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DOE OSTI2021

Nickel-based superalloy single crystals fabricated via electron beam melting

Additive manufacturing technologies have emerged as potential disruptive processes whose possible impacts range from supply chain logistics, prototyping, and novel materials synthesis. Numerous works illustrate the ability to control mi- crostructure in fusion based processes and a few recent authors have even produced single crystals. However, a number of open questions remain regarding the process window which enables printing of single crystals. Furthermore, it has been observed that these additively manufactured single crystals exhibit a preferred $\langle$011$\rangle$ secondary orientation. In this work we investigate fabrication conditions which enable printing of single crystals via electron beam melting. A space filling design of experiments is utilized to efficiently explore the fabrication space. Single crystals were success- fully obtained using both commercially available powders and custom melt alloys. Microstructures obtained via these exploratory experiments exhibited a continuum of columnar structures ranging from weakly textured polycrystals, near single crystal, and fully single crystalline material. Complex geometry experiments are performed to study the grain selection mechanism. We find that the grain selection mechanism is independent of the bulk scale geometry and must therefore by driven by local heat transfer and solidification dynamics. Furthermore, grain selection is shown to be driven by competing driving forces; one which prefers epitaxial growth and another which is driven by the imposed scan pattern geometry. Finally, a mechanism is proposed for the anomalous secondary orientation preference.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Reactive capture and electrochemical conversion of CO 2 with ionic liquids and deep eutectic solvents

Ionic liquids (ILs) and deep eutectic solvents (DESs) have tremendous potential for reactive capture and conversion (RCC) of CO 2 due to their wide electrochemical stability window, low volatility, and high CO 2 solubility. There is environmental and economic interest in the direct utilization of the captured CO 2 using electrified and modular processes that forgo the thermal- or pressure-swing regeneration steps to concentrate CO 2 , eliminating the need to compress, transport, or store the gas. The conventional electrochemical conversion of CO 2 with aqueous electrolytes presents limited CO 2 solubility and high energy requirement to achieve industrially relevant products. Additionally, aqueous systems have competitive hydrogen evolution. In the past decade, there has been significant progress toward the design of ILs and DESs, and their composites to separate CO 2 from dilute streams. In parallel, but not necessarily in synergy, there have been studies focused on a few select ILs and DESs for electrochemical reduction of CO 2 , often diluting them with aqueous or non-aqueous solvents. The resulting electrode–electrolyte interfaces present a complex speciation for RCC. In this review, we describe how the ILs and DESs are tuned for RCC and specifically address the CO 2 chemisorption and electroreduction mechanisms. Critical bulk and interfacial properties of ILs and DESs are discussed in the context of RCC, and the potential of these electrolytes are presented through a techno-economic evaluation.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Subsurface microbial communities as a tool for characterizing regional-scale groundwater flow

Subsurface microbial community distribution patterns are influenced by biogeochemical and groundwater fluxes and may inform hydraulic connections along groundwater-flow paths. This study examined the regional-scale microbial community of the Death Valley Regional Flow System and evaluated whether subsurface communities can be used to identify groundwater-flow paths between recharge and discharge areas. Samples were collected from 36 sites in three groundwater basins: Pahute Mesa–Oasis Valley (PMOV), Ash Meadows (AM), and Alkali Flat–Furnace Creek Ranch (AFFCR). Microbial diversity within and between communities varied by location, and communities were separated into two overall groups that affiliated with the AM and PMOV/AFFCR basins. Network analysis revealed patterns between clusters of common microbes that represented groundwaters with similar geochemical conditions and largely corroborated hydraulic connections between recharge and discharge areas. Null model analyses identified deterministic and stochastic ecological processes contributing to microbial community assemblages. Most communities were more different than expected and governed by dispersal limitation, geochemical differences, or undominating processes. However, certain communities from sites located within or near the Nevada National Security Site were more similar than expected and dominated by homogeneous dispersal or selection. Overall, the (dis)similarities between the microbial communities of DVRFS recharge and discharge areas supported previously documented hydraulic connections between: (1) Spring Mountains and Ash Meadows; (2) Frenchman and Yucca Flat and Amargosa Desert; and (3) Amargosa Desert and Death Valley. However, only a portion of the flow path between Pahute Mesa and Oasis Valley could be supported by microbial community analyses, likely due to well-associated artifacts in samples from the two Oasis Valley sites. This study demonstrates the utility of combining microbial data with hydrologic, geologic, and water-chemistry information to comprehensively characterize groundwater systems, highlighting both strengths and limitations of this approach.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Harnessing Peptide Binding to Capture and Reclaim Phosphate

