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DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2025

Detection of Br and I as atomic anions using liquid sampling – atmospheric pressure glow discharge/Orbitrap mass spectrometry

The quantification and determination of halogen isotope composition is essential in many fields including geochemistry and nuclear forensics. Detection of the halogen elements is commonly attempted with inductively coupled plasma mass spectrometry (ICP-MS) however, there are multiple challenges faced. Most significantly this includes very poor ionization efficiency and the potential for isobaric interferences from either matrix or plasma species. Thus, sensitivity and accuracy are often limiting issues. Here a liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source is coupled with an ultrahigh resolution Orbitrap MS to perform halogen detection of bromine and iodine as initial analytes. This facilitates simple and sensitive detection of these elements as atomic anions from simple aqueous salt solutions. Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) energies were optimized to produce the maximum response of Br − and I − . The LS-APGD conditions were optimized using a design of experiments (DOE) approach for Br − and I − concurrently. Response curves for Br − and I − solutions determined limit of detection (LOD) values of 50 pg and 5 pg, respectively, in 20 μL aliquots. The curves indicated response factors of 0.67 for 79 Br − and 0.90 for 127 I − . Br isotope ratios were determined with precision between 0.7 and 7.3% RSD, with the isotope ratios determined with precision 0.8% RSD at concentrations above the limit of quantification. Preliminary tests were conducted to evaluate the effect of different cations (Na + , K + , and Mg 2+ ) on Br − responses, with no discernible impacts observed on the halogen signal responses. This study demonstrates the potential use of the LS-APGD-Orbitrap-MS for the detection of multiple halogens while avoiding interferences, minimizing sample preparation, and overcoming ionization barriers.

Lang, Dehlia A. [Clemson University, SC (United St↗

DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Process design for recovering rare-earth elements from mine tailings with low rare-earth concentrations via sequential leaching and solvent extraction

Rare earth elements (REEs) are essential for advanced technologies and yet face significant supply chain risks due to their concentrated global production and limited domestic availability. Addressing this challenge requires efficient processes capable of upgrading low-grade secondary resources such as mine tailings. In this study, we developed a novel separation flowsheet that integrates sequential leaching and 2-stage solvent extraction (SX) processes to recover high-purity heavy REEs (HREEs) and light REEs (LREEs) from a simulated mine-tailing concentrate containing 2.4 wt% total REEs (TREEs; 0.6 wt% LREEs and 1.8 wt% HREEs). Sequential leaching with controlled pH adjustment selectively precipitated REEs while retaining the large amount of impurities in the solution, producing an REE-enriched leachate by following leaching processes with roughly twice the REE concentration and half the impurity concentration compared to that of single-step leaching. The optimized SX flowsheet employed Cyanex 572 to extract HREEs and Fe over LREEs, followed by Fe removal using tributyl phosphate (TBP), while the raffinate stream was processed by SX with di(2-ethylhexyl)phosphoric acid (D2EHPA) to recover LREEs under optimized conditions balancing both extraction efficiency and purity. Although increased extractant availability in the organic phase improved LREE recovery, it also increased co-extraction of Ca, underscoring trade-offs in process optimization. Both HREE- and LREE-rich solutions were subsequently precipitated into solid products via oxalate precipitation, resulting in high-purity REE solids containing ∼92.0 wt% HREEs (∼95.7 wt% TREEs) and ∼92.8 wt% LREEs (∼94.0 wt% TREEs). In conclusion, this proof-of-concept study using simulated mine tailings demonstrates a promising approach for upgrading low-grade REE resources, while highlighting the need for future validation with real materials.

Mine tailings↗

DOE OSTI2025

Potential Cost Reduction in New Nuclear Deployments Based on Recent AP1000 Experience

The completion of Vogtle Units 3 and 4, despite significant cost and schedule overruns, a major bankruptcy, and a pandemic, demonstrates that the U.S. nuclear industry can still develop new supply chains, train a highly skilled workforce, and build large nuclear power plants. Vogtle, being a first-of-a-kind (FOAK) plant, faced overruns, but FOAK overruns can decrease with more deployments through a combination of design standardization and modularization, transferring and implementing lessons learned between consecutive projects, and innovation. This report quantifies the potential cost and schedule reductions in future AP1000 plants in the US and identifies the barriers in achieving those reductions. It starts with a summary and analysis of the cost and construction timeline data for AP1000 plants that started construction in the United States (Vogtle) and China. Motivated by the steep schedule reductions from the first to second series of AP1000 plants in China, a cost reduction model was developed to quantify potential reductions for future AP1000 builds in the US. Results show that within about 3 more builds, AP1000s can become economically attractive in large markets in the U.S., without leveraging the Investment Tax Credit (ITC) or Production Tax Credit (PTC), and much sooner if these tax credits are available. Importantly the financial risks for future AP1000 builds in the US might be much smaller than observed in the FOAK plants. The study also identifies various barriers for future builds including the availability of a supply chain and a skilled workforce, and emphasizes the importance of making investments to gather and implement lessons learned between consecutive projects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Tough and circular glass fiber composites via a tailored dynamic boronic ester interface

