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At least 613 records · Page 34

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DOE OSTI2018

Collab Fracture Characterization: Preliminary Results from the Modeling and Flow Testing of Experiment 1

The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. At the first experimental site, a hydraulic fracture will be created in metamorphic rock at the 4850 level in the former Homestake Mine. The experimental concept is for fracturing to be initiated at an injection well and continue until it intersects a production well approximately 10 meters away. Initial step-pressure flow testing of the fracture are to be performed to describe pressure-aperture-flow relationships. The flow field between the injector and producer boreholes will be characterized using a series of forced gradient dipole tracer tests. A number of fracture characterization models have been used to assist in the design of the tracer tests. The tracer test program will use conservative, sorbing, and particulate tracers, as well as sampling for radon gas to describe the fracture surface area (i.e., potential heat transfer area), flow pathway distribution, and volume. Tracer concentration at the production well (i.e. break through curves) will be measured using a series of inline sensors, liquid sampling devices, filtering of the effluent, and the measuring of gas concentrations in the effluent. Tracer monitoring data will be compared against results from a collection of numerical simulators developed at U.S. national laboratories and universities for the purpose of validating these flow and transport models. Ultimately these same models will be used to predict the heat transfer characteristics of the fracture and will be compared to a thermal characterization field test where cold water will be injected at the injection well and the temperature will be measured at the production well. This paper discusses the early tracer transport model predictions and parameters sensitivity that affect the tracer breakthrough curves at the production well.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory

Characterization techniques for TRU waste from the Analytical Laboratory Hot Cells at Idaho National Laboratory Since the first reactor went hot in 1942, there has been a never ending desire to improve the nuclear reactor and the fuel that it burns. The Idaho National Laboratory has been a key player in reactor and fuel development. As more research projects begin, the spectrum of waste parameters grows. Over the years, the Analytical Laboratory (AL) at the Materials and Fuels Complex (MFC) at Idaho National Laboratory (INL) has supported a large range of various research projects. Each project brings its own challenge and analytical technique that, in turn, yields its own unique waste parameters. The AL Hot Cells at INL consist of 6 small cells that can support around 12 different fuel research projects at a time along with infrastructure and environmental samples. Each different project yields different levels of transuranic (TRU) material, RCRA constituents, dose, and contamination levels. The waste parameters from each project dictate a path forward for the waste Low-Level Waste (LLW), Contact Handled TRU (CH-TRU), Mixed Contact Handled TRU (M-CH-TRU), Remote Handled TRU (RH-TRU), or Mixed Remote Handled TRU (M-RH-TRU). With visual examination techniques and process knowledge as an aid, there have been waste handling methods developed that allow debris and sample material to be segregated. Unique characterization mechanisms have been applied for each debris and sample material that are generally more accurate than smear and surface contamination data. For debris found in the Hot Cells, analytical data is gathered and conservatively summarized for each fuel type and research method. This consists of data from Quadrupole Inductively Coupled Plasma Mass Spectrometry (Q-ICP-MS), Gamma Spectroscopy, and some data verification through Thermal Ionization Mass Spectrometry (TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS). This data gives a range of TRU isotope concentrations. Fuel age is taken into account to determine the dominant TRU activity isotopes. Cesium concentrations are also gathered. Each parameter/fuel type then ties a measured Cesium number to TRU content based on historical analytical data. Debris in the hot cells is segregated by project and packaged in individual 2 gallon metal paint cans. Each can is counted by gamma spectroscopy in a calculated and measured geometry. Using process knowledge to determine the fuel or project type, newly counted cesium numbers can be applied to the historic analytical data and TRU content can be estimated. The TRU content allows waste debris to be segregated between CH-TRU and RH-TRU. Packaging techniques can allow for the minimization if RH-TRU waste. Sample material received into the AL must be dissolved into a liquid matrix in order to be analyzed in most cases. When analysis is complete, these liquids go through a consolidation, complexation, neutralization, and solidification process where real time analytical data can be used to calculate TRU content and RCRA constitutes. These calculations allow for no further analytical data to be gathered. The waste matrix is considered stable and acceptable by all AK [define AK] documents. The unique techniques used in characterizing and handling the Analytical Laboratories Hot Cell waste can be applied to various situations and various laboratories. They allow for more accurate data for waste constituents and also for the minimization of RH-TRU waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2018

Collab Fracture Characterization: Preliminary Results from the Modeling and Flow Testing of Experiment 1

