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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Frontiers in Thermal Transport and Energy Conversion: Proceedings of a Workshop

Thermal transport and energy conversion has remained an active field for at least 200 years, with numerous opportunities for discoveries and new applications. Recently, experiments have advanced researchers’ understanding of basic physics, and how new discoveries might translate into applications in energy, materials, quantum technologies, and other areas. The National Academies convened a workshop on April 11, 2019 to identify and assess the frontier of current research in the field of thermal transport and energy conversion. Discussions involved topics related to thermal transport and quasi-particle hydrodynamics, thermal transport beyond the quasiparticle paradigm, the thermal hall effect from neutral spin excitations in frustrated quantum magnets, quantization of the thermal hall conductivity at small hall angles, and thermal spin transport, including spin-Seebeck and magnon drag effects. These topics were strategically selected with the goal of uncovering key challenges, opportunities, and issues in order to guide future efforts and investments to advance the field. This publication offers a condensed summary of the discussions and presentations from the workshop, which was unclassified and open to the public.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Immersive Visualization for Scientific Data Analysis

We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.

immersive analytics↗
DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗
DOE OSTI2026

A Solid State Zwitterionic Plastic Crystal With High Static Dielectric Constant

Solid materials with a high dielectric constant have a wide range of applications in the energy storage field. In this research, an imidazolium-based zwitterion is designed, synthesized, and confirmed to have a plastic crystal phase based on the following experimental and computational evidence: (i) the presence of long-range order with weak intermolecular forces and competing attractive-repulsive interactions along different crystallographic directions; (ii) the observation of more than one endotherm on heating including a solid-solid phase transition at T s-s = −26 °C and melting of the plastic crystal at T m = 72°C; (iii) a low entropy of fusion at melting (2.1 JK −1 mol −1 ); (iv) a strongly anisotropic morphology; (v) relatively fast dynamics originating from short-range degrees of freedom. Furthermore, it exhibits a very high dielectric constant in the plastic crystal solid state (147 at −10°C and 103 at 70°C) due to the rotational degrees of freedom of plastic crystals that arise from weak net intermolecular interactions of zwitterions due to only having two carbons between the anion and cation. This material conveniently remains in the plastic crystal phase within 50 K of ambient temperature. This discovery opens new opportunities in the search for solid-state high dielectric constant materials.

dielectric relaxation↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Robust and tunable oxide nanoscrolls for solar-driven H 2 generation and storage

Hydrogen gas is a promising alternative to fossil fuels due to its high energy output and environmentally safe byproducts. Various morphologies of photocatalytic materials have been explored for high-efficiency H 2 production, for instance, quasi-1D nanoscroll structures that provide a larger surface-to-volume ratio. Recently, we predicted layer-by-layer formation of stable oxide nanoscrolls directly from dichalcogenide precursors, eliminating the need for costly formation of two-dimensional oxides for a roll-up synthesis of nanoscrolls. Here, in this study, we evaluate the suitability of those oxide nanoscroll materials—MoO 3 , WO 3 , PdO 2 , HfO 2 , and GeO 2 —for solar-driven photocatalytic H 2 production and storage. Using excited state theory coupled with Bethe–Salpeter equation simulations, we discern their electronic and optical properties as a function of interlayer scroll spacing and find them to be highly conducive for solar-driven photocatalysis. Additionally, using ab initio molecular dynamics simulations, we show that they are also suitable for H 2 storage as the nanoscrolls exhibit an effective trapping of hydrogen, even in the presence of defects and vacancies in the oxides. This work thus demonstrates the discovery of robust and tunable oxide nanoscrolls as materials for advancing solar-driven hydrogen technologies.

