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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗
DOE OSTI2022

Electrochemical and photoelectrochemical reduction of furfurals

Electrochemical cells and photoelectrochemical cells for the reduction of furfurals are provided. Also provided are methods of using the cells to carry out the reduction reactions. Using the cells and methods, furfurals can be converted into furan alcohols or linear ketones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reduction of the Waste Occupied Area by Nuclide Separation and Horizontal Emplacement of the Waste Packages - 20422

Two of the key objectives for realizing the advanced nuclear fuel cycle are to reduce the volume of high-level radioactive waste and to reduce the foot-print of the geological repository. To achieve these objectives, research and development on nuclide separation technologies is a prerequisite. In this context, it is necessary to consider the vitrification conditions and the design of the geological repository where separation technologies would be implemented to achieve effective reductions in the volume of vitrified waste and the foot-print of the geological repository. In this study, the effects of the vitrification conditions on the number of vitrified waste units and the disposal area were evaluated, based on the assumption that separation of Cs and Sr from the spent nuclear fuel had been performed. In addition, with reference to the design of the geological repository, the impact on the foot-print of the repository for different emplacement methods of the waste packages was examined. As a result, it was clarified that the waste-occupied area could be reduced by horizontal emplacement of the vitrified waste with low heat generation. In addition, it was demonstrated that a low waste-loaded vitrified waste contributed to a reduction in the foot-print of the geological repository in the case of horizontal emplacement, although the number of vitrified waste units increased. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Variance reduction techniques for Monte Carlo neutron noise simulations

The small fluctuations of the neutron flux caused by small perturbations of the macroscopic cross-sections take the name of neutron noise. Advanced Monte Carlo methods have been recently proposed in order to solve the neutron noise equations in the frequency domain, which allows establishing reference solutions to validate faster but approximate deterministic solvers. Due to the presence of particles carrying two statistical weights (for the real and imaginary components of the noise field), both of which may be positive or negative, Monte-Carlo simulations of neutron noise pose distinct challenges in terms of variance reduction. In this work we investigate two variance-reduction techniques, namely branchless collisions and weight cancellation, and probe their effectiveness for a benchmark con- figuration concerning the noise field induced by a pin with oscillating cross sections in a fuel assembly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Dilute alloy catalysts for electrochemical CO 2 reduction

A product includes a dilute alloy catalyst for carbon dioxide reduction. The catalyst has a majority component and at least one minority component. The majority component is present in a concentration of greater than 90 atomic percent of the catalyst. The majority component is copper, and each minority component is selected from the group consisting of: a transition metal, a main group metal, a lanthanide, and a semimetal. A method includes forming a product on a cathode. The product includes a dilute alloy catalyst for carbon dioxide reduction. The catalyst has a majority component and at least one minority component. The majority component is present in a concentration of greater than 90 atomic percent of the catalyst. The majority component is copper, and each minority component is selected from the group consisting of: a transition metal, a main group metal, a lanthanide, and a semimetal.

Biener, Juergen↗
DOE OSTI2024

Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations

Input-centric program optimization aims to optimize code by considering the relations between program inputs and program behaviors. Despite its promise, a long-standing barrier for its adoption is the difficulty of automatically identifying critical features of complex inputs. This paper introduces a novel technique, reductive analysis through compiler-guided Large Language Models (LLMs), to solve the problem through a synergy between compilers and LLMs. It uses a reductive approach to overcome the scalability and other limitations of LLMs in program code analysis. The solution, for the first time, automates the identification of critical input features without heavy instrumentation or profiling, cutting the time needed for input identification by 44× (or 450× for local LLMs), reduced from 9.6 hours to 13 minutes (with remote LLMs) or 77 seconds (with local LLMs) on average, making input characterization possible to be integrated into the workflow of program compilations. Optimizations on those identified input features show similar or even better results than those identified by previous profiling-based methods, leading to optimizations that yield 92.6% accuracy in selecting the appropriate adaptive OpenMP parallelization decisions, and 20-30% performance improvement of serverless computing while reducing resource usage by 50-60%.