With rising consumer demands, society is tapping into wastewater as an innovative source to recycle depleting resources. Novel reclamation technologies have been recently explored for this purpose, including several that optimize natural biological processes for targeted reclamation. However, this emerging field has a noticeable dearth of synthetic material technologies that are programmed to capture, release, and recycle specified targets; and of the novel materials that do exist, synthetic platforms incorporating biologically inspired mechanisms are rare. We present here a prototype of a materials platform utilizing peptide amphiphiles that has been molecularly engineered to sequester, release, and reclaim phosphate through a stimuli-responsive pH trigger, exploiting a protein-inspired binding mechanism that is incorporated directly into the self-assembled material network. This material is able to harvest and controllably release phosphate for multiple cycles of reuse, and it is selective over nitrate and nitrite. We have determined by simulations that the binding conformation of the peptide becomes constrained in the dense micelle corona at high pH such that phosphate is expelled when it otherwise would be preferentially bound. However, at neutral pH, this dense structure conversely employs multichain binding to further stabilize phosphate when it would otherwise be unbound, opening opportunities for higher-order conformational binding design to be engineered into this controllably packed corona. With this work, we are pioneering a new platform to be readily altered to capture other valuable targets, presenting a new class of capture and release materials for recycling resources on the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Characterization of edge damage induced on REBCO superconducting tape by mechanical slitting

Abstract Rare-earth barium-copper-oxide (REBCO) superconductors are high-field superconductors fabricated in a tape geometry that can be utilized in magnet applications well in excess of 20 T. Due to the multilayer architecture of the tape, delamination is one cause of mechanical failure in REBCO tapes. During a mechanical slitting step in the manufacturing process, edge cracks can be introduced into the tape. These cracks are thought to be potential initiation sites for crack propagation in the tapes when subjected to stresses in the fabrication and operation of magnet systems. We sought to understand which layers were the mechanically weakest by locating the crack initiation layer and identifying the geometrical conditions of the slitter that promoted or suppressed crack formation. The described cracking was investigated by selectively etching and characterizing each layer with scanning electron microscopy, laser confocal microscopy, and digital image analysis. Our analysis showed that the average crack lengths in the REBCO, LaMnO 3 (LMO) and Al 2 O 3 layers were 34 μ m, 28 μ m, and 15 μ m, respectively. The total number of cracks measured in 30 mm of wire length was between 3000 and 5700 depending on the layer and their crack densities were 102 cracks mm −1 for REBCO, 108 cracks mm −1 for LMO, and 183 cracks mm −1 for Al 2 O 3 . These results indicated that there are separate crack initiation mechanisms for the REBCO and the LMO layers, as detailed in the paper. With a better understanding of the crack growth behavior exhibited by REBCO tapes, the fabrication process can be improved to provide a more mechanically stable and cost-effective superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Study of CC2p0$\pi$ Event Topologies in the MicroBooNE Detector

The precise measurement of neutrino oscillation parameters is a critical component of understanding neutrino oscillations. As future accelerator neutrino experiments will utilize 40 Ar as the target nucleus it is important to understand the influence that nuclear effects have on neutrino-nucleon cross-sections. Processes that lead to 2 particle-2 hole (2p2h) states are of particular interest, such as Meson Exchange Currents (MEC) and Short Range Nucleon-Nucleon Correlations. In the MicroBooNE detector, a charged-current interaction that produced a 2p2h state would create a 3 track topology with 1 muon and 2 protons connected to a single interaction vertex (CC2p0$\pi$). We present here a study of MicroBooNE’s potential sensitivity to a variety of MEC models as well as CC2p0$\pi$ events selected from a combination of data collected from the first 3 years of running of the MicroBooNE experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Verification of EvaluateFLux Utility Program