Chemical compounds↗
DOE OSTI2026

Latent Catalysis as a Platform for Accessing Diverse Material Properties in Vat Photopolymerization 3D Printing

Vat photopolymerization (VP) 3D printing is an attractive strategy to manufacture customized polymer parts. The properties of printed materials are limited by the need to employ a low viscosity liquid resin and achieve rapid polymerization kinetics. To circumvent this limitation, dual‐cure methods have been developed using reagents embedded in the liquid resin formulation; however, the reagent‐based approach requires the discovery and optimization of new chemistry for each desired material. Here, in this work, we demonstrate a catalytic, dual‐cure platform that enables access to both Nylon‐6 and polyester interpenetrating networks through VP 3D printing under a universal approach. Structure–reactivity relationships of the latent NHC catalysts led to the identification of a magnesium chloride–NHC adduct as a latent catalyst that is orthogonal to radical polymerization and can be unmasked at elevated temperatures post‐printing to initiate ring‐opening polymerization of lactones and lactams. This strategy results in access to semicrystalline materials, which are a challenging morphology to access via VP 3D printing, that have attractive mechanical properties and can be printed at high resolution. This work represents the first photochemical‐based 3D printing of Nylon‐based materials and demonstrates the value of catalytic approaches to access new material properties in VP 3D printing.

Colliver, Cali N. [University of North Carolina, C↗
DOE OSTI2021

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

40 Years of SCES at Los Alamos

Reports of unconventional superconductivity in UBe 13 in 1983 and soon thereafter of the possible coexistence of bulk superconductivity and spin fluctuations in UPt 3 marked the beginning of a 40-year adventure in the study of strongly correlated quantum materials and phenomena at Los Alamos. The subsequent discovery and exploration of heavy-fermion magnetism, cuprates, Kondo insulators, Ce- and Pu-115 superconductors and, more broadly, quantum states of narrow-band systems provided challenges for the next 30 years. Progress was not made in a vacuum but benfitted from significant advances in the Americas, Asia and Europe as well as from essential collaborations, visitors and Los Alamos students and postdocs, many subsequently setting their own course in SCES. As often the case, serendipity played a role in shaping this history.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Rare-earth monopnictides: Family of antiferromagnets hosting magnetic Fermi arcs

We report since the discovery of topological insulators a great deal of research effort has been devoted to magnetic topological materials, in which nontrivial spin properties can be controlled by magnetic fields, culminating in a wealth of fundamental phenomena and possible applications. The main focus was on ferromagnetic materials that can host Weyl fermions and therefore spin-textured Fermi arcs. The recent discovery of Fermi arcs and new magnetic band splitting in the antiferromagnet (AFM) NdBi has opened up new avenues for exploration. Here we show that these uncharted effects are not restricted to this specific compound, but also emerge in CeBi and NdSb when they undergo paramagnetic to AFM transition. Our data show that the Fermi arcs in NdSb have twofold symmetry, leading to strong anisotropy that may enhance effects of spin textures on transport properties. Our findings thus demonstrate that the RBi and RSb series are materials that host magnetic Fermi arcs and may be a potential platform for modern spintronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.

augmentation↗
DOE OSTI2026

Tuning the Functionalities of Porous Liquids for Emergent Gas-Capture Properties

Type 3 Porous Liquids (PLs) are a class of materials with the potential to revolutionize gas capture, storage, and utilization. These PLs are formed by suspending sorbent nanoparticles (e.g., metal−organic frameworks) in sterically excluded solvents, creating permanent porosity for gas capture in a processable, low-viscosity phase. Herein, a computational study revealed sorbent surface functionalization strategies to enhance CO 2 sorption, and the molecular structural signatures underpinning the enhancements in gas uptake. PLs composed of a ZIF-8 surface functionalized with 3-amino-1,2,4-triazole (Atz) in glyceryl triacetate were targeted for emergent CO 2 capture, exceeding that of the unfunctionalized ZIF-8 PL. ZIF-8 was surface functionalized with Atz at various surface coverage fractions (f), and classical molecular dynamics simulations predicted an increase in CO 2 sorption capacity with increasing f, up to f = 0.75. Additionally, detailed structural analyses revealed that solvent orientational order, derived from the solvent triplet-angle distribution, can identify the gas-capture potential of a PL without requiring computationally expensive direct modeling of the CO 2 sorption. Combined with experimental validation, initial computational screening of PL compositions promises to accelerate the discovery of PL compositions for novel gas separation materials platforms.