Input-Centric Optimization↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗
DOE OSTI2022

Distinct Bimetallic Cooperativity Among Water Reduction Catalysts Containing [Co III Co III ], [Ni II Ni II ], and [Zn II Zn II ] Cores

We report three binuclear species [LCo III 2 (μ-Pz) 2 ](ClO 4 ) 3 (1), [LNi II 2 (CH 3 OH) 2 Cl 2 ]ClO 4 (2), and [LZn II 2 Cl 2 ]PF 6 (3) supported by the deprotonated form of the ligand 2,6-bis[bis(2-pyridylmethyl) amino-methyl]-4-methylphenol were synthesized, structurally characterized as solids and in solution, and had their electrochemical and spectroscopic behavior established. Species 1–3 had their water reduction ability studied aiming to interrogate the possible cooperative catalytic activity between two neighboring metal centers. Species 1 and 2 reduced H 2 O to H 2 effectively at an applied potential of -1.6 V Ag/AgCl , yielding turnover numbers of 2,820 and 2,290, respectively, after 30 minutes. Species 3 lacked activity and was used as a negative control to eliminate the possibility of ligand-based catalysis. Pre- and post-catalytic data gave evidence of the molecular nature of the process within the timeframe of the experiments. Species 1 showed structural, rather than electronic cooperativity, while species 2 displayed no obvious cooperativity. DFT methods complemented the experimental results determining plausible mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Theoretical Investigation of the Adsorbate and Potential–Induced Stability of Cu Facets During Electrochemical CO 2 and CO Reduction

The activity and product selectivity of electrocatalysts for reactions like the carbon dioxide reduction reaction (CO 2 RR) are intimately dependent on the catalyst's structure and composition. While engineering catalytic surfaces can improve performance, discovering the key sets of rational design principles remains challenging due to limitations in modeling catalyst stability under operating conditions. Herein, we perform first-principles density functional calculations adopting implicit solvation methods with potential control to study the influence of adsorbates and applied potential on the stability of different facets of model Cu electrocatalysts. Using coverage dependencies extracted from microkinetic models, we describe an approach for calculating potential and adsorbate-dependent contributions to surface energies under reaction conditions, where Wulff constructions are used to understand the morphological evolution of Cu electrocatalysts under CO 2 RR conditions. Here we identify that CO*, a key reaction intermediate, exhibits higher kinetically and thermodynamically accessible coverages on (100) relative to (111) facets, which can translate into an increased relative stabilization of the (100) facet during CO 2 RR. Our results support the known tendency for increased (111) faceting of Cu nanoparticles under more reducing conditions and that the relative increase in (100) faceting observed under CO 2 RR conditions is likely attributed to differences in CO* coverage between these facets.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Towards defect engineering in hexagonal MoS 2 nanosheets for tuning hydrogen evolution and nitrogen reduction reactions

Combined computational and experimental approaches were used to evaluate defective 2H-MoS 2 nanosheets for their activity and selectivity for hydrogen evolution reaction (HER) and nitrogen reduction reaction (NRR). Density functional theory calculations were used to understand the relationship between HER and NRR activity on the ideal basal MoS 2 plane, seven grain-boundaries, ten single-/few-atom vacancies and anti-sites, and zigzag and armchair edge sites. The results confirm that 2H-MoS 2 should contain several defects with high activity for HER: armchair and zigzag edges, VS vacancy, MoS 2 anti-site, and S-S and Mo-Mo grain boundaries. Considering Gibbs free energy change for all the steps in the NRR mechanism and kinetic barriers for a key NRR step, we have found that activation of perspective NRR selective sites in 2H-MoS 2 , namely V MoS6 and clusters of S-vacancies, would require large overpotential, conditions at which HER dominates. The DFT conclusions are supported by the electrochemical studies of NRR activity and selectivity under aqueous conditions, which show an increase in NRR activity but a decrease in Faradaic efficiency as applied cell potential becomes more negative. The results of this work therefore highlight the challenges in activating natural 2H-MoS 2 for NRR, which would require additional material engineering or reaction condition optimization as a way to suppress HER, decrease the NRR overpotential or preferentially both.

