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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Science Area 1: Standard Award: Model-Data Fusion to Examine Multiscale Dynamical Controls on Snow Cover and Critical Zone Moisture Inputs (Final Report)

In many mountain watersheds of the world, seasonal snowpacks play an important role as natural reservoirs of water. Seasonal snowpacks accumulate water during cold, wet winter months that subsequently melts. Downstream communities depend on water from melting seasonal snowpacks to support agricultural, industrial, and municipal water needs. Rapidly melting snowpacks can also present a flooding hazard, particularly if snowpacks melt at rates faster than anticipated and where adequate reservoir capacity is unavailable to buffer river flows associated with melt. The spatial and temporal dynamics of snow accumulation and melt also play an important role in supporting upland ecosystems in mountain landscapes. Snowmelt provides soil moisture that enable terrestrial ecosystem productivity and exert control on soil microorganisms that play important roles in global carbon cycles. Climate warming is gradually decreasing the amount of precipitation in mountain watersheds arriving as snow, presenting potentially profound disruptions to mountain ecosystems, as well as downstream delivery of water. The overarching goal of this project was to understand how interactions between the near-surface atmosphere and surface topography control the input, accumulation, retention, and release of water from mountain snowpacks. Over a 5-year period, this project pursued an approach combining high-resolution regional climate modeling, satellite and airborne remote sensing data, and ground-based observations to develop and analyze benchmark datasets to address overarching science questions and hypotheses. Key products include a continuous, long-term, high spatiotemporal resolution (1 km/1 hr) dataset characterizing key climate variables in the Upper Colorado River Basin. The dataset included historical estimates of precipitation, temperature, humidity, solar and longwave radiation, and wind speeds. Additionally, the project developed a 20+ year long, 30 m spatial, daily temporal multi-sensor dataset characterizing snow presence/absence in the East/Taylor River watersheds in the Central Rocky Mountains of Colorado. The project supported training of 1 postdoctoral scholar, 1 Ph.D. student, and 1 M.S. student.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Sun-Earth Day, 2001

To promote awareness of the Sun-Earth connection, NASA's Marshall Space Flight Center, in collaboration with the Stanford SOLAR Center, sponsored a one-day Sun-Earth Day event on April 27, 2001. Although "celebrated" on only one day, teachers and students from across the nation, prepared for over a month in advance. Workshops were held in March to train teachers. Students performed experiments, results of which were shared through video clips and an internet web cast. Our poster includes highlights from student experiments (grades 2 - 12), lessons learned from the teacher workshops and the event itself, and plans for Sun-Earth Day 2002.

Adams, Mitzi L.↗
NASA NTRS2015

Ultrafast High Accuracy PCRTM_SOLAR Model for Cloudy Atmosphere

An ultrafast high accuracy PCRTM_SOLAR model is developed based on PCA compression and principal component-based radiative transfer model (PCRTM). A fast algorithm for simulation of multi-scattering properties of cloud and/or aerosols is integrated into the fast infrared PCRTM. We completed radiance simulation and training for instruments, such as IASI, AIRS, CrIS, NASTI and SHIS, under diverse conditions. The new model is 5 orders faster than 52-stream DISORT with very high accuracy for cloudy sky radiative transfer simulation. It is suitable for hyperspectral remote data assimilation and cloudy sky retrievals.

Yang, Qiguang↗
DOE OSTI2021

An Overview of Issues Related to IEEE Std 1547-2018 Requirements Regarding Voltage and Reactive Power Control

The revised Institute of Electrical and Electronics Engineers (IEEE) 1547 Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces (IEEE Std 1547-2018) was published in April 2018. This standard is one of the foundational documents in the United States needed for integrating distributed energy resources (DERs), including solar energy systems, with the electric distribution grid. The revised standard contains 11 chapters (clauses) and 8 annexes that comprise 136 pages. The revision is significantly different from the 2003 version, and it contains new concepts and new technical requirements. Each clause specifies information or requirements that apply to certain aspects important to the interconnection of DERs to the electric power system. Implementing the requirements necessitates a careful study of the underlying technical concept and requires the appropriate information required to calculate relevant settings and configurations. Various stakeholders have different roles in implementing the standard, and portions of the standard are directed toward a specific audience who must possess specialized information and technical training to use and apply the requirements. This document aims to (1) summarize a specific portion of the standard as concisely as possible and (2) provide the reader with introductory knowledge and information to support the utilization of the requirements.

