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At least 343 records · Page 19

Advances in additive manufacturing, materials, and applications with AI/ML

There is high interest in making digital manufacturing a central facet of the new manufacturing landscape. However, in the materials science world, there is much work and opportunity to realize the full potential of artificial intelligence/machine learning (AI/ML) with regard to the structure–composition–processing–property (SCPP) relationship. For polymers (thermoplastics, thermosets, elastomers) and composites (nanocomposites), the origin of their high performance and even recyclability starts with design and formulation. Processing methods enable more property development based on curing, shape-factor forming, and anisotropic directionality. In subtractive manufacturing, high-performance and engineering polymers can be shaped and milled to very high tolerance and specifications and used as replacements for metals and alloys. In conclusion, this typically relies on digital manufacturing methods but tends to be wasteful in materials.

Lara-Ceniceros, Tania E. [Centro de Investigación ↗

X-ray fluorescence mapping: Insights into mesoscale structure impact on battery functional electrochemistry

Abstract Electron transfer and ion transport occurs over multiple-length scales ranging from the atomic to mesoscale within battery materials and electrodes. Micro-X-ray fluorescence (µ-XRF) is an important characterization tool as it can resolve structural, compositional, and redox information while providing insight into the spatial distribution of an electroactive material. In this work, µ-XRF mapping is used to probe the distribution of iron within thin planar slurry-based and thick porous carbon nanotube (CNT)-based magnetite (Fe 3 O 4 ) electrodes. Notably, the porous CNT-based electrode showed homogenous distribution of Fe within the electrode whereas the planar electrode demonstrated distinct Fe aggregates. This information was used to rationalize the electrochemistry observed by cyclic voltammetry and galvanostatic cycling. The thick porous electrode delivered 215% more capacity per gram of magnetite during the first discharge, consistent with increased electrode homogeneity enabling effective ion access and electron transfer. Graphical Abstract

Sadique, Nahian↗

Understanding sextupole

In this study, we reassess the dynamics within a simple accelerator lattice featuring a single degree of freedom and incorporating a sextupole magnet. In the initial segment, we revisit the H\'enon quadratic map, a representation of a general transformation with quadratic nonlinearity. In the subsequent section, we unveil that a conventional sextupole is essentially a composite structure, comprising an integrable McMillan sextupole and octupole, along with non-integrable corrections of higher orders. This fresh perspective sheds light on the fundamental nature of the sextupole magnet, providing a more nuanced understanding of its far-from-trivial chaotic dynamics. Importantly, it enables the description of driving terms of the second and third orders and introduces associated nonlinear Courant-Snyder invariant.

43 PARTICLE ACCELERATORS↗

The energetics and dynamics of confinement in flexible frameworks and molecular confinement

This project is part of an ongoing and long-term collaboration among Alexandra Navrotsky (who moved recently from UC Davis to Arizona State University), Nancy Ross at Virginia Tech, and Brian Woodfield at Brigham Young University (BYU) on the thermodynamics and lattice dynamics of nanophase and porous materials. Navrotsky emphasizes energetics of formation and guest molecule sorption by ambient and high temperature calorimetry, Woodfield contributes cryogenic heat capacity measurements, and Ross uses inelastic neutron scattering, X-ray and neutron diffraction, and high pressure studies to probe lattice dynamics and phase transitions. Porous frameworks form the chemical and structural basis for critical technologies in separations, catalysis, nuclear waste containment and biomedical applications. Hundreds of zeolites and metal organic frameworks (MOFs) have been synthesized, and their ability to separate and store hydrogen, methane and carbon dioxide has been investigated both experimentally and theoretically. Nevertheless, a fundamental and systematic molecular-level understanding of the thermodynamic and structural factors governing the stability and guest-host interactions in these materials lags behind focused studies of specific systems. Because the guest molecules interact with each other and with the host framework, molecular confinement is a finely balanced and complex phenomenon. The ability of the guest molecules to bind and diffuse through the pores is determined by the nature of the host framework which, in turn, responds to the nature and concentration of guest molecules and to pressure and temperature. The work explores how framework flexibility, tailored by structure, composition, temperature and pressure, is a general phenomenon, similar in nature but variable in extent, in both zeolites and MOFs and is part of a free energy landscape in which framework-guest interactions, pressure, and temperature result in changes in framework geometry and, in some cases, phase transitions. These subtle and/or pronounced changes in lattice geometry, energetics, and dynamics can play a decisive role in confinement and in differentiating the binding of molecules of similar size.

