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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗
DOE OSTI2023

Medium-Range Ordering in the Ionic Glass Electrolytes LiPON and LiSiPON

In this work, we provide an in-depth structural characterization of the amorphous ionic glasses LiPON and LiSiPON with high Li content. Based on ab initio molecular dynamics simulations, the structure of these materials is an inverted structure with either isolated polyanion tetrahedra or polyanion dimers in a Li+ matrix. Based on neutron scattering data, this type of inverted structure leads to a significant amount of medium-range ordering in the structure, as demonstrated by two sharp diffraction peaks and a periodic structural oscillation in the density function G(r). While this medium-range ordering is commonly observed in liquids and metallic glasses, it has not previously been observed in oxides. On a local scale, adding N and Si increases the number of anion bridges and polyanion dimer structures, leading to higher ionic conductivity. In the medium-range ordering, the addition of Si leads to more disorder in the polyanion substructure but a significant increase in the ordering of the O substructure. Finally, we demonstrate that this inverted structure with medium-range ordering results in a glassy material that is both mechanically stiff and ductile on the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Neutron irradiation & thermomechanical experiment (NITE) - design

For the reliable long-term operation of fusion power plants, it is crucial to understand and predict the lifetime of materials in use. These materials include all structural and functional materials utilized at the first wall, blanket, magnets, and shielding. The key challenge is, that the harsh environment including high heat fluxes, high thermal stress and stress cycling, neutron irradiation, and sputtering on such materials should not be viewed separately. Currently, the synergistic loads cannot be evaluated experimentally because of the lack of adequate facilities. The purpose of that work is to design a synergetic Neutron Irradiation and Thermomechanical Experiment (NITE) for fusion materials. This design will leverage the existing Advanced-Test-Reactor (ATR), a fission reactor at the Idaho National Laboratory. We also acknowledge that with existing fission reactors the exact fusion condition cannot be created, and the limitations are critically discussed. The combination of neutron irradiation with a high heat flux is the focus. This is realized with an irradiation capsule design that includes a TRISO fueled region inside the capsule to enable a steady-state heat flux on one side of the specimen. In conclusion, the experimental design modeling showed that steady-state heat fluxes of 2.4 MW/m 2 with a thermal gradient of above 250°C can be achieved in a 5 mm thick specimen.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Unraveling the Nature of Vibrational Dynamics in CsPbI 3 by Inelastic Neutron Scattering and Molecular Dynamics Simulations

Cesium lead iodide, CsPbI 3 , is an optoelectronic material of large interest for various technological applications; however, fundamental questions surrounding the vibrational dynamics of this material, especially regarding its role in structural phase transitions, remain to be elucidated. Here, in a combined variable temperature inelastic neutron scattering (INS) and machine-learning based molecular dynamics (MD) simulation study, we show that the stable phase at room temperature, i.e., the nonperovskite δ-phase, exhibits phonon modes with weak anharmonicity with only a weak temperature dependence from 10 K all the way up to the transition to the cubic perovskite α-phase at approximately 600 K. In contrast, the α-phase features anharmonic and damped vibrational dynamics, mainly associated with overdamped tilting motions of the PbI 6 octahedra. Crucially, these overdamped tilting modes, which relate to the tetragonal and orthorhombic distorted perovskite phases (β- and γ-phase, respectively) formed at lower temperatures, stay overdamped by more than 100 K above the respective phase transition. This suggests a flat energy landscape of octahedral tilting motions in α-CsPbI3 and with structural fluctuations on the picosecond time scale with tilting patterns that locally resemble the structure of the β- and γ-phases. The vibrational dynamics of α-CsPbI 3 are also characterized by pronounced anharmonic motions with large thermal displacements of the Cs + ions, but these modes remain underdamped at 600 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Biochemical approaches for synthesis of performance-advantaged polymers from lignocellulosic biomass

Lignocellulose is an abundant renewable feedstock for production of sustainable fuels, chemicals, and materials. The structural complexity of lignocellulose provides key material properties and inspires the design of advanced materials. However, this same complexity also presents challenges for conversion of lignocellulosic biomass into new materials with consistent properties. Conventional physical and chemical strategies for valorization of biomass to new materials are often limited by the technical challenges of precisely manipulating complex feedstocks, sensitivity to feedstock variability, and associated costs. In contrast, biological approaches are capable of selectively manipulating complex architectures under mild conditions. Recent advances demonstrate the potential of biological and hybrid biochemical methods to tailor biomass-derived polymers and generate new materials. This review provides an overview of biological strategies to valorize lignocellulosic biomass into novel materials, highlighting approaches for in planta engineering, biochemical modification of natural biomass polymers, microbial funneling of deconstructed biomass, and direct biosynthesis of novel polymers. In combination, these approaches open new avenues for the synthesis of performance-advantaged materials from lignocellulosic biomass.

