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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Hybrid Simulation of Solar-Wind-Like Turbulence

We present 2.5D hybrid simulations of the spectral and thermodynamic evolution of an initial state of magnetic field and plasma variables that in many ways represents solar wind fluctuations. In accordance with Helios near-Sun high-speed stream observations, we start with Alfvénic fluctuations along a mean magnetic field in which the fluctuations in the magnitude of the magnetic field are minimized. Since fluctuations in the radial flow speed are the dominant free energy in the observed fluctuations, we include a field-aligned v∥(k⊥) with an k(exp −1) spectrum of velocity fluctuations to drive the turbulent evolution. The flow rapidly distorts the Alfvénic fluctuations, yielding spectra (determined by spacecraft-like cuts) transverse to the field that become comparable to the k∥ fluctuations, as in spacecraft observations. The initial near constancy of the magnetic field is lost during the evolution; we show this also takes place observationally. We find some evolution in the anisotropy of the thermal fluctuations, consistent with expectations based on Helios data. We present 2D spectra of the fluctuations, showing the evolution of the power spectrum and cross-helicity. Despite simplifying assumptions, many aspects of simulations and observations agree. The greatly faster evolution in the simulations is at least in part due to the small scales being simulated, but also to the non-equilibrium initial conditions and the relatively low overall Alfvénicity of the initial fluctuations.

Roberts, D. Aaron↗
NASA NTRS2022

Yonkers Urban Development II: Leveraging NASA Earth Observations to Support Modeling Urban Cooling Interventions and Urban Heat Vulnerability in Yonkers, New York

The City of Yonkers, New York, located in Westchester County, is experiencing rising temperatures which are a growing threat to the health and safety of its residents. Furthermore, the risk of heat-related illnesses and mortality disproportionately affects neighborhoods in Yonkers historically subjected to race-based housing segregation. To better understand these inequities, Groundwork Hudson Valley and NASA DEVELOP collaborated for a second term to evaluate community-level heat vulnerability, landcover distribution, street-level thermal comfort, and modeled urban cooling interventions. This team applied 2019 5-year American Community Survey (ACS) data and social and biophysical heat vulnerability variables established by the New York State Department of Health (NYSDOH), along with land surface temperature (LST) data collected from Landsat 8 Thermal Infrared Sensor (TIRS), and ISS ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to identify communities in Yonkers in need of prioritized cooling intervention at the census tract level. Data from the Real-Time Mesoscale Analysis (RTMA) provided relevant meteorological data for the ENVI-met model to conduct street-level thermal observations and model tree canopy cooling interventions in the Yonkers neighborhoods of Kimball and Old 7th Ward. The project results will support the prioritization and equitable distribution of cooling infrastructure in identified neighborhoods. Additionally, Groundwork Hudson Valley will use the analyses as a heat literacy tool to improve advocacy efforts and inform both residents and officials about how investment in deliberate modification to tree canopy cover improves the city’s thermal environment and helps mitigate extreme heat.

Tamara Barbakova↗
NASA NTRS2023

Extended Kalman Filter Performance on the Artemis-1 Mission

The Artemis Program is NASA’s campaign to explore the Moon and beyond. Artemis-1, the uncrewed exoLEO test flight of the Orion spacecraft, was completed in 2022. There were four navigation Extended Kalman Filters (EKFs) that are part of the Orion navigation system. The Atmospheric Extended Kalman Filter (ATMEKF) estimates the vehicle position, velocity, and attitude (referred to as the vehicle state) during the ascent and entry phases of flight. Once Orion is outside of Earths atmosphere, the Earth Orbit Extended Kalman Filter (EOEKF) and CisLunar Extended Kalman Filter (CLEKF) estimate the translational states, depending on the phase of flight, while the Attitude Extended Kalman Filter (ATTEKF) estimates the rotational state of the vehicle. The Kalman filters propagate the vehicle state forward in time using a combination of dynamics models and the output data from the Inertial Measurement Unit (IMU). The filters update the vehicle states and associated uncertainties, in the form of the covariance matrix, using pseudorange measurements from GPS (in ATMEKF/EOEKF), optical navigation measurements of the Earth or Moon (in CLEKF), and star tracker measurements (in ATTEKF). Simultaneously, the Kalman filters estimate error sources in the sensors, which are included in the state vectors as Exponentially Correlated Random Variables (ECRVs). This paper will summarize the performance of these filters during the Artemis-1 mission.

Artemis-1↗
NASA NTRSDate not supplied

Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.