14 SOLAR ENERGY↗
NASA NTRS2000

STS-97 Crew Interviews: Michael J. Bloomfield

Live footage of a preflight interview with Pilot Michael J. Bloomfield is shown. The interview addresses many different questions including why Bloomfield became interested in the space program, the events and people that influence him and ultimately led to his interest, and his vigorous training in the astronaut program. Other interesting information that this one-on-one interview discusses are the main goals of the STS-97 mission, its scheduled docking with the new International Space Station (ISS), and its delivery of the first set of U.S.-provided solar arrays, batteries, and radiators. Bloomfield briefly discusses his responsibilities during the much-anticipated docking as well as during the scheduled space-walks.

Source record↗
NASA NTRS2000

STS-97 Crew Interviews: Brent W. Jett Jr.

Live footage of a preflight interview with Commander Brent W. Jett Jr. is shown. The interview addresses many different questions including why Jett became interested in the space program, the events that led to his interest, and his vigorous training in the astronaut program. Other interesting information that this one-on-one interview discusses are the main goals of the STS-97 mission, its scheduled docking with the new International Space Station (ISS), and its delivery of the first set of U.S.-provided solar arrays, batteries, and radiators. Jett mentions his responsibilities during the much-anticipated docking as well as during the scheduled space-walks.

Source record↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

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

Global and regional perspectives on optimizing thermo-responsive dynamic windows for energy-efficient buildings

Architectural thermo-responsive dynamic windows offer an autonomous solution for solar heat regulation, thereby reducing building energy consumption. Previous work has emphasized the significance of thermo-responsive windows in hot climates due to their role in solar heat control and subsequent energy conservation; conversely, our study provides a different perspective. Through a global-scale analysis, we explore over 100 material samples and execute more than 2.8 million simulations across over two thousand global locations. World heatmap results, derived from well-trained artificial neural network models, reveal that thermo-responsive windows are especially useful in climates where buildings demand both heating and cooling energy, whereas thermo-responsive windows with optimal transition temperatures show no dynamic features in most of low-latitude tropical regions. Additionally, this study provides a practical guideline and an open-source mapping tool to optimize the intrinsic properties of thermo-responsive materials and evaluate their energy performance for sustainable buildings at various geographical scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Project LOCOST: Laser or Chemical Hybrid Orbital Space Transport

A potential mission in the late 1990s is the servicing of spacecraft assets located in GEO. The Geosynchronous Operations Support Center (GeoShack) will be supported by a space transfer vehicle based at the Space Station (SS). The vehicle will transport cargo between the SS and the GeoShack. A proposed unmanned, laser or chemical hybrid orbital space transfer vehicle (LOCOST) can be used to efficiently transfer cargo between the two orbits. A preliminary design shows that an unmanned, laser/chemical hybrid vehicle results in the fuel savings needed while still providing fast trip times. The LOCOST vehicle receives a 12 MW laser beam from one Earth orbiting, solar pumped, iodide Laser Power Station (LPS). Two Energy Relay Units (ERU) provide laser beam support during periods of line-of-sight blockage by the Earth. The baseline mission specifies a 13 day round trip transfer time. The ship's configuration consist of an optical train, one hydrogen laser engine, two chemical engines, a 18 m by 29 m box truss, a mission-flexible payload module, and propellant tanks. Overall vehicle dry mass is 8,000 kg. Outbound cargo mass is 20,000 kg, and inbound cargo mass is 6,000 kg. The baseline mission needs 93,000 kg of propellants to complete the scenario. Fully fueled, outbound mission mass is 121,000 kg. A regeneratively cooled, single plasma, laser engine design producing a maximum of 768 N of thrust is utilized along with two traditional chemical engines. The payload module is designed to hold 40,000 kg of cargo, though the baseline mission specifies less. A proposed design of a laser/chemical hybrid vehicle provides a trip time and propellant efficient means to transport cargo from the SS to a GeoShack. Its unique, hybrid propulsion system provides safety through redundancy, allows baseline missions to be efficiently executed, while still allowing for the possibility of larger cargo transfers.