Qian, Liangyu [ORNL] (ORCID:0009000212029938)↗
DOE OSTI2022

Electron-hole symmetry in quasiparticle spectral weight of cuprates observed via infrared and photoemission spectroscopy

Here we performed an optical spectroscopy study of single crystals of Pr 0.85 LaCe 0.15 CuO 4–δ (PLCCO) to revisit the electron-hole asymmetry, which has been understood as a fundamental property of cuprates. Four differently annealed samples—as-grown, reduced, optimally oxygenated, and overoxygenated samples—were prepared, which have superconducting transition temperatures T c = 0, 15, 24, and 18 K, respectively. We observed that the low-energy quasiparticle spectral weights of all the PLCCO samples are significantly small in comparison with those of other electron-doped cuprate families. Instead, they are rather close to those of their hole-doped counterpart La 2–x Sr x CuO 4 . Accordingly, estimated effective carrier numbers N eff per Cu atom of superconducting samples are also very small, despite their relatively high critical temperatures. A complementary photoemission study reveals that the low-energy quasiparticle spectral weight of PLCCO is much smaller than that of Nd 1.85 Ce 0.15 CuO 4–δ , consistent with the optical results. Our observations demonstrate that PLCCO provides the electron-hole symmetry in the quasiparticle spectral weight and highlight the importance of Cu 3d–O 2p hybridization to understand the low-energy spectral weight transfer in doped cuprates.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molybdenum disulfide and related materials for water treatment

Catalytic water treatment is provided using an active material driven with an optical and/or electrical excitation. The active material is MoS2, MoSe2, WS2, WSe2, MoxW1-xS2, MoxW1-xSe2, MoSySe2-y, WSySe2-y, or MoxW1-xSySe2-y; wherein 0 < x < 1 and 0 < y < 2. The active material is configured as one or more layered nanostructures having exposed layer edges. A metal catalyst is disposed on the active material. The combined structure of active material and metal catalyst is disposed in the water to be treated. The excitation is provided to the active material to generate one or more reactive oxygen species by dissociation of water, wherein the reactive oxygen species provide water treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Operando Freezing Cryogenic Electron Microscopy of Active Battery Materials

Abstract Understanding structural and chemical evolution of battery materials during operation is critical to achieving safe, efficient, and long-lasting energy storage. Cryogenic electron microscopy (cryo-EM) has become a valuable tool in battery characterization, leveraging low temperatures to improve stability of sensitive materials under electron beam irradiation. However, typical cryo-EM sample preparations leave extended time between the electrochemical point of interest and ex situ freezing of samples, during which active structures may relax, degrade, or otherwise evolve. Here, we detail a method for operando freezing cryo-EM to preserve and characterize native electrode and interfacial structures that arise during battery cycling, based on an operando plunge freezer and cold sample removal process. We validate the method on multiple electrode materials and quantify and discuss the freezing rate achieved. Operando freezing cryo-EM can be used to directly visualize transient features that arise at active electrochemical interfaces, to enable deeper understanding of structural evolution and interfacial chemistry in batteries and other electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Core–Multishell-Structured Digital-Gradient Cathode Materials with Enhanced Mechanical and Electrochemical Durability

Ni-rich cathode materials provide high energy density, but their structural and surface instability limits their cyclability and thermal stability. As one of the approaches to mitigate this problem, cathode materials comprising Ni-rich high-capacity core wrapped in Mn-rich multiple shells are produced successfully. In contrast to the conventional batch-type process for concentration-gradient materials, a digital-gradient cascade coprecipitation process described here achieves the improvements in productivity and quality consistency needed to move toward large-scale manufacturing. The core-multishell cathode materials produced in this manner not only have longer cycle life and improved rate performance compared to homogeneous Ni-rich cathode materials having the same overall composition, but also show remarkably enhanced thermal stability and low impedance growth characteristics. In a novel attempt to determine the correlation between the mechanical properties of the core-multishell cathode particles and their electrochemical cyclabilities, their breaking force and elasticity were successfully measured using a statistical approach, which indicates that a cathode particle with stable surface composition as well as high breaking force has improved capacity retention and durability. These results guide the realization of long life and high thermal stability in Ni-rich cathode materials through heterogeneous particle engineering.

