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C is rhombohedral graphite-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of two C sheets oriented in the (0, 0, 1) direction. C is bonded in a trigonal planar geometry to three equivalent C atoms. All C–C bond lengths are 1.42 Å.
N2 is graphite-like structured and crystallizes in the cubic I2_13 space group. The structure is three-dimensional. N is bonded in a trigonal non-coplanar geometry to three equivalent N atoms. All N–N bond lengths are 1.41 Å.
C is rhombohedral graphite-like structured and crystallizes in the cubic I4_132 space group. The structure is three-dimensional. C is bonded in a trigonal planar geometry to three equivalent C atoms. All C–C bond lengths are 1.46 Å.
C is rhombohedral graphite-like structured and crystallizes in the hexagonal P6/mmm space group. The structure is two-dimensional and consists of one C sheet oriented in the (0, 0, 1) direction. C is bonded in a trigonal planar geometry to three equivalent C atoms. All C–C bond lengths are 1.42 Å.
Ge is graphite structured and crystallizes in the hexagonal P6_3mc space group. The structure is two-dimensional and consists of two Ge sheets oriented in the (0, 0, 1) direction. Ge is bonded in a trigonal non-coplanar geometry to three equivalent Ge atoms. All Ge–Ge bond lengths are 2.44 Å.
C is Graphite structured and crystallizes in the orthorhombic Cmme space group. The structure is two-dimensional and consists of two C sheets oriented in the (0, 0, 1) direction. C is bonded in a trigonal planar geometry to three equivalent C atoms. There is two shorter (1.42 Å) and one longer (1.43 Å) C–C bond length.
CuS is graphite-derived structured and crystallizes in the trigonal P3m1 space group. The structure is two-dimensional and consists of one CuS sheet oriented in the (0, 0, 1) direction. Cu2+ is bonded in a trigonal non-coplanar geometry to three equivalent S2- atoms. All Cu–S bond lengths are 2.15 Å. S2- is bonded in a trigonal non-coplanar geometry to three equivalent Cu2+ atoms.
This is the Critical Minerals and Materials Matchmaker (CM3) survey form. CM3 is an online information resource created to help connect users across the critical minerals and materials supply chain. The survey is designed to allow organizations to self-identify their critical minerals and materials-aligned activities and interests, and an interactive map that displays those on-going activities in a dynamic way. To include your critical minerals management activity or activities in CM3, please open and fill out the Critical Minerals and Materials Survey. If your organization has many ongoing or planned activities that would be onerous to enter in the form, or if your activities are difficult to geolocate (such as a transport network), please email the team at edxspatial@netl.doe.gov. This initiative is aligned with the approach of DOE’s H2 Matchmaker and Carbon Matchmaker. Read more information on H2 Matchmaker and Carbon Matchmaker. Below are some questions to help understand if you should fill out the CM3 survey: Does your company work with elements such as lithium, cobalt, copper, graphite, nickel, rare earth minerals, or platinum group metals? Does your organization have research and development activities related to critical materials or their supply chains? Does your company currently work in the critical minerals or materials supply chain? Do you have prospective work in critical minerals or materials in the next 5 years? Does your company mine, process, refine or distribute critical minerals or materials? Do you want to network with other facilities or organizations working in the same areas? Are you curious about the critical mineral and material activity in your surrounding area? Are you interested in aligning your potential needs across the supply chain to different geographic areas within the U.S? For more information, please see the CM3 website (https://www.energy.gov/fecm/articles/critical-minerals-materials-matchmaker-cm3) or email our team at edxspatial@netl.doe.gov.
Considering that toxic metals can affect metabolic processes in microorganisms adversely, it can be hypothesized that these metals in water matrices would induce a decrease in metabolic activity of the biofilm microorganisms populating the surface of a sensing electrode, which could be registered as a change in the open-circuit potential (OCP) generated by the biofilm microorganisms. The goal of this study was to test this hypothesis and demonstrate the underlying principle that microbial potentiometric sensor (MPS) technology could be used for long-term and real-time monitoring and detection of rapid changes in metal concentrations in realistic aquatic environments. To address the goal, four objective were addressed: (1) a batch reactor with three graphite-based MPS electrodes was fabricated; (2) a set of single-ion solutions and one multiple ion solution were prepared reflecting realistic concentrations of metals found in electroplating wastewaters; (3) the responses of the MPS to the simultaneous presence of multiple toxic metal ions in a single solution were measured; and (4) the changes of the MPS signals to the presence of individual metal ion solutions were examined. While the hypothesis was validated, the study also revealed that the MPS was sufficiently sensitive to not only detect, but also quantify, toxic metal ion concentrations in aqueous solutions. The coefficients of determination, which were R 2 > 0.995, and responsiveness of <1 μmol/L for some toxic metal cations, strongly support the performance of MPS technology in the echelons of expensive analytical tools capable detecting and measuring trace elements.The magnitude of the MPS response was toxic metal specific. When the molar concertation normalizes the inhibition portion of the signal area, the assessed sensitivity order was: Se>Cd>Pb>Ag>Ni> Zn. The study provides valuable information for enforcement agents, environmental professionals, and wastewater treatment operators, so toxic metal pollution and its detrimental impacts can be prevented and mitigated.