Sumanth Nethi↗
NASA NTRS2024

NPCC4: Tail Risk, Climate Drivers of Extreme Heat, and New Methods for Extreme Event Projections

We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the “hot models” associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC's climate projections. We describe the state of the science on temperature variability within NYC and explain both the large-scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure.

NPCC4↗
DOE OSTI2016

Method for characterization of the rate of movement of an oxidation front in cementitious materials

Disclosed are methods for determining the redox condition of cementitious materials. The methods are leaching methods that utilize a redox active transition metal indicator that is present in the cementitious material and exhibits variable solubility depending upon the oxidation state of the indicator. When the leaching process is carried out under anaerobic conditions, the presence or absence of the indicator in the leachate can be utilized to determine the redox condition of and location of the oxidation front in the material that has been subjected to the leaching process.

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

Bison Fuel Performance Modeling on RIA in a PWR

This report is prepared to fulfill a Level 2 milestone for CASL on “deliver and document RIA fuel performance modeling using Bison fuel performance code.” The work performed is focused on modeling the full length commercial fuel rod response under a design basis Reactivity Initiated Accident (RIA), in particular, a hypothetical Control Rod Ejection Accident (CREA) in a Pressurized Water Reactor (PWR). Fuel rods with different burnups, 0 (fresh fuel), 20, 40, and 60 GWd/tU have been modeled with a reactivity worth insertion of $1.24 at Hot Zero Power (HZP) condition. Bison failure modeling method, and a brief summary of validations on selected RIA cases from CABRI and NSRR testing facilities are also described. The work performed has demonstrated the successful application of Bison code in the modeling of thermal and mechanical responses in RIAs for PWRs. In particular, Bison code has a more mature capability in the modeling of the frictional contact between pellet and cladding of the high burnup fuels, which is critical for modeling the Pellet Clad Mechanical Interaction (PCMI). The modeling of RIA using Bison code shows fuels in PWR environment could have different responses than in the simulated RIA tests, and results indicate that a transition from PCMI to Departure from Nucleate Boiling (DNB) could happen at relatively lower enthalpies: a better heat transfer from the fuel to coolant could increase cladding temperature and ductility, but at the same time also reduces the margin to DNB, which could cause high temperature oxidation and ballooning type of failures. The precise determination of the transition into DNB may rely on a more accurate input of the power histories in the simulated RIAs from VERA, the core simulator of CASL. The limiting conditions in PWRs could be quite different than those tests with non-typical testing conditions, and the improvement of the modeling of thermal responses, such as, clad coolant heat transfer, gap heat transfer, clad thermal inertia, and radial power profiles could be important considerations of the RIAs. In modeling the fuel responses in a fast transient, a number of variables could affect the initial burnup states, and some material models, e.g., fuel mechanical models, fission gas release, and corrosion models, and parameters can affect modeling results as well. Those uncertainties and advanced modeling capabilities need to be investigated in the future study of RIAs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Market Designs for High Levels of Variable Generation: Preprint

Variable renewable generation is increasing in penetration in modern power systems, leading to higher variability in the supply and price of electricity as well as lower average spot prices. This raises new challenges, particularly in ensuring sufficient capacity and flexibility from conventional technologies. Because the fixed costs and lifetimes of electricity generation investments are significant, designing markets and regulations that ensure the efficient integration of renewable generation is a significant challenge. This papers reviews the state of play of market designs for high levels of variable generation in the United States and Europe and considers new developments in both regions.

17 WIND ENERGY↗
DOE OSTI2016

Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS

Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions--native resolution (134 BAs), state-level, and NERC region level--and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States

In snow-dominated regions, a critical source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, we demonstrate that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy-balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature-only precipitation partitioning approach. Poor model skill in high-altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large-domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results indicate that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher-humidity Pacific Northwest to 4.0 °C in the colder, lower-humidity Rocky Mountains.

58 GEOSCIENCES↗
NASA NTRS1983

Solar variability and quasi-stationary planetary wave behavior

A steady state analysis of winter winds shows that the speed of the upper atmospheric polar vortex approaches and occasionally exceeds the value required to reflect externally forced planetary wave 1 energy back towards the surface of the earth. Historical observations suggest that solar UV variability and geomagnetic disturbances are two primary forces which modulate the planetary wave trapping mechanism. Through the solar-terrestrial process described, small changes in the sun's behavior can be physically linked to both shortand long-term changes in tropospheric weather patterns.

