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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 721 records · Page 40

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗

Reproducible emission from nonlinear random lasers

Multiple scattering of light serves as a mechanism for feedback in random lasers. Consequently, internal spatial mode patterns, lasing wavelengths, and output directionality can all be random. Strong mode interaction can occur in such devices due to spatially overlapping modes resulting in nonlinearity with respect to the pump input power. Nevertheless, temporal coherence and lasing mode amplitude can be fixed at a constant pumping rate. This is a property desirable for applications where unique randomness is exploited but expected to be reliable over time, such as physical unclonable functions. Random lasers can also be cheaply and easily fabricated, exhibit relatively low lasing thresholds and high emission intensity. However, the precise scattering properties of such structures and fluctuations in the pump field can make device emission irreproducible, thereby limiting random laser applications. Here, in this work, we directly compare the random lasing spectra from zinc oxide samples fabricated in four distinct ways: spin-coating, sputtering, solgel deposition, and atomic layer deposition. The particular method of fabrication has a strong impact. Samples made through atomic layer deposition here exhibit both reproducibility and strong nonlinearity desirable for applications. Randomness in emission spectra persists across hundreds of repeated and averaged measurements irrespective of spatial location and is demonstrably nonlinear with respect to input signal intensity.

47 OTHER INSTRUMENTATION↗

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein–protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein–protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning–based methods to unlock the widespread application of designed protein–protein interfaces and self-assembling protein nanomaterials in biotechnology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effects of Structural Constraints on Excited-State Properties in Dimeric Cu(I) Diimine Complexes

Copper(I) bis-diimine complexes have played important roles in light-activated processes that can lead to their potential applications in photocatalysis and chemical sensing. Their metal-to-ligand charge-transfer (MLCT) excited-state properties are tunable by various structural factors. Dimeric Cu(I) complexes with connecting diimine derivative ligands offer another structural tuning platform for the excited-state properties. Here, we investigate excited-state properties in two covalently connected dimeric Cu(I)'s with varying structural constraints exerted by the number of carbons in the polyethylene bridge (C0 and C4) connecting the two copper(I) diimine moieties. An interesting feature of Cu(I) diimine complexes is their ability to flatten following a photoinduced structural change. Herein, we observe larger structural constraints and more structural rearrangement required upon excitation of the longer bridged complex C4 to achieve a conformation toward a more flattened tetrahedral coordination geometry compared to the shorter bridged C0. Vibrational wavepacket analysis of these complexes further supports the effect of these structural constraints where we observe a more rapid dephasing of the C0 complex, as opposed to the C4 complex, despite similar normal mode vibrations. The experimental results were supplemented by TDDFT calculations. In conclusion, the studies provide insight into using metal-metal interactions through constraints to tune excited-state dynamics and pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SPC-3184 Rev 7 MARVEL Reactor Structures Build to Print Fabrication Specification

Idaho National Laboratory (INL) is developing a microreactor to produce electrical power utilizing a small nuclear core and under the Microreactor Application Research Validation and Evaluation (MARVEL) program. This procurement specification defines the requirements for fabrication of the MARVEL Primary Coolant System (PCS), Fuel and Core System (FCS), and Reactor Sub-Structure Assemblies, including the Upper Confinement Structure.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

A Review of Polymeric Nanoparticle Design, Synthesis, and Biomedical Applications

Polymeric nanoparticles (PNPs) are solid particles that contain macromolecular polymers ranging in size from 1-1000 nm. They have a wide variety of structures that are determined by their identity as either natural biopolymers, synthetic polymers or a combination of both. In the design of PNPs, the polymeric system, size, internal structure, additional molecules, and synthetic pathway can be engineered, allowing for a multitude of properties including differing responses to physiological environments and the incorporation of a drug. The easily accessible fine tuning of PNPs allows for a multitude of applications in medicine. PNPs allow for increased pharmacokinetics, efficacy, safety, and targeting strength when utilized as carriers for drugs. Currently there are a wide range of PNPs approved by the Food and Drug Administration for use in clinical settings and many more being investigated in clinical studies. There is also research that is being conducted for the usage of PNPs in controlled drug delivery systems and as carriers through the blood brain barrier to aid in treating neurological diseases.