36 MATERIALS SCIENCE↗

Highly Ordered Hierarchical Anodes for Extreme Fast Charging Batteries (Final Report)

The goal of this project is to enable extreme fast charging (XFC) of Li-ion batteries, which was accomplished through a combination of 1) rational design and manufacturing of hierarchically structured anode architectures; 2) blending graphite/hard carbon into a bulk hybrid anode; 3) engineering artificial solid-electrolyte interphase (SEI) coatings with reduced interphase impedance; 4) computational modeling of coupled transport, kinetic, and electrochemical phenomena; and 5) improved fundamental understanding of lithium plating through operando analysis. This work integrated structural, compositional, and surface modification of graphite anodes, multi-physics modeling of ion transport, electrochemical activity, and heat transfer, advanced strategies to detect Li plating, and semi-automated roll-to-roll cell assembly. The unique facilities at the University of Michigan (UM) and Sandia National Laboratories (SNL) were leveraged to manufacture, prototype, and characterize commercially relevant >2Ah and >180 Wh/kg cells with a target of <20% capacity fade over 500 XFC cycles.

25 ENERGY STORAGE↗

Durable Mn-Based PGM-Free Catalysts for Polymer Electrolyte Membrane Fuel Cells

This proposed project aims to develop and evaluate novel manganese based, nitrogen-derived, PGM-free electrocatalysts (denoted as Mn-N-C) to fully address the membrane electrolyte assemblies (MEA)’s ionomer degradation issue resulting from iron. Four thrusts will be pursed in this proposed project. First, advanced first-principles computation methods will be employed to accelerate the rational catalyst design and synthesis. Second, an effective hydro-gel method will be used to maximize atomic Mn active sites embedded in carbon matrix. Next, state-of-the art methods in fuel cell companies will be used to fabricate MEAs containing the Mn-N-C catalysts. Finally, industry standards will be rigorously followed to evaluate fuel cell performance and durability of the Mn-N-C catalysts. With successful completion of the project, it is expected that the following outcomes will be achieved. (1) A set of MEAs containing the Mn-N-C catalysts and with active area large than 50 cm 2 for independent testing, (2) testing results demonstrating that the MEAs of Mn-N-C catalysts have mass activity of 0.044 A/cm 2 at 0.9 VIR-free and H 2 /air performance of 0.50 V at 1.0 A/cm 2 ; (3) fundamental understanding of the composition-structure-property relation of the PGM-free Mn- N-C catalysts, and (4) computational data, measurement data, and publications deposited into the database of ElectroCat Consortium.

08 HYDROGEN↗

The Influence of Environment on Post-Detonation Chemistry and Debris Formation (Abbreviated Final Report: 20-SI-006)

Predicting, responding to, or interpreting the chemical record preserved in debris derived from nuclear events can be challenging due to chemical fractionation. Chemical fractionation is where different species of the evolving radionuclide inventory segregate and/or are lost from the system over the timescales of debris formation. Both historic data and recent research suggest that the interaction and character of the local environment may exert controls on chemical fractionation by influencing the cooling and evolution of the associated fireball as well as the composition of the vapor term and resultant speciation. Prior to this work, an integrated platform permitting dynamic and concurrent consideration of physical and chemical evolution of early time post-detonation event environments did not exist. Our work merged historic data and experimental approaches to support development of a computational framework able to simulate fundamental processes (e.g., entrainment of local environment, oxidation chemistry, and cooling time scales) that may perturb the radionuclide inventory captured in post-detonation debris. Work with historic debris confirmed that entrained environmental material affect debris composition, structure, and radionuclide incorporation. Complementary work utilizing a readily controllable and tunable benchtop setup (a plasma flow reactor) simulated the late cooling of a nuclear fireball (e.g., T < 6000 K) and bounded the sensitivity of actinide speciation and particle size distribution to variations in oxygen concentration and cooling rates. Concurrent laser ablation and laser heating experiments were used to investigate the chemistry and physics of processes occurring in vaporized and/or rapidly heated actinides and other elements in the presence of oxygen. A more computationally efficient microphysical model was developed for predicting and evolving size distributions of particles forming from mixed vapor terms and simulating particle formation processes under a variety of extreme conditions. Continued study of historic nuclear event film confirmed that shockwave data and physics codes agree to within the uncertainty of the data. Good agreement was achieved for thermal emission from an airburst, however the paucity of low-temperature molecular opacity data for mixtures of air, bomb debris, entrained dirt, and water vapor complicate agreement for more elaborate scenarios. A multiphysics code (ALE3D) was modified to bring the necessary physics and chemistry, including these new data and insights, onto a single platform. Code development included improved initialization of large physical systems, modernization of chemistry capabilities, and modifications to enable inclusion of particle transport.

