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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Tritium Extraction from Water

Tritiated water production is ubiquitous in facilities that handle tritium gas. Sources range from decontamination efforts, to the deliberate conversion of elemental tritium to tritiated water in processes that strive to reduce emissions to the environment, to gaseous effluents to the environment. At low concentrations, ranging from a few μCi/L to mCi/L, high throughputs are required to process the high-volume, low-activity water. Combined Electrolysis Catalytic Exchange (CECE) shows promise by offering high throughput, reliability, economic viability, and facile coupling to isotopic separation systems if necessary. This paper will discuss the features of two production-scale CECE facilities: a 7-m 3 /h throughput system that uses an alkaline electrolysis cell and a 21-m 3 /h throughput system that uses a proton exchange membrane (PEM) electrolysis cell. Finally, the former is in service and has been modified to improve reliability; the latter is in the initial stages of commissioning.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Extraction of Mercury Utilizing 3D Printed Media – Mercury Extraction Coalescer (MEC) (Revision 1)

This project was undertaken to address the concern of mercury species present in chemical reprocessing facilities and environmental remediation sites. Typical elemental mercury removal is performed by steam stripping. As efficient as this process is, the vaporized elemental mercury is often condensed along with acidic condensate. The acidic conditions re­-dissolve the elemental mercury, transferring it back into the aqueous stream, and undoing the energy-intensive steam stripping process. This project looked to design and produce a 3D printed component to act as a mercury vapor/liquid coalescer utilizing structural and chemical features to segregate elemental mercury from an aqueous solution or process offgas and improving elemental mercury retention and removal from the recirculated condensate stream.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Ab Initio Molecular Dynamics Simulations of Amorphous Calcium Carbonate: Interpretation of Pair Distribution Function and X-Ray Absorption Spectroscopy Data

The structure and transformation of hydrous amorphous calcium carbonate (ACC) are key to understanding biomineralization pathways and their relationship with the properties of the resulting material. Quantitative interpretation of scattering experiments aimed at elucidating the structure of ACC is challenging, due to the amorphous nature of this material, and, therefore, requires models for the structure and the scattering physics. Here, we generate physically realistic ensembles of hydrated ACC structures and their vibrational disorder from ab initio molecular dynamics (AIMD) simulations with an emphasis on enabling the consistent interpretation of the finer details of three complementary structural probes: neutron and x-ray pair distribution experiments and x-ray absorption spectroscopy (XAS). In each case, we simulate the signal directly in reciprocal space and then manipulate it into the real-space pair distribution function (PDF) or spectrum using the same procedures for the experimental and theoretical data. Good agreement with experiment was obtained across the three techniques with the simulations accounting well for all features in the spectra. Remaining small discrepancies pointed to differences between real samples and the idealized simulated systems such as deviations from the nominal CaCO3·nH2O stoichiometry. Additionally, the simulations offered a more accurate description of the local coordination environment of calcium than previous shell-by-shell fits to spectra of synthetic ACC and classical molecular dynamics simulations. This work demonstrates that AIMD is a powerful approach for extracting detailed structural information from neutron PDF, x-ray PDF, and XAS of amorphous carbonate phases.

Prange, Micah P.↗
DOE OSTI2021

Utilizing distributed acoustic sensing and ocean bottom fiber optic cables for submarine structural characterization

The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that provides observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield. Here, we report a marine DAS application which demonstrates the strength and limitation of this new technique on submarine structural characterization. Based on ambient noise DAS records on a 20 km section of a fiber optic cable offshore of Moss Landing, CA, in Monterey Bay, we extract Scholte waves from DAS ambient noise records using interferometry techniques and invert the resulting multimodal dispersion curves to recover a high resolution 2D shear-wave velocity image of the near seafloor sediments. We show for the first time that the migration of coherently scattered Scholte waves observed on DAS records can provide an approach for resolving sharp lateral contrasts in subsurface properties, particularly shallow faults and depositional features near the seafloor. Our results provide improved constraints on shallow submarine features in Monterey Bay, including fault zones and paleo-channel deposits, thus highlighting one of many possible geophysical uses of the marine cable network.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