2H-MoS2↗
DOE OSTI2022

Multi-pass flow-through reductive catalytic fractionation

Reductive catalytic fractionation (RCF) is a promising lignin-first biorefining strategy that selectively extracts and depolymerizes lignin from whole biomass. Flow-through (FT)-RCF enables physical separation of the biomass and catalyst, but this process configuration typically operates at high solvent-to-biomass ratios. Here, we demonstrate multi-pass FT-RCF, wherein the lignin-enriched solvent obtained after an initial FT-RCF step is recycled and used in subsequent FT-RCF without intermediate lignin recovery. Multi-pass FT-RCF reduces the solvent-to-biomass ratio from 48 to 1.9 L/kg, which is a lower solvent loading than is accessible in batch reactors, without negative impacts on delignification and monomer yield with up to 12 wt % lignin in the solvent. Overall, this work demonstrates that solvent demand in RCF processes, which is a key cost and energy driver to enable this process at scale, can be reduced by recycling lignin oil between RCF reactions.

09 BIOMASS FUELS↗
DOE OSTI2025

Temperature-dependent solid electrolyte interphase reactions drive performance in lithium-mediated nitrogen reduction to ammonia

The solid electrolyte interphase (SEI) is a vital component to control mass transport and selectivity in the lithium-mediated reduction of N 2 to NH 3 (Li-N 2 R). Finding strategies that generate the optimal SEI, a complex network of organic and inorganic species, can potentially improve Li-N 2 R performance. Here, we unravel structure-property relationships of the SEI by correlating its composition with the NH 3 faradaic efficiency (FENH3). By modifying the reaction temperature, we alter electrolyte decomposition reactions and observe changes in the SEI that explain FE NH3 trends between electrolyte solvents. We quantify a complex reaction environment at elevated temperatures where SEI formation is counteracted by etching reactions. This tradeoff leads to temporal fluctuations of FE NH3 , but the maximal FE NH3 can reach up to 40%, the highest value reported for batch cells at ambient pressure, thus far. In conclusion, our work underscores the potential of novel electrolytes that steer SEI selectivity and, ultimately, improve Li-N 2 R performance.

electrocatalytic nitrogen reduction↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Robust Copper-Based Nanosponge Architecture Decorated by Ruthenium with Enhanced Electrocatalytic Performance for Ambient Nitrogen Reduction to Ammonia

Electrochemical conversion of nitrogen to green ammonia is an attractive alternative to the Haber–Bosch process. However, it is currently bottlenecked by the lack of highly efficient electrocatalysts to drive the sluggish nitrogen reduction reaction (N2RR). In this work, we strategically design a cost-effective bimetallic Ru–Cu mixture catalyst in a nanosponge (NS) architecture via a rapid and facile method. The porous NS mixture catalysts exhibit a large electrochemical active surface area and enhanced specific activity arising from the charge redistribution for improved activation and adsorption of the activated nitrogen species. Benefiting from the synergistic effect of the Cu constituent on morphology decoration and thermodynamic suppression of the competing hydrogen evolution reaction, the optimized Ru 0.15 Cu 0.85 NS catalyst presents an impressive N 2 RR performance with an ammonia yield rate of 26.25 μg h –1 mg cat. –1 (corresponding to 10.5 μg h –1 cm –2 ) and Faradic efficiency of 4.39% as well as superior stability in alkaline medium, which was superior to that of monometallic Ru and Cu nanostructures. Additionally, this work develops a new bimetallic combination of Ru and Cu, which promotes the strategy to design efficient electrocatalysts for electrochemical ammonia production under ambient conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

