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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

On the high-temperature stability of the Al 8 Cu 3 Ce intermetallic in an additively manufactured Al-Cu-Ce-Zr alloy

High-temperature resistant eutectic Al alloys are crucial materials for lightweight and energy efficient design in the automotive and aviation industries. Additive manufacturing offers a pathway to refine eutectic microstructures and develop novel alloys with superior high-temperature strength. High-volume fraction intermetallic Al-Cu-Ce alloys have been developed to deliver high-temperature strength in combination with reduced hot-tearing susceptibility. Zr is added to provide additional strengthening via nanoscale Al 3 Zr precipitation, and to stabilize and avoid coarsening of the Al 8 Cu 3 Ce phase. However, the detailed interaction between Zr and Al 8 Cu 3 Ce remains unexplored. In this work, we show with synchrotron X-ray diffraction that laser powder bed fusion fabricated Al-Cu-Ce and Al-Cu-Ce-Zr alloys contain predominantly the Al 8 Cu 3 Ce intermetallic in the as-fabricated condition. Heat treatment of the Al-Cu-Ce alloy results in the Al 8 Cu 3 Ce → Al 8 Cu 4 Ce phase transformation. In the Al-Cu-Ce-Zr alloy, minor fractions of (Al,Cu,Si) 4 Ce and Al 2 Cu-θ are found in the as-fabricated condition, while Al 8 Cu 3 Ce remains stable during heat treatment. Atom probe microscopy quantifies intermetallic stoichiometries and reveals how Zr is enriched at the Al-matrix/Al 8 Cu 3 Ce interface acting as a diffusion barrier against solute exchange. Calibrated thermodynamic modeling underpins this as a kinetic effect. Here, a qualitative microstructural model summarizes, how Zr stabilizes Al 8 Cu 3 Ce against phase transformations and coarsening.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of acetic acid exposure on metal contact degradation of different crystalline silicon solar cell technologies

Degradation due to acetic acid in photovoltaic (PV) modules has been a commonly observed phenomenon for both damp-heat exposure and outdoor operations. Acetic acid is a degradation byproduct of ethylene-vinyl acetate (EVA), a common module encapsulant. To address this issue, robust metallization pastes and cell technologies are being developed. However, it is important to assess how these technologies perform in an acetic acid environment and withstand degradation before they are implemented in the solar market. In this work, we investigate the impact of acetic acid exposure on four different cell groups: monofacial passivated emitter and rear contact (PERC) cells with advanced telluride-based front contact pastes, bifacial PERC cells with novel aluminum rear contact pastes, bifacial tunnel oxide passivated contacts (TOPCon) cells, and silicon heterojunction (SHJ) cells. These cells were exposed to acetic acid for different time increments. The recombination losses were characterized by Suns-VOC, and multi-variate regression analysis of intensity-dependent photoluminescence (PL) images with Griddler AI. Resistive losses were tracked with the transmission line method (TLM). Samples showing severe performance degradation were selected for further materials characterization to understand the root cause. Top-down and cross-sectional scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) were performed to investigate the change in materials properties. Our study shows that the front contacts of the bifacial TOPCon cells and monofacial PERC cells were significantly affected by acetic acid exposure. Here, the SHJ cells were found to be the most stable.

14 SOLAR ENERGY↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Copper–Carbon Nanotube Composites Enabled by Electrospinning for Advanced Conductors

The power losses associated with the electrical resistance of copper (Cu) have generated considerable interest in the development of advanced conductors that incorporate carbon nanotubes (CNTs) into the Cu matrix—ultraconductive Cu (UCC) composites—to increase energy efficiency in various industrial and residential applications, ranging from electric power transmission and rotating machinery to electronic devices. To meet this demand, we describe an electrospinning-based polymer nanofiber templating strategy for the fabrication of UCC composites with electrical and mechanical performance exceeding that of Cu. Our approach involves electrospinning of polyvinylpyrrolidone (PVP)-based solutions containing CNTs into aligned PVP/CNT nanofibers onto Cu foil substrates, followed by vacuum-assisted thermal removal of organic solvent/polymer from the CNT matrix to achieve a uniformly distributed CNT layer on the Cu surface. Following additional Cu deposition, the Cu–CNT–Cu composites demonstrated similar electrical conductivity, higher current carrying capacity, and improved mechanical properties compared with those obtained from reference Cu. Importantly, after the heat treatment, Raman analysis of the CNT network displayed an increased metallic character that supports the enhanced electrical properties of the UCC composites. Thus, we believe that these performance characteristics together with the commercial viability of the present approach could open new possibilities in designing advanced conductors for a broad range of electrical systems and industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A novel path to runaway electron mitigation via deuterium injection and current-driven MHD instability