The fusion-fission process in the reaction {sup 34}S+{sup 186}W near the interaction barrier

The reaction {sup 34}S+{sup 186}W at E{sub lab}=160 MeV was investigated with the aim of diving into the features of the fusion-fission process. Gamma rays in coincidence with binary reaction fragments were measured using the high efficiency gamma-ray spectrometer ORGAM at the TANDEM Accelerator facility of I.P.N., Orsay, and the time-of-flight spectrometer for fission fragments (FF) registration CORSET of the Flerov Laboratory of Nuclear Reactions (FLNR), Dubna. The coupling of the ORGAM and CORSET setups offers the unique opportunity of extracting details for characterizing the fusion-fission process and gives information regarding production of neutron-rich heavy nuclei. The FF–γ coincidence method is of better use then the γ – γ coincidence method when dealing with low statistic measurements and also offers the opportunity to precisely correct the Dopler shift for in-flight emitted gamma rays. Evidence of symmetric and asymmetric fission modes were observed in the mass and TKE distributions, occurring due to shell effects in the fragments. Coincident measurements allow for discrimination between the gamma rays by accepting a specific range within the mass distribution of the reaction products. Details regarding the experimental setup, methods of processing the acquisitioned data and preliminary results are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Classification of local chemical environments from x-ray absorption spectra using supervised machine learning

We report that x-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry, and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semiquantitative and not transferable. In this paper, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition-metal families. Lastly, we found that spectral features beyond the preedge region play an important role in the local structure classification problem especially for the late 3d transition-metal elements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Multi-stage circulating fluidized bed syngas cooling

A method and apparatus for cooling hot gas streams in the temperature range 800.degree. C. to 1600.degree. C. using multi-stage circulating fluid bed (CFB) coolers is disclosed. The invention relates to cooling the hot syngas from coal gasifiers in which the hot syngas entrains substances that foul, erode and corrode heat transfer surfaces upon contact in conventional coolers. The hot syngas is cooled by extracting and indirectly transferring heat to heat transfer surfaces with circulating inert solid particles in CFB syngas coolers. The CFB syngas coolers are staged to facilitate generation of steam at multiple conditions and hot boiler feed water that are necessary for power generation in an IGCC process. The multi-stage syngas cooler can include internally circulating fluid bed coolers, externally circulating fluid bed coolers and hybrid coolers that incorporate features of both internally and externally circulating fluid bed coolers. Higher process efficiencies can be realized as the invention can handle hot syngas from various types of gasifiers without the need for a less efficient precooling step.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Multi-stage circulating fluidized bed syngas cooling

A method and apparatus for cooling hot gas streams in the temperature range 800° C. to 1600° C. using multi-stage circulating fluid bed (CFB) coolers is disclosed. The invention relates to cooling the hot syngas from coal gasifiers in which the hot syngas entrains substances that foul, erode and corrode heat transfer surfaces upon contact in conventional coolers. The hot syngas is cooled by extracting and indirectly transferring heat to heat transfer surfaces with circulating inert solid particles in CFB syngas coolers. The CFB syngas coolers are staged to facilitate generation of steam at multiple conditions and hot boiler feed water that are necessary for power generation in an IGCC process. The multi-stage syngas cooler can include internally circulating fluid bed coolers, externally circulating fluid bed coolers and hybrid coolers that incorporate features of both internally and externally circulating fluid bed coolers.