Springer, B. D.↗
NASA NTRSDate not supplied

Detecting Risk and Anomalies in Airplane Dynamics Through Entropic Analysis of Time Series Data

Despite recent efforts to move away from traditional threshold exceedance detection methods for aircraft state monitoring, modern aircraft still rely on safety thresholds to communicate to pilots the identification of an anomaly in the aircraft when a threshold is surpassed. Current anomaly detection methods mainly depend on uninterpretable machine learning models to learn complex patterns and relationships contained in the time series data of aircraft. Although these methods are capable of identifying known anomalies, their deficiency in interpretability presents a challenge when translating them to different aircraft. To overcome this deficiency, entropic analysis of aircraft dynamics seeks to characterize the complexity, or lack thereof, of the aircraft dynamics prior to the development of a risk scenario. This complexity characterization provides a more straightforward summary of state changes in the dynamics of flight variables. To build a foundation for entropic analysis, we analyzed the complexity of unstable approaches, an anomalous event present in many of today’s aviation accidents. The analysis revealed a statistically significant difference in the complexity distribution of flight variables under a stable approach versus an unstable approach. These differences in complexity were especially notable minutes before an approach was identified as unstable. Moreover, the multiscale entropic analysis revealed the presence of signal complexity at multiple time scales across multiple time windows before landing. By capturing state changes and corrections in the aircraft dynamics using entropy, advanced, yet still interpretable, sensor systems based on entropic frameworks from this study can be constructed in the future using classical machine learning approaches.

Risk detection↗
DOE OSTI2015

Thickness-dependent metal-insulator transition in epitaxial SrRuO{sub 3} ultrathin films

Transport characteristics of ultrathin SrRuO{sub 3} films, deposited epitaxially on TiO{sub 2}-terminated SrTiO{sub 3} (001) single-crystal substrates, were studied as a function of film thickness. Evolution from a metallic to an insulating behavior is observed as the film thickness decreases from 20 to 4 unit cells. In films thicker than 4 unit cells, the transport behavior obeys the Drude low temperature conductivity with quantum corrections, which can be attributed to weak localization. Fitting the data with 2-dimensional localization model indicates that electron-phonon collisions are the main inelastic relaxation mechanism. In the film of 4 unit cells in thickness, the transport behavior follows variable range hopping model, indicating a strongly localized state. Magnetoresistance measurements reveal a likely magnetic anisotropy with the magnetic easy axis along the out-of-plane direction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Catalytic N 2 Reduction to Silylamines and Thermodynamics of N 2 Binding at Square Planar Fe

The geometric constraints imposed by a tetradentate P4N2 ligand play an essential role in stabilizing square planar Fe complexes with changes in metal oxidation state. A combination of high-pressure electrochemistry and variable temperature UV-vis spectroscopy were used to obtain these thermodynamic measurements, while X-ray crystallography, 57Fe Mössbauer spectroscopy, and EPR spectroscopy were used to fully characterize these new compounds. Analysis of Fe0, FeI, and FeII complexes reveals that the free energy of N2 binding across three oxidation states spans more than 37 kcal mol-1. The square pyramidal Fe0(N2)(P4N2) complex catalyzes the conversion of N2 to N(SiR3)3 (R = Me, Et) at room temperature, representing the highest turnover number (TON) of any Fe-based N2 silylation catalyst to date (up to 65 equiv N(SiMe3)3 per Fe center). Elevated N2 pressures (> 1 atm) have a dramatic effect on catalysis, increasing N2 solubility and the thermodynamic N2 binding affinity at Fe0(N2)(P4N2). Acknowledgment. This research was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences. EPR experiments were performed using EMSL, a national scientific user facility sponsored by the DOE’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for the U.S. DOE. Computational resources were provided by the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. The authors thank Prof. Yisong Alex Guo at Carnegie Mellon University for recording Mössbauer data for some complexes and Emma Wellington and Kaye Kuphal for their assistance with the collection of Mössbauer data at Colgate University, Dr. Katarzyna Grubel for X-ray assistance, and Dr. Rosalie Chu for mass spectrometry assistance. The authors also thank Dr. Aaron Appel and Dr. Alex Kendall for helpful discussions.

iron, thermodynamics, dinitrogen, square planer, M↗
DOE OSTI2022

Iron precipitation under controlled oxygen flow: Mineralogical implications for BIF precursors in the Archean ocean

