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

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At least 487 records · Page 27

Layer spacing gradient (NaLi) 1–x CoO 2 for electrochemical Li extraction

Designing materials with high Li to Na selectivity is the key for direct extraction of Li from unconventional sources, such as seawater, without complex separations. However, material discovery is hindered by an insufficient understanding on how a material's composition and structure relate to Li selectivity during electrochemical co-intercalation. Here, we synthesized a core-shell structured (NaLi) 1-x CoO 2 with a core Li phase (Li 0.94 CoO 2 ), a shell Na phase (Na 0.51 CoO 2 ), and a transition intermediate phase via Na ion-exchange of parent Li 1-x CoO 2 . We reveal that the chemical compositions and spatial relation of these phases play critical roles in governing high Li to Na selectivity. The core Li phase restricts large layer spacing expansion and inhibits Na intercalation. The shell Na phase serves to maintain the electrode stability against further Na ion exchange. With the layer spacing gradient design, we achieved a high Li selectivity of 1.5 x 10 5 with a recovery of similar to 7.6:1 Li to Na from 1:20,000 initial ratio.

36 MATERIALS SCIENCE↗

Experimental methodology and theoretical framework in describing constrained plastic flow of FCC microscale tensile specimens

Small scale mechanical testing (SSMT) allows for the quantification of the mechanical responses for microscale and nanoscale material volumes. It has significant potential in improving our fundamental understanding of plastic deformation and in providing an engineering solution for testing complex and/or critical materials. Importantly, previous studies have revealed that plastic intermittency, manifested as strain bursts, are characteristic of the plastic flow behavior at the small length scale. In this work, interfacial constraint has been utilized to suppress strain bursts in order to stabilize plasticity. A new parameter, blocked volume ratio (BVR), is introduced to quantify the degree of constraint for a microscale specimen due to an interface. Here, we showed that the plastic flow behavior in FCC Ni-based metals is highly dependent on the BVR of the specimen.

36 MATERIALS SCIENCE↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗

Integrated Approach to Post-Irradiation Examination of Nuclear Materials at Idaho National Laboratory

Idaho national Laboratory (INL) is the U.S. lead national laboratory for the Department of Energy’s Office of Nuclear Energy (DOE-NE), providing much of the nuclear research, development and demonstration capability needed to move nuclear innovation forward to deployment. INL’s Materials and Fuels Complex hosts a unique combination of personnel, facilities and infrastructure and offers the ability to perform post-irradiation examinations (PIE) of nuclear materials spanning multiple length scales. The ability to combine engineering-scale analysis and sub-microscopic characterization provides valuable insights into fuel and structural material behavior and degradation mechanisms. The holistic approach is used to accelerate these materials demonstration and deployment. Selected studies will be presented, highlighting the impact of these techniques on improving fuel reliability, safety, and efficiency, thereby advancing the development of sustainable and advanced nuclear energy technologies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Biomolecular assembly processes in the design of novel functional materials

Nature admits innumerable examples where efficient, remarkably parallelized, and precise organization of modular building blocks (e.g., molecules, particles) leads to diverse, complex, and functional structures. Unraveling such processes in order to mimic Nature’s design strategies remains the Holy Grail in establishing a multiscale paradigm for programmable materials production. Success in this endeavor would revolutionize the efficiency and precision with which complex materials are designed and synthesized, and would spawn novel, functional structures tailored precisely for applications. Among promising approaches to this bottom-up strategy is the use of particle-tethered, complementary single-stranded DNA (ssDNA) for programming particle assembly mediated by DNA hybridization into complex structures. The overall goal of our work was to develop a comprehensive computational and theoretical strategy aimed at overcoming current barriers in self-assembly of DNA-functionalized particles (DFPs).

36 MATERIALS SCIENCE↗

Dimensionality-Induced Change in Topological Order in Multiferroic Oxide Superlattices

We construct ferroelectric (LuFeO 3 ) m /(LuFe 2 O 4 ) superlattices with varying index m to study the effect of confinement on topological defects. We observe a thickness-dependent transition from neutral to charged domain walls and the emergence of fractional vortices. In thin LuFeO 3 layers, the volume fraction of domain walls grows, lowering the symmetry from P6 3 cm to P3c1 before reaching the nonpolar P6 3 /mmc state, analogous to the group-subgroup sequence observed at the high-temperature ferroelectric to paraelectric transition. Our study shows how dimensional confinement stabilizes textures beyond those in bulk ferroelectric systems.