Dixon, Alan↗
DOE OSTI2024

Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV

Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are measured at the relay location. The objective of this work is to develop a machine learning model that can identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of waveforms. The overall testing accuracy of the trained model exceeds 95%.

Ramesh, Meghana↗
DOE OSTI2020

Forecasting Megaelectron-Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms

We present the recent progress in upgrading a predictive model for megaelectron-volt (MeV) electrons inside the Earth's outer Van Allen belt. This updated model, called PreMevE 2.0, provides improved forecasts, particularly at outer L-shells, by adding upstream solar wind speeds to the model's input parameter list that originally includes precipitating electrons observed at low Earth orbits and MeV electron fluxes in situ measured by a geosynchronous satellite. Furthermore, based on several kinds of linear and artificial neural networks algorithms, a list of models was constructed, trained, validated, and tested with 42-month MeV electron observations from Van Allen Probes. Out-of-sample test results from these models show that, with optimized model hyperparameters and input parameter combinations, the top performer from each category of models has the similar capability of making reliable 1-day (2-day) forecasts of 1-MeV electron flux distributions with performance efficiency values ~0.87 (~0.82) averaged over the L-shell range of 2.8–6.6, significantly outperforming the previous version of PreMevE particularly at L-shells > ~4.5. Interestingly, the linear regression model is often the most successful when compared to other models, which suggests the relationship between dynamics of trapped 1-MeV electrons and precipitating electrons is dominated by linear components. Results also show that PreMevE 2.0 can reasonably well predict the onsets of MeV electron events in 2-day forecasts. PreMevE 2.0 is designed to be driven by observations from longstanding space infrastructure to make high-fidelity forecasts for MeV electrons, thus can be an invaluable space weather forecasting tool for the future.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1999

Electron Microscopy and Image Analysis for Selected Materials

This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

Williams, George↗
NASA NTRS2018

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of 140 Pg C year 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates

CO2↗
NASA NTRS2003

Solar Pumped Solid State Lasers for Space Solar Power: Experimental Path

We outline an experimentally based strategy designed to lead to solar pumped solid state laser oscillators useful for space solar power. Our method involves solar pumping a novel solid state gain element specifically designed to provide efficient conversion of sunlight in space to coherent laser light. Kilowatt and higher average power is sought from each gain element. Multiple such modular gain elements can be used to accumulate total average power of interest for power beaming in space, e.g., 100 kilowatts and more. Where desirable the high average power can also be produced as a train of pulses having high peak power (e.g., greater than 10(exp 10 watts). The modular nature of the basic gain element supports an experimental strategy in which the core technology can be validated by experiments on a single gain element. We propose to do this experimental validation both in terrestrial locations and also on a smaller scale in space. We describe a terrestrial experiment that includes diagnostics and the option of locating the laser beam path in vacuum environment. We describe a space based experiment designed to be compatible with the Japanese Experimental Module (JEM) on the International Space Station (ISS). We anticipate the gain elements will be based on low temperature (approx. 100 degrees Kelvin) operation of high thermal conductivity (k approx. 100 W/cm-K) diamond and sapphire (k approx. 4 W/cm-K). The basic gain element will be formed by sequences of thin alternating layers of diamond and Ti:sapphire with special attention given to the material interfaces. We anticipate this strategy will lead to a particularly simple, robust, and easily maintained low mass modelocked multi-element laser oscillator useful for space solar power.

Fork, Richard L.↗
DOE OSTI2025

Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics

As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.