gas capture↗
DOE OSTI2024

Computational discovery of fast interstitial oxygen conductors

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here, in this study, we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La 4 Mn 5 Si 4 O 22+δ , a representative member of the perrierite/chevkinite family. We found that La 4 Mn 5 Si 4 O 22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO 2 , and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Probing Electron Correlations in 1D Electronic Materials Using Single Quantum Channels (Final Technical Report)

This research focuses on the investigation of strong electron correlations in quasi-one-dimensional oxide nanostructures. The archetypical system consisting of heterostructures formed from an ultrathin layer of LaAlO 3 and TiO 2 -terminated SrTiO 3 offers a unique laboratory for probing strong correlations due to intrinsic richness of the physical system and its ability to be modulated at extreme nanoscale dimensions. Under suitable conditions, SrTiO 3 is known to exhibit a variety of important properties associated with strong electronic correlations, especially superconductivity, magnetism. Understanding the nature of these electronic correlations at fundamental scales, and in reduced spatial dimensions, will provide the scientific basis for new energy technologies, information technologies, and materials. The research program is motivated by recent discoveries and technical advances in the PIs' lab, specifically, (1) pioneering work to create conductive nanostructures to be created in LaAlO 3 /SrTiO 3 heterointerfaces with 2 nm spatial resolution, (2) the discovery of a novel correlated electronic phases in which electrons pair without forming a superconductor, and (3) the demonstrated ability to form high-quality quantum point contacts (QPCs) and achieve ballistic electron transport over micrometer-scales in LaAlO 3 /SrTiO 3 nanowires. The program has three major components. First, we have press forward to develop a full microscopic understanding of the mechanism for electron pairing in LaAlO 3 /SrTiO 3 nanostructures. Understanding the “glue” that leads to pairing and superconductivity in a doped semiconductor will help to form a scientific basis for the correlated oxide nanoelectronics platform being developed. Second, we have used their ability to create well-defined quantum channels in nanowires allows to allow novel “scattering” experiments to be performed that probe fundamentally new quasiparticle excitations using an unprecedented combination of experimental methodologies. Experiments already underway with artificially defined superlattices (5 nm period) demonstrate the feasibility of this approach. Third, we have investigated 1D correlations in proximally coupled nanowires, which are predicted exhibit Majorana-like zero modes in the absence of superconductivity or spin-orbit coupling. These studies provide insight not only into new and emergent phenomena in the rich class of complex oxides, but also fundamental mechanisms of superconductivity and magnetism. Thematically, the research aims to combine some of the most important and interesting challenges in the physics of semiconductor nanostructures and correlated electronic materials. The increased understanding of electron correlations can pave the way to the development of new materials with outstanding or novel properties, which could be relevant for energy-related technologies such as low-power high performance computing, quantum computation, or the development of novel materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗
DOE OSTI2021

Efficient Removal of Per- and Polyfluoroalkyl Substances from Water with Zirconium-Based Metal–Organic Frameworks