Relativistic electron (RE) beams at high current density (low safety factor, qa) yet very low free-electron density accessed with D2 secondary injection in the DIII-D and JET tokamak are found to exhibit large-scale MHD instabilities that benignly terminate the RE beam. In JET, this technique has enabled termination of MA-level RE currents without measurable first-wall heating. This scenario thus offers an unexpected alternate pathway to achieve RE mitigation without collisional dissipation. Benign termination is explained by two synergistic effects. First, during the MHD-driven RE loss events both experiment and MHD orbit-loss modeling supports a significant increase in the wetted area of the RE loss. Second, as previously identified at JET and DIII-D, the fast kink loss timescale precludes RE beam regeneration and the resulting dangerous conversion of magnetic to RE kinetic energy. During the termination, the RE kinetic energy is lost to the wall, but the current fully transfers to the cold bulk thus enabling benign Ohmic dissipation of the magnetic energy on longer timescales via a conventional current quench. Hydrogenic (D2) secondary injection is found to be the only injected species that enables access to the benign termination. D2 injection: 1) facilitates access to low qa in existing devices (via reduced collisionality & resistivity), 2) minimizes the RE avalanche by ‘purging’ the high-Z atoms from the RE beam, 3) drives recombination of the background plasma, reducing the density and Alfven time, thus accelerating the MHD growth. Furthermore, this phenomenon is found to be accessible when crossing the low qa stability boundary with rising current, falling toroidal field, or contracting minor radius - the latter being the expected scenario for vertically unstable RE beams in ITER. While unexpected, this path scales favorably to fusion-grade tokamaks and offers a novel RE mitigation scenario in principle accessible with the day-one disruption mitigation system (DMS) of ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Size-Dependent Adsorption and Adhesion Energetics of Ag Nanoparticles on Graphene Films on Ni(111) by Calorimetry

Interest in the use of carbon supports for late transition metal nanoparticle catalysts has expanded rapidly due to the increasing importance of electrocatalysts for clean energy and environmental technologies and the use and storage of renewable electricity. Compared to oxide supports, almost nothing is known about the effect of metal nanoparticle size on the energies of the metal atoms within carbon-supported nanoparticles, yet these energies are crucial for understanding their surface reactivity and sintering kinetics. Here, the growth morphology and adsorption energetics of vapor-deposited Ag onto clean graphene/Ni(111) surfaces have been studied using a combination of single-crystal adsorption calorimetry (SCAC) and He + low-energy ion scattering (LEIS). The differential heat of Ag adsorption is 207 kJ/mol for making ~30 atom Ag particles on graphene terraces at 100 K and 16 kJ/mol higher for making ~9 atom Ag clusters at defect sites at the same temperature. The heat of adsorption increases rapidly with Ag coverage as 3D Ag nanoparticles nucleate and grow in size, asymptotically reaching within 5 kJ/mol of the bulk Ag sublimation enthalpy (285 kJ/mol) by 2 ML. The heats of adsorption and Ag nanoparticle densities from LEIS (~10 16 /m 2 ) were combined to provide the Ag/graphene adhesion energy (E adh = 1.8 J/m 2 in the large-particle limit) and the Ag chemical potential (μ) versus effective particle diameter (D). The Ag chemical potential was well-fitted by μ(D) = (3γ v /M – E adh )(1 + (1.5 nm)/D)(2V m /D), where γ v /M is the surface energy of bulk Ag and V m is its molar volume. The same equation is known to fit similar data for late transition metals on clean surfaces of metal oxide single crystals. The adhesion energy of Ag measured here on graphene falls within the wide range measured for Ag on those oxide surfaces and is almost as large as on the oxide that binds Ag particles most strongly, namely CeO 2 (111), which is well-known to be very effective at resisting catalyst deactivation by metal sintering. Furthermore, these results imply that carbon supports will be effective at resisting sintering and that Ag particles smaller than 6 nm on graphene will bind small adsorbed reaction intermediates more weakly than supports with weaker adhesion to Ag, like MgO(100).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DOE Zero Energy Ready Manufactured Housing: Subject Matter Expert Technical Assistance Summary

