

79 ASTRONOMY AND ASTROPHYSICS↗