Liu, Guohai↗
DOE OSTI2025

Simulation and analysis of small angle scattering (SAS) patterns of Ni-based superalloy microstructures generated by a phase-field model

This paper investigates the relationship between microstructural features and small-angle scattering (SAS) patterns in Ni-based superalloys using a combined phase-field and SAS simulation approach coupled with microstructure analyses. The simulated SAS patterns accurately capture key experimental observations previously reported in the literature, including the time-dependent transition from circular to square-shaped precipitates and the development of anisotropic SAS patterns. Importantly, our analysis reveals the correlations between characteristic length scales extracted from SAS profiles and microstructural descriptors, such as precipitate size and inter-precipitate distance. These findings provide a comprehensive understanding of the link between SAS profiles and microstructure evolution in Ni-based superalloys, offering valuable insights for materials characterization and design.

Microstructure↗
DOE OSTI2022

Excess noise in high-current diamond diodes

We report the results of an investigation of low-frequency excess noise in high-current diamond diodes. It was found that the electronic excess noise of the diamond diodes is dominated by the 1/f and generation-recombination noise, which reveals itself as Lorentzian spectral features (f is the frequency). The generation-recombination bulges are characteristic of diamond diodes with lower turn-on voltages. The noise spectral density dependence on forward current, I, reveals three distinctive regions in all examined devices—it scales as I 2 at the low (I < 10 μA) and high (I > 10 mA) currents and, rather unusually, remains nearly constant at the intermediate current range. The characteristic trap time constants, extracted from the noise data, show a uniquely strong dependence on current. Interestingly, the performance of the diamond diodes improves with the increasing temperature. The obtained results are important for the development of noise spectroscopy-based approaches for device reliability assessment for high-power diamond electronics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗
DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗
DOE OSTI2022

Recovering Native-Like Lignin from Poplar Biomass Using Flow-Through Solvolysis

Lignin valorization, frequently involving a depolymerization step, is necessary for economic production of lignocellulosic biofuels. However, isolation of lignin frequently degrades the structure such that depolymerization yields from the isolated lignin are decreased relative to the "native" lignin present in the plant. In this work we show that by using flow-through extraction with methanol at 225 degrees C, combined with rapid quenching, we can isolate a "native-like" lignin that produces monomer yields comparable the lignin in the parent biomass under reductive catalytic fractionation (RCF) conditions. This isolated, native-like lignin is shelf-stable at room temperature on a time scale of months, and can be concentrated to a lignin oil and reconstituted in methanol without losing activity. These features will facilitate the study of intrinsic lignin properties and steady-state depolymerization processes.

BIOMASS FUELS↗
DOE OSTI2017

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of the spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 - ), total acid (H + ), neodymium (Nd 3+ ), sodium (Na + ), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Dark matter on top

We consider a simplified model of fermionic dark matter which couples exclusively to the right-handed top quark via a renormalizable interaction with a color-charged scalar. We first compute the relic abundance of this type of dark matter and investigate constraints placed on the model parameter space by the latest direct detection data. We also perform a detailed analysis for the production of dark matter at the LHC for this model. We find several kinematic variables that allow for a clean signal extraction and we show that the parameter space of this model will be well probed during LHC Run-II. Finally, we investigate the possibility of detecting this type of dark matter via its annihilations into gamma rays. We compute the continuum and the line emission (which includes a possible ''Higgs in Space!'' line) and its possible discovery by future gamma-ray telescopes. We find that the annihilation spectrum has distinctive features which may distinguish it from other models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Investigations on MGy ionizing dose effects in thin oxides of micro-electronic devices