14 SOLAR ENERGY↗
NASA NTRS2022

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗
DOE OSTI2020

Poster Abstract: Investigating Occupancy Profiles Using Convolutional Neural Networks: Preprint

In this paper, we implement a convolutional neural networks based autoencoder to investigate occupancy profiles. We use the American time use survey data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Trace Elemental Abundances in Calcium-Aluminum-Rich Inclusions in CV Chondrites

Introduction: Calcium-aluminum-rich inclusions (CAIs), are the first formed solids that define the age of the Solar System [1,2]. CAIs are thought to have condensed from nebular gas [3,4] within the first <1 Ma of Solar System formation [5,6]. CAIs have experienced numerous early Solar System processes including condensation, evaporation, melting, recrystallization, and aqueous alteration [e.g., 7]. The chemical, mineralogical, and textural diversity among CAIs results from a range of chemical and physical processes recorded during nebular and parent body epoch. This study aims to explore the mineralogical, textural, and chemical compositions of CAIs including the trace elemental abundances in CAI phases to determine the early Solar System processes recorded in them. Samples and Analytical Methods: We analyzed one CAI each from CV3 chondrites Northwest Africa (NWA) 5508 designated as ‘Saguaro’, and Northwest Africa (NWA) 12772 designated as ‘Hoopoe’. Back-scatter electron (BSE) images were collected using a Phenom XL scanning electron microscope (SEM) at the Lunar and Planetary Institute (LPI) and the JEOL JXA-8530F electron probe microanalyzer (EPMA) at Johnson Space Center (JSC)-NASA. Additionally, energy dispersive X-ray spectrometry (EDS) elemental maps of select areas for these samples were collected using a 15.0kV beam energy and a 40µA emission current. Using the EPMA, wavelength-dispersive X-ray spectroscopy (WDS) quantitative data were collected. In-situ trace element measurements for both CAIs were determined at JSC-NASA using a Photon Machines 193nm laser ablation system and a Thermo-Scientific Element-XR inductively coupled plasma mass spectrometer (ICP-MS). Analyses consisted of 30s ablations at 10Hz, spot sizes of 20-25µm, and a fluence of 6.0 J/cm2 for anorthite and melilite, and a 3.5 J/cm2 fluence for all other phases. NIST612 was used to correct for instrument drift, while BHVO-2g was used as a primary calibration standard. BCR-2g and in-house mineral standards were regularly measured as unknowns to ensure accuracy. Results: Saguaro is a coarse-grained CAI, ~11 x 6 mm in dimensions. Saguaro contains spinel, Al-rich pyroxene, anorthite, Mg-rich melilite, and minor perovskite in its interior and is therefore classified as a Type B CAI. Individual melilite grains shows normal compositional zoning with an Ak content ranging from ~24 to 54 with no apparent trend from the core to the edge of the CAI. The spinel appears euhedral and occurs both as clusters and as spinel palisades [8]. Two rim sequences surround most of the sample: the inner rim being a Wark-Lovering (WL) rim (~10-35 µm) containing pyroxene, spinel, and melilite (or anorthite), and the outer rim is a finer-grained, thicker (~100 µm), accretionary rim (Fig. 1). The mineral phases in Saguaro record an overall flat REE pattern with an average negative Eu anomaly in pyroxene, and an average positive Eu anomaly in anorthite and melilite respectively. Anorthite, melilite, and pyroxene have a minor depletion in Tm (Fig. 2). The Hoopoe CAI is a compact, coarse-grained ~6 × 4 mm in size. The major mineralogy includes hibonite, spinel, melilite, anorthite, and perovskite. Therefore, it is classified as a compact transitional type A and B. (?)zoning was observed in some hibonites. Individual melilite grains show both reverse and normal zoning, where the Ak content ranges from ~6- to 28. Melilite shows two distinct textures. One texture consisted of smooth melilite that appeared homogenous, while the second appeared to consist of many fine fractures. The spinel also often appears clustered. The WL-rim sequence surrounding Hoopoe is ~25 µm thick and composed of spinel, perovskite, hibonite, and melilite/anorthite. It is then partially