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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Arsenic Removal by Continuous Flow-Through Iron Electrocoagulation and Downstream Steps under Environmentally Relevant Conditions

Arsenic (As) contamination in drinking water is a major health concern. This study investigated As(III) and As(V) removal by continuous flow-through iron electrocoagulation (EC) under varying iron doses and water chemistry conditions. Effective arsenic removal (>90% of 100 µg/L) was achieved with a low iron dose of 5 mg/L and a short EC reactor residence time of 11 s that was followed by flocculation and settling. Removal was primarily through adsorption of As(III) and As(V) to the EC-generated Fe(III) oxyhydroxide solids ferrihydrite and lepidocrocite. For influents containing As(III), oxidation of As(III) to As(V) also occurred. The removal processes occurred in both the EC reactor and downstream treatment. EC-generated reactive solids with high surface area and adsorption capacity provided better arsenic removal than with preformed adsorbents. The removed arsenic remained largely immobile in the EC-generated solids. The extent of arsenic removal was generally similar across pH 6 to 8. Phosphate (1 mg/L as P) inhibited the removal of both arsenic species. Dissolved silica (20 mg/L as SiO2) suppressed As(III) removal but had little effect on the amount of As(V) removed. In addition to thermodynamic effects on adsorption equilibria, pH and co-occurring phosphate and silica influenced removal rates by affecting iron oxidation, nucleation, and aggregation kinetics. The results integrate mechanistic understanding with treatment performance, providing insight relevant to both fundamental research and the application of EC in water treatment.

Arsenic, electrocoagulation, iron oxide, phosphate↗
DOE OSTI2021

Permafrost Promotes Shallow Groundwater Flow and Warmer Headwater Streams

Abstract The presence of permafrost influences the flow paths of water through Arctic landscapes and thereby has the potential to impact stream discharge and thermal regimes. Observations from 11 headwater streams in Alaska showed that July water temperatures were higher in catchments with more near‐surface permafrost. We apply a fully coupled cryohydrology model to investigate if the impact of permafrost on flow path depth could cause the same pattern in temperatures of groundwater discharging from hillslopes to streams. The model simulates surface energy and water balances, snow, and subsurface water and energy balances for two‐dimensional hillslope model cases with varying permafrost extent. We find that hillslopes with continuous permafrost have more shallow flow paths and twice as high rates of evapotranspiration, compared to hillslopes with no permafrost. For our simulated cases, 6.7% of the horizontal water flux moves through the top organic soil layers when there is continuous permafrost, while only 0.5% moves through organic layers without permafrost. The deeper flow paths in permafrost‐free simulations buffer seasonal temperature extremes, so that summer groundwater discharge temperatures are highest with continuous permafrost. Our results suggest that permafrost thawing alters groundwater flow paths and can lead to decreases in summer stream temperatures and reductions in evapotranspiration in headwater catchments. These changes are of potential importance for stream biotic components of ecosystems, however, the full impact remains unknown.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Tandem bulk oxygen diffusion and surface reactions in reducible metal oxides control redox cycle dynamics

The interplay between bulk oxygen diffusion and surface reactions in reducible metal oxides is key in heterogeneous catalysts, but direct measurements of oxygen mobility, transient kinetics, and in situ spectroscopies have been lacking. Here, we reveal complex dynamic behavior of ceria-zirconia by H 2 using transient kinetics via mass spectrometry and in situ Raman and near-ambient pressure x-ray photoelectron spectroscopies. Molecular dynamics simulations with a machine learning potential delineate competitive oxygen diffusion mechanisms, with an optimal mobility at intermediate reductions. We expose a compensation between vacancy availability and lattice distortion at intermediate to high reductions and Frenkel defects at low reductions, underscoring a potential deficiency of 16 O/ 18 O exchange experiments in deducing oxygen mobility. Vacancies in proximity require electron localization on Ce atoms further away. The continuous replenishment of surface oxygen results in a varying reduction rate, with H 2 dissociation being the rate-limiting step. Multiscale transient simulations, consistent with experiments, indicate catalysts of potentially spatially varying oxidation states. The approach is broadly applicable to reducible oxide materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Residential Solar-Adopter Income and Demographic Trends: 2022 Update