36 MATERIALS SCIENCE↗

Cyclic phase transition of substrate-modulated two-dimensional dusty plasma driven by gyroscopic forces

Langevin dynamical simulations are performed to investigate the formation of clusters and voids of a two-dimensional periodic substrate (2DPS)-modulated two-dimensional dusty plasma (2DDP) driven by a gyroscopic force. It is discovered that, as the frequency of the gyroscopic force decreases gradually, the substrate-modulated 2DDP undergoes the cyclic transition between the ordered cluster and void phases. Between the observed ordered cluster and void phases, the studied 2DDP exhibits a more uniform arrangement of particles. The discovered cyclic transition is attributed to the symmetry of the time-averaged potential landscape induced by the 2DPS in the reference frame of the moving particle, as confirmed by superimposing the particle locations on the effective potential landscape under various conditions.

36 MATERIALS SCIENCE↗

Autonomous Polycrystalline Material Decomposition For Hyperspectral Neutron Tomography

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously.We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data.

Samin nur chowdhury, Mohammad↗

NSUF BRR Cask Shipment Summary Report

On September 16, 2021 the Idaho National Laboratory shipped a Battelle Research Reactor (BRR) cask loaded with five Nuclear Science User Facilities (NSUF) experiments and one Accident Tolerant Fuels (ATF) experiment from the Advanced Test Reactor (ATR) to the Hot Fuel Examination Facility (HFEF) at the INL Materials and Fuels Complex (MFC) for post-irradiation examination.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Edge-Confined Excitons in Monolayer Black Phosphorus

Quantum confinement of two-dimensional excitons in van der Waals materials via electrostatic trapping, lithographic patterning, Moiré potentials, and chemical implantation has enabled significant advances in tailoring light emission from nanostructures. While such approaches rely on complex preparation of materials, natural edges are a ubiquitous feature in layered materials and provide a different approach for investigating quantum-confined excitons. Here, we observe that certain edge sites of monolayer black phosphorus (BP) strongly localize the intrinsic quasi-one-dimensional excitons, yielding sharp spectral lines in photoluminescence, with nearly an order of magnitude line width reduction. Through structural characterization of BP edges using transmission electron microscopy and first-principles GW plus Bethe–Salpeter equation (GW-BSE) calculations of exemplary BP nanoribbons, we find that certain atomic reconstructions can strongly quantum-confine excitons resulting in distinct emission features, mediated by local strain and screening. Here we observe linearly polarized luminescence emission from edge reconstructions that preserve the mirror symmetry of the parent BP lattice, in agreement with calculations. Furthermore, we demonstrate efficient electrical switching of localized edge excitonic luminescence, whose sites act as excitonic transistors for emission. Localized emission from BP edges motivates exploration of nanoribbons and quantum dots as hosts for tunable narrowband light generation, with future potential to create atomic-like structures for quantum information processing applications as well as exploration of exotic phases that may reside in atomic edge structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A single-atom library for guided monometallic and concentration-complex multimetallic designs

Atomically dispersed single-atom catalysts have the potential to bridge heterogeneous and homogeneous catalysis. Dozens of single-atom catalysts have been developed, and they exhibit notable catalytic activity and selectivity that are not achievable on metal surfaces. Although promising, there is limited knowledge about the boundaries for the monometallic single-atom phase space, not to mention multimetallic phase spaces. Here, single-atom catalysts based on 37 monometallic elements are synthesized using a dissolution-and-carbonization method, characterized and analyzed to build the largest reported library of single-atom catalysts. In conjunction with in situ studies, we uncover unified principles on the oxidation state, coordination number, bond length, coordination element and metal loading of single atoms to guide the design of single-atom catalysts with atomically dispersed atoms anchored on N-doped carbon. We utilize the library to open up complex multimetallic phase spaces for single-atom catalysts and demonstrate that there is no fundamental limit on using single-atom anchor sites as structural units to assemble concentration-complex single-atom catalyst materials with up to 12 different elements. Furthermore, our work offers a single-atom library spanning from monometallic to concentration-complex multimetallic materials for the rational design of single-atom catalysts.

36 MATERIALS SCIENCE↗

High temperature transition aluminas in gamma-Al2O3: Review

High temperature treated transition aluminas display adsorptive and catalytic properties that are in many ways comparable to those of their low temperature counterpart gamma-Al2O3. While being important industrial catalysts as well as catalytic supports, their very basic crystallographic and structural characteristics remain poorly understood and thus actively studied. In this review, we critically examine the crystallography and structural complexity of these materials. Specifically, we address the crystallography of delta- and theta-Al2O3 polymorphs and show how structural intergrowth and disorder are accommodated in these phases. The structural complexity at the scale of overall microstructure is also examined, and the challenges and recent progress in quantification of the structure at ensemble level are discussed. Most pertinently to catalysis, we review the surfaces properties of high temperature treated Al2O3 and discuss the implications for understanding attributes relevant to heterogeneous catalysis.