Banded iron formation (BIF) deposition in the Archean ocean is thought to have been initiated by the oxidation of dissolved iron into ferric primary precursor phases that descended through the water column and were deposited on the seafloor. Effective interpretation of the trace element and isotopic composition of BIFs in the geologic record requires an understanding of the identity and longevity of those precursor phases. Temporal and spatial variation in oxidant concentration and ocean chemistry may have driven precursor mineralogy. Furthermore, precursor phases may undergo transitions during descent and burial. This experimental study follows the evolution of iron mineralogy, speciation, and redox state during the precipitation of iron under variable oxygen fluxes and in fluids containing different iron-complexing anions (chloride, sulfate, and phosphate). Additionally, suspensions collected after incomplete oxidation were anoxically aged to simulate changes to precursor mineralogy during descent below the seawater redoxcline. Results from X-ray diffraction, sequential dissolution, colorimetric determination of Fe redox state, and Fe K-edge X-ray absorption fine structure spectroscopy showed that under all experimental conditions, intermediate precipitates collected before complete iron uptake contained mineralized ferrous iron. Purely ferric mineral assemblages were not observed until iron was removed from solution. In low-oxygen experiments, intermediate precipitates contained phases with stoichiometric Fe 2+ , including magnetite, vivianite, and green rust (GR). Chloride green rust (GR1) had more ferric iron than sulfate green rust (GR2) and more readily transitioned to magnetite via disproportionation. GR2 was stable over a broader range of iron uptake; magnetite appeared mainly as an additional oxidation product in sulfate solution. In high oxygen experiments, mineralized ferrous iron occurred in amorphous phases or as a non-stoichiometric component in ferric oxides; final ferric assemblages were less crystalline than in low-oxygen equivalents. Low concentrations of phosphate increased the total iron oxidation rate, but also increased the ferrous content of intermediate precipitates. The incorporation of phosphate stabilized GR2 and facilitated GR2 precipitation over a larger range of iron uptake. In higher concentrations of phosphate, vivianite and poorly crystalline ferric hydroxides formed in place of GR. Here, these findings suggest that over a range of possible ocean chemistries, mixed-valence phases may have been short lived but relevant precursors to BIF. Additionally, anionic chemistry and oxidant concentration are shown to influence the crystallinity and chemical resistivity of final ferric assemblages, affecting their reactivity during later anoxic burial.

58 GEOSCIENCES↗
DOE OSTI2023

Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorologically relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks to months is an area of active research and development. NASA's Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1–3-month lead times in the HMA region, including a portion of the Indian subcontinent, during the retrospective forecast period, 1981–2016. To assess forecast skill, we evaluate 2 m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to the similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from an anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system's ability to forecast HMA hydrometeorology.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗
DOE OSTI2023

The effect of composition, temperature and pressure on the oxidation state and coordination environment of copper in silicate melts

Copper is a redox variable element that may occur as Cu 0 , Cu + , and Cu 2+ in the Earth’s crust. The oxidation state will affect its partitioning between coexisting minerals, melts and fluids and hence its behaviour in magmatic processes. Copper bearing silicate glasses were quenched from melts with 23 synthetic compositions (19 CaO-Na 2 O-MgO-Al 2 O 3 -SiO 2 (CNMAS), two “granites” containing K 2 O ± H 2 O, and Fe-bearing “MORB” and “andesite”) equilibrated at oxygen fugacities (fO 2 ), expressed in log units relative to the fayalite-magnetite-quartz (FMQ) buffer, ranging from −0.7 to 14, temperatures from 900 to 1500 °C and pressures from 0 to 2.5 GPa. Cu K-edge X-ray absorption near edge structure (XANES) spectra were recorded from the glasses and a pre-edge feature in the XANES spectra was found to scale with the proportion of Cu + . Cu + /ΣCu (where ΣCu = Cu + + Cu 2+ ) was quantified by fitting the intensity of the pre-edge feature as a function of fO 2 to the thermodynamically expected relationship. Cu + /ΣCu was found to only weakly depend on melt composition, with more basic melts (e.g., basalts rather than granites) preferentially stabilising Cu 2+ . Increasing temperature stabilises Cu + , while increasing pressure had little effect on Cu + /ΣCu in CNMAS melts but preferentially stabilised Cu 2+ in granite melts. Cu + /ΣCu can be predicted in silicate melts by the empirical equation: log(Cu 2+ /Cu + ) = 0.25(ΔFMQ + 8.58 − 25050/T + 940P/T – 0.02P) − 4.73 + 5400/T + 1.99Λ + (280P – 90P2) where T is temperature in K, P is pressure in GPa and is the optical basicity of the composition. The effects of fO 2 , melt composition, temperature and pressure on Cu + /ΣCu indicate that Cu + will be the dominant oxidation state in terrestrial silicate melts (e.g., Cu + /ΣCu = 99% in an andesitic melt at 900 °C, 1 GPa and ΔFMQ = 1). The electron exchange reaction Cu 2+ + Fe 2+ → Cu + + Fe 3+ occurs on cooling and, given the abundance of Fe in natural melts, the oxidation state of Cu in natural glasses is unlikely to correspond to that in the original melt.

Copper↗