60 APPLIED LIFE SCIENCES↗

Doping Induced Magnetic and Electronic Phase Transitions in the Ferrimagnetic Half-Metallic Mn 4 Al 11 Compound

The future of spintronics and semiconductor applications demands materials with tailored electronic and magnetic properties. Here, this study uses density functional theory to investigate the electronic structure of the half-metallic compound Mn 4 Al 11 under uniaxial strain and in its Ge-substituted derivatives. Strain analysis shows that although the half-metallic band gap collapses under strain beyond −2%, the ferrimagnetic character remains stable. Ge substitution at six inequivalent Al sites in Mn 4 Al 11 results in varying degrees of metallicity and magnetic properties. Substitution at Al═ (000) induces a metal-to-insulator transition with an indirect semiconducting gap of 0.14 eV. Bonding and hybridization analyses reveal that local Mn–Al interactions due to Ge substitution significantly modify the local electronic structure, causing both electronic and magnetic phase transitions. This work highlights the effectiveness of substitutional doping in tuning half-metallicity and magnetic properties in inorganic solids, enabling the design of materials for future technological applications.

alloys↗

Structure and mechanical properties of grain boundaries in molybdenum disulfide (MoS 2 )

Molybdenum disulfide (MoS 2 ) is a two-dimensional material widely used as a lubricant in many applications involving mechanical loading under a wide range of operating temperatures. Since many synthesis and processing techniques yield MoS 2 in its polycrystalline form, establishing grain boundary (GB) structure–property relations is key to designing MoS 2 microstructures with tailored properties. Here, we employ classical, reactive atomistic simulations to investigate the structure and mechanical behavior of a wide range of GBs in MoS 2 as a function of temperature. Using a bicrystal MoS 2 geometry, we characterize the atomic structure and calculate the energy of several low-angle GBs. Then, we simulate the tensile deformation behavior of MoS 2 bicrystals at several temperatures. Our results reveal that at temperatures above 100 K, the deformation of MoS 2 bicrystals is characterized by the nucleation of shear bands from GBs that grow, with subsequent loading, into the MoS 2 crystals. At low temperatures, the tensile deformation is characterized by the nucleation and propagation of deformation fronts, resulting in altered bond angles and bond lengths. Quantitative analysis reveals a decrease in the ultimate tensile stress and ultimate failure strain of MoS 2 bicrystals with the increase in temperature. Furthermore, our simulations of the mechanical behavior of metastable GBs reveal that the strength and ductility decrease with the increase in energy of these boundary structures. In broad terms, our work provides future avenues to employ GB engineering as a strategy to tailor the properties of MoS 2 microstructures.

Moore, Robert D. [Lehigh Univ., Bethlehem, PA (Uni↗

Multiscale Modeling of Silicon Carbide Cladding for Nuclear Applications: Thermal Performance Modeling

The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. In addition to the microstructural features, the properties of individual constituents of the composites and fiber tow architecture determine the bulk properties. Models for determining the relationship between the individual constituents’ properties and the bulk properties of SiC composites for nuclear applications are absent, although empirical relationships exist in the literature. Here, a hierarchical multiscale modeling approach was presented to address this challenge. This modular approach addressed this difficulty by dividing the various aspects of the composite material into separate models at different length scales, with the evaluated property from the lower-length-scale model serving as an input to the higher-length-scale model. The multiscale model considered the properties of various individual constituents of the composite material (fiber, matrix, and interphase), the porosity in the matrix, the fiber volume fraction, the composite architecture, the tow thickness, etc. By considering inhomogeneous and anisotropic contributions intrinsically, our bottom-up multiscale modeling strategy is naturally physics-informed, bridging constitutive law from micromechanics to meso-mechanics and structural mechanics. The effects that these various physical attributes and thermo-physical properties have on the composite’s bulk thermal properties were easily evaluated and demonstrated through the various analyses presented herein. Since silicon carbide fiber-reinforced SiC CMCs are also promising thermal–structural materials with a broad range of high-end technology applications beyond nuclear applications, we envision that the multiscale modeling method we present here may prove helpful in future efforts to develop and construct reinforced CMCs and other advanced composite nuclear materials, such as MAX phase materials, that can service under harsh environments of ultrahigh temperatures, oxidation, corrosion, and/or irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Resolving local structural motifs across the phase evolution of zinc titanates with computational x-ray absorption spectroscopy