Manufactured homes offer American consumers an affordable option for decent single-family detached housing. For working-class American families in many U.S. markets, manufactured homes are the first step toward home ownership. They now make up 10% of all new homes constructed in the United States, with higher percentages in the south and in rural communities. To help encourage the production of homes that are more durable, healthy, efficient, and disaster resistant, the U.S. Department of Energy is bringing its building science research to the manufactured housing industry through DOE’s Zero Energy Ready Manufactured Home (ZER-MH) program, which provides technical assistance and voluntary guidelines to manufactured home builders. Homes built to these guidelines are better able to handle power outages and less likely to experience moisture issues, offering a better product option for American families. This higher quality is evidenced by energy modeling which shows homes manufactured to these voluntary guidelines will typically use half the energy of manufactured homes built to the current minimum requirements of the U.S. Department of Housing and Urban Development (HUD)’s Manufactured Housing and Construction Safety Standard (MHCSS). These homes can also reduce critical energy demand during the busiest hours of the day, typically late afternoon and early evening in the summer when air conditioning demand is highest and mornings in the winter when furnaces and heaters are heating up. Reducing electricity demand during these peak periods when electricity rates are at their highest reduces costs for American families while freeing up capacity on overburdened energy distribution networks. Builders participating in the DOE ZER-MH program are eligible for a tax incentive via the 45L tax credit, which helps to offset the costs of ZER-MH upgrades, enabling builders to offer these certified homes at no additional cost. Together these factors enable manufactured homes to offer home buyers a housing option that is both affordable to finance and affordable to operate, with lower monthly mortgage payments and lower monthly energy bills.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

In-Situ Observation of FCC→HCP Transformation-Induced Plasticity Behavior During Dynamic Deformation of CoCrNi Multi-principal Element Alloys

CoCrNi multi-principal element alloys (MPEAs) have potential as high toughness, blast resistant materials, but limited studies of their high strain rate behavior have been performed. Their high toughness has been attributed to low stacking fault energies and the activation of toughness enhancing deformation mechanisms, such as twinning- and transformation-induced plasticity (TWIP and TRIP, respectively), that may be affected by increased strain rate. This work examines a series of CoCrNi MPEAs tested with a miniaturized pressure bar, while simultaneous synchrotron x-ray diffraction and imaging are performed, and compares these results to those collected during quasi-static deformation at elevated temperatures. Here, the collected diffraction indicates that TRIP was active at elevated strain rates for the most Co-rich alloys containing 50 or 55 at. pct. Co, while TRIP behavior was suppressed at elevated strain rates in a 40 at. pct. Co alloy anticipated to experience TRIP. The suppression of TRIP in the lower Co alloy at high strain rates is attributed to adiabatic heating, consistent with suppression of TRIP as a result of elevated temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Two-stage crash process in resistive drift ballooning mode driven ELM crash

We report a two-stage crash process in edge localized mode (ELM) driven by resistive drift-ballooning modes (RDBMs) numerically simulated in a full annular torus domain with a scale-separated four-field reduced MHD (RMHD) model using the BOUT++ framework. In the early nonlinear phase, the small first crash is triggered by linearly unstable RDBMs, and m/n=2/1 magnetic islands are nonlinearly excited by nonlinear coupling of RDBMs as well as their higher harmonics. Here, m is the poloidal mode number, n is the toroidal mode number, the q = 2 rational surface exists near the pressure gradient peak, and q is the safety factor. Simultaneously, middle-n RDBM turbulence develops but is poloidally localized around X-points of the magnetic islands, leading to the small energy loss. The second large crash occurs in the late nonlinear phase. Higher harmonics of m/n=2/1 magnetic islands well develop around the q = 2 surface via nonlinear coupling and make the magnetic field stochastic by magnetic island overlapping. Turbulence heat transport develops at X-points of higher harmonics of m/n=2/1 magnetic islands, resulting in the turbulence spreading in the poloidal direction. The large second crash is triggered when the turbulence covers the whole poloidal region so that the magnetic island generation and magnetic field stochastization before the large crash can be interpreted as ELM precursors. It is concluded that the ELM trigger is attributed to the turbulent spreading in the poloidal direction in synchronization with the magnetic field stochastization and the crash is driven by E × B convection rather than the conventional Rechester–Rosenbluth anomalous electron heat transport.

