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In this work the rapid alloy selection and subsequent processability via additive manufacturing of nickelbased superalloys was investigated. Rapid alloy selection protocols developed within the project produced two viable nickel-based superalloys. This phase I CRADA demonstrates a viable route for selecting new nickel-based superalloys for additive manufacturing.
Advances in catalysis science are critical to the progress of the United States. Catalysis is employed, in one way or another, in the production of almost every chemical and product manufactured. The catalysis research that we undertook here, regarding the role of specific facets of cobalt carbide in the Fischer-Tropsch production of low molecular weight olefins (FTO), is motivated by the work of many previous investigators. In particular two research groups from China had discovered FTO catalysts that work very selectively for low molecular weight olefins coupled with low production of methane. These are potentially very important findings that could find significant industrial utility if improvements can be discovered. With the research undertaken here we chose to focus on one of these studies and especially the Co 2 C surfaces that they implicate as being responsible for this exciting FTO catalysis with low production of methane. In this study, Zhong and co-workers investigated cobalt-manganese oxide catalysts for the Fischer-Tropsch to Olefins process. After reaching steady-state their catalyst showed a good selectivity to light olefins and low selectivity to methane with a carbon monoxide conversion of 31.8% at one atmosphere (with a H 2 /CO ratio of two and a temperature of 250°C). Lower H 2 /CO ratios and lower pressures increased the olefin production. Their catalyst characterization studies indicated that cobalt carbide quadrangular nanoprisms with preferentially exposed (101) and (020) facets were important in favoring lower olefin production and inhibiting methane formation. The authors stated that these cobalt carbide nanoprisms are a promising new catalyst system for directly converting synthesis gas to lower olefins. Motivated by the studies of Zhong and co-workers, our goal with the research here was to contribute to a better understanding of catalysis science through the study of surface chemical reactions related to the Fischer-Tropsch to Olefins reaction on specific facets of planar model cobalt carbide catalysts in ultrahigh vacuum.
Gallium Nitride (GaN) has a great potential in high-power and high-frequency applications due to its wide energy gap and good transport property. So far all commercial GaN optoelectronic and electronic devices have planar junctions and heterostructures prepared by epitaxial growth. To take the advantage of the merits of GaN material properties, more sophisticated device configurations such as current-aperture vertical electron transistors (CAVETs), junction field-effect transistors (JFETs), and super-junction (SJ) devices require the ability to form in-plane, lateral junctions by selective area doping (SAD). In this project, we explored a novel approach of realizing SAD through selective-area etching (SAE) followed by selective-area growth (SAG).
In the past decade software has grown significantly in complexity and scale. Likewise the number of dependencies for most software has increased with typical software packages depending on tens to hundreds of other packages. Such large numbers of dependencies place a significant burden on package managers to correctly maintain dependency lists and constraints between them. Due to this package managers have incorporated sophisticated tooling such as SAT solvers into their dependency management mechanisms. Despite these tools package managers still rely on many human annotated constraints for dependency and version selection. These are error-prone and require a significant amount of labor to constantly update and test. In this paper we propose a methodology to make use of historical build results in selecting the version for package dependencies. Our method utilizes the flexibility of the Spack package manager’s heavily parameterized package configurations to incorporate a machine learning model trained to predict the probability of build outcomes. This work is able to build and install packages with a 13% higher success rate than the default version selection mechanism in Spack.
In this work the rapid alloy selection and subsequent processability via additive manufacturing of nickelbased superalloys was investigated. Rapid alloy selection protocols developed within the project produced two viable nickel-based superalloys. This phase I CRADA demonstrates a viable route for selecting new nickel-based superalloys for additive manufacturing.
A mini split cooling system will be used to maintain temperature requirements in a mobile secure transport system. While the split cooling system was designed to be used in static residential or commercial applications, it was selected for this transportation application due to a unique set of security requirements. However, the system’s ability maintain reliability and survive prolonged long-term shock and vibration is a significant concern. The mitigation strategy is to select vibration isolation mounts and perform lifetime shock and vibration testing to demonstrate survivability. The goal of this study is to generate a finite element model of the system and perform modal analysis to inform selection of vibration mounts to minimize the amount of vibrational energy transferred to the split cooling system. The scope of this report is limited to study of the condensing unit only, and geometric variation of the assembly will not be allowed.
Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.
The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.
In recent years, an abundance of natural gas obtained from shale deposits has created opportunities for the US chemical industry to establish new and more efficient processes for the production of chemical intermediates and polymers. For example, the replacement of petroleum-derived naphtha as a to shale-derived ethane as feedstocks has made the production of ethylene/polyethylene less costly. However, this shift in feedstock has led to lower production of propylene from steam crackers, and new “on-purpose” light olefin production technologies have been implemented to bridge the gap between propylene supply and demand. The most important of these processes is direct propane dehydrogenation (PDH), but catalyst deactivation and high temperatures lead to high capital and operating costs. Oxidative dehydrogenation of propane (ODHP) is an efficient alternative to PDH, but high selectivity to CO2 from previous metal oxide catalyst prevents implementation. Hexagonal boron nitride (hBN) and other boron-containing materials have been recently developed as highly selective catalysts for both ODHP and oxidative cracking, surpassing previous systems. Much is still unknown in regard to the mechanism and the dynamic surface changes that occur under reaction conditions. This research project aims to combine in situ/operando spectroscopic investigation and kinetic studies to elucidate the interplay between the proposed gas-phase mechanism and the surface structures on the working catalyst. These insights will be leveraged to create a framework for optimizing reaction conditions and material synthesis for the next generation of boron-containing selective oxidation catalysts.