Resolving the local structure motifs that characterize phase evolution as a function of composition is a key challenge in structure characterization of complex materials. Here, in this study, we combine first-principles simulations and x-ray absorption near-edge structures (XANES) analysis to gain insights into the structure evolution revealed by measurements across a combinatorial zinc titanate thin film, which was grown with smoothly varying composition over a wide range of the Ti:Zn ratio. Specifically, we propose a cluster blind-signal-separation (cBSS) method for XANES spectral analysis based on a library of the structures and spectra of representative local motifs. In addition to motifs from zinc titanate crystals, two types of Ti-defect models constructed in this study are key to the understanding of the structure characteristics in the Zn-rich region. The cBSS method makes use of both spectral clustering of the simulated site-XANES spectra library and the BSS procedure to construct high-fidelity spectral basis functions from an experimental spectral sequence. The method provides a rigorous measure of the spectral sensitivity and basis completeness. The results of the XANES analysis are corroborated with other experimental modalities, including x-ray diffraction and spectroscopic ellipsometry, to validate the cBSS method. The calculated motif weights resulting from fitting the XANES spectra with the cBSS basis probe the atomic structure characteristics of both crystalline and amorphous phases as a function of the Ti/Zn composition. The insights of the local structure motif evolution are pivotal to the understanding of the nonmonotonic trend in the optical gap, which may lead to potential applications through tuning the optical properties of zinc titanate. The workflow of the XANES spectral analysis developed in this work can be generalized to construct the structure-property relationship in a broad material space.

36 MATERIALS SCIENCE↗

Barrier reinforcement for enhanced perovskite solar cell stability under reverse bias

Stability of perovskite solar cells (PSCs) under light, heat, humidity and their combinations have been notably improved recently. However, PSCs have poor reverse-bias stability that limits their real-world application. Here we report a systematic study on the degradation mechanisms of p-i-n structure PSCs under reverse bias. The oxidation of iodide by injected holes at the cathode side initialize the reverse-bias-induced degradation, then the generated neutral iodine oxidizes metal electrode such as copper, followed by drift of Cu+ into perovskites and its reduction by injected electrons, resulting in localized metallic filaments and thus device breakdown. A reinforced barrier with combined lithium fluoride, tin oxide and indium tin oxide at the cathode side reduces device dark current and avoids the corrosion of Cu0. It dramatically increases breakdown voltage to above -20 V and improved the T90 lifetime of PSCs to ~1,000 h under -1.6 V. The modified minimodule also maintained over 90% of its initial performance after 720 h of shadow tests.

degradation mechanisms↗

2024 roadmap on magnetic microscopy techniques and their applications in materials science

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

2D materials↗

N-Doped Graphene (N-G)/MOF(ZIF-8)-Based/Derived Materials for Electrochemical Energy Applications: Synthesis, Characteristics, and Functionality

In recent years, graphene-type materials originating from metal–organic frameworks (MOFs) or integrated with MOFs have exhibited notable performances across various applications. However, a comprehensive understanding of these complex materials and their functionalities remains obscure. While some studies have reviewed graphene/MOF composites from different perspectives, due to their structural–functional intricacies, it is crucial to conduct more in-depth reviews focusing on specific sets of graphene/MOF composites designed for particular applications. In this review, we thoroughly investigate the syntheses, characteristics, and performances of N-G/MOF(ZIF-8)-based/derived materials employed in electrochemical energy conversion and storage systems. Special attention is given to realizing their fundamental functionalities. The discussions are divided into three segments based on the application of N-G/ZIF-8-based/derived materials as electrode materials for batteries, electrodes for electrochemical capacitors, and electrocatalysts. As electrodes for batteries, N-G/MOF(ZIF-8) materials can mitigate issues like an electrode volume expansion for Li-ion batteries and the ‘shuttle effect’ for Li-S batteries. As electrodes for electrochemical capacitors, these materials can considerably improve the ion transfer rate and electronic conductivity, thereby enhancing the specific capacitance while maintaining the structural stability. Also, it was observed that these materials could occasionally outperform standard platinum-based catalysts for the electrochemical oxygen reduction reaction (ORR). The reported electrochemical performances and structural parameters of these materials were carefully tabulated in uniform units and scales. Through a critical analysis of the present synthesis trends, characteristics, and functionalities of these materials, specific aspects were identified that required further exploration to fully utilize their inherent capabilities.

