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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Damage Tolerance Assessment of Laser Clad Repairs of Coarse Grain Ti-6Al-4V

Background: Laser cladding offers a promising approach to repairing aviation components, with the ability to restore the geometric shape and the static and fatigue strength. Objective: The damage tolerance of a coarse grain Ti-6Al-4 V material is assessed via three sets of samples: (i) baseline, (ii) laser clad repaired, and (iii) laser cladding followed by heat treatment. Method: The specimens were subjected to fatigue loading using a marker band technique, in which cracks initiated from pre-defined laser slots and the small crack growth rates were measured by quantitative fractography. Additional characterization was performed on these samples, including hardness measurements, microstructure analysis, and multimodal residual stress analyses. Results: The region of the specimen undergoing laser cladding exhibited higher hardness values and a compressive residual stress, based on the slitting method. The samples subjected to heat treatment after cladding displayed negligible differences in grain size and minimal residual stress relaxation. Conclusions: The results demonstrated significant variability, with data demonstrating that cracks in the laser clad region (exhibiting the largest compressive residual stress) grew faster than cracks in the substrate region of the sample or in the baseline cases. Finally, these counterintuitive results are rationalized with additional analyses from synchrotron-based energy dispersive X-ray diffraction and high energy X-ray diffraction, demonstrating significant spatial variability in the residual elastic strains in the millimeter-sized prior β grains within the material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An empirical calibration of the serpentine-water oxygen isotope fractionation at T = 25–100°C

The ability to precisely constrain the physical–chemical conditions of serpentinization, such as temperature and fluid source, is limited by the accuracy of calibrations for oxygen isotope fractionation between serpentine and water – i.e., 1000 lnα( serpentine-water ) – which disagree by up to 20‰ when extrapolated to T<200°C. In this study, we present a new empirical calibration of 1000 lnα( serpentine-water ) aiming to improve applications of oxygen isotope geochemistry to very low-T serpentinization (T<100°C). We used the high-spatial resolution capabilities of Secondary Ion Mass Spectrometry (SIMS) to analyze oxygen isotope ratios in mineral pairs of calcite + serpentine, quartz + serpentine and talc + serpentine co-crystallized at scales≤50μm in six serpentinite samples from the Samail ophiolite (Oman). SIMS analysis shows that the mineral pairs are relatively homogenous in oxygen isotope ratios with variability in δ 18 O values ≤2‰ (2s). Clumped isotope thermometry and petrological constraints indicate crystallization temperatures from 25 to 100°C for the investigated samples. These independent constraints on temperature allowed us to derive 1000 lnα( serpentine-water ) by combining Δ 18 O( quartz-serpentine ), Δ 18 O( talc-serpentine ) and Δ 18 O( calcite-serpentine ) measured by SIMS in the investigated samples with Δ 18 O( quartz-water ), Δ 18 O( talc-water ) and Δ 18 O( calcite-water ) calibrations available in the literature. Our empirical calibration of 1000 lnα( serpentine-water ) = 1.04±0.20 (2SE) × 10 6 /T 2 (T in K), from T = 25 to 100°C, is within uncertainty of former high-temperature empirical calibrations extrapolated to T<100°C and matches experimental calibrations when extrapolated to T=250°C. In conclusion, the new serpentine-water calibration enables more accurate reconstructions of fluid-rock interactions occurring during low-temperature serpentinization processes in various tectonic settings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Evidence for Black Holes in Green Peas from WISE Colors and Variability

We explore the presence of active galactic nuclei (AGNs)/black holes in Green Pea galaxies (GPs), motivated by the presence of high-ionization emission lines such as He ii and [Ne iii] in their optical spectra. In order to identify AGN candidates, we used mid-infrared (MIR) photometric observations from the all-sky Wide-field Infrared Survey Explorer (WISE) mission for a sample of 1004 GPs. Considering only >5σ detections with no contamination from neighboring sources in AllWISE, we select 31 GPs out of 134 as candidate AGNs based on a stringent three-band WISE color diagnostic. Using multi-epoch photometry in W1 and W2 bands based on time-resolved unWISE coadd images, we find two sources exhibiting variability in both the WISE bands among 112 GPs with W1 ≤16 mag and no contamination from neighboring sources in unWISE. These two variable sources were selected as AGNs by the WISE three-band color diagnostic as well. Compared to variable AGN fractions observed among low-mass galaxy samples in previous studies, we find a higher fraction (~1.8%) of MIR variable sources among GPs, which demonstrates the uniqueness and importance of studying these extreme objects. Through this work, we demonstrate that MIR diagnostics are promising tools to select AGNs that may be missed by other selection techniques (including optical emission-line ratios and X-ray emission) in star formation-dominated, low-mass, low-metallicity galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The early chemical enrichment histories of two Sculptor group dwarf galaxies as revealed by RR lyrae variables