Seto, H. (ORCID:0000000259278643)↗
DOE OSTI2023

Computationally Guided Design of Multiple Impurities Tolerant Electrode (Final Report)

The current project was based on a combined experimental and computational approach, which can help recommend better cathode materials under multiple impurities conditions. The PI will mainly take in charge of experimental and computational thermodynamics of the selected cathode materials for the SOFC applications under multiple impurities. While the co-PI will run the electrochemical tests of the cathodes recommended and eventually the long-term degradation tests. At the end of the project, a multiple tolerant cathode material based on the combined experimental and computational approach will be recommended and the reliability of the commonly used accelerated testing will be evaluated. It will address multiple impurities poisoning effect of SO2, CO2, Cr and H2O on the LSM, LSCF and LNO cathodes by identifying the formation of the detrimental secondary phases by XRD, SEM and TEM techniques. And further recommended cathode material will be subjected to electrochemical testing and the most promising ones will be applied to long-term tests. The hybrid approach the PI proposed will not only be applied to the design of multiple impurities tolerant cathodes but will also be considered in the future oxygen electrode applications in SOECs or reversible SOCs. This hybrid computational and experimental approach includes four sections: 1) Investigation of single impurity poisoning on LSM, LSCF and LNO cathodes in the presence of SO2, CO2, Cr. In this section, LSM, LSCF cathodes from FuelCellMaterials and LNO cathodes from Sol-Gel synthesis will be heat-treated in the above single impurity. And the formation of the secondary phases as well as the corresponding simulations will be cross compared, which shows good agreement between each other. Meanwhile, the accelerated testing approach will be evaluated in these systems compared with the previous published work to understand the reliability of the approach. 2) Investigation of multiple impurities poisoning on LSM, LSCF and LNO cathodes in the presence of Cr+H2O, SO2+Cr and SO2+Cr+H2O conditions. We have also applied these 3 candidate cathodes under these multiple impurities’ conditions and the long-term degradation mechanism of the multiple impurities will be understood with the help of the combined experimental and computational approach. Meanwhile, the synergistic effect of those impurities will be compared with the individual ones in the same cathode system to further reveal the actual operating conditions. 3) Electrochemical testing and polarization of the recommended cathode. We have demonstrated very low polarization resistance in LSCF (core)-LSM (shell) electrodes using MSD process and analyzed impedance spectra using DRT analysis and confirming that the low polarization resistance in LSM infiltrated MSD cells is due to reduction in polarization resistance associated with O2-adsorption process. 4). Long-term degradation testing. We have developed a versatile MSD-based process to deposit various continuous coatings onto porous scaffolds and established baseline for longer term Cr-impurity testing in future projects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning assisted prediction of tungsten heavy alloy plasma facing component performance for fusion energy applications

