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431 records · Page 9

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing

Development of a high fidelity CFD model for solvent evaporation and transport in porous structure during battery electrode drying

An efficient battery manufacturing process is the key to the mass production of Electric Vehicles (EV), in which drying is one of the most energy-intensive steps significantly influencing the battery cell performance. An accurate 3D CFD model for drying is essential for predicting the drying mechanism and optimizing its parameters. By optimizing the drying process, it is possible to reduce energy consumption and cost during battery manufacturing, minimize binder loading and maximize active material loading to achieve superior electrochemical performances and facilitate wider and faster public adoption of EV. This project aims to optimize the drying process during electrode manufacturing by leveraging high-fidelity, porous electrode simulations for solvent evaporation. By optimizing this process, we seek to reduce energy consumption during battery manufacturing, while minimizing binder loading and maximizing active material loading, with the overall goal of enhancing electrical vehicle performance.

Horner, Jeffrey Scott [Sandia National Laboratorie

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE

Time-Resolved Stochastic Dynamics of Quantum Thermal Machines

Steady-state quantum thermal machines are typically characterized by a continuous flow of heat between different reservoirs. However, at the level of discrete stochastic realizations, heat flow is unraveled as a series of abrupt quantum jumps, each representing an exchange of finite quanta with the environment. Here, in this work, we present a framework that resolves the dynamics of quantum thermal machines into cycles classified as enginelike, coolinglike, or idle. We analyze the statistics of individual cycle types and their durations, enabling us to determine both the fraction of cycles useful for thermodynamic tasks and the average waiting time between cycles of a given type. Central to our analysis is the notion of intermittency, which captures the operational consistency of the machine by assessing the frequency and distribution of idle cycles. Our framework offers a novel approach to characterizing thermal machines, with significant relevance to experiments involving mesoscopic transport through quantum dots.

full counting statistics

Advancing technologies for lignin-based jet fuel production in aqueous phase

Integrating lignin into a cellulosic ethanol plant for the co-production of lignin-based jet fuel (LJF) in aqueous phase offers a significant opportunity to boost operational efficiency, economic viability, carbon conversion, and the overall sustainability of biofuel and chemical production. LJF is lignin-structure-based jet fuel blendstocks primarily composed of alkyl-substituted mono-, bi-, and tri-cyclohexanes. It exhibits high energy density, potential for low emissions, and favourable blend characteristics that comply with drop-in specifications. An overview of lignin feedstock, catalytic processes, LJF chemical compositions, fuel properties tests, and techno-economic analysis (TEA) and life cycle assessment (LCA) indicate that (1) the reactivity of lignin plays a crucial role in its structure transformation to LJF molecules; (2) catalytic processing of lignin to LJF can occur through a simultaneous depolymerization and hydrodeoxygenation process, bypassing the intermediate step of producing and upgrading lignin-derived oil; (3) LJF's uniqueness molecules making it more promising for high energy content and low emission jet fuel properties for next generation sustainable aviation fuel (SAF); and (4) TEA and LCA demonstrate that LJF is not only potentially cost-effective but also offers favourable carbon footprint compared to other SAFs. In conclusion, this review highlights the most recent advancements in LJF technology, along with the challenges and opportunities that lie ahead in fulfilling its potential.

09 BIOMASS FUELS

Sintering effects on NpO 2 grain size and morphology: The role of precursor Np phase

Neptunium dioxide (NpO 2 ) is a key phase in nuclear material processing as a target material for the production of plutonium-238 ( 238 Pu) and has been historically synthesized via the calcination of a Np oxalate precursor. Alternative synthesis methods for NpO 2 are now more prevalent, necessitating their study and comparison with the more common oxalate route. The purpose of this work was to investigate the microstructural properties of NpO 2 synthesized from a nitrate-based Np precursor phase via the assessment of NpO 2 particle size and morphology as a function of calcination temperature and time. Scanning electron microscopy (SEM) was used to probe the primary grain size and morphology of NpO 2 after calcination at temperatures ranging from 700 to 1100 °C and hold times ranging from 1 to 10 h. Post-image analysis using ImageJ software enabled the quantification of mean particle diameter. This analysis indicated that particle diameter increases with both increasing calcination temperature and hold time. Primary particles were shown to be clumped in irregular patterns into the overall rough, blocky aggregates, but this macroscopic morphology was not affected by calcination time or temperature. Although trends in primary grain size of NpO 2 were consistent with available literature from other Np precursor phases, the macroscopic morphology of the NpO 2 aggregates was quite different than reported for other precursors. Through comparison with historical literature on Np oxalate, this work emphasizes the importance of Np precursor on the physical properties of NpO 2 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA

Formation and Shape Changing of Conductive Helical Ribbons via Deposition of Highly Stressed Films on Mechanically Responsive Substrates

Abstract This work demonstrates that the electrodeposition of highly stressed films on compliant ribbons is a robust process to obtain helical structures with excellent mechanical stability and potentially high thermal and electrical conductance. Electrodeposition on end‐tethered ribbons alters their axial and bending stiffness while imparting mechanical stress to drive the formation of a helix with a microscale diameter and pitch in a controlled and scalable manner. The process generates helices with diameters and pitches between 80 and 200 µm and lengths as large as several millimeters. The approach is amenable to parallel processing a large number of 3D structures on any substrate, including large‐area semiconductor wafers. This phenomenon is explained in terms of the change of stress gradients as material is added. Applications of the fabricated helices include antennas, metamaterials, and slow‐wave structures in frequency ranges not previously attainable.

Chemistry

Theoretical perspective on phase stability and polarization switching in ferroelectric hafnia

Fluorite-based ferroelectric materials are revolutionizing the application space of polar semiconductors. These materials leverage robust polarization of extremely thin films, compatibility with the silicon chip processing, and decades of manufacturing experience to enable a new generation of ferroelectric memory devices. As a new paradigm for ferroelectrics, understanding of phase transitions and the switching mechanism in fluorites is essential both for advancing applications and for fundamental science. In this article, we outline the recent progress that has been made to understand the relative phase stability, phase transition and order parameter coupling, ferroelectric switching through unique nucleation and growth processes, and how defects affect these phases and processes. The main challenges, opportunities, and next steps for leveraging these materials for next-generation devices are reviewed.

Condensed Matter Physics

Characterization of ceramic fuel powder packing fractions to support INFLUX

Triply periodic minimal surface (TPMS)-based structures show marked potential in novel nuclear reactor fuel designs, as their high surface area-to-volume ratio increases the efficiency of heat transfer out of the fuel, enabling safer, more innovative reactor designs. This milestone report addresses the role of dUO 2 powder processing route on the fill behavior of TPMS-based cladding shells to understand and advance the feasibility of manufacturing TPMS-based nuclear fuel forms. dUO 2 powder was processed through either a dry granulation route, varying consolidation pressure, or through milling, varying milling time, milling method and milled size distribution. The lowest tapped bulk densities (TBD), but best powder flowabilities, were obtained when testing unprocessed dUO 2 powder which was prone to self-agglomeration and formed low-density spheroids. The highest TBD and lowest flowabilities were obtained when using powder produced by hammer-milling dUO 2 powder to pass through a 200-mesh sieve, which led to particles with angular morphologies. Powder produced by dry granulation exhibited TBD that varied according to the consolidation pressure used to form the initial pellets and exhibited improved flowabilities when compared to hammer-milled material. Because of the large span of granule sizes formed as well as the irregular shape associated with the granules, a packing fraction of 0.69 was achieved, exceeding the analytical solution for random close packing of mono-sized spheres. TPMS polymer shells were loaded with unprocessed, granulated, and hammer-milled dUO 2 powders, and their qualitative packing behaviors were analyzed using x-ray computed tomography (xCT). TBDs calculated after loading TPMS polymer shells were 10-20% lower when compared to tapped bulk density measurements taken in a glass graduated cylinder, indicating a non-trivial impact on the tapped bulk density of either the TPMS channel size, TPMS channel surface material, powder cohesiveness, or a combination of the two parameters. A metallic zircaloy-4 TPMS shell will be loaded with hammer-milled dUO 2 powder upon receipt of the shell from Oak Ridge National Laboratory (ORNL) and shipped to Idaho National Labs (INL) for subsequent hot isostatic pressing (HIP) densification experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry

MARVEL Reactor Digital Engineering Developments

The MARVEL reactor project has served to introduce a new generation of engineers to the processes required to transform a reactor design from simply an idea on paper into what will be an approved, constructed, and operational nuclear power system. Much as there have been advances in materials, analysis, and evaluation methodologies over the 50 years since the last reactor was built at INL, so too has the technology for managing the engineering process itself advanced. Digital Engineering tools and methods provide improved coordination between previously siloed engineering disciplines, reduced burdens of non-value-added data transcription processes and bring forward insights and improvements that might otherwise fall later in the design stage, where changes are much more costly. While the tools and techniques to support the full digital engineering vision are not yet complete, the MARVEL design processes provide valuable demonstrations and validations of key aspects and illuminate further areas for implementation by subsequent projects.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN

Biochemical properties of glycerol kinase from the hypersaline-adapted archaeon Haloferax volcanii

ABSTRACT Extremophilic microorganisms are promising candidates for industrial and analytical biocatalysis.Haloferax volcanii, a halophilic archaeon that prefers glycerol over glucose, channels this substrate into central metabolism through glycerol kinase (GK). Here, we report the biochemical properties ofH. volcaniiGK and its potential for biotechnological applications. An N-terminal His-tagged GK was functionalin vivoand yielded 3 mg/L culture—4.5 times more enzyme than a C-terminal StrepII-tagged version. Size exclusion chromatography revealed a glycerol-induced oligomeric shift from homodimer to a dimer-dominant state with detectable tetramer. The purified enzyme showed robust activity across broad pH and salinity ranges, with optimal activity at 100 mM NaCl and 50°C–60°C. It retained catalytic activity in 5%–10% dimethyl sulfoxide (DMSO) and crude glycerol containing methanol. His-GK was freeze-thaw stable and thermotolerant in 2 M NaCl buffers. In the absence of ligands, the enzyme’s melting temperature (T m ) was 80°C. Glycerol increased the T m to 85°C, and combinations with MgCl₂ (84°C) or ATP (88°C) provided further stabilization. The highest T m (89°C) occurred with all three ligands, suggesting a cumulative stabilizing effect. Kinetic analyses revealed positive cooperativity for glycerol, ATP, and Mg² + ; Mn² + and Co² + also supported the activity.H. volcaniiGK is the first known GK to exhibit positive cooperativity with glycerol and ATP. Its high stability and substrate flexibility support its use in biodiesel waste valorization,in vitrobiocatalysis, and biosensor development—applications demanding robust, specific, and stable enzymes. IMPORTANCE This study reveals thatH. volcaniiGK exhibits positive cooperativity for glycerol, ATP, and Mg² + , a kinetic feature not previously reported for glycerol kinases. This behavior enables steep, switch-like responses to small substrate changes, offering unique advantages for biosensor design. Importantly,H. volcaniiGK also maintains high activity under extreme salinity, temperature, broad pH, and solvent conditions that typically limit enzyme use in industrial and environmental applications. These traits make this GK an ideal candidate for enzyme-based biosensors, which often suffer from poor tolerance to pH, solvent, and thermal stress. Its robustness supports its use in cross-linked enzyme crystals, an immobilization method that enhances enzyme stability and reusability under harsh conditions. Moreover, GKs are already employed in Mg² + detection kits; however,H. volcaniiGK’s ability to tolerate and respond to diverse divalent cations (e.g., Co² + , Mn² + ) broadens their potential for pollutant detection and environmental monitoring. These features collectively positionH. volcaniiGK as a valuable biocatalyst for biosensing,in vitrodiagnostics, and biotechnological applications requiring both precision and durability.

Biotechnology & Applied Microbiology

First Wall Design of a Tokamak Pilot Plant Using a Monte Carlo Model for 3-D Heat Flux Deposition

We present a method for calculating the heat fluxes deposited on nonaxisymmetric tokamak first wall components, allowing for a first-of-its-kind model for power handling in the tokamak far scrape-off layer (SOL). The DIV3D Monte Carlo model features strict global power conservation and can calculate the finite cross-field plasma transport into magnetically-shadowed regions, which is significant when dealing with meter-scale shadows introduced by components such as poloidal limiters or antennas. As a case study, we apply the DIV3D model to inform the distribution of first wall poloidal limiters in an ARC-class reactor device. We demonstrate that discrete protection limiters can efficiently reduce peak heat fluxes on recessed breeder wall components in the presence of significant far-SOL plasma fluxes. By varying the toroidal periodicity and radial standoff depth of the limiters, we demonstrate one of the tradeoffs that must be considered in first wall design: more limiters provide greater protection, but at the cost of reduced breeding performance. We also present the impact that radial misalignments between limiters would have on first wall power loading.