We present the results of our analysis of the RR Lyrae (RRL) variable stars detected in two transition-type dwarf galaxies (dTrans), ESO294-G010 and ESO410-G005 in the Sculptor group, which is known to be one of the closest neighboring galaxy groups to our Local Group. Using deep archival images from the Advanced Camera for Surveys on board the Hubble Space Telescope, we have identified a sample of RRL candidates in both dTrans galaxies (219 RRab (RR0) and 13 RRc (RR1) variables in ESO294-G010; 225 RRab and 44 RRc stars in ESO410-G005). The metallicities of the individual RRab stars are calculated via the period-amplitude-[Fe/H] relation derived by Alcock et al. This yields mean metallicities of ([Fe/H]){sub ESO294} = –1.77 ± 0.03 and ([Fe/H]){sub ESO410} = –1.64 ± 0.03. The RRL metallicity distribution functions (MDFs) are investigated further via simple chemical evolution models; these reveal the relics of the early chemical enrichment processes for these two dTrans galaxies. In the case of both galaxies, the shapes of the RRL MDFs are well described by pre-enrichment models. This suggests two possible channels for the early chemical evolution for these Sculptor group dTrans galaxies: (1) the ancient stellar populations of our target dwarf galaxies might have formed from the star forming gas which was already enriched through 'prompt initial enrichment' or an 'initial nucleosynthetic spike' from the very first massive stars, or (2) this pre-enrichment state might have been achieved by the end products from more evolved systems of their nearest neighbor, NGC 55. We also study the environmental effects of the formation and evolution of our target dTrans galaxies by comparing their properties with those of 79 volume limited (D {sub ☉} < 2 Mpc) dwarf galaxy samples in terms of the luminosity-metallicity relation and the H I gas content. The presence of these RRL stars strongly supports the idea that although the Sculptor Group galaxies have a considerably different environment from the Local Group (e.g., no giant host galaxies, loosely bound and very low local density), they share a common epoch of early star formation with the dwarf satellite galaxies in the Local Group.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs

Generalized polynomial chaos (gPC) expansions allow us to represent the solution of a stochastic system using a series of polynomial chaos basis functions. The number of gPC terms increases dramatically as the dimension of the random input variables increases. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs when the corresponding deterministic solver is computationally expensive, evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solutions, in both spatial and random domains, by coupling Bayesian model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spatial points, via (1) the Bayesian model average (BMA) or (2) the median probability model, and their construction as spatial functions on the spatial domain via spline interpolation. The former accounts for the model uncertainty and provides Bayes-optimal predictions; while the latter provides a sparse representation of the stochastic solutions by evaluating the expansion on a subset of dominating gPC bases. Moreover, the proposed methods quantify the importance of the gPC bases in the probabilistic sense through inclusion probabilities. We design a Markov chain Monte Carlo (MCMC) sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed methods are suitable for, but not restricted to, problems whose stochastic solutions are sparse in the stochastic space with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the accuracy and performance of the proposed methods and make comparisons with other approaches on solving elliptic SPDEs with 1-, 14- and 40-random dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII

A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Stable-carbon isotope ratios for sourcing the nerve-agent precursor methylphosphonic dichloride and its products

The ability to connect a chemical threat agent to a specific batch of a synthetic precursor can provide a fingerprint to contribute to effective forensic investigations. Stable isotope analysis can leverage intrinsic, natural isotopic variability within the molecules of a threat agent to unlock embedded chemical fingerprints in the material. Methylphosphonic dichloride (DC) is a chemical precursor to the nerve agent sarin. It is converted to methylphosphonic difluoride (DF) as part of the sarin synthesis process. We used a suite of commercially available DC stocks to both evaluate the potential for δ13C analysis to be used as a fingerprinting tool in sarin-related investigations and to develop sample preparation techniques (using chemical hydrolysis) that can simplify isotopic analysis of DC and its synthetic products. We demonstrate that natural isotopic variability in DC results in at least three distinct, isotope-resolved clusters within the thirteen stocks we analyzed. Isotopic variability in the carbon feedstock (i.e., methanol) used for DC synthesis is likely inherited by the DC samples we measured. We demonstrate that the hydrolysis of DC and DF to methylphosphonic acid (MPA) can be used as a preparative step for isotopic analysis because the reaction does not impart a measureable isotopic fractionation. MPA is more chemically stable, less toxic, and easier to handle than DC or DF. Further, the hydrolysis method we demonstrated can be applied to a suite of other precursors or to sarin itself, thereby providing a potentially valuable forensic tool.