Single-walled carbon nanotubes (SWCNTs) are among the most promising nanostructures because of their special and unusual physico-chemical properties that make them strong candidates for a variety of applications. Since their discovery, chemical vapor deposition (CVD) methods have become the preferred synthesis method. CVD uses a carbon-containing precursor species, which is decomposed over a catalyst yielding C atoms that eventually evolve in carbon nanostructures. CVD temperatures are in the order of 1000K or lower, and pressures in the range of 1-5 atm, although some processes use higher pressures. Depending on the nature of the catalyst, and on the conditions of pressure, temperature, and presence of other inert compounds, the resultant nanostructures may include nanofibers, nano-onions, multiple-, or single-wall carbon nanotubes, among others. When the CVD synthesis process is appropriately tuned, however, it is possible to obtain only SWCNTs, which are geometrically characterized by their diameter and by their chiral angle. The chiral indexes (n,m) determine the degree of helical twist of the graphite lattice along the nanotube axis, where the term chirality is derived from the left and right-handed helicity of the tubes defined by their chiral indexes (n,m) and (m,n) respectively. The main goal of this research is to build fundamental understanding that will help elucidating the nature and structure of the actual catalyst in a combined catalyst/support system, which we claim is essential especially when the catalyst is of nanodimensions. Given that we are considerably advanced in the understanding of selective single-walled carbon nanotube catalyst synthesis, we take this process as a case study. Thus, we focus on the understanding of the selective growth of SWCNTs on supported nanocatalysts in systems where the catalyst synthesis takes place in the same reactor preceding the nanotube synthesis. Once we understand what type of catalyst structure would be necessary for this specific growth, we will try to elucidate the catalytic synthesis process that leads to such catalytic structure. Thus, our aim of controlling nanostructure synthesis may start to be delineated.
The Advanced High-Temperature Pebble Bed Reactor concept leverages a particle-based fuel format consisting of discrete spherical graphite pebbles arrayed in a packed bed architecture. Thermal regulation achieved via flow of gas (e.g., helium) or liquid (e.g., molten salt) coolants through the void spaces between pebbles in the bed (characteristic pebble diameters: ~ 6 cm, gas cooled; ~ 3 cm, liquid cooled). This design is of interest owing to inherent advantages including passive safety features and highly efficient heat transfer characteristics that enable high power densities to be achieved. Fully leveraging these advantages requires a fundamental understanding of the complex coolant flow structure within the pebble-shaped fuel bed. Unfortunately, complexities associated with non-invasively probing the micro-scale flow phenomena within the complex network of void spaces between pebbles have hindered efforts to characterize the underlying transport phenomena. Without this fundamental foundation, it becomes challenging to rationally construct macroscopic transport models based upon volume averages of micro-scale parameters that accurately capture the observed flow behavior. Consequently, this lack of fundamental knowledge has limited previous work to coarse comparisons between theory and extremely limited experimental data. Moreover, these comparisons have been made only in terms of integral or macroscopic results. Therefore, multipoint measurements with a high level of spatial and temporal resolution are critically needed to fully map the complex flow patterns and to provide data at high spatial density to permit accurate volume averaging in the pebble bed. For this project, we performed a coordinated experimental and computational effort to quantitatively map the full-field velocity and temperature fields in the interstitial spaces within the pebble bed. The major accomplishments of this project included the important knowledge acquired for the flow mixing and heat transfer in the interstitial pore scales, the effects of flow mixing in the near-wall and far-wall regions, and the by-pass flows of pebble bed systems. The produced high-resolution experimental data (not broadly available in existing literature) were then used for the numerical studies enabling the refinement and validation of standard simulation tools (Nek5000).