Total ionizing dose (TID) effects have been studied for a long time in micro-electronic components designed to operate in natural and artificial environments. In most cases, TID induces both charge trapping in the bulk of irradiated oxides and the buildup of interface traps located at semiconductor/dielectric interfaces. Such effects result from basic mechanisms driven by both the shape of the electric field which stands into the oxide and by fabrication process parameters inducing pre-existing traps in the oxide's bulk. From the pioneering studies based on 'thick' oxide technologies to the most recent ones dedicated to innovative technologies, most studies concluded that the impact of total ionizing dose effects reduces with the oxide thinning. This is specifically the case for the gate-oxide of Metal-Oxide-Semiconductor Field Effect Transistors (MOSFET) for which it is generally considered that TID is not a major issue anymore at kGy dose ranges. TID effects are now mainly due to charge trapping in the field oxides such as Shallow Trench Isolation. This creates either parasitic conduction paths or Radiation-Induced Narrow Channel Effects (RINCE). Static current-voltage (I-V) electrical characteristics are then modified through a significant increase of the off-current of NMOS transistors or by shifting the whole I-V curves (of both NMOS and PMOS transistors). Based on these assumptions, no significant shift of I-V curves should be observed in modern bulk CMOS technologies. However, such phenomenon may not be directly extrapolated to higher TID ranges, typically of several MGy for which only few data are available in the literature. This paper presents evidences of large threshold voltage shifts measured at MGy dose levels despite the fact that transistors are designed in a submicron bulk technology which features a 7-nm thin gate-oxide on GO2 transistors dedicated to mixed analog/digital integrated circuits. Such electrical shifts are encountered on PMOS transistors of different widths, W{sub NARROW} = 0.24 μm and W{sub WIDE} = 10 μm. The devices are irradiated using 10 keV X-rays at several total dose steps up to 3 MGy. On the one hand, negative threshold voltage shifts of more than 3.3 V are extracted after 3 MGy on narrow transistors. Even very high, this voltage shift is consistent with RINCE in narrow open layout transistors. On the other hand, voltage shifts greater than 2.5 V are extracted on wide transistors. Obviously, this result should not be associated neither to positive charge trapping in the thin gate oxide nor to any RINCE in this very wide transistor geometry. NMOS and PMOS transistors exhibit a clear asymmetrical behaviour. But contrary to what is commonly observed at 'low' TID range, the PMOS transistor is here more impacted than the NMOS one. The final paper will thus provide an extensive study of these phenomena using other device designs and geometries tested with dedicated TID experiments to discuss whether or not this effect revealed at very high TID, i.e. several MGy, may be attributed to an enhanced high-TID induced charge trapping mechanism in thin gate oxides. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Vadose zone monitoring at the radioactive waste management sites at the Nevada national security site - 15294

Two lysimeter facilities continuously monitor the vadose zone at the radioactive waste disposal sites at the Nevada National Security Site. The time-series data collected at these facilities support the performance assessments of the radioactive waste disposal sites, the closure cover designs, and the long-term monitoring decisions for these sites. The Weighing Lysimeter Facility at the Area 5 Radioactive Waste Management Site (RWMS) consists of a bare-soil weighing lysimeter and a vegetated weighing lysimeter. The upper surfaces of both weighing lysimeters are exposed to precipitation. The Drainage Lysimeter Facility at the Area 3 RWMS includes bare-soil lysimeters that receive precipitation or precipitation supplemented with irrigation equal to double precipitation and vegetated lysimeters that have the same upper boundary conditions as the bare-soil lysimeters. Lysimeter sensors are connected to data-loggers that are accessed remotely for data acquisition. Lysimeter water balance measurements show the arid climate and native vegetation at the NNSS provide a natural sustainable system to effectively prevent transport of contaminants from the RWMSs through the vadose zone to groundwater. Transport by infiltration and percolation of present day precipitation at the RWMSs is prevented. The native desert vegetation is adapted to extract moisture from very dry soils and maintains negative matric potentials at the base of the root zone effectively buffering the deep vadose zone from episodic precipitation events at the surface. The very negative matric potential at the base of the root zone effectively intercepts all infiltration from precipitation and draws moisture upward from the vadose zone below the root zone. This system is sustainable because it does not rely on engineered features and mimics the stable landscapes and no groundwater recharge of the current condition surrounding the RWMSs. The system depends on establishing native vegetation in the covers above the waste cells and a stable climate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