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on data for roughly 1.9 million residential rooftop solar systems installed through 2019, representing 82% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: -Solar adopters generally skew towards higher incomes, though that trend continues to diminish over time. -Solar adopter incomes vary considerably and encompass many low-to-moderate income (LMI) households. -Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes. -Solar adopters differ from the broader U.S. population in terms of a variety of other demographic and socioeconomic measures. -State-level comparisons indicate that solar-adopters tend to live in neighborhoods with relatively high non-Hispanic White and Asian populations, and with relatively low Hispanic and Black populations.

14 SOLAR ENERGY↗
DOE OSTI2022

Residential Solar-Adopter Income and Demographic Trends: 2021 Update [Slides]

The report describes income, demographic, and other socio-economic trends among U.S. residential rooftop solar adopters. The report is based on data for roughly 1.9 million residential rooftop solar systems installed through 2019, representing 82% of all U.S. systems. With its unique size, geographic scope, and level of detail, this report is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and researchers. Key findings include the following: Solar adopters generally skew towards higher incomes, though that trend continues to diminish over time. Solar adopter incomes vary considerably and encompass many low-to-moderate income (LMI) households. Solar-adopter incomes are consistently higher for systems paired with battery storage, for host-owned systems, and for systems installed on single-family homes. Solar adopters differ from the broader U.S. population in terms of a variety of other demographic and socioeconomic measures. State-level comparisons indicate that solar-adopters tend to live in neighborhoods with relatively high non-Hispanic White and Asian populations, and with relatively low Hispanic and Black populations. In conjunction with the report, Berkeley Lab has published an updated accompanying set of online data visualizations that allow users to further explore the underlying data. Berkeley Lab is also offering related analytical support to states, local agencies, and other organizations on issues related to solar adoption among low-to-moderate income households.

14 SOLAR ENERGY↗
DOE OSTI2020

Extinction Dynamics of the Foot-and-Mouth Disease Virus Carrier State Under Natural Conditions

Foot-and-mouth disease (FMD) is one of the most economically important livestock diseases worldwide. Following the clinical phase of FMD, a large proportion of ruminants remain persistently infected for extended periods. Although extinction of this carrier state occurs continuously at the animal and population levels, studies vary widely in their estimates of the duration of persistent infection. There is a need for robust statistical models to capture the dynamics of persistent infection for the sake of guiding FMD control and trade policies. The goal of the current study was to develop and assess statistical models to describe the extinction of FMD virus (FMDV) persistent infection using data from primary longitudinal studies of naturally infected cattle and Asian buffalo in Vietnam and India. Specifically, accelerated failure time (AFT) models and generalized linear mixed models (GLMM) were developed to predict the probability of persistent infection in seropositive animals and identified carriers at the individual animal level at sequential time points after outbreaks. The primary studies were analyzed by country and combined using an individual-participant data meta-analysis approach. The models estimated similar trends in the duration of persistent infection for the study/species groups included in the analyses, however the significance of the trends differed between the models. The overall probabilities of persistent infection were similar as predicted by the AFT and GLMM models: 6 months: 99% (AFT) /80% (GLMM), 12 months: 51% (AFT) /32% (GLMM), 18 months: 6% (AFT) /5% (GLMM), 24 months: 0.8% (AFT) /0.6% (GLMM). These models utilizing diverse and robust data sets predict higher probabilities of persistence than previously published, suggesting greater endurance of carriers subsequent to an outbreak. This study demonstrates the utility of statistical models to investigate the dynamics of persistent infection and the importance of large datasets, which can be achieved by combining data from several smaller studies in meta-analyses. Results of this study enhance current knowledge of the FMDV carrier state and may inform policy decisions regarding FMDV persistent infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Recovery of Natural Gas Equipment Emissions into Gas Compression Engines for the Reduction of Potential Greenhouse Gas Emissions