Glass fiber reinforced polymer (GFRP) composites are valued for their strength and cost-effectiveness. However, traditional GFRPs often face challenges for end-of-life recycling due to their non-depolymerizable thermoset matrices, and long-term performance due to inadequate interfacial adhesion, which can lead to fiber–matrix delamination. Here, in this work, we have designed dynamic fiber–matrix interfaces to allow tough and closed-loop recyclable GFRPs by utilizing a vitrimer, derived from upcycled polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (SEBS) with boronic ester (S-Bpin) and amine-based diol crosslinker. The boronic ester groups in S-Bpin form dynamic covalent bonds with the naturally present hydroxyl groups on the unsized GF surface, which eliminates the need for fiber sizing and enables facile closed-loop recyclability of both the fibers and the vitrimer matrix. The resulting strong fiber–matrix interface, depicted by the Raman mapping, leads to a 552% increase in-plane shear toughness (6.2 ± 0.3 MJ m -3 ) and 27% ultimate tensile strength (361 ± 89.2 MPa) compared to those of the conventional epoxy-based matrix (0.95 ± 0.05 MJ m -3 and 264 ± 59.7 MPa, respectively). The network rearrangement through dynamic boronic ester exchange enables fast thermoformability and repairability of micro-cracks at elevated temperatures. Additionally, both the matrix and composite demonstrate strong adhesion to various surfaces including steel and glasses exhibiting ≥6 MPa lap shear strength, which expands their suitability for diverse industrial applications. The readily created dynamic interface between boronic ester functionalized vitrimer and neat GFs presents a promising strategy for developing closed-loop recyclable, multifunctional structural materials, offering a sustainable alternative to non-recyclable thermoset GFRPs and contributes to a circular economy in composite materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗

DOE OSTI2025

Ampere-level co-electrosynthesis of formate from CO 2 reduction paired with formaldehyde dehydrogenation reactions

Current catalysts face challenges with low formate selectivity at high current densities during the CO 2 electroreduction. Here, we showcase a versatile strategy to enhance the formate production on p-block metal-based catalysts by incorporating noble metal atoms on their surface, refining oxygen affinity, and tuning adsorption of the critical oxygen-bound *OCHO intermediate. The formate yield is observed to afford a volcano-like dependence on the *OCHO binding strength across a series of modified catalysts. The rhodium-dispersed indium oxide (Rh/In 2 O 3 ) catalyst exhibits impressive performances, achieving Faradaic efficiencies (FEs) of formate exceeding 90% across a broad current density range of 0.20 to 1.21 A cm −2 . In situ Raman spectroscopy and theoretical calculations reveal that the oxophilic Rh site facilitates *OCHO formation by optimizing its adsorption energy, placing Rh/In 2 O 3 near the volcano-shaped apex. A bipolar electrosynthesis system, coupling the CO 2 reduction at the cathode with the formaldehyde oxidative dehydrogenation at the anode, further boosts the FE of formate to nearly 190% with pure hydrogen generation under an ampere-level current density and a low cell voltage of 2.5 V in a membrane electrode assembly cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2024

Low Melting Temperature Gallium–Indium Liquid Metal Anode for Solid-State Li-Ion Batteries