Co-Extrusion (CoEx) is a deposition technology developed at PARC that uses engineered fluidic channels to cause multiple streams of dissimilar fluids to impart shape to one another. Here, we describe the use of CoEx to produce structured cathode electrode films to improve electrochemical performance of thick electrode films. CoEx enables us to create structured electrode films that have periodic regions of enhanced ionic conductivity, enabling higher utilization of thicker cathode films at high discharge rates. While increasing electrode thickness is a known method for increasing energy density of battery electrodes, this increase in energy density typically also results in reduced power density. This project, a collaboration between the Palo Alto Research Center, Oak Ridge National Laboratories, and Ford Motor Company, seeks to first validate the benefits of CoEx at the coin cell level, before performing the necessary R&D effort to scale production up to large (10+ Ah) pouch cells that will show increased energy density without a reduction in power density. We used conventional battery materials (commercially available NMC, PVDF, carbon black, and graphite) to produce structured electrodes via CoEx and compared them to thick and thin conventionally produced (unstructured) battery electrodes. CoEx electrodes show improved high rate performance compared to unstructured electrodes, resulting in higher energy density pouch cells compared to the baseline electrode of 7 and 11% in volumetric and gravimetric energy density, respectively. These electrodes also showed substantially improved electrochemical performance at high discharge rates compared to thick conventional electrodes.
Deep learning has been successfully applied to the segmentation of 3D Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify untrustworthy predictions. Recent UQ architectures include Monte Carlo dropout networks (MCDNs), which approximate deep Gaussian processes, and Bayesian neural networks (BNNs), which learn the distribution of the weight space. BNNs are advantageous over MCDNs for UQ but are thought to be computationally infeasible in high dimension, and neither architecture has produced interpretable geometric uncertainty maps. We propose a novel 3D Bayesian convolutional neural network (BCNN), the first deep learning method which generates statistically credible geometric uncertainty maps and scales for application to 3D data. We present experimental results on CT scans of graphite electrodes and laser-welded metals and show that our BCNN outperforms an MCDN in recent uncertainty metrics. The geometric uncertainty maps generated by our BCNN capture distributions of sigmoid values that are interpretable as confidence intervals, critical for applications that rely on deep learning for high-consequence decisions.
Scientific Achievement: Revealed the effect of graphite substrate on H binding / hydrogen evolution reaction (HER) activity of organic molecular electrocatalysts (OMECs); and demonstrated the effect of molecular modification on calculated HER activity descriptor. Significance and Impact: Understanding of HER processes is important for discovering low-cost OMECs for hydrogen production and energy application; design of OMEC structures remains challenging due to the unexplored redox properties of organic molecules; and density functional theory (DFT) has been proposed to provide needed insights into HER activity for improved materials design. Research Details: DFT calculations on the binding energy of HER intermediates are used for predicting reduction potentials and and calculated H adsorption energy as activity descriptor; and LANL Institutional Computing (IC) resources are essential for successful project execution given the size of structures involved in modeling molecule-support interaction.
A conceptual design has been completed for a new subcritical assembly at Oak Ridge National Laboratory (ORNL). This subcritical assembly will support the Nuclear Criticality Safety Program (NCSP) training and education program to augment current subcritical assembly capabilities at Sandia National Laboratories (SNL) and at the National Criticality Experiments Research Center (NCERC). The proposed subcritical assembly uses legacy AGN-201M research reactor fuel plates that are available at the Y-12 National Security Complex. This subcritical assembly contains approximately 620 grams of 235 U as UO 2 particulates distributed homogeneously in radiation-stabilized polyethylene. The fuel plates will have a graphite neutron reflector to obtain a core multiplication, $M$, from 10 to 50, corresponding to a k eff of 0.9 to 0.95, respectively. The subcritical assembly will be able to support at least four experiments for the training courses: the addition of fissile material to increase neutron multiplication (mass), a core separation experiment (interaction), the effect of adding moderators to the core (moderation), and the effect of adding neutron absorbers to the core (poison/absorption). The proposed ORNL subcritical assembly will provide additional student bandwidth to support NCSP training courses and to provide for some backup capacity in the event that nuclear facility operations are disrupted at SNL or NCERC due to some unforeseen issue. The subcritical assembly will be designed to be an inherently safe subcritical assembly—subcritical under all normal and abnormal conditions—and it will provide the capability to conduct hands-on training to support NCSP and general nuclear criticality safety (NCS) staff training and qualification goals.