Since the turn of the millennium, the United States (U.S.) oil and natural gas (ONG) industry has nearly doubled its natural gas production rate. As a result, the ONG industry has recently come under increasing scrutiny for its contributions to greenhouse gas (GHG) emissions. Consequently, various solutions to this problem have been proposed and formulated to reduce the impacts of GHG emissions on the environment. West Virginia University (WVU) have found it important to research the impacts of recovering vented gas streams into prime-mover engines. The U.S. Department of Energy (DOE) and National Energy Technology Laboratory (NETL) have granted WVU funding to research and develop a “Methane Mitigator” (M2) - a “Scalable Vent Mitigation Strategy to Simultaneously Reduce Methane Emissions and Fuel Consumption from the Compression Industry.” One of the main areas of interest for this research was the collection of emissions from natural gas equipment into a Caterpillar G3508J natural gas compression engine. The parameters being analyzed from the engine were brake-specific emissions and power output. The emissions sources considered for this research were pneumatic controllers (PCs), reciprocating compressor vents, and the engine’s open crankcase breather. The compressor vent and PC emissions were simulated using a mass flow controller (MFC) and flowed into the engine using two separate methods: (1) directly into the air intake, and (2) through a retrofitted closed crankcase ventilation system (CCV), serving as a buffer volume. The crankcase emissions were quantified without the CCV, and the impact on exhaust emissions from circulating the crankcase gases into the intake was measured. The simulated compressor vent and PC flows from the MFC had limited effect on the steady state operation of the engine and resulting performance. When the simulated flows were fed directly into the engine’s air intake, the changes within the engine’s continuous performance and emission parameters were larger but lasted for shorter durations. Conversely, when the simulated flows were fed into the CCV before entering the air intake, the changes in the engine’s performance and emission parameters were less pronounced for continuous analysis but lasted for longer durations. In either case, the continuous emission changes in both emissions and performance varied in size depending on the test scenario being run, but the cycle average changes in emissions and performance showed little impact overall compared to the engine’s baseline operation. As a result, the inclusion of a CCV shows a decrease in baseline carbon dioxide equivalent (CO2-eq.) engine emissions (from combined exhaust and open crankcase) of almost 4%. Likewise, the CCV inclusion reduced baseline total methane (CH4) from combined exhaust and open crankcase by upwards of 16%. These atmospheric emissions only decreased further with the inclusions of collected PC and compressor vent flows. The resulting changes in time-averaged rated exhaust behavior (or lack thereof) prove that the proposed M2 system could likely be deployed at sites with modern lean-burn natural gas engines as a viable option for reducing and eliminating potential GHG sources that would have otherwise been unutilized as energy sources.

03 NATURAL GAS↗
DOE OSTI2025

From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)

Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling Value Flows in Utility Rate Structures

As the increased adoption of distributed energy resources continues to challenge flat utility rate structures, time-varying rates and more dynamic mechanisms like transactive energy systems can better leverage customer-sited distributed energy resources to provide grid services. However, adopting new utility policies can be a timely process and requires a high level of transparency into the energy system. A wide range of stakeholders must understand who may be affected by policy changes and how. This work employs the valuation methodology developed under Pacific Northwest National Laboratory’s Transactive Systems Program to outline the functional differences in value flow under a series of conventional rate structures and a transactive energy system. The resulting value model illustrates the nuances that arise and highlights future avenues of work that will be necessary as utilities across the country continue to develop new rate structures and market mechanisms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Future grid mix impacts on whole-building life cycle assessment