Enhanced Geothermal Systems, EGS Collab, Sanford U↗

DOE OSTI2019

Materials Characterization and Interrogation using Terahertz Radiation

Terahertz (THz) radiation is a powerful tool for characterizing materials and interrogating complex systems. The low-energy, non-ionizing nature of THz photons make it ideal for use as a non-destructive, non-contacting probe. Identification of materials by their unique THz response is possible through various absorption mechanisms, including vibrational (phonon) and rotational spectra. The Drude-like response of materials allows for non-contact electrical conductivity measurements as well. In the additive and advanced manufacturing realm, high sampling rates (kHz) and ability to extract electrical conductivity and composition makes in-situ THz imaging and spectroscopy a prime candidate for monitoring melt-pool dynamics in metal 3D printers. The fiberization of pulsed lasers systems enables the use of THz radiation to monitor oxide layer growth and characterize microstructure in challenging environments, whether ex-situ (e.g. post irradiation hot-cells) or in-situ (e.g. Transient Test Reactor, TREAT), through time of flight measurements and unique vibrational spectra of materials, providing crucial data on previously out of reach processes. While security imaging using THz radiation is already employed in airport body scanners, further miniaturization and ruggedization of THz systems enables materials characterization during field operations, adding a necessary and complementary tool to the arsenal of spectroscopic technologies available to the military and first responders. Cybersecurity is also a major focus for the coming decades; the use of THz interrogation methods to monitor integrated circuit operations provides a non-contacting, undetectable probe for system verification.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Wabash CarbonSAFE Subtask 4.1 - Application of Policy Frameworks for Improved Carbon Capture and Storage Social Site Characterization & Stakeholder Engagement

Anthropogenic climate change threatens environmental and human health globally. Limiting these threats requires large-scale and innovative greenhouse gas mitigation responses across carbon-intensive energy and industrial sectors. Carbon capture and storage (CCS) technologies present potential opportunities for mitigating climate change while maintaining a diverse energy resource portfolio. Successful development of CCS physical sites requires effective and efficient project management solutions that elicit and incorporate the concerns and perspectives of diverse stakeholders. Due to the urgency of climate change mitigation technology implementation and the costs of CCS development, CCS project developers cannot risk setbacks by poor stakeholder assessment that concern management processes. Thus, this report presents four prominent policy frameworks and associated case studies as opportunities to improve CCS social site characterization and stakeholder engagement. After comparing the relative effectiveness and efficiencies of each framework with regard to CCS, this report concludes that the Advocacy Coalition Framework, Narrative Policy Framework, and Policy Conflict Framework can improve the CCS social site characterization process, while the Collaborative Governance Framework paired with the Q-Methodology provides an ideal framework for direct stakeholder engagement. Overall, this report finds that the Narrative Policy Framework and the Collaborative Governance Framework are most ideally suited for the purposes of CCS social site characterization and stakeholder engagement.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Measurements and Characterization

The Measurements and Characterization scientists at the National Renewable Energy Laboratory provide characterization support, conduct collaborative research, and develop new measurement capabilities and diagnostics to advance the photovoltaic (PV) generation of energy. We use state-of- the-art characterization tools to solve problems in all phases of material and device development. Results lead to an increased understanding that drives advances in the performance, reliability, cost, and manufacturability of PV materials, devices, and systems.

14 SOLAR ENERGY↗

DOE OSTI2019

Site Characterization and Adaptation - Lessons Learned from the Port Granby LLRW Project in Canada - 19255

The Port Hope Area Initiative (PHAI) is a community-based solution for the long-term management of historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the Town of Port Hope, Ontario. The Eldorado refinery, on the shores of Lake Ontario, began refining radium-226 from pitchblende ore, later transitioning to the refining of uranium ore. Process residues were deposited at the Welcome Waste Management Facility in the Town of Port Hope until the mid-1950's switching to the Port Granby Waste Management Facility (PGWMF), which continued to accept waste until the late 1980's. The Port Granby Project is part of the broader PHAI being completed by Canadian Nuclear Laboratories (CNL). The scope of remediation activities at Port Granby includes: 1) constructing a new, secure long-term waste management facility (LTWMF) and supporting infrastructure, and 2) excavating and relocating LLRW and marginally contaminated soil from the Port Granby Waste Management Facility (PGWMF) to the newly constructed LTWMF. Remediation design required extensive site investigations of waste buried in unmarked and unlined trenches across the PGWMF area. Investigations included reviews of historical records, interviews with past staff of Eldorado and Atomic Energy Canada Limited (the entity managing the PHAI cleanup prior to CNL), radiological surveys, and the advancement of boreholes across the site to facilitate the physical sampling of soil and groundwater contaminants indicative of the presence of LLRW. This paper describes the challenges associated with characterizing and quantifying waste prior to remediation and lessons-learned to adjust procedural adaptations during the characterization phase of a project to better anticipate the unanticipated nature and growth of the contamination profile that often occurs during remediation. Despite substantial pre-remediation investigative efforts, current PGWMF remediation activities have resulted in growth of both the areal and vertical extent of contamination. The contamination profile was shown to be dramatically influenced by leaching of contaminants deeper into native soils. In addition, remedial excavation discovered specific waste types such as unidentified compressed gas cylinders outside of the location indicated by historical records, as well as a quantity of unknown liquids and unmarked drums. These discoveries have resulted in higher waste quantity estimates and have required a partial redesign of the LTWMF mound (a higher final elevation and steeper slopes) to accommodate the increased waste volume. In addition, the means and methods necessary to execute the remedial construction had to be modified to account for the new conceptual model developed from the new contamination data. Despite extensive historical review and site characterization to define the nature and extent of LLRW buried at the PGWMF, actual conditions encountered during remediation efforts necessitated taking an adaptive approach to both the design of the LTWMF and the means and methods used for excavation and transport of waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2025