The EvaluateFlux program is a post-processing utility program for DIF3D, specifically DIF3D-VARIANT which handles Cartesian and hexagonal geometries. The EvaluateFlux program was developed to allow users to obtain flux and power traverses through the geometry domain, and its initial purpose was to facilitate foil analysis by evaluating the flux solution from DIF3D-VARIANT and combining it with foil cross section data. The EvaluateFlux program can calculate the neutron flux, as well as the reaction rates, at any user provided evaluation point. It does this by identifying the spatial mesh associated with the evaluation point and then evaluates the polynomial based neutron flux moments stored in the NHFLUX file at that point. The output of EvaluateFlux varies depending on the input setup. The maximum output includes the neutron flux and microscopic and macroscopic reaction rates at each evaluation point. The purpose of this work is to verify the outputs of EvaluateFlux. Simple models that have hand calculatable results are first defined and used to verify the EvaluateFlux outputs. More complex cases are then added where a duplicate program of EvaluateFlux that uses PrintTables outputs of the binary files is used to verify the EvaluateFlux outputs. In those complex cases, hand calculations of selected evaluation points were also displayed to confirm the software verification. For all the tests done, the hand calculations agreed well with those calculated by EvaluateFlux. For the larger complex problems, the duplicate program that can process hundreds of evaluation points was able to identify that zero points within some meshes have large errors. This aspect was attributed to the truncation error on the input provided to the duplicate program and is not a concern for the accuracy of the EvaluateFlux software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Verification of the EvaluateFlux Utility Program

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Integration of genome-scale metabolic model with biorefinery process model reveals market-competitive carbon-negative sustainable aviation fuel utilizing microbial cell mass lipids and biogenic CO 2

Producing scalable, economically viable, low-carbon biofuels or biochemicals hinges on more efficient bioconversion processes. While microbial conversion can offer robust solutions, the native microbial growth process often redirects a large fraction of carbon to CO 2 and cell mass. By integrating genome-scale metabolic models with techno-economic and life cycle assessment models, this study analyzes the effects of converting cell mass lipids to hydrocarbon fuels, and CO 2 to methanol on the facility’s costs and life-cycle carbon footprint. Results show that upgrading microbial lipids or both microbial lipids and CO 2 using renewable hydrogen produces carbon-negative bisabolene. Additionally, on-site electrolytic hydrogen production offers a supply of pure oxygen to use in place of air for bioconversion and fuel combustion in the boiler. To reach cost parity with conventional jet fuel, renewable hydrogen needs to be produced at less than $\$2.2$ to $\$3.1$/kg, with a bisabolene yield of 80% of the theoretical yield, along with cell mass and CO 2 yields of 22 wt% and 54 wt%, respectively. The economic combination of cell mass, CO 2 , and bisabolene yields demonstrated in this study provides practical insights for prioritizing research, selecting suitable hosts, and determining necessary engineered production levels.

09 BIOMASS FUELS↗

DOE OSTI2023

Dual Tunability for Uncatalyzed N‐Alkylation of Primary Amines Enabled by Plasma‐Microdroplet Fusion

Abstract The fusion of non‐thermal plasma with charged microdroplets facilitates catalyst‐free N‐alkylation for a variety of primary amines, without halide salt biproduct generation. Significant reaction enhancement (up to >200×) is observed over microdroplet reactions generated from electrospray. This enhancement for the plasma‐microdroplet system is attributed to the combined effects of energetic collisions and the presence of reactive oxygen species (ROS). The ROS (e.g., O 2 ⋅ − ) act as a proton sink to increase abundance of free neutral amines in the charged microdroplet environment. The effect of ROS on N‐alkylation is confirmed through three unique experiments: (i) utilization of radical scavenging reagent, (ii) characterization of internal energy distribution, and (iii) controls performed without plasma, which lacked reaction acceleration. Establishing plasma discharge in the wake of charged microdroplets as a green synthetic methodology overcomes two major challenges within conventional gas‐phase plasma chemistry, including the lack of selectivity and product scale‐up. Both limitations are overcome here, where dual tunability is achieved by controlling reagent concentration and residence time in the microdroplet environment, affording single or double N‐alkylated products. Products are readily collected yielding milligram quantities in eight hours. These results showcase a novel synthetic strategy that represents a straightforward and sustainable C−N bond‐forming process.