Moran, James J.↗
DOE OSTI2023

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R 2 =0.61. Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3 mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

Abstract Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

ColorMap ND : A Data-Driven Approach and Tool for Mapping Multivariate Data to Color

A wide variety of color schemes have been devised for mapping scalar data to color. We address the challenge of color-mapping multivariate data. While a number of methods can map low-dimensional data to color, for example, using bilinear or barycentric interpolation for two or three variables, these methods do not scale to higher data dimensions. Likewise, schemes that take a more artistic approach through color mixing and the like also face limits when it comes to the number of variables they can encode. Our approach does not have these limitations. It is data driven in that it determines a proper and consistent color map from first embedding the data samples into a circular interactive multivariate color mapping display (ICD) and then fusing this display with a convex (CIE HCL) color space. The variables (data attributes) are arranged in terms of their similarity and mapped to the ICD’s boundary to control the embedding. Using this layout, the color of a multivariate data sample is then obtained via modified generalized barycentric coordinate interpolation of the map. In conclusion, the system we devised has facilities for contrast and feature enhancement, supports both regular and irregular grids, can deal with multi-field as well as multispectral data, and can produce heat maps, choropleth maps, and diagrams such as scatterplots.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

CALiPER Report 20.3: Robustness of LED PAR38 Lamps

Three samples of 40 of the Series 20 PAR38 lamps underwent multi-stress testing, whereby samples were subjected to increasing levels of simultaneous thermal, humidity, electrical, and vibrational stress. The results do not explicitly predict expected lifetime or reliability, but they can be compared with one another, as well as with benchmark conventional products, to assess the relative robustness of the product designs. On average, the 32 LED lamp models tested were substantially more robust than the conventional benchmark lamps. As with other performance attributes, however, there was great variability in the robustness and design maturity of the LED lamps. Several LED lamp samples failed within the first one or two levels of the ten-level stress plan, while all three samples of some lamp models completed all ten levels. One potential area of improvement is design maturity, given that more than 25% of the lamp models demonstrated a difference in failure level for the three samples that was greater than or equal to the maximum for the benchmarks. At the same time, the fact that nearly 75% of the lamp models exhibited better design maturity than the benchmarks is noteworthy, given the relative stage of development for the technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Enhanced magnetoresistance induced by oxygen deficiency in La{sub 0.4}Ca{sub 0.6}MnO{sub 3-δ} oxides

We report electrical features and magnetoresistance behavior of the oxygen deficient La{sub 0.4}Ca{sub 0.6}MnO{sub 3-δ} perovskites (δ = 0, 0.15, and 0.2). These samples will be referred to as S0, S15, and S20, respectively. The dependence of electrical transport on temperature and magnetic field is systematically investigated between 2 K and 400 K in magnetic field ranging up to 5 T. The parent compound shows a stable charge ordering/antiferromagnetic state with a semiconductor-like behavior in all considered temperature range. The variable range hopping and thermally activated hopping models are found to fit well with the electrical resistivity data at low and high temperatures, respectively. Oxygen deficiency tends to weaken the charge ordering and induce ferromagnetism and metallicity at low temperature. Metal insulator transition appears at higher fields for lower oxygen deficit (S15 sample) and without field for the S20 sample. The resistivity data for S15 sample are discussed in the framework of the variable-range hopping model. Abnormal transport properties were observed in the S20 sample, characterized by the double metal-insulator transitions and low minimum behavior. These results are discussed in terms of phenomenological percolation model, based on the phase segregation of ferromagnetic metallic clusters and paramagnetic insulating regions. While the parent compound shows no magnetoresistance, a large magnetoresistance is observed in the deficient samples at low temperature reaching 90% and 75% at 2 T for S15 and S20 samples, respectively. Noticeably, these values reached 98% and 91% at 5 T. The appearance of colossal magnetoresistance is attributed to the spin dependent hopping between spin clusters and/or ferromagnetic domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Abnormal Transport and Magnetic Properties Induced by Change of the Phase Volume Ratio for YBa{sub 2}Cu{sub 3}O{sub 7-δ}/La{sub 0.67}Sr{sub 0.33}MnO{sub 3} Hybrids