Building construction and operation are a significant contribution to global greenhouse gas emissions, so understanding and mitigating emissions is crucial for reliable and realistic emissions accounting. Whole-building Life Cycle Assessment (WBLCA) is an emissions accounting method that considers lifetime environmental impacts of a building during its construction, operation, and eventual end-of-life. When performing WBLCAs, emission calculations from the building's operation over the entire building lifespan are typically based on today's energy grid mixes. This method does not consider changes or advancements in the clean energy proportion within the grid mix and can over or under-inflate results, skewing the ratio of embodied vs. operational environmental impacts. While a variety of prediction tools estimate what future grid emissions might be, predictions can vary widely. To predict the clean energy ratio within future grid mixes and the potential impact these changes might have on WBLCA, annual data from several existing U.S. grid models was averaged and probabilistic modeling was used to extend the usable projections of shorter forecasts. Results show that clean energy sources will likely continue to increase over time, although the rate of growth varies by model. On average, by 2085, the clean energy penetration of the grid is projected to reach ~81% and renewable energy is projected to reach ~71%, although no widespread consensus is reached. To understand how the future grid mix impacts lifetime building emissions within a WBLCA context, the team analyzed two 2021 IECC-compliant all-electric residential buildings: one built from traditional materials and construction processes and the other built with carbon sequestering materials and modular assembly, with a portion of energy generated on site. The results indicate that a moderate estimate of future electricity grid mixes shows a reduction of yearly operational emissions for traditional residential buildings of 55% between 2025 and 2085, and a corresponding reduction of 48% of total emissions over a 60 year building lifespan. This study offers a nuanced approach to account for the variability of future grid mix models and provides an average trend-line based on a robust collection of scenarios.

Life Cycle Assessment (LCA)↗
DOE OSTI2022

Tidal frequencies and quasiperiodic subsurface water level variations dominate redox dynamics in a salt marsh system

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. Whilst our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical drivers within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions subjected to different degrees of tidal inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied biogeochemical characteristics and hydrological interactions at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at 0.7 m depth at each marsh position. To understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, we used wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects key drivers of biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is nonlinear, and their lead–lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL shows relatively minimal change during events. Furthermore, this work highlights the importance of high frequency in situ measurements, such as Eh, to help explain factors that govern subsurface biogeochemistry and hydrological processes in salt marshes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Autogenous acid production using a regulated bio-oxidation method for economical recovery of REEs and critical metals from coal-based resources

Acid is a necessary component for the successful leaching of minerals containing REEs (rare earth elements) and other sources. Pyrite (FeS2), which is present in REE containing minerals, can be used to produce acid. Pyrite is utilized as a source to create ferric sulfate and sulfuric acid in the presence of A. Ferroxidans. Such microorganism-based bio-oxidation necessitates the establishment of optimal conditions for continual acid production. For efficient acid generation, additional optimization was done by varying residence time, solid percentage, and airflow rate. A step-by-step analysis and associated details are offered in this work, which used pyrite to create acid for leaching. For the bio-oxidation of pyrite with varying purity, an automated bioreactor was used. Scanning electron microscopy with energy-dispersive X-ray spectroscopy was used to examine the morphology and elemental content of bioleaching sediments. Eh and pH measurements were continuously done to ensure healthy operation of the bioreactors. The analysis suggested that, the Eh value in the range of ∼ 640–680 mV, pH range of ∼ 1.3–1.5 and residence time of 4–6 days with 6% solid feed are favorable conditions of operation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Transcription initiation as a control point in plastid gene expression

The extensive processing and protein-assisted stabilization of transcripts have been taken as evidence for a viewpoint that the control of gene expression had shifted entirely in evolution from transcriptional in the bacterial endosymbiont to posttranscriptional in the plastid. This suggestion is however at odds with many observations on plastid gene transcription. Chloroplasts of flowering plants and mosses contain two or more RNA polymerases with distinct promoter preference and division of labor for the coordinated synthesis of plastid RNAs. Plant and algal plastids further possess multiple nonredundant sigma factors that function as transcription initiation factors. The controlled accumulation of plastid sigma factors and modification of their activity by sigma-binding proteins and phosphorylation constitute additional transcriptional regulatory strategies. Plant and algal plastids also contain dedicated one- or two-component transcriptional regulators. Finally, transcription initiation thus continues to form a critical control point at which varied developmental and environmental signals intersect with plastid gene expression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Pulsed laminar falling films in vertical tubes: Maintaining a continuous liquid film with reduced film thickness