This poster highlights the use of computational hydrogen electrode approach to explain the improved activity and selectivity of Ag nanoparticle catalysts for CO2 to CO conversion observed in the experiments. The calculations predict a charge transfer from the Ag nanoparticle to a defective carbon surface, stabilizing the *COOH intermediate through reduced antibonding orbital overlap, significantly reducing the *COOH formation energy barrier, and improving CO2-to-CO conversion selectivity compared with Ag nanocluster on defect-free carbon. These results provide new insights into carbon-supported electrocatalysts for CO2RR and introduce a new approach for creating active and selective nanocatalysts.
Our BES Separation Science program project, DE-SC0021007, supported our efforts to begin to understand the mechanisms underlying selectivity of a novel class of lanthanide-binding proteins discovered by our laboratory, called lanmodulin (LanM), and to leverage these proteins for recovery and separations of trivalent rare earth elements (REEs) as well as of trivalent actinides. Overall, our work provides important insights into how higher-order (e.g., secondary, tertiary, and quaternary) protein structure modulates selectivity profiles of proteins that bind f-elements highly selectively. These results are important for advancing the concept of protein-based separations of REEs and, perhaps, of other critical minerals.
Various short baseline neutrino experiments observe anomalies that challenge the three-flavor neutrino oscillation model, consistent with a hypothetical “sterile” neutrino that does not interact via the weak force. This poster presents a near detector event selection at the Short-Baseline Near Detector (SBND), developed for the first search for neutral current (NC) disappearance at short-baseline experiments. NC disappearance provides ``smoking gun” insight into the sterile neutrino question because NC interactions are equally sensitive to all three active neutrinos, meaning any change in the NC interaction rate between near and far detectors cannot be explained by oscillations among known flavor states. This analysis focuses on the NC1p topology, the most common NC interaction type at the Short-Baseline Neutrino (SBN) Program. NC topologies are inherently challenging due to their low light yield and the absence of an outgoing charged lepton, which complicates identification and leads to poor energy reconstruction as the outgoing neutrino carries away a large portion of the neutrino’s initial energy. This work presents an NC1p event selection at the near detector, highlighting SBND’s impressive detector capabilities, including trigger efficiency studies. Combined with a novel kinematic energy reconstruction technique leveraging the detector’s outstanding hadronic detail, this analysis establishes a robust near detector foundation to target an NC disappearance measurement consistent with the 3$+$1 sterile neutrino model. This SBND selection will soon be combined with the far detector to conduct an NC disappearance search, providing unique insight and complementary information to traditional charged current searches and advancing the SBN Program’s goal to resolve the sterile neutrino question, including 3$+$1 searches and beyond.
The NuMI Off-axis $\nu_e$ Experiment (NOvA) is a long-baseline neutrino oscillation experiment that studies a neutrino beam produced by the Neutrinos at the Main Injector (NuMI) facility at Fermilab to constrain the PNFS mixing angles, the neutrino mass hierarchy, and the CP-violating phase $\delta_{CP}$. These parameters are extracted by comparing the spectra of muon neutrinos and electron neutrinos measured at the near and far detectors, using an extrapolation of the Near Detector spectra. Accurate event selection is critical to this process, as only interactions with well-reconstructed energies and interaction types should be included in the oscillation analysis to reduce systematic uncertainties. In this work, we describe the selection criteria used for muon and electron neutrinos in the Near and Far Detector. We demonstrate the effectiveness of the criteria by examining the efficiency and purity of the event selections as well as their impact on key variables used in the oscillation analysis.
In the work documented in this interim report, the experiment selection toolkit web site was demonstrated and q C coverage metric methodology was validated for IEU-MET-FAST-002-001, MIX-COMP-THERM 004-004, and HEU-SOL-THERM-013-003 experiments. 𝑞 𝐶 is an information-theoretic measure based on mutual information that quantifies the ability of candidate benchmark experiments to reduce the bias and uncertainty of a target criticality safety application. The metric and an accompanying open-source Python toolkit with a web-based interface were tested against a benchmark set of 425 experiments drawn from the International Criticality Safety Benchmark Evaluation Project Handbook. The interface is hosted at https://edim.covdef.com. It accepts sensitivity data files produced by the TSUNAMI-IP module of the SCALE code system and supports both (i) deterministic analysis using the ENDF/B-VII.0 covariance library and (ii) stochastic analysis based on user-supplied keff samples. Demonstrations on representative applications across a range of material composition, spectrum, and form show that q C -guided benchmark selection achieves greater uncertainty reduction with fewer experiments and yields more stable posterior bias and uncertainty estimates than traditional similarity coefficient ( c k )–based selection, while also capturing valuable low-ck experiments that one-to-one metrics overlook.
