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DOE OSTI2020

Using a carbon vacancy reduction material to increase average carrier lifetime in a silicon carbide semiconductor device

A semiconductor die and a process for fabricating the semiconductor die are disclosed. The semiconductor die has a substrate and a silicon carbide (SiC) epitaxial structure on the substrate. The SiC epitaxial structure includes at least a first N-type SiC layer, at least a first P-type SiC layer, and carbon vacancy reduction material, which has been implanted into a surface of the SiC epitaxial structure. Further, the SiC epitaxial structure has been annealed to mobilize the carbon vacancy reduction material to diffuse carbon atoms substantially throughout the SiC epitaxial structure, thereby increasing an average carrier lifetime in the SiC epitaxial structure.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Electrochemical cells for direct oxide reduction, and related methods

A method of direct oxide reduction includes forming a molten salt electrolyte in an electrochemical cell, disposing at least one metal oxide in the electrochemical cell, disposing a counter electrode comprising a material selected from the group consisting of osmium, ruthenium, rhodium, iridium, palladium, platinum, silver, gold, lithium iridate, lithium ruthenate, a lithium rhodate, a lithium tin oxygen compound, a lithium manganese compound, strontium ruthenium ternary compounds, calcium iridate, strontium iridate, calcium platinate, strontium platinate, magnesium ruthenate, magnesium iridate, sodium ruthenate, sodium iridate, potassium iridate, and potassium ruthenate in the electrochemical cell, and applying a current between the counter electrode and the at least one metal oxide to reduce the at least one metal oxide. Related methods of direct oxide reduction and related electrochemical cells are also disclosed.

Tripathy, Prabhat K.↗

DOE OSTI2021

A Study on Computational Cost Reduction of Simulations of Phase-Change Material (PCM) Embedded Heat Exchangers

Thermal storage can be implemented using Phase-Change Materials (PCM), which absorb significant latent heat with a relatively small temperature change. PCM phase-change processes are transient and are driven by thermal diffusion and natural convection – the latter, especially for melting process. Modeling and simulation of PCM heat exchangers (HX’s) is typically computationally intensive due to the relatively complex time-dependent physics. Most of the PCM modeling work in the literature uses high-order modeling tools such as Computational Fluid Dynamics (CFD) and Lattice-Boltzmann Method (LBM). For design purposes, the existing PCM modeling approaches are not practical, limiting researchers in their ability to investigate new ideas and different PCM’s with faster turnarounds. This paper presents a study investigating the reduction of computational cost of PCM embedded HX’s CFD models by evaluating the feasibility of spatial reduction without losing accuracy. The analysis consists of comparing full and partial domain under full melting conditions. The subject of this study is a single straight tube with circular transverse fins in the vertical orientation, using PCM’s with 35oC nominal melting temperature. Different tube and fin dimensions are investigated. Results indicate that the reduced domain reproduces - in half the run time - the same behavior as the full domain since the buoyancy effects are localized and patterned. The outputs from the partial domain simulation were used to build a non-general correlation for the PCM heat transfer characteristics and demonstrated how it can be implemented in a Finite Control Volume Reduced Order Model (ROM). The ROM can accurately reproduce the CFD simulations at 4 to 5 orders of magnitude faster.

25 ENERGY STORAGE↗

DOE OSTI2021

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.

Choi, Kyoung-Shin↗

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

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↗