Tungsten and tungsten heavy alloys (WHAs), known for their remarkably high hardness, durability, and corrosion resistance, play a critical role in the thriving development of nuclear fusion reactors in recent years. However, the exploration in tungsten alloys for the nuclear-related applications has been limited by the difficulty of manufacturing and the complexity of experiments to reproduce the environment of nuclear reaction. Therefore, this project aims to utilize nanoscale simulation methods such as density functional theory (DFT) and molecular dynamics (MD) with the help of machine learning techniques to not only understand the mechanisms of tungsten alloys but also allow us to computationally predict their mechanical behaviors under extreme environments. One critical problem of the application of WHAs in nuclear reactors is the surface melting. In the current design of the SPARC reactor, the WHA, W97Ni2.1Fe0.9 or W97NiFe, is chosen to be the first wall components to confine the plasma where the particles are fiercely moving and colliding into each other to create nuclear fusion reaction. This process will generate extremely high heat flux onto these WHA tiles, leaving high surface temperature that could possibly melt the surface of the WHA tiles, As illustrated in Fig. 1(a). a laser experiment previously done illustrates that a rough surface damage would be made after the surface melting where the matrix area mainly composed of nickel and iron as shown in Fig. 1(b), will first melt and then leave vacancies between these tungsten grains. Unfortunately, these kinds of roughness on the first-wall components could be deadly to the plasma inside a Tokmak reactor because the heat that is supposed to dissipate at a designed ratio through the tiles may in turn be excessively absorbed and accumulated on any uneven area of the surface, which will eventually make the whole nuclear reaction fail. In this project, we will introduce a machine learning potential, Allegro, based on DFT calculation and then build a MD model for W-Ni-Fe alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental observation of spin glass state in the highly disordered quaternary Heusler alloy FeRuMnGa

The realization of the spin-glass (SG) state in Heusler alloys is very rare despite the presence of inherent structural and elemental disorder in those compounds. Although a few half- and full-Heusler alloys are known to exhibit the SG state, there is hardly any manifestation of the same in cases of quaternary Heusler compounds. Here we report the observation of a SG state in a highly disordered equiatomic quaternary Heusler compound: FeRuMnGa where the SG state is in between the canonical SG and the cluster glass. Different intricate features of the SG state including nonequilibrium magnetic dynamics at low temperatures in the compound are unveiled through our comprehensive magnetic, heat capacity, and neutron-diffraction studies. Further the structural disorder in the sample is neither conventional A2 - nor B2 -type whereas those two types are commonly observed for Heusler compounds. The presence of disorder also plays a significant role in electron transport properties of the alloy, which is reflected in its exhibition of semimetallic behavior and anomalous Hall effect at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Challenges to Reliable Production Nitrogen Doping of Nb for SRF Accelerating Cavities

Over the last several years, alloying of the surface layer of niobium SRF cavities has been demonstrated to beneficially lower the superconducting RF surface resistance. Nitrogen, titanium, and oxygen have all been demonstrated as effective alloying agents, occupying interstitial sites in the niobium lattice within the RF penetration depth and even deeper, when allowed to thermally diffuse into the surface at appropriate temperatures. The use of nitrogen for this function has been often termed ’nitrogen doping’ and is being applied in the LCLS-II and LCLS-II HE projects. We report characterization studies of the distribution of nitrogen into the exposed niobium surface and how such distribution is affected by the quality of the vacuum furnace environment in which the doping takes place, and the complexity of nitride crystal growth on different grain orientations of surface niobium. Using state-of-the-art quantification methods by dynamic secondary ion mass spectrometry (SIMS) depth profiling in niobium, we identify several phenomena involving furnace-sourced contamination. We also highlight a potential issue with N₂ flow constraints from the flange ’caps’ used during heat treatments.

Reece, Charles↗
DOE OSTI2021

Short-range thermal magnon diffusion in magnetic garnet

Here, using the spin Seebeck effect (SSE), we study the propagation distance of thermally induced spin currents inside a magnetic insulator thin film in the short-range regime. We disambiguate spin currents driven by temperature and chemical potential gradients by comparing the SSE signal before and after adding a heat-sinking capping layer on the same device. We report that the measured spin decay behavior near the heat source is well accounted for by a diffusion model where the magnon diffusion length is in submicron range, in other words, two orders of magnitude smaller than previous estimates inferred from the long-range behavior. Our results highlight the caveat in applying a diffusive theory to describe thermally generated magnon transport, where a single decay length may not capture the behavior on all length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Relationship between magnetoresistance behavior and magnetic states in intercalated compounds Fe x TiS 2