Falling-liquid-film heat transfer is important to a wide variety of industrial applications, including two-phase thermosyphons, falling-film evaporators in the food industry, heat exchangers in fuel-ethanol processing, and the chemical service industry. Particularly for laminar falling films, the heat transfer increases as the film thickness decreases, hence it is desirable to keep the films as thin as possible. This article presents a periodically-pulsed flow concept to reduce the average mass flow inside a vertical tube. The technique provides thinner films than can be achieved with continuous flow. A drainage model is adopted to predict the film thickness as a function of flow rate and location along the tube. The concept is confirmed experimentally by measuring the film thickness in real time in a circular tube using a laser-based absorption technique. A new heat-transfer-based film continuity measurement is also integrated into the optical film measurement setup. Water at ambient temperature and pressure is used for this work. Film thickness reductions of up to 60%, and liquid-film volume-flow reductions of up to 80% compared to the minimum continuous-flow film have been obtained. The time-varying film thickness profile can also be controlled to a limited extent, which may be of interest for process industries.

42 ENGINEERING↗
DOE OSTI2020

The effect of moisture on hydrocarbon-based solvent liquefaction of pine, cellulose and lignin

In this report we examined the effect of moisture on the solvent liquefaction of loblolly pine, cellulose, and lignin in tetralin at 280 °C and pressures ranging from 15 to 70 bar. Solvent liquefaction experiments were conducted in a quasi-batch reactor capable of continuous pressure control and external vapor condensation. Moisture was varied by the controlled addition of de-ionized water. Control over the system pressure subsequently impacted the removal of water vapor from the reactor. Liquid yield decreased by 25, 21, and 35 wt%, for pine, cellulose, and lignin, respectively, when moisture content was increased from 1 to 50 wt% at 42 bar. Humins were observed in the solid residue from liquefaction of wet cellulose. Wet lignin yielded a substantial amount of solid residue compared to dry lignin with a corresponding decrease in total phenolic monomer production. It was concluded that the ionic dissociation of water was an important factor in loss of liquid yield in the presence of water. Although water was less than 20 wt% of the solvent loading in these experiments, it strongly influenced the liquefaction of biomass and biomass components.

09 BIOMASS FUELS↗
DOE OSTI2025

An innovative radial gradient material design using hot isostatic pressing for applications in extreme environments

Functionally graded materials (FGMs) are highly advanced continuous or discontinuous structures whose structural and material properties vary along a singular geometric dimension either in the axial or radial direction. Here, the radial gradient FGM design makes for an optimal structural design to incorporate a bi-metallic structure with a copper-based high entropy alloy (Cu-HEA) with good mechanical properties and high irradiation resistance, and Chromium (Cr) with great corrosion resistance. This study focuses on the experimental design of a metal powder loading mechanism to fabricate a bi-metallic radial gradient structure using Cu-HEA and 99.9 % pure Cr metal powders. The powder loading strategy uses custom-designed concentric cylindrical dividers to separate the individual compositions. Two benchtop trial runs were performed for design optimization. The optimized design was then implemented to eventually load the HEA and Cr powders for consolidation via powder metallurgy hot isostatic pressing (PM-HIP). The electron microscopy analysis reveals the successful fabrication of the radial gradient structure with the chemical mapping analysis, demonstrating the gradual composition shift from the HEA at the center to the pure-Cr at the periphery via a three-step gradient.

High Entropy Alloys (HEAs)↗