This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Sup- port for Advanced Non-Light Water Reactors.” The tasks completed for this report are: • Task 3a: This task demonstrates the effectiveness of Griffin’s methods for interpolating cross sections and SPH correction factors as a function of control drum rotation. The Monte Carlo code Serpent is used to generate reference results. The accuracy of Griffin-calculated eigenvalues and reaction rates are studied as a function of control drum rotation for cross section and SPH factor libraries of varying fidelity. Higher-fidelity libraries require less interpolation and are therefore inherently more accurate; however, they require more computational resources to create. Overall, Griffin is able to accurately model eigenvalue and reaction rates over the full range of drum rotation using a library generated from three to five discrete drum rotations for a particular microreactor design. • Task 3c: This preliminary task successfully demonstrates the performance of a coupled neutron-photon transport calculation with Griffin using the spherical harmonics approximation. The Griffin solutions are compared to reference solutions from the continuous energy Monte Carlo codes MCNP and Serpent. Furthermore, this work also demonstrates how to prepare the neutron and photon libraries using NJOY, MCNP, and Serpent with the latest ENDF/B-VIII data for 235 U and graphite. The Griffin total energy deposition in the active core region is calculated within 0.3% of the reference, but it degrades in the reflector regions with a maximum difference of -1.5%. Overall, the results look promising and future analysis should use an improved data stream and focus on determining the potential cancellation of error within each energy group.
BISON is a finite element-based nuclear fuel performance code. Among its unique characteristics are its ability to model 1D, 2D, and 3D geometries and its applicability to a wide variety of nuclear fuels. For the last eight years, BISON has included a beginning capability to model tri-structural isotropic (TRISO) fuel. Recently, interest in TRISO fuel has grown, and a significant effort has been made to improve BISON’s capabilities in this area. Capability development has occurred for each material present in TRISO fuel particles: the buffer, inner pyrolytic carbon, silicon carbide, and outer pyrolytic carbon layers, as well as the fuel kernel. New elastic, creep, swelling, thermal expansion, thermal conductivity, and fission gas release (FGR) models are available. New models for the graphite matrix are also now available. Another important addition is the ability to perform statistical failure analysis of large samples of fuel particles. This new capability, which continues to grow, enables evaluation of failure due to pressure or crack formation by analyzing many thousands of particles. This enables realistic calculations of fission product release from the many particles in a TRISO-fueled reactor. These capabilities were checked via regression and verification tests. A large number of code benchmarking problems were also run, showing that BISON’s results closely match those of other software tools. Finally, a significant validation effort was completed in which fission product release, measured as part of the AGR-1 capsule experiments, was compared to BISON outputs. BISON outputs compared very well to the experimental data and to PARFUME results. Interest in BISON’s TRISO capabilities is growing, with the U.S. Nuclear Regulatory Commission (NRC) and Westinghouse Electric Company receiving training during the past year. Multiple other entities have expressed interest in or are actively using BISON. Kairos Power, LLC, has a strong partnership with Idaho National Laboratory (INL) regarding the use of BISON for TRISO analysis. While its capabilities still continue to grow, BISON has already become a powerful tool for TRISO analysis.
This project involved a collaborative effort, led by Dr. Rajesh Maingi of Princeton Plasma Physics Laboratory, to understand and control the plasma-material interface to improve long pulse discharge control and performance in the EAST devices. The focus is on long pulse recycling control and optimization, which can be challenging because of their broad range of materials used for plasma-facing components (PFC) and wall conditioning techniques. The purpose of the EAST device is to demonstrate long-pulse, stable, high performance plasma operation, providing a test-bed for key physics and technology issues for next step devices. As part of these tests, EAST has deployed carbon for the lower divertor PFC, tungsten for the upper divertor, and molybdenum for the main wall. In addition wall coatings are deployed for long pulse recycling control: lithium (Li) via several delivery tools, boronization, and recently also siliconization; for example, in 2013, a layer of SiC was deposited on all of the graphite tiles. Oxygen is also present at trace levels, making for a complex mix of materials and plasma-materials interactions (PMI). This mix of materials and the limited durability of wall coatings, coupled to partially optimized cryo-pumping, lead to evolution of the recycling off the PFCs, which restricts the duration of stationary, high performance, long pulse discharges. Comparing the wall conditioning techniques, Li enables the highest energy confinement and lowest recycling for the longest duration, while also suppressing ELMs in certain discharges. More specifically, research activities at the University of Tennessee focused on heat flux profile measurements in EAST, modeling the surface response, including the bonding energetics and chemical interactions between Li with carbon, tungsten, hydrogen and impurities, in addition to post situ experimental characterization and analysis of the divertor plasma facing components in EAST. This final report describes the overall progress within each of these tasks at the University of Tennessee within the project.