From Molecules to Modules: Advanced Characterization of Membrane Systems

Membrane technologies can enhance the efficiency and selectivity of chemical separations in energy-water systems. Advanced characterization tools are critical for discerning separation mechanisms, revealing degradation processes, and designing novel materials and material systems for new and emerging challenges. The pursuit of next-generation membranes for water and energy applications requires understanding phenomena at the molecular scale, mesoscale, and macroscale. This perspective highlights advanced characterization techniques for elucidating and enhancing membrane performance, while addressing fundamental trade-offs involved in characterizing membranes under realistic conditions.

Zhu, Yaguang [Princeton University, NJ (United Sta↗

DOE OSTI2019

Snapshots of a Migrating H‐Atom: Characterization of a Reactive Iron(III) Indenide Hydride and its Nearly Isoenergetic Ring‐Protonated Iron(I) Isomer

Abstract We report the characterization of an S = iron π‐complex, [Fe(η 6 ‐IndH)(depe)] + (Ind=Indenide (C 9 H 7 − ), depe=1,2‐bis(diethylphosphino)ethane), which results via C−H elimination from a transient Fe III hydride, [Fe(η 3 :η 2 ‐Ind)(depe)H] + . Owing to weak M−H/C−H bonds, these species appear to undergo proton‐coupled electron transfer (PCET) to release H 2 through bimolecular recombination. Mechanistic information, gained from stoichiometric as well as computational studies, reveal the open‐shell π‐arene complex to have a BDFE C‐H value of ≈50 kcal mol −1 , roughly equal to the BDFE Fe‐H of its Fe III −H precursor (Δ G °≈0 between them). Markedly, this reactivity differs from related Fe(η 5 ‐Cp/Cp*) compounds, for which terminal Fe III −H cations are isolable and have been structurally characterized, highlighting the effect of a benzannulated ring (indene). Overall, this study provides a structural, thermochemical, and mechanistic foundation for the characterization of indenide/indene PCET precursors and outlines a valuable approach for the differentiation of a ring‐ versus a metal‐bound H‐atom by way of continuous‐wave (CW) and pulse EPR (HYSCORE) spectroscopic measurements.

Drover, Marcus W.↗

DOE OSTI2019

Snapshots of a Migrating H‐Atom: Characterization of a Reactive Iron(III) Indenide Hydride and its Nearly Isoenergetic Ring‐Protonated Iron(I) Isomer

Drover, Marcus W.↗

DOE OSTI2021

Cloning and characterization of a panel of mitochondrial targeting sequences for compartmentalization engineering in Saccharomyces cerevisiae

Mitochondrion is generally considered as the most promising subcellular organelle for compartmentalization engineering. Much progress has been made in reconstituting whole metabolic pathways in the mitochondria of yeast to harness the precursor pools (i.e., pyruvate and acetyl-CoA), bypass competing pathways, and minimize transportation limitations. However, only a few mitochondrial targeting sequences (MTSs) have been characterized (i.e., MTS of COX4), limiting the application of compartmentalization engineering for multigene biosynthetic pathways in the mitochondria of yeast. In the present study, based on the mitochondrial proteome, a total of 20 MTSs were cloned and the efficiency of these MTSs in targeting heterologous proteins, including the Escherichia coli FabI and enhanced green fluorescence protein (EGFP) into the mitochondria was evaluated by growth complementation and confocal microscopy. After systematic characterization, six of the well!performed MTSs were chosen for the colocalization of complete biosynthetic pathways into the mitochondria. As proof of concept, the full alpha-santalene biosynthetic pathway consisting of 10 expression cassettes capable of converting acety coA to alpha-santalene was compartmentalized into the mitochondria, leading to a 3.7 fold improvement in the production of alpha-santalene. Furthermore, the newly characterized MTSs should contribute to the expanded metabolic engineering and synthetic biology toolbox for yeast mitochondrial compartmentalization engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Synthesis and Characterization of a Masked Terminal Nickel-Oxide Complex