Kovarik, Libor↗

Crosslink V.0.11.x User Manual

CrossLink is a novel two-dimensional and three-dimensional geometry and mesh generation software package developed by the Simulation Tools team at Los Alamos National Laboratory. This software represents the third generation of topology-based mesh generation technology developed by the Department of Defense and the Department of Energy with a special focus on complex multi-material hydrodynamic applications, mesh scalability, and high-order element mesh generation. The topology-based meshing approach offered by CrossLink enables users to quickly and easily mesh complex geometries in a repeatable and robust manner. CrossLink’s topology-based meshing approach is well-suited for parametric design studies, parametric design optimization, damage scenario assessment, and iterative design modification (i.e. feature addition and/or removal). CrossLink’s python API allows workflow scripting of the geometry creation and mesh generation process for traceability, repeatability, data provenance, and version control. CrossLink consists of three main components: a graphical user interface (GUI), a geometry creation and mesh generation engine, and a python API that provides a workflow scripting interface to the geometry and meshing functions.

97 MATHEMATICS AND COMPUTING↗

Bimetallics in a Nutshell: Complexes Supported by Chelating Naphthyridine-Based Ligands

Bimetallic motifs are a structural feature common to some of the most effective and synthetically useful catalysts known, including in the active sites of many metalloenzymes and on the surfaces of industrially relevant heterogeneous materials. However, the complexity of these systems often hampers detailed studies of their fundamental properties. To glean valuable mechanistic insight into how these catalysts function, this research group has prepared a family of dinucleating 1,8-naphthyridine ligands that bind two first-row transition metals in close proximity, originally designed to help mimic the proposed active site of metal oxide surfaces. Of the various bimetallic combinations examined, dicopper(I) is particularly versatile, as neutral bridging ligands adopt a variety of different binding modes depending on the configuration of frontier orbitals available to interact with the Cu centers. Organodicopper complexes are readily accessible, either through the traditional route of salt metathesis or via the activation of tetraarylborate anions through aryl group abstraction by a dicopper(I) unit. The resulting bridging aryl complexes engage in C-H bond activations, notably with terminal alkynes to afford bridging alkynyl species. In this work, the μ-hydrocarbyl complexes are surprisingly tolerant of water and elevated temperatures. This stability was leveraged to isolate a species that typically represents a fleeting intermediate in Cu-catalyzed azide-alkyne coupling (CuAAC); reaction of a bridging alkynyl complex with an organic azide afforded the first example of a well-defined, symmetrically bridged dicopper triazolide. This complex was shown to be an intermediate during CuAAC, providing support for a proposed bimetallic mechanism. These platforms are not limited to formally low oxidation states; chemical oxidation of the hydrocarbyl complexes cleanly results in formation of mixed valence Cu I Cu II complexes with varying degrees of distortion in both the bridging moiety and the dicopper core. Higher oxidation states, e.g. , dicopper(II), are easily accessed via oxidation of a dicopper(I) compound with air to give a Cu II 2 (μ-OH) 2 complex. Reduction of this compound with silanes resulted in the unexpected formation of pentametallic copper(I) dihydride clusters or trimetallic monohydride complexes, depending on the nature of the silane. Finally, development of an unsymmetrical naphthyridine ligand with mixed donor side-arms enables selective synthesis of an isostructural series of six heterobimetallic complexes, demonstrating the power of ligand design in the preparation of heterometallic assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for instance, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3–55 atoms with mean absolute errors in the range of 0.11–0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure–property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal–metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3–55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Shot noise and differential conductance as signatures of putative topological superconductivity in FeSe 0.45 ⁢Te 0.55

We present a theory for the differential shot noise, dS/dV, as measured via shot-noise scanning tunneling spectroscopy, and the differential conductance, dI/dV, for tunneling into Majorana zero modes (MZMs) in the putative topological superconductor FeSe 0.45 Te 0.55 . We demonstrate that for tunneling into chiral Majorana edge modes near domain walls, as well as MZMs localized in vortex cores and at the end of defect lines, dS/dV vanishes whenever dI/dV reaches a quantized value proportional to the quantum of conductance. Furthermore, these results are independent of the particular orbital tunneling path, thus establishing a vanishing dS/dV concomitant with a quantized dI/dV, as universal signatures for Majorana modes in two-dimensional topological superconductors, irrespective of the material’s specific complex electronic band structure.

Iron-based superconductors↗

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