Per- and polyfluoroalkyl substances (PFASs) are an emerging class of contaminants raising increased levels of concern due to their toxic, bioaccumulative, and persistent nature. Current solutions for removing PFAS from contaminated water rely on adsorption-based methods where commonly used sorbents, for example, activated carbons and ion-exchange resins, exhibit low adsorption capacity and a long equilibration time. Motivated by the generally deficient performance of these current materials, this work addresses the need for the discovery of advanced sorbents for high capacity and efficient PFAS removal. The zirconium-based metal–organic framework (MOF) NU-1000 was characterized for the adsorption of three perfluorosulfonic acids (PFSAs, C4–C8) and six perfluorinated carboxylic acids (PFCAs, C1–C9) from aqueous solutions. Here, the results indicate that NU-1000 exhibits outstanding adsorption capacities of 400–620 mg/g for PFSAs and 201–604 mg/g for PFCAs coupled with ultrafast adsorption kinetics featuring equilibrium times of <1 min. Complementary density functional theory calculations reveal that the PFAS@MOF adsorption mechanism is dominated by a combination of hydrogen bonding, electrostatic, and hydrophobic non-covalent PFAS–MOF interactions. Excellent regeneration and reusability characteristics were found, particularly nearly quantitative removal and recovery rates of NU-1000 after five consecutive adsoption and desorption cycles of PFAS. Additional adsorption testing using PFAS-contaminated groundwater samples obtained from U.S. Air Force bases revealed impressive PFAS removal rates of 75–98% within 10 min regardless of the presence of co-contaminants. To the best of our knowledge, the suite of herein presented PFAS sorption characteristics—capacity, kinetics, regeneration, and reusability—significantly outperforms other current sorbents, rendering NU-1000 as a promising platform for the rapid and effective removal of PFAS from aqueous media.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Light-Induced Melting of Competing Stripe Orders without Introducing Superconductivity in La 2 − x Ba x CuO 4

The ultrafast manipulation of quantum material has led to many novel and significant discoveries. Among them, the light-induced transient superconductivity in cuprates achieved by melting competing stripe orders represents a highly appealing accomplishment. However, recent investigations have shown that the notion of photoinduced superconductivity remains a topic of controversy, and its elucidation solely through c -axis time-resolved terahertz spectroscopy remains an arduous task. Here, we measure the in-plane and out-of-plane transient terahertz responses simultaneously in the stripe-ordered nonsuperconducting La 2 − x Ba x CuO 4 after near-infrared excitations. We find that although a pump-induced reflectivity edge appears in the c -axis reflectance spectrum, the reflectivity along the CuO 2 planes decreases simultaneously, indicating an enhancement in the scattering rate of quasiparticles. This in-plane transient response is clearly distinct from the features associated with superconducting condensation. Therefore, we conclude the out-of-plane transient responses cannot be explained by an equivalent of Josephson tunneling. Notably, those pump-induced terahertz responses remain consistent even when we vary the near-infrared optical pump wavelengths and hole concentrations. Our results provide critical evidence that transient three-dimensional superconductivity cannot be induced by melting the competing stripe orders with pump pulses whose photon energy is much higher than the superconducting gap of cuprates. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metal-hydrogen-pi-bonded organic frameworks

We report the synthesis and characterization of a new series of permanently porous, three-dimensional metal–organic frameworks (MOFs), M-HAF-2 (M = Fe, Ga, or In), constructed from tetratopic, hydroxamate-based, chelating linkers. Here, the structure of M-HAF-2 was determined by three-dimensional electron diffraction (3D ED), revealing a unique interpenetrated hcb-a net topology. This unusual topology is enabled by the presence of free hydroxamic acid groups, which lead to the formation of a diverse network of cooperative interactions comprising metal–hydroxamate coordination interactions at single metal nodes, staggered π–π interactions between linkers, and H-bonding interactions between metal-coordinated and free hydroxamate groups. Such extensive, multimodal interconnectivity is reminiscent of the complex, noncovalent interaction networks of proteins and endows M-HAF-2 frameworks with high thermal and chemical stability and allows them to readily undergo postsynthetic metal ion exchange (PSE) between trivalent metal ions. We demonstrate that M-HAF-2 can serve as versatile porous materials for ionic separations, aided by one-dimensional channels lined by continuously π-stacked aromatic groups and H-bonding hydroxamate functionalities. As an addition to the small group of hydroxamic acid-based MOFs, M-HAF-2 represents a structural merger between MOFs and hydrogen-bonded organic frameworks (HOFs) and illustrates the utility of non-canonical metal-coordinating functionalities in the discovery of new bonding and topological patterns in reticular materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