25 ENERGY STORAGE↗
DOE OSTI2021

The surface triple-coupling on single crystalline cathode for lithium ion batteries

Single crystalline (SC) cathode materials, which are less susceptible to micro/nano-cracks formation and offer better structure stability compared to the polycrystalline counterpart, have attained great attention. However, the parasitic side reactions at the cathode-electrolyte interface induces the loss of active species, which consequently leads to continual degradation of the electrochemical performances. Herein, a triple coupling of concentration-gradient Na+, F- co-doping and surface NaF coating are exploited for the first time on SC LiNi 0.5C o 0.2 Mn 0.3 O 2 cathode by the hydrolysis of NaPF 6 . This process regulates the external structure of materials by constructing a “sandwich” configuration from surface to bulk: rock salt - mixing zone - layered phase. The detailed interface transformation mechanism is revealed by Neutron powder diffraction (NPD), spherical aberration corrected high-resolution scanning transmission electron microscopy (HR-STEM), electron energy loss spectroscopy (EELS), and Ar+ sputtering assisted X-ray photoelectron spectroscopy (XPS). The synergistic effects endow the SC cathode with outstanding capacity retentions: 91.3% at 25 °C and 85% at 45 °C, after 500 cycles at 5 C between 3.0 and 4.5 V. In addition, a high full-cell reversible capacity of 168.9 mAh g -1 with a capacity retention of 92.4% is achieved after 300 cycles at 1 C. Multiple characterizations further indicate that these superior results are mainly ascribed to the overall structure integrity of SC material, the thin cathode electrolyte interface, high content of lithium fluoride, and the low solubility of transition metal ions. This work opens a new avenue to construct a benign interface towards high-performance lithium ion batteries.

lithium ion batteries↗
DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Cellulose regulated lignin/cellulose-based carbon materials with hierarchical porous structure for energy storage

Lignin has gained extensive attention as an ideal carbon precursor due to its abundance and high carbon content. However, the agglomeration of lignin and additional corrosive and unrecyclable reagents in direct pyrolysis still limit the development of lignin-based porous carbons. Herein, a facile and eco-friendly strategy was proposed to fabricate hierarchical porous lignin/cellulose-based carbon materials (LCs). In the process, cellulose nanofibrils acted as the skeleton of the bio-aerogels, which supported lignin and benefit the preparation of the LCs. Moreover, the specific surface area and the graphitization degree of LCs can be regulated by varying the cellulose content. Without activation, the bio-based carbon material (LC30) had a high specific surface area of 1770 m 2 g –1 , which displayed high specific capacitance of 216.2 F g –1 at the current density of 0.5 A g –1 . The supercapacitor based on LC30 also showed outstanding energy density of 12.3 Wh kg –1 at the power density of 50 W kg –1 . The sustainable raw material, simple and harmless preparation process, and remarkable electrochemical performance enable LC30, a promising supercapacitor electrode for energy storage.

25 ENERGY STORAGE↗
DOE OSTI2021

Methods and apparatus for additively manufacturing structures using in situ formed additive manufacturing materials

A method of additively manufacturing a structure comprises nuclear reactor comprises disposing a feed material on a surface of a substrate in a reaction vessel, disposing at least one material formulated and configured to react with the feed material in the reaction vessel, and exposing the feed material and the at least one material to energy from an energy source to react the feed material and the at least one material to form an additive manufacturing material and reaction by-products. The additive manufacturing material is separated from the reaction by-products and exposed to energy from the energy source to form inter-granular bonds between particles of the additive manufacturing material and form a layer of a structure comprising the additive manufacturing material. Related apparatuses and methods are disclosed.

van Rooyen, Isabella J.↗
DOE OSTI2023

Machine learning the electronic structure of matter across temperatures

Here, we introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions.

Fiedler, Lenz↗