Two elaborately designed hybrids consisting of superconducting YBa{sub 2}Cu{sub 3}O{sub 7-δ} (YBCO) and ferromagnetic La{sub 0.67}Sr{sub 0.33}MnO{sub 3} (LSMO), namely 20 × (YBCO(m)/LSMO(100 − m)) are prepared on single crystal SrTiO{sub 3} (STO) by pulsed laser deposition (PLD) with 20 being repetition periods and m = 90 and 10, respectively, which represents YBCO pulse number in a period. The films show highly c-oriented and epitaxial quality, suggested by XRD 𝜃–2𝜃 scans and pole figures. The 20 × (YBCO90/LSMO10) sample with a large YBCO volume fraction exhibits the typical character of a metal superconductor at about 90 K, while its transition width reaches 9.1 K, which is nearly eight times of that of the pure YBCO film. Above the superconducting transition temperature, a superparamagnetic signal rather than ferromagnetic behavior is observed, which may be associated with a diluted system consisting of the ferromagnetic nanoparticles embedded in the superconducting films. For the hybrid with a small YBCO volume fraction, i.e., the 20 × (YBCO10/LSMO90) sample, its transport behavior at normal state is insulating, and it is found that the Mott variable range hopping conduction dominates in this insulating phase in the temperature range 70–310 K. Obviously distinguishable insulator to superconductor (IS) transition occurs at 52 K, but the superconducting diamagnetic signals are very weak in the magnetic measurements below the IS temperature. For this sample, the magnetization loop at 100 K is ferromagnetic, yet manifested by a reduced coercive field and residual magnetization as compared with a pure LSMO film. In fact, both hybrids represent two kinds of nanocomposites, i.e., superconducting nanoparticles in a ferromagnetic matrix and ferromagnetic nanoparticles in a superconducting matrix, which benefits to study interfacial interactions due to an increased interface to value ratio and reduced size effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Leaf chlorophyll content as a proxy for leaf photosynthetic capacity

Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO 2 levels and improving carbon cycle modelling. However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013-2015), approximately every 10 days for leaf chlorophyll content (Chl Leaf ) and canopy structure. Leaf nitrogen (N Area ) was also measured during 2014. Leaf photosynthesis was measured during 2014-2015 using a Li-6400 gas-exchange system, with A-Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax 25 ) and N Area ( R 2 = 0.62, P < 0.001), whereas Chl Leaf demonstrated a much stronger correlation with Vcmax 25 ( R 2 = 0.78, P < 0.001). The relationship between Chl Leaf and N Area was also weak ( R 2 = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax 25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive Chl Leaf . TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple N Area - Vcmax 25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that Chl Leaf provides a more accurate, direct proxy for Vcmax 25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Automatic classification of time-variable X-ray sources

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Three Dimensional Variable-Wavelength X-Ray Bragg Coherent Diffraction Imaging

Here, we present and demonstrate a formalism by which three-dimensional (3D) Bragg x-ray coherent diffraction imaging (BCDI) can be implemented without moving the sample by scanning the energy of the incident x-ray beam. This capability is made possible by introducing a 3D Fourier transform that accounts for x-ray wavelength variability. We also demonstrate the approach by inverting coherent Bragg diffraction patterns from a gold nanocrystal measured with an x-ray energy scan. Furthermore, variable-wavelength BCDI will expand the breadth of feasible in situ 3D strain imaging experiments towards more diverse materials environments, especially where sample manipulation is difficult.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Lead exposure to children from consumption of backyard chicken eggs

Highlights: • Backyard chicken eggs from most households providing egg samples (98%) contained detectable concentrations of lead. • IIn prediction models, backyard egg consumption increased overall blood lead levels in children • Infants < 1 year of age experience the greatest increase in predicted blood lead levels from backyard egg consumption. • Mean household egg lead concentration was strongly positively correlated with coop soil lead concentrations. Backyard chicken ownership is rapidly increasing in urban areas in the United States, largely as a way to provide eggs for household consumption. Despite elevated levels of environmental lead contamination in many US cities, the role of backyard chicken eggs as a pathway for lead exposure, particularly for children, has received limited scrutiny. To characterize lead exposure from consumption of backyard chicken eggs for children and predict related effects on blood lead level (BLL), we conducted a cross-sectional study of backyard chicken owners in the Greater Boston area (n = 51). We interviewed participants regarding egg consumption by household members and collected backyard eggs (n = 201) and coop soil samples (n = 48) for analysis. Inductively coupled plasma mass spectrometry (ICP-MS) was used to evaluate lead concentration in homogenized eggs and an X-ray fluorescence (XRF) portable device was used to assess soil lead levels in the laboratory. We used the USEPA's Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK) to assess the relative contribution of backyard egg consumption to aggregate BLL in children. Four scenarios were developed in the IEUBK model to address variability in egg consumption rates and egg lead contamination. Lead was detected in egg samples from 98% of the households that provided egg samples. Mean household lead concentration was 0.10 μg/g (SD: 0.18). Egg lead concentrations ranged from below the limit of detection (0.0014 μg/g) to 1.798 μg/g (<1.4–1198 ppb). Egg lead levels were strongly positively correlated with lead concentration in coop soil (r = 0.64; p < 0.001). In modeled scenarios where a child < 7 years frequently ate eggs highly contaminated with lead, BLLs are predicted to increase by 0.9–1.5 μg/dL. In three other scenarios reflecting more moderate egg lead contamination and consumption rates, BLLs were predicted to increase from 0.1 to 0.8 μg/dL. Consumption of backyard chicken eggs can contribute to lead exposure in children. Soil lead remediation prior to chicken ownership may reduce lead exposure from backyard eggs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Summertime Aerosol across the North Slope of Alaska Field Campaign Report