surrounded by an outer accretionary rim (~75µm). Like before, refractory metal nuggets appeared concentrated near the WL rims. Other metal assemblages rich in Fe and Ni were also observed. All major mineral phases in Hoopoe display relatively flat REE patterns, except for varying Eu and Tm between phases (Fig. 2). There is a prominent negative Eu anomaly in perovskite and an average positive Eu anomaly in spinel, anorthite, and hibonite respectively (Fig. 2, 3). The mixed phases along the rim of the CAI also display a negative Eu anomaly, and all phases the CAI were depleted in Pb. Discussion: The CV3 CAIs analyzed in this study were classified based on their mineralogy and textures into Type A versus Type B CAIs [10]. Hibonite appears to be pseudomorphically replacing the spinel, (i.e., is hibonite in composition, but appears in the shape of spinel). Spinel palisades. The presence of spinel palisades present in Saguaro are consistent with the melting and recrystallization experienced by this CAI. Trace elemental analyses. Saguaro and Hoopoe display similar trace element patterns to each other, with both appearing generally flat, with anomalies in Eu, and Tm. Melilite and anorthite display positive Eu anomalies in both CAIs, in addition to the hibonite in Hoopoe (Fig. 2). The phases that are depleted in Eu are pyroxene and perovskite in both Saguaro and Hoopoe, respectively (Fig. 3). Given that Eu is volatile in reducing environments [11], this could possibly indicate reducing conditions at the time anorthite and melilite crystallized, with the gas they formed from containing Eu. As these CAIs continued to form, this gas as a result would become depleted in Eu. This also could be supported by the propensity of anorthite and melilite to take up Eu from its surroundings and incorporate it into their structure [12]. In addition, analyzing the assemblage of the phases in the Saguaro, melilite and anorthite (Eu enriched) often surround the pyroxene (Eu depleted) as they are crystallized. This intergrowth of phases and the proximity of the phases would support that the Eu is being incorporated into some phases, preventing it from incorporating into other. Trace elemental analyses of the CAI rims will be evaluated in more detail, as they are complicated by the transient signal being composed of a mixture of mineral phases. Broadly, however, the patterns in the rims of both CAIs are comparable to each other, and for Hoopoe, to the mixed phase patterns in the core (Fig. 3). Other studies have found that CAI rims can be depleted in Ce and Yb [13], however we did not observe these anomalies in the two CAIs discussed here. Given their similarity, the trace elemental analyses of the mixed interior (i.e. core) and rim phases could be interpreted as forming from similar, if not the same, reservoirs. The REE abundance between the rim and core of Hoopoe are also similar, indicating they may have formed from a gas of the same or similar composition. Acknowledgments: We thank the ASU Center for Meteorite Studies for loaning the samples used in this work and Tabb Prissel for his assistance with the analysis. Mouti Al-Hashimi thanks Sam Crossley and Cyrena Goodrich for their help with the LPI SEM training. This work was supported by the LPI Summer Intern Program in Planetary Science and the LPI Cooperative Agreement. References: [1] Connelly J.N. (2012) Science, 338, 651-655. [2] MacPherson G. J. (2014) Treatise on Geochem., 2, 139-179. [3] Grossman L. (1972) GCA, 36, 597-619. [4] Ebel, D.S. (2006) Meteorites and the Early Solar System II (D. S. Lauretta & H. Y. McSween, Eds.) 253-277. [5] MacPherson G. J. (2012) Earth Planet. Sci. Lett., 331-332, 43-54. [6] MacPherson G.J. (2017) GCA, 201, 65-82. [7] Krot A.N. (1995) Meteoritics & Planet. Sci., 30, 748-775. [8] Wark and Lovering (1982) GCA, 46, 2595-2607. [9] Palme H. and Jones A. (2003) Treatise on Geochemistry (H. D. Holland and K. K. Turekian Eds.), 1, 41-61. [10] Grossman L. (1980) Ann. Rev. Earth Planet. Sci., 8, 559-608. [11] Floss C. et al. (1996) GCA, 60, 1975-1997. [12] Mason B. and Martin P. M. (1974) Earth Planet. Sci. Lett., 22, 141-144. [13] Wark B. and Boynton W. V. (2001) Meteoritics & Planet. Sci., 36, 1135-1166.

X Mouti↗