Electrochemistry↗

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model

Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.

diffusion model↗

Electronic Structure Distortions in Chromium Chelates Impair Redox Kinetics in Flow Batteries

Aminopolycarboxylate chelates are emerging as a promising class of electrolyte materials for aqueous redox flow batteries, offering tunable redox potentials, solubility, and pH stability through careful selection of ligands and transition metal ions. Despite their potential, the impact of molecular structure modifications on the electronic and electrochemical properties of these chelates remains underexplored. Here, in this study, we examine how introducing a hydroxyl group, often employed for its solubilizing properties, to the backbone of CrPDTA, a reference chelate material, significantly changes the thermodynamics and kinetics of the chelate's redox process. We correlate changes in molecular and electronic structures to different electrochemical responses resulting from the hydroxyl addition and show that the introduction of this functional group leads to a distortion in the octahedral coordination of chromium. Furthermore, increased anisotropic spin density and nonintegral oxidation state changes in the Cr metal center result in a larger barrier for electron transfer in CrPDTA‐OH. It is demonstrated that preserving a hexacoordinate chelate structure across a broad pH range is crucial for efficient flow battery application and it is emphasized that ligand modifications must avoid distorting the octahedral coordination of the transition metal.

25 ENERGY STORAGE↗

Dielectric Engineering of ZnO@ZIF‐8 for High‐Performance Triboelectric Nanogenerators and Self‐Powered Humidity Sensors

Porous metal oxide–metal-organic framework (MO x @MOF) hybrids offer synergistic effects that enhance surface charge density, electronic structure, and textural properties, making them ideal for self-powered sensing applications. Here, uniform, and pinhole-free ternary porous ZnO–PTFE@ZIF-8 hybrid films are developed on room-temperature co-sputtered ZnO–polytetrafluoroethylene (PTFE) composite films, where ZnO serves as a self-sacrificial precursor to precisely regulate ZIF-8 (Zn-MeIm 2 ) growth through solvothermal methods. The resulting TENG achieves a high output power density of 0.67 mW cm −2 , attributed to the synergistic effects of fluorine-rich PTFE and methyl-functionalized ZIF-8, which enhance surface charge density and dielectric response. By tuning PTFE content in the ZnO–PTFE composite, the dielectric constant and triboelectric output is optimized. The ZP60@ZIF-8-based device also demonstrates excellent humidity sensing performance, with a wide detection range (20–99% RH) and ultrahigh sensitivity (R V % of 19 900%, R I % of 19 325% at 99% RH). These results position ZP@ZIF-8-based TENGs as promising platforms for next-generation self-powered sensors and smart wearable electronics.

42 ENGINEERING↗

Evaluating consumer 3D printing nozzles as a low cost alternative for mesophase pitch-derived carbon fiber production

Synthetic fibers, such as Kevlar fibers, SiC fibers, and carbon fibers, are essential components for constructing high performance structures. Whether for engineering, sports, energy storage (batteries and supercapacitors), or aerospace applications, fiber microstructure plays a critical role in fiber properties and functionalities. However, studying fiber nozzle configurations and spinning parameters to achieve the desired microstructure remains challenging, costly, and time consuming. Here, mesophase pitch-derived fibers were used as an example to demonstrate that low cost, commercially available 3D printer nozzles can “print” fibers. Four different nozzles were used to “print” fibers and the effects of their features on fiber properties were observed and compared to other lab spun and commercial pitch- derived CF. A longer orifice length resulted in higher modulus fiber whereas a larger draw-down ratio yielded a stronger fiber. Furthermore, the findings provide a new opportunity for 3D printer hardware application and open up opportunities for developing low-cost fibers.

36 MATERIALS SCIENCE↗