In exploring terminal nickel-oxo complexes, postulated to be the active oxidant in natural and non-natural oxidation reactions, we report the synthesis of the pseudo-trigonal bipyramidal Ni II complexes (K)[Ni II (L Ph )(DMF)] (1[DMF]) and (NMe 4 ) 2 [Ni II (L Ph )(OAc)] (1[OAc]) (L Ph =2,2’,2’’-nitrilo-tris-(N-phenylacetamide); DMF=N,N-dimethylformamide; - OAc=acetate). Both complexes were characterized using NMR, FTIR, ESI-MS, and X-ray crystallography, showing the L Ph ligand to bind in a tetradentate fashion, together with an ancillary donor. The reaction of 1[OAc] with peroxyphenyl acetic acid (PPAA) resulted in the formation of [(L Ph )Ni III -O-H∙∙∙OAc] 2- , 2, that displays many of the characteristics of a terminal Ni=O species. 2 was characterized by UV-Vis, EPR, and XAS spectroscopies and ESI-MS. 2 decayed to yield a Ni II -phenolate complex 3 (through aromatic electrophilic substitution) that was characterized by NMR, FTIR, ESI-MS, and X-ray crystallography. 2 was capable of hydroxylation of hydrocarbons and epoxidation of olefins, as well as oxygen atom transfer oxidation of phosphines at exceptional rates. While the oxo-wall remains standing, this complex represents an excellent example of a masked metal-oxide that displays all of the properties expected of the ever elusive terminal M=O beyond the oxo-wall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Single Atom Catalysts: A Review of Characterization Methods

Abstract Single atom catalysts (SACs) harbor a potential to exceed nanoparticle catalysts in terms of activity, stability and selectivity in a growing number of chemical reactions. Although their investigation is attracting significant attention, important fundamental questions focusing on key physicochemical properties of SACs (e. g., structure – property relationships, structural dynamics, reaction‐driven restructuring) remain unanswered. A main challenge for research in the field is how to reliably characterize the environments of single atoms in the presence of complicating factors such as low weight loadings, strong metal‐support interactions, and atomic and multiscale heterogeneity of bonding in the single atom sites. This review addresses this challenge – identifying catalytically relevant features of physicochemical properties of single atoms (charge state, electronic structure, atomic configuration, bonding interactions with a support) and surveying advanced tools/methods for characterizing them. The review places a strong emphasis on multimodal methods exploiting X‐ray absorption, emission and photoelectron spectroscopies, and provides several examples from the authors’ research that demonstrate their use as powerful tools for SAC characterization.

Kottwitz, Matthew↗

DOE OSTI2023

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Finally, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗

DOE OSTI2019

Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges

Characterization of a parallel application’s communication patterns can be useful for performance analysis, debugging, and system design. However, obtaining and interpreting a characterization can be difficult. AChax implements an approach that uses search and a library of known communication patterns to automatically characterize communication patterns. Our approach has some limitations that reduce its effectiveness for the patterns and pattern combinations used by some real-world applications. By viewing AChax’s pattern recognition problem as an image recognition problem, it may be possible to use deep learning to address these limitations. In this position paper, we present our current ideas regarding the benefits and challenges of integrating deep learning into AChax and our conclusion that a hybrid approach combining deep learning classification, regression, and the existing AChax approach may be the best long-term solution to the problem of parameterizing recognized communication patterns.

Roth, Philip↗

DOE OSTI2019

Characterization of renal stone composition by using fast kilovoltage switching dual-energy computed tomography compared to laboratory stone analysis: a pilot study

Purpose: To prospectively examine the diagnostic performance of fast kilovoltage switching dual-energy computed tomography (DECT) in characterization of in vivo renal stone composition compared with postoperative stone analysis. Methods: Consecutive consenting patients scheduled for endoscopic kidney stone surgery in a tertiary referral hospital from June 2015 to January 2016 were enrolled. Patients were preoperatively scanned with single-source, fast kilovoltage switching DECT. Stone compositions were determined regarding the effective atomic number measurements. Results of the stone compositions from DECT were compared to postoperative infrared spectroscopy stone analysis as the standard reference. Results: For the 39 patients enrolled in the study, DECT was able to detect uric acid stone with sensitivity of 88.9% and specificity of 100%. There was 100% positive predictive value, 96.8% negative predictive value, and 97.4% accuracy. For non-contrast CT scan, sensitivity was 88.9%, specificity was 96.7%, positive predictive value was 88.9%, negative predictive value was 96.7%, and accuracy was 94.8%. Of the 39 samples examined, 21 (54%) were single composition, whereas 18 (46%) were combined. Single composition stones were correctly characterized by DECT in 100% (8/8) for uric acid. Whereas the result of uric acid stone containing stone discrimination in mixed composition was not so good with Z{sub eff} alone, iodine imaging can compensated this fault. Conclusions: DECT provides excellent accuracy in characterizing uric acid stone compositions. With the addition of iodine image, all of uric acid-containing stones can be determined by the DECT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