Solid-state Li-ion batteries are attracting attention for their enhanced safety features, higher energy density, and broader operational temperature range compared to systems based on liquid electrolytes. However, current solid-state Li-ion batteries face performance challenges, such as suboptimal cycling and poor rate capabilities, often due to inadequate interfacial contact between the solid electrolyte and electrodes. To address this issue, we incorporated a gallium–indium (Ga–In) liquid metal as the anode in a solid-state Li-ion battery setup, employing Li 6 PS 5 Cl as the solid electrolyte. Operating at room temperature, this configuration achieved an initial capacity of 389 mAh g –1 and maintained 88% of this capacity after 30 cycles at a 0.05 C rate. It also demonstrated a capacity retention of 66% after 500 cycles at a 0.5 C rate. In comparison to solid anode materials, such as tin, the Ga–In liquid metal exhibited superior cycling stability and rate capacity, which is due to the self-healing and fluid properties of the alloy that ensure stable interfacial contact with solid electrolytes. In situ X-ray diffraction (XRD) and ex situ scanning electron microscope (SEM) analyses revealed that indium does not directly participate in the lithiation/delithiation process. Instead, it helps maintain the alloy’s low melting point, facilitating its return to a liquid state after delithiation. In a comparative analysis of stack pressure during cycling in cells utilizing Ga–In liquid metal and tin, the Ga–In liquid metal cell demonstrated an ability to buffer pressure increases associated with deformation. In conclusion, these findings suggest a promising approach for enhancing solid-state batteries by integrating liquid metal anodes, which improve interfacial contact and stability.

Alloys↗

DOE OSTI2025

A Versatile Simulated Data Transport Layer for in Situ Workflows Performance Evaluation

In situ processing does not only allow scientific applications to face the explosion in data volume and velocity but also to address the time constraints of many simulation-analysis workflows by providing scientists with early insights about their applications at runtime. Multiple frameworks implement the concept of a data transport layer (DTL) to enable such in situ workflows. These tools are very versatile, directly or indirectly access the data generated on the same node, another node of the same compute cluster, or a completely distinct node, and allow data publishers and subscribers to run on the same computing resources or not. This versatility puts on researchers the onus of taking key decisions related to resource allocation and how to transport data to ensure the most efficient execution of their in situ workflows. However, domain scientists and workflow practitioners lack the appropriate tools to assess the respective performance of particular design and deployment options. In this paper we introduce a versatile simulated DTL designed to provide researchers with insights on the respective performance of different execution scenarios of in situ workflows. This open-source, standalone library builds on the SimGrid toolkit and can be linked to any SimGrid-based simulator. It facilitates the evaluation of the performance behavior, at scale, of different data transport configurations and the study of the effects of resource allocation strategies. We demonstrate the scalability, versatility, and accuracy of this simulated DTL by reproducing the execution of two synthetic benchmarks and of a real-world in situ workflow composed of an MPI application and a parallel data analysis. Results of simulations run on a single core show that the proposed library can simulate the interactions of tens of thousands of simulated processes deployed on two interconnected commodity clusters in a few seconds, and the execution by a thousand simulated processes of an in situ workflow in less than three minutes.

Suter, Fred [ORNL] (ORCID:0000000319021955)↗

DOE OSTI2026

Developing the hybrid scenario in DIII-D and KSTAR for W-compatible long-pulse operation

Long-pulse, high-performance operation with tungsten plasma-facing components is a crucial challenge for achieving steady-state (SS) goals in ITER and future fusion devices. This paper presents a joint international effort between the DIII-D and KSTAR tokamaks aimed at demonstrating the compatibility of the high q 95 ~ 6 hybrid scenario with a tungsten divertor and superconducting coils. A DIII-D hybrid scenario recipe is successfully tailored to KSTAR’s operational constraints, which facilitates cross-device studies. The results show successful 30 s long-pulse operation in KSTAR at a normalized beta (β N = 2.4) with a benign m/n = 4/3 mode and effective mitigation of tungsten accumulation. Despite this success, the KSTAR scenario exhibits a greater than 25% reduction in thermal confinement compared to the DIII-D reference. To investigate this discrepancy, we performed a predictive TGYRO validation study, which suggests that the difference is likely caused by a weaker temperature pedestal in the KSTAR discharge due to a higher and narrower density pedestal. In addition, such lower thermal confinement leads to higher impurity accumulation, possibly due to a change in neoclassical transport with increased inward pinch, which further degrades global confinement. We also report that stationary, long-pulse operation with sustained thermal confinement requires consistent fueling and wall conditioning. These findings highlight the importance of optimized fueling and real-time wall conditioning in achieving a high temperature pedestal and strong global confinement during stable long-pulse operation. We propose potential approaches toward fully SS, long-pulse operation, including real-time wall conditioning using an impurity dropper in KSTAR and future tokamaks.

DIII-D↗

DOE OSTI2025

Lithium Divertor Targets and Walls for the ASTER Liquid Stellarator Reactor, Distributed Divertor

Stellarators may have advantages for certain liquid metal options as Plasma Facing Components (PFC) for divertor targets and first walls due to the wide range of possible magnetic configurations, which additionally are free of disruptions and fast field variations. In a previous work (V. Queral et al., IEEE Trans. Plasma Sci. 52, 2024), a concept of stellarator reactor (ASTER-CP) based on swirling Li-molten salts and liquid lithium floating on the molten salt as PFC was presented. The divertor matters were not studied then and, thus, they are being studied and experimentally tested now. The ASTER-CP reactor concept, the initial liquid metal experiments and potential concepts for the ASTER-CP divertor and first wall are reported. Concerning the experiments, several small scale experiments of galinstan in a small rotating cylinder under magnetic field have been produced, including one experiment with high viscosity galinstan-mixture for increased thickness of layer. An experiment of floating lithium on the molten salt LiCl-PbCl2 gave fast volatilization/decomposition of the molten salt. Particularly for divertors, the traditional free-flow, Capillary Porous Systems and ‘divertorlets’ have been studied for application to ASTER-CP. Surface waves (hot spots), lack of enough surface fluid turbulence and excessive fluid speed are the main issues found in fast free-flow. The perhaps original concept of Distributed Divertor and Equi-power Surface is tentatively proposed and studied, taking advantage of stellarator fields and low recycling regime.

distributed divertor↗

DOE OSTI2025

Microstructure characteristics of LPBF&HIP fabricated graded composite transition joint between ferritic steel and austenitic stainless steel

Graded composite transition joints (GCTJs) offer a promising alternative to conventional dissimilar metal welds (DMWs) by enabling smooth compositional and microstructural transitions. However, GCTJs fabricated solely through additive manufacturing (AM) face challenges such as heat accumulation, complex parameter control, and elemental segregation. In this study, we propose a novel approach that relies on AM to design a spatially graded structure in one alloy and then employs hot isostatic pressing (HIP) as a diffusion bonding method to join it with a second alloy. Here, this method combines the flexibility of AM with the powder net-shaping advantage of HIP. Specifically, a series of closely packed austenitic stainless steel 304 conical structures were printed using laser powder bed fusion (LPBF) and then combined with ferritic steel P91 powder via HIP. By using electron backscatter diffraction (EBSD), electron probe microanalysis (EPMA), and transmission electron microscopy (TEM) techniques, the microstructure characteristics of the GCTJ of 304&P91, especially the interdiffusion zone (IDZ), have been systematically investigated. The microstructure at the interface transitions from austenite-ferrite (A+F) to austenite-martensite-ferrite (A+M+F), and finally to martensite-ferrite (M+F) due to diffusion. Additionally, the diffusion width between 304 and P91 increases with the volume fraction of P91. This unique design also ensures a gradual transition in both hardness and thermal expansion coefficient from 304 to P91, thereby enabling a smooth gradient in functional properties. Overall, this study proposes a novel approach for fabricating GCTJs and contributes to advancing design concepts in the field of dissimilar metal joining.

Additively manufacturing (AM)↗

DOE OSTI2025

Impact of the d 0 transition metal on local structural transformations in disordered rock salt cathodes

Although it is widely accepted that the long-range (average) crystal structure plays a critical role in determining the electrochemical performance of battery materials, the relationship between local structural features and electrochemical performance is rarely studied. Disordered rock salt oxides (DRX), which have become serious contenders for next generation Li-ion electrode materials, provide an ideal platform for exploring correlations between local structure and electrochemical performance as they exhibit a simple face-centered cubic structure and combine long-range disorder and short-range order on the cation sublattice. This work examines the Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 series of DRX cathodes and investigates the links between local structure rearrangements and capacity activation. The end-member Li 1.1 Mn 0.7 Zr 0.2 O 2 compound exhibits a low capacity in the as-synthesized state, attributed to unfavorable short-range order that hinders Li-ion transport, yet its capacity increases seven-fold, from 20 to 140 mAh g −1 , after chemical delithiation followed by a 400 °C heat treatment. Capacity activation is associated with the appearance of local spinel-like structural features that depart from the short-range order originally present in the material, without significant change to the bulk composition and average crystal structure. Investigation of a series of Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 (x ≤ 0.2) DRX compounds reveals that the correlation length of the spinel-like ordering that emerges during the heat treatment strongly depends on the Zr : Ti ratio. Yet, dramatic capacity activation and electrochemical (pseudo-)plateaus reminiscent of Mn-based spinel cathodes are observed for all compounds irrespective of the size of the ordered domains. To explain this phenomenon, we propose that the DRX phase undergoes a complete transformation to a spinel-like domain structure, which improves bulk Li-ion transport regardless of domain size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