Chemistry↗

DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗

DOE OSTI2022

A SIMPLE TOOL TO EVALUATE COLOR FOR LEGIBILITY AND DISTINGUISHABILITY WHEN DESIGNING DIGITAL DISPLAYS

Color is important in helping users effectively process information like text and symbols when performing tasks on digital displays. Interface designers in safety- and mission-critical industries often utilize human factors design guidelines and standards to make important design decisions like the use of color for legibility and distinguishability. However, such guidance sometimes requires additional interpretation and calculation to readily apply to the design question at hand. This work describes a simple tool that can be used to evaluate the adequacy of color choices from a palette for being legible or distinguishable. The tool eliminates having to manually convert colors to a readable format while providing a specific set of colors that are acceptable from select human factors standards. This paper provides an exemplary case study applied to a digital modification at a nuclear power plant to highlight how the tool may be used in safety and mission-critical industries.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗

DOE OSTI2023

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Biochemical Conversion of Herbaceous Biomass to Renewable Diesel: Net Greenhouse Gas and Air Pollutant Trade-offs

This study examines greenhouse gas (GHG) and criteria air pollutant (CAP) emissions trade-offs for renewable diesel across 12 scenarios, involving different biochemical conversion designs, biorefinery scales, and feedstocks. A conventional design uses lignin for on-site heat and power, which exports excess power to the grid. An alternative design exports lignin pellets, offsetting other pellet production methods but requiring grid electricity to meet biorefinery power demands. Net emissions were quantified in Iowa and Georgia, selected considering feedstock availability, coproduct displacement, and regional power grids, assuming grid-exported power avoids coal or low-carbon electricity. Results for the conventional design remained consistent across the electricity displacement scenarios. When comparing lignin utilization strategies, pelletizing lignin reduces sulfur dioxide, carbon monoxide, nitrogen oxides, and volatile organic compounds (net emissions –0.66 mg MJ –1 , 25 mg MJ –1 , 25 mg MJ –1 , 7.8 mg MJ –1 , respectively). However, lignin pelletization increases net particulate matter (fine and coarse) and ammonia (net emissions of 4.7 mg MJ –1 , 13 mg MJ –1 , and 0.26 mg MJ –1 , respectively), alongside indirect GHG emissions due to grid electricity dependence. Additionally, processing 2000 tonnes corn stover daily minimizes emissions for both designs. Only lignin pelletization with renewable electricity and additional particulate matter and ammonia controls reduces all CAP and GHG emissions simultaneously.

09 BIOMASS FUELS↗

DOE OSTI2022

Mitigation of Leakage and Water Ingress for a Promising Phase Change Material (PCM) Containing Polyethylene Glycol (PEG)

A promising and cost-effective phase change material (PCM) containing polyethylene glycol (PEG) 600, 800, and/or 1000 has been synthesized. It is shape stabilized using a sol-gel method and displays promising thermal properties. These properties make the PCMs good candidates for thermal batteries and other thermal energy storage applications in the building sector. However, PEG leakage is observed at elevated temperatures, and due to the hygroscopic nature of PEG, moisture ingress is also observed, which can have a significant effect on performance and durability. Thus, we have developed hydrophobic coatings to prevent PEG leakage and mitigate moisture ingress. The selected approach involves a secondary sol-gel process during which a thin layer formulated with hydrophobic precursors is deposited onto the surface of the PCM samples. Several coating formulations have been investigated, including formulations composed of tetraethyl orthosilicate (TEOS), diethoxy(dimethyl)silane (DEDMS), triethoxymethylsilane (TEMS), and n-octyltriethoxysilane (OTES). We find that this is effective at increasing hydrophobicity of the PCM substrates. It also decreases substrate leakage.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

DOE OSTI2025

Mixed Element Microparticle Characterization by Electron Probe Microanalysis

Abstract Microparticle compositional characterization for nuclear forensics has traditionally been achieved by “gold-standard” destructive analytical techniques such as large geometry-secondary ion mass spectrometry and fission track-thermal ionization mass spectrometry, but long processing times and total sample consumption eliminate the option of subsequent analysis by other methods. Electron probe microanalysis has long been widely employed for rapid, nondestructive, micrometer-scale compositional measurements and imaging in fields such as material science and geology and may therefore need to be reevaluated as an alternative tool for microparticle analysis for the nuclear forensics community. This study presents the use of electron probe microanalysis for imaging and quantitative characterization of homogeneous microparticles utilizing a calibration curve based on high-precision quadrupole-inductively coupled plasma-mass spectrometry analyses. Samples of opportunity synthesized at Savannah River National Laboratory, nickel-doped cerium oxide microparticles, were selected as analogs for plutonium-doped uranium oxide microparticles. Positive detection and accurate quantification of variable amounts of nickel dopant down to trace levels (10s of parts per million) suggest applicability of this technique to other mixed element systems (e.g., actinides). Quantitative single-particle characterization via electron probe microanalyzer may thus provide a high fidelity, nondestructive complement to techniques currently in use.

Riche, Alexis T. (ORCID:0009000121978126)↗

DOE OSTI2022

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