Selective electrochemical hydrogenation (ECH) of biomass-derived unsaturated organic molecules has enormous potential for sustainable chemical production. However, an efficient catalyst is essential to perform an ECH reaction consisting of superior product selectivity and a higher conversion rate. Here, we examined the ECH performance of reduced metal nanostructures, i.e., reduced Ag (rAg) and reduced copper (rCu) prepared via electrochemical or thermal oxidation and electrochemical reduction process, respectively. Surface morphological analysis suggests the formation of nanocoral and entangled nanowire structure formation for rAg and rCu catalysts. rCu exhibits a slight enhancement in ECH reaction performance in comparison to the pristine Cu. However, the rAg exhibits more than two times higher ECH performance without compromising the selectivity for 5-(HydroxyMethyl) Furfural (HMF) to 2,5-bis(HydroxyMethyl)-Furan (BHMF) formation in comparison to the Ag film. Moreover, a similar ECH current density was recorded at a reduced working potential of 220 mV for rAg. This high performance of rAg is attributed to the formation of new catalytically active sites during the Ag oxidation and reduction processes. This study demonstrates that rAg can potentially be used for the ECH process with minimum energy consumption and a higher production rate.
In the last decades, the selective liquid phase oxidation of alcohols to the corresponding carbonyl compounds has been a subject of growing interest. Research has focused on green methods that use “clean” oxidants such as O 2 in combination with supported metal nanoparticles as the catalyst. Among the alcohols, benzyl alcohol is one of the most studied substrates. Indeed, benzyl alcohol can be converted to benzaldehyde, largely for use in the pharmaceutical and agricultural industries. This conversion serves as model reaction in testing new potential catalysts, that can then be applied to other systems. Pd based catalysts have been extensively studied as active catalytic metals for alcohol oxidation for their high activity and selectivity to the corresponding aldehyde. Several catalytic materials obtained by careful control of the morphology of Pd nanoparticles, (including bimetallic systems) and by tuning the support properties have been developed. Moreover, reaction conditions, including solvent, temperature, pressure and alcohol concentration have been investigated to tune the selectivity to the desired products. Different reaction mechanisms and microkinetic models have been proposed. The aim of this review is to provide a critical description of the recent advances on Pd catalyzed benzyl alcohol oxidation.
Carbon capture has been an important topic of the twenty-first century because of the elevating carbon dioxide (CO2) levels in the atmosphere. CO2 in the atmosphere is above 420 parts per million (ppm) as of 2022, 70 ppm higher than 50 years ago. Carbon capture research and development has mostly been centered around higher concentration flue gas streams. For example, flue gas streams from steel and cement industries have been largely ignored due to lower associated CO2 concentrations and higher capture and processing costs. Capture technologies such as solvent-based, adsorption-based, cryogenic distillation, and pressure-swing adsorption are under research, but many suffer from higher costs and life cycle impacts. Membrane-based capture processes are considered cost-effective and environmentally friendly alternatives. Over the past three decades, our research group at Idaho National Laboratory has led the development of several polyphosphazene polymer chemistries and has demonstrated their selectivity for CO2 over nitrogen (N2). Poly[bis((2-methoxyethoxy)ethoxy)phosphazene] (MEEP) has shown the highest selectivity. A comprehensive life cycle assessment (LCA) was performed to determine the life cycle feasibility of the MEEP polymer material compared to other CO2-selective membranes and separation processes. The MEEP-based membrane processes emit at least 42% less equivalent CO2 than Pebax-based membrane processes. Similarly, MEEP-based membrane processes produce 34–72% less CO2 than conventional separation processes. In all studied categories, MEEP-based membranes report lower emissions than Pebax-based membranes and conventional separation processes.
The gold contained in copper ores is an important resource for the gold industry. In some cases, elemental gold is present and can be recovered by selective flotation. It has been reported that the gold grade and recovery can be increased, without sacrificing the copper recovery, by replacing AERO 3477 (diisobutyl dithiophosphate (DTP)) with AERO 7249 (mixture of diisobutyl monothiophosphate (MTP) and diisobutyl dithiophosphate (DTP)) as the main collector. The fundamental understanding of the improvement in selectivity with the addition of MTP in the flotation of elemental gold from pyrite is limited and is the subject of this paper. In this regard, the hydrophobicity and selectivity of DTP and MTP in the flotation of pyrite and gold are compared and discussed. Density functional theory (DFT) was used to examine the electron density, reactivity, highest occupied molecular orbital (HOMO), and lowest unoccupied molecular orbital (LUMO) of the MTP and DTP collectors. The interaction energies for the adsorption of MTP and DTP from fresh pyrite, oxidized pyrite and gold surfaces were calculated and discussed with respect to the experimental results reported in the literature. Molecular dynamics simulation (MDS) was used to examine the adsorption state of MTP and DTP on the pyrite (100) and Au (111) surfaces.