07 ISOTOPE AND RADIATION SOURCES↗

Porous Organic Cage Membranes for Molecular Gas Separations (Final Technical Report for DE-SC0021357)

This proposal aims at demonstrating the development of a novel family of membranes, composed of porous organic cages (POC) which offer the possibility of displaying high separation performance for challenging molecular gas separations relevant to natural gas purification, and olefin/paraffin separation. The proposed POCs synthesized in membrane form will display the most desirable properties of polymers (facile processability and flexibility) and inorganic materials (hierarchically ordered pores with molecular sieving properties) leading to highly selective and permeable membranes. POCs should display distinctive structural, compositional, adsorption and transport properties than those of conventional porous materials, opening the doors for a new research direction in membrane science, and gas separations. Our preliminary results demonstrate the feasibility of preparing POC crystals with controlled size, and continuous POC membranes with remarkable high permeances, and separation ability for CO 2 /CH 4 , N 2 /CH 4 and C 3 H 6 /C 3 H 8 separations serving as a solid foundation for our proposed work. Fundamentally, this proposal aims at elucidating separation mechanisms of different gas mixtures related to natural gas composition, and olefin/paraffin separation over porous organic cage membranes. The proposed research will result in fundamental understanding of adsorption and transport properties of industrially relevant gas molecules through novel microporous membranes, and may lead to the development of a cost effective membrane technology for natural gas purification, and olefin/paraffin separation surpassing the conventional benchmark technology distillation. Furthermore, we aim at demonstrating selective water transport through POC membranes, which can be positively impactful in numerous industrial applications in which water is present. The ability to fabricate thin, chemically and mechanically stable POC membranes for societal relevant gas separations constitute a new and distinctive direction in membrane science. Our proposed work aims at addressing some of the challenges recognized in the Research Agenda for Transforming Separation Science . Specifically: (a) advancing understanding of complex mixtures on separation performance; (b) exploring thermodynamic and kinetic mechanisms through the elucidation of separation mechanisms, and (c) study potential stability issues of the membranes to be assessed by evaluating the long term membrane stability and performance at various temperatures and pressures. The team is uniquely qualified to execute the proposed work. The PI has solid expertise in the rational molecular engineering design of porous crystalline membranes for molecular gas separations. The PNNL collaborator has extensive experience in the synthesis, characterization, and functional applications of microporous crystals, with particular emphasis on gas adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials

Catalysts are critical for most large-scale energy intensive chemical transformation processes, such as energy storage, liquid fuel production, and the formation of chemical building blocks. The catalyst composition, structure, and morphology impact the performance under reaction conditions and influences properties like activity and selectivity. Many industrial catalysts offer imperfect activity/selectivity, or contain expensive metals. Further, catalysts can deactivate over time as the morphology changes or harsh reactive environments alter the surface structure. Methods to automatically model and predict the kinetics of how catalyst surfaces will restructure would enable engineers to design around these challenges, improve performance, increase longevity. This project investigated a specific type of machine learning model, deep reinforcement learning, machine learning models to act as surrogates for the physical system, and compared the results of these approaches with a specialized high-throughput experimental synthesis and measurement platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Additive Manufacturing of Dissolvable Mandrels

ORNL collaborated with Mitsubishi Chemical America to investigate different grades of vinyl alcohol co-polymers as a potential feedstock material for large-scale material extrusion additive manufacturing (AM). Dissolvable polymers and can find potential applications in AM tooling for complex, hollow, and trapped composite structures without the need for specialized molds and tools. This CRADA Phase I work involved analysis of three different developmental grades of vinyl alcohol co-polymers for thermal and rheological properties, followed by print trials on the Big Area Additive Manufacturing (BAAM) system using these materials. Finally, printed part properties, including mechanical and thermal performance, as well as dissolvability were evaluated. The properties and performance evaluated in this phase of the project have provided guidelines to further develop these vinyl alcohol co-polymers to enable large-scale printing at high deposition rates and obtain parts that satisfy high temperature molds and dies requirements.