1 H NMR Quantification of Aromatic Monomers from Reductive Catalytic Fractionation

Reductive catalytic fractionation (RCF) can produce high yields of aromatic monomers from lignin in native biomass. Quantification of these aromatic monomers is a well-known but demanding task, in part due to the lack of commercially available standards. Here, we demonstrate 1 H NMR spectroscopy as a complementary method to rapidly quantify aromatic monomer concentrations in RCF oils. The method exhibited good agreement with measurements from ultrahigh pressure liquid chromatography (UHPLC) for 96 RCF oils with varying monomer selectivity, with average absolute deviations of individual monomer yields between 0.5 and 1.1 wt % (relative 11–17%) and R 2 values above 0.9 compared to conventional UHPLC quantification. Quantification of S-type monomers, including for 4-ethylsyringol and 4-propenylsyringol, was generally reliable. The validity of G-type monomer quantifications depended on reaction selectivity due to overlap between peaks of 4-ethylguaiacol and 4-(3-hydroxypropyl)-guaiacol. The method could be applied on crude RCF oils without needing to perform the liquid–liquid extraction typically done for RCF reactions, thereby providing a convenient way to quantify lignin extraction and aromatic monomer yield. Overall, 1 H NMR spectroscopy can serve as a rapid primary quantification or secondary validation method for RCF monomer yield and selectivity measurements.

1H NMR spectroscopy↗
DOE OSTI2021

Carbon dioxide and nitrogen reduction reactions using 2D transition metal dichalcogenide (TMDC) and carbide/nitride (MXene) catalysts

Improving the carbon dioxide and nitrogen reduction reactions (CO 2 RR and NRR) can reduce anthropogenic greenhouse gas emissions while selectively producing chemicals needed for the fuel, plastic, and chemical industries. Efficient CO 2 RR can be used to replace fossil fuels as well as repurpose captured CO 2 , while new NRR pathways can be used to supplement or replace the energy intensive Haber–Bosch process for NH 3 generation with no CO 2 emissions. Therefore, this review article focuses on (photo)electrocatalytic and photocatalytic conversion of CO 2 and N 2 molecules into useful products, such as carbon monoxide, methanol, formic acid, and ammonia, using 2D transition metal dichalcogenides (TMDCs) and metal carbides/nitrides (MXenes). Additionally, these highly tunable 2D catalysts will be evaluated for their ability to selectively and efficiently undergo CO 2 RR and NRR by controlling defects, phases, edge sites, interfaces, and functional groups. We first address the CO 2 RR and NRR challenges, with a particular focus on theoretical mechanisms and minimum energy pathways. We follow this discussion with a detailed review of state-of-the-art 2D TMDC and MXene experimental catalysts for CO 2 RR and NRR (photo)electrocatalytic and photocatalytic reactions, and then address areas of opportunity for these catalytic reactions.

14 SOLAR ENERGY↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nanocolumnar Pt:Ni Alloy Thin Films by High Pressure Sputtering for Oxygen Reduction Reaction

Self-supported nanocolumnar Pt:Ni thin films (TFs) with varying Pt:Ni atomic ratios and Pt mass loadings were produced on a microporous layer (MPL)-like surface composed of carbon particles by high pressure sputtering and examined as oxygen reduction reaction (ORR) electrocatalysts for polymer electrolyte membrane fuel cells. Cauliflower-like microstructures were observed from scanning electron microscopy imaging. Various Pt:Ni atomic ratios were obtained by simply changing the relative deposition power between Pt and Ni source and investigated by X-ray diffraction and quartz crystal microbalance analysis. Additionally, electrochemical characterization of the Pt:Ni-TF/MPL-like-layer/glassy-carbon samples was conducted through benchtop cyclic voltammetry and rotating disk electrode measurements. The electrochemically active surface area (ECSA) was found to be between 22–42 m 2 g -1 for different Pt:Ni atomic ratios. Lower Pt mass loadings exhibited a higher ECSA and the catalytic activity of all Pt:Ni ratios increased with the increase in Pt mass loading. The ORR activity of the Pt:Ni-TFs increased in the order of 3:1 < 1:1 < 1:3 with exhibiting a specific activity of 1781 μA cm -2 and mass activity of 0.66 A mg -1 for the Ni-rich film with 1:3 ratio. The catalytic performance of Pt:Ni-TFs were higher than traditional high surface area carbon supported Pt nanoparticles, elemental Pt nanorods, and Pt-Ni nanorods.

08 HYDROGEN↗