The Summertime Aerosol across the North Slope of Alaska U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility field campaign, a collaborative effort between the University of Michigan and Baylor University, was focused on chemically characterizing sources of summertime aerosol impacting the Utqiagvik (Barrow, NSA) and the Oliktok Point (AMF3) ARM sites. This campaign was co-funded by a grant from the National Oceanic and Atmospheric Administration (NOAA, PI: Kerri Pratt, Co-I: Rebecca Sheesley) titled “Assessment of Atmospheric Aerosols Resulting from Oil and Gas Extraction Activities near the North Slope of Alaska”. The ARM field campaign took place on the North Slope of Alaska (NSA) from August 21 to September 30, 2015 and August 12 to September 20, 2016, with filter sampling occurring both years at both the NSA Barrow and AMF3 Oliktok Point sites. Online intensive measurements occurred at Barrow in 2015 and at Oliktok Point in 2016, coordinated with the arrival of the ARM Aerosol Observing System (AOS) in August 2016 at Oliktok Point. During 2015, the University of Michigan deployed the newly constructed aerosol time-of-flight mass spectrometer (ATOFMS), as well as a microanalysis particle sampler (MPS) to collect particles for offline analysis by computer-controlled scanning electron microscopy with energy-dispersive x-ray spectroscopy (CCSEM-EDX). Unfortunately, an issue with the time-of-flight mass spectrometer on the ATOFMS limited the number of particles that produced mass spectra during the 2015 study. This issue was subsequently fixed for the 2016 deployment. During 2016, the ATOFMS was deployed to the AMF3 at the Oliktok Point field site, in addition to a micro-orifice uniform deposition impactor (MOUDI) to collect particles for offline analysis by CCSEM-EDX. Notably, these measurements, to our knowledge, represent the first real-time, single-particle chemical characterization (aerosol mixing state) measurements at Utqiagvik and Oliktok Point. Online aerosol measurements also included: PM2.5 (particulate matter <2.5 μm) black carbon mass concentrations (aethalometer; Baylor) and size-resolved (0.013-20 μm) number concentrations (scanning mobility particle sizer, SMPS, and aerodynamic particle sizer, APS; Michigan). Baylor conducted filter sampling at both the Utqiagvik and Oliktok Point locations during both intensive operational periods (IOPs). Baylor deployed a total suspended particulate matter (TSP) sampler to Oliktok Point, and continuous one-week samples were collected in 2015 for a total of seven samples; during 2016, continuous 3-7 day samples were collected, for a total of 13 filters. Baylor deployed a TSP sampler and a PM1.0 sampler at Utqiagvik, Alaska; continuous 4-7 day filter samples were collected in 2015 for a total of eight samples. To supplement this campaign, PI Sheesley had a simultaneous DOE ARM field campaign at Utqiagvik and Oliktok Point (https://www.arm.gov/research/ campaigns/amf2016bbc), the goal of which was to collected continuous one-week TSP samples at both sites from June 2016 to September 2017. These results provide better context in terms of annual trends and for year-to-year variability and a final report with data was already submitted by PI Sheesley for that project. Analysis by Baylor focuses on TSP samples because of the extremely low PM loading on the PM1.0 sampler. Michigan deployed a three-stage impactor (0.07-5 μm PM collection) at Utqiagvik and a 10-stage impactor (0.056-10 μm PM collection) at Oliktok Point, during the 2015 and 2016 campaigns, respectively.

Arctic processes, North Slope of Alaska, Barrow, O↗