Here, temperature and field-dependent neutron powder diffraction (NPD) measurements have been performed to reveal the nature of the unusual evolution of the magnetoresistance behavior with increasing Fe content in the intercalated compounds Fe x TiS 2 (with x=0.25,0.33,0.50,0.55) synthesized by solid-phase reaction method with prolonged homogenization heat treatment. As derived from neutron diffraction measurements, both the Fe 0.25 TiS 2 and the Fe 0.50 TiS 2 compound exhibit an antiferromagnetic (AFM) order below their respective Néel temperatures T N ≈52 K and T N ≈140K, which results in the presence of a large magnetoresistance accompanying the field-induced phase transition from AFM to the ferromagnetic (FM) state. At low temperatures, this AFM-FM transition is irreversible, confirmed by the irreversibility of changes in the NPD patterns and the presence of remnant magnetoresistance. In contrast, Fe 0.33 TiS 2 shows short-range magnetic order at T f ≈44 K due to a triangular network of intercalated Fe atoms and frustrations of exchange interactions with a field-induced FM alignment of Fe magnetic moments in an applied magnetic field as revealed by NPD measurements. The field-induced transformations of the cluster glass magnetic state in this compound lead to a significant decrease in electrical resistivity. According to NPD data, a reduced impact of an external magnetic field on the electrical resistivity of the compound Fe 0.55 TiS 2 can be ascribed to the presence of ferromagnetic or ferrimagnetic order in compounds with the Fe concentrations above x=0.50. The results obtained indicate that the distribution of the Fe atoms, along with their concentration in Fe x TiS 2 layered compounds, plays a decisive role in the formation of the magnetic state and the behavior of the magnetoresistance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-Entropy Alloys for Accelerator Beam Window Applications

Development of novel high-entropy alloys (HEAs) is currently underway for potential use as beam windows in future multi-megawatt target systems at Fermilab. HEAs encompass a new class of materials with a vast design space allowing for material properties to be tailored for particular applications and to potentially offer improved resistance to beam-induced radiation damage and thermal shock effects. The alloy systems being studied consist of several compositions of AlCoCrMnTiV with 4 6 component elements. These alloys are all predicted by CALPHAD simulation to have a single-phase BCC crystal structure and low density, with some compositions displaying ordered, nanoscale precipitates. This presentation will briefly discuss alloy design and synthesis before giving a detailed description of the characterization studies of these HEAs in both the pristine state and post-irradiation by low-energy heavy ions to high damage levels. Electron microscopy techniques to quantify elemental homogeneity and composition, determine grain size, shape, and orientation, and quantify lattice parameters, defect structures and precipitate phases are all being used to study alloy microstructures. Mechanical properties of the alloys at the microscale will be reported. The evolution of these properties as a function of radiation damage will also be described. Bulk thermal characteristics of these HEAs have been tested to measure specific heat capacity and coefficient of thermal expansion as a function of temperature. To determine bulk tensile properties a miniature tensile testing apparatus is under development; it s commissioning will be covered briefly. The talk will conclude with our plans for alloy down-selection.

Burleigh, A. [Fermilab]↗
DOE OSTI2024

High-Temperature Oxidation Behavior of Wrought and Additive Manufactured Alloys in Direct-Fired Supercritical CO2 Power Cycle Environments

Materials selection is a key concern for corrosion resistance in elevated temperature and pressure direct-fired supercritical CO2 (sCO2) power cycles. The effect of elevated pressure on corrosion resistance can be critical, as impurities within the supercritical fluid such as H2O and O2 can have additional corrosion effects than found at ambient pressure conditions. Several wrought nickel-based commercial alloys (230, 263, 282, 617, 625, 740H) were exposed to direct-fired conditions (95CO2 – 4H2O – 1 O2) at 750 °C at both atmospheric pressure (0.1 MPa) and supercritical conditions (20 MPa) for over 2,000 h. Additionally, additive manufactured (AM) 282 produced by laser powder bed fusion (LPBF) were also included in the exposures. Supercritical conditions affected the oxidation behavior of several alloys. Most notably, water-vapor assisted Cr-oxide volatilization was observed at 750 °C supercritical conditions. Many alloys did not form sufficient protective Cr-oxide scales at supercritical conditions, leading to increased oxidation at elevated pressure. Differences in oxidation behavior were also observed for 282 AM samples compared to their wrought counterparts, with the differences also dependent on the LPBF build parameters and post-production treatments (heat treatment and surface finishing procedures). The AM samples formed Mo-rich carbides in the alloy beneath the oxide following sCO2 exposure, implying potentially higher levels of carbon uptake relative to the wrought

Carney, Casey↗