36 MATERIALS SCIENCE↗

Understanding Sextupole

In this study, we reassess the dynamics within a simple accelerator lattice featuring a single degree of freedom and incorporating a sextupole magnet. In the initial segment, we revisit the H\'enon quadratic map, a representation of a general transformation with quadratic nonlinearity. In the subsequent section, we unveil that a conventional sextupole is essentially a composite structure, comprising an integrable McMillan sextupole and octupole, along with non-integrable corrections of higher orders. This fresh perspective sheds light on the fundamental nature of the sextupole magnet, providing a more nuanced understanding of its far-from-trivial chaotic dynamics. Importantly, it enables the description of driving terms of the second and third orders and introduces associated nonlinear Courant-Snyder invariant.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗

Tunable Few-Layer van der Waals Crystals and Heterostructures as Emerging Energy and Quantum Materials (Final Technical Report)

2D and layered (van der Waals) semiconductors offer extraordinary opportunities for manipulating optically excited charge carriers, many-body excitations, and non-charge based quantum numbers. To date, research has focused on a limited group of materials, mostly transition metal dichalcogenides in the monolayer limit. Other van der Waals semiconductors, and especially few-layer to multilayer crystals and their heterostructures, carry large potential for the discovery of phenomena of interest for future energy and information technologies. But they remain largely unexplored, often due to a lack of access to high-quality materials and approaches for measuring their properties at the relevant scales. The goal of this project was to develop an EPSCoR-State/National Laboratory Partnership that addresses the challenges of preparing high-quality van der Waals semiconductors and of probing their structure, composition, and especially their optoelectronic and photonic properties, near the atomic scale using electron microscopy techniques. A central component of the project was the development of advanced methods for electron microscopy and electron-excited spectroscopy, taking advantage of unique samples as well as leading capabilities and expertise at the partner institutions. Efforts to advance leading-edge techniques was supported by ancillary developments, such as precision sample preparation for electron microscopy/spectroscopy, coordinated chemical imaging, and analytical electron microscopy. Experiments in materials synthesis and technique development were closely linked to theory and computation. The results obtained under this project yielded multifaceted benefits to the involved partners and their institutions, DOE-BES, the wider scientific community, and society at large, particularly in the State of Nebraska through dividends from knowledge and human capital generated under the project.

36 MATERIALS SCIENCE↗

AL-CRADA-2025-01 Final Report: American-Made Wind Turbine Materials Recycling Prize Accelerate! Phase 2

This report summarizes the Phase 2 achievements of the Wind Turbine Materials Recycling Prize project led by Critical Materials Recycling (CMR) in partnership with Ames National Laboratory. The team demonstrated the technical feasibility and economic viability of recovering and reusing rare-earth Nd-Fe-B magnets from end-of-life (EOL) wind turbine generators. Through mechanical disassembly, demagnetization, and precision processing, magnets were harvested and characterized to assess structural, compositional, and magnetic properties. Results showed that recovered magnets performed on par with commercial-grade counterparts, enabling their integration into actuator and motor prototypes without redesign. System-level modeling and finite-element simulations validated operational performance, while Life Cycle Cost (LCC) analysis revealed up to 20% material cost savings and 2–3% reductions in overall motor system costs. Life Cycle Assessment (LCA) further confirmed substantial environmental benefits, including a ~94% reduction in global warming potential relative to virgin magnet production. The project’s success was enabled by a tightly integrated partnership between industry and national lab experts, illustrating a viable path for domestic magnet-to-magnet recycling. These findings support a scalable, circular solution for rare-earth material recovery, reducing dependence on imported critical minerals and advancing sustainable clean energy technologies.

Lograsso, Thomas [Ames Laboratory (AMES), Ames, IA↗

Microwave-Assisted Fabrication of High Energy Density Binary Metal Sulfides for Enhanced Performance in Battery Applications

Nanomaterials have found use in a number of relevant energy applications. In particular, nanoscale motifs of binary metal sulfides can function as conversion materials, similar to that of analogous metal oxides, nitrides, or phosphides, and are characterized by their high theoretical capacity and correspondingly low cost. This review focuses on structure–composition–property relationships of specific relevance to battery applications, emanating from systematic attempts to either (1) vary and alter the dimension of nanoscale architectures or (2) introduce conductive carbon-based entities, such as carbon nanotubes and graphene-derived species. In this study, we will primarily concern ourselves with probing metal sulfide nanostructures generated by a microwave-mediated synthetic approach, which we have explored extensively in recent years. This particular fabrication protocol represents a relatively facile, flexible, and effective means with which to simultaneously control both chemical composition and physical morphology within these systems to tailor them for energy storage applications.

25 ENERGY STORAGE↗

The AGORA High-resolution Galaxy Simulations Comparison Project. VI. Similarities and Differences in the Circumgalactic Medium

Abstract We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

79 ASTRONOMY AND ASTROPHYSICS↗

DNA-functionalized scaffolds for drug capture applications

This disclosure is directed to structured compositions, including DNA-functionalized scaffolds, for drug capture, and methods and devices for sequestering chemotherapeutics from physiological fluids using the functionalized scaffolds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