Monte Carlo methods

A holistic platform for accelerating sorbent-based carbon capture

Abstract Reducing carbon dioxide (CO 2 ) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO 2 from various sources and deliver it to different sinks 1,2 . The quest for optimal solutions for specific source–sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design 3 and engineers on optimizing processes 4,5 , which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant’s lifetime, is typically one of the final steps 6 . Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO 2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Science & Technology - Other Topics

Automation of Solid Oxyde Electrolyzer Cell (SOEC) & Stack Assembly

The work performed under this agreement before the No/Go decision amounted to the following: 1. Preliminary manufacturing requirements defined by business sensitivity, baseline processes & risk assessment; 2. Process & materials development, in-line gauge exploration, and preliminary automation work used to reduce risk & further refine equipment specifications; 3. “Request for Quote” issued to multiple suppliers for major equipment; 4. Application specific equipment, hardware, and fixturing will be more beneficial to fabricate inhouse; 5.Current proposals & estimates meet cycle time, capital spend, & direct labor; space is on target but requires awareness; 6. Team will continue to explore opportunities to reduce risk (dry time, traceability); 7. Go / No-Go, Purchase Orders, Equipment build & commissioning next All other future task were not completed because during the Go /No-Go decision it was confirmed this project was a "No-Go" and did not proceed past BP1; these included the following: 1. Preliminary manufacturing requirements defined by business sensitivity, baseline processes & risk assessment; 2. Process & materials development, in-line gauge exploration, and preliminary automation work used to reduce risk & further refine equipment specifications; 3. “Request for Quote” issued to multiple suppliers for major equipment; 4. Application specific equipment, hardware, and fixturing will be more beneficial to fabricate inhouse; 5. Current proposals & estimates meet cycle time, capital spend, & direct labor; space is on target but requires awareness; 6. Team will continue to explore opportunities to reduce risk (dry time, traceability) In final preparation of the termination of the Automation of Solid Oxyde Electrolyzer Cell (SOEC) & Stack Assembly, Cummins has purchased no equipment with government funds, and there is no government-owned property in Cummins possession related to this project. Minimal labor and travel were completed and paid. This acts as the Final technical report and concludes our business with DOE on this grant.

36 MATERIALS SCIENCE

Spatiotemporal Studies of Soluble Inorganic Nanostructures with X‐rays and Neutrons

This Review addresses the use of X-ray and neutron scattering as well as X-ray absorption to describe how inorganic nanostructured materials assemble, evolve, and function in solution. We first provide an overview of techniques and instrumentation (both large user facilities and benchtop). We review recent studies of soluble inorganic nanostructure assembly, covering the disciplines of materials synthesis, processes in nature, nuclear materials, and the widely applicable fundamental processes of hydrophobic interactions and ion pairing. Reviewed studies cover size regimes and length scales ranging from sub-Ångström (coordination chemistry and ion pairing) to several nanometers (molecular clusters, i.e. polyoxometalates, polyoxocations, and metal-organic polyhedra), to the mesoscale (supramolecular assembly processes). Reviewed studies predominantly exploit 1) SAXS/WAXS/SANS (small- and wide-angle X-ray or neutron scattering), 2) PDF (pair-distribution function analysis of X-ray total scattering), and 3) XANES and EXAFS (X-ray absorption near-edge structure and extended X-ray absorption fine structure, respectively). While the scattering techniques provide structural information, X-ray absorption yields the oxidation state in addition to the local coordination. Our goal for this Review is to provide information and inspiration for the inorganic/materials science communities that may benefit from elucidating the role of solution speciation in natural and synthetic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Microstructural Engineering of Cu-Rich Nanoprecipitate formation in NiCoFeCrCu0.12 High-Entropy Alloy via Severe Plastic Deformation for Enhanced Irradiation Tolerance

This study demonstrates a defect-engineering approach for controlling Cu-rich precipitates in FeNiCrCoCu0.2 high-entropy alloys (Cu-HEAs), delivering a novel pathway for next-generation nuclear reactor materials with superior irradiation resistance. This work establishes that severe plastic deformation (SPD) processing via Shear Assisted Processing and Extrusion (ShAPE) and Friction Stir Layer Deposition (FSLD) creates dense dislocation networks and subgrain boundaries that fundamentally alter precipitation behavior under identical thermal treatments. Atom probe tomography (APT) indicates that SPD produces a metastable, atomically homogeneous solid solution that, upon moderate heat treatment (500°C/10 hour), develops remarkedly stronger Cu clustering than the as-cast counterpart. High-temperature exposure (800°C/100 h) produces near-pure Cu precipitates (~90 at% Cu) with significantly enhanced defect-sink efficacy in SPD-processed alloys: precipitate sizes of 50-60 nm and number densities of 2.7-3.8 × 10¹7 m?³, compared to 89 nm and 0.44 × 10¹7 m?³ in as-cast materials. Collectively, the findings establish defect-mediated precipitation control as a scalable, high-impact route to tailor sink density and distribution in HEAs, enabling microstructures optimized for irradiation tolerance and mechanical robustness in nuclear reactor environments.

Meher, Subhashish

Tree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments

In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH