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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Manganese-rich high entropy oxides for lithium-ion batteries:materials design approaches to address voltage fade

Lithium- and manganese-rich oxides are of interest as lithium-ion battery cathode materials as Mn is earth abundant, low cost, and can deliver high capacity. Herein, a high entropy strategy was used to prepare Mn rich high entropy oxide (HEO) materials by including four additional metals (Ni, Co, Fe and Al) in the compositions using a mild co-precipitation method. Two HEOs (Li x Ni 0.1 Mn 0.6 Co 0.1 Al 0.1 Fe 0.1 O y , where x = 1.5 for HEO-L and x = 0.5 for HEO-H) with layered and spinel-layered hybrid structures were investigated where the morphology, elemental composition, structure, atomic level phase distribution, and electrochemistry were determined. The HEO-L samples involve a Li 2 TMO 3 layered structure with ~39% stacking faults. HEO-H is a hybrid structure comprised of 80 wt% spinel and 20 wt% LiMO 2 layered structure. The high entropy manganese-rich HEO-L showed higher capacity and 93% retention of the average voltage after 100 cycles while HEO-H showed higher capacity retention and near 100% average voltage retention. Finally, operando X-ray absorption spectroscopy revealed that the Ni, Co, and Mn are redox active in both materials while the Fe center remains at the Fe 3+ oxidation state throughout cycling, where the changes in the oxidation states for both materials during discharge were consistent with the delivered electrochemical capacity rationalizing the observed electrochemistry.

25 ENERGY STORAGE↗
DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory

Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments. Due to their unique crystallographic features and behavior in nature environment as exemplified by their natural analogues, ceramic waste forms are capable of incorporating problematic nuclear waste elements while showing promising chemical durability in aqueous environments. Recent studies of apatite- and hollandite-structured waste forms demonstrated an approach that can predict the compositions of ceramic waste forms and their long-term dissolution rate by a combination of computational techniques including machine learning, first-principles thermodynamics calculations, and modeling using kinetic rate equations based on critical laboratory experiments. By integrating the predictions of elemental incorporation and degradation kinetics in a holistic framework, the approach could be promising for the design of advanced ceramic waste forms with optimized incorporation capacity and environmental degradation performance. Such an approach could provide a path for accelerated ceramic waste form development and performance prediction for problematic nuclear waste elements.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗
DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Gamma (K5 Based) Compressor Blade Material Design - Alpha Extrusion on a Small Scale

The objective of the 2003 study is to develop a mechanically processed, high temperature TiAl alloy for use in the Turbine Based Combined Cycle compressor. An aging study with 3 temperatures and 4 time periods at each temperature will be conducted to determine the optimum aging condition. High-magnification microscopic analyses shall be employed to define the size distribution of carbide as a function of aging temperature/time. The contractor shall define optimum materials' conditions by conducting tensile and creep testing for two over-aging conditions for both alloys and fatigue testing for one over-aging condition for each alloy.

Kim, Young-Won↗
DOE OSTI2022

Recent Advances in 3D Printed Sensors: Materials, Design, and Manufacturing

Sensors are of great importance in different aspects of research and industry. Future sensors will require high-efficient and low-cost manufacturing, as well as high-performance functionality in areas, such as mechanical sensing, biomedical, and optical applications. Recent advances in 3D printing open a new paradigm for sensors fabrication as a precision, customizable, and seamless process. Here, in this article, the state-of-the-art 3D printing methods in sensors manufacturing is reviewed and the performance of the 3D printed sensing materials and devices is summarized. Special attention is paid to emerging multimaterial printing and 4D printing technologies, which will benefit the fabrication of a new generation of structures with multifunctionalities. The content on 3D printed sensors covers piezoelectric sensors, medical, and optical sensing devices. The performance of 3D printed sensors in comparison with the sensors made by traditional manufacturing is also covered. Finally, section 4 provides the viewpoints on the future development of 3D printed sensors.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Approach and Issues Relating to Shield Material Design to Protect Astronauts from Space Radiation

One major obstacle to human space exploration is the possible limitations imposed by the adverse effects of long-term exposure to the space environment. Even before human spaceflight began, the potentially brief exposure of astronauts to the very intense random solar energetic particle (SEP) events was of great concern. A new challenge appears in deep space exploration from exposure to the low-intensity heavy-ion flux of the galactic cosmic rays (GCR) since the missions are of long duration and the accumulated exposures can be high. Since aluminum (traditionally used in spacecraft to avoid potential radiation risks) leads to prohibitively expensive mission launch costs, alternative materials need to be explored. An overview of the materials related issues and their impact on human space exploration will be given.

Wilson, J. W.↗
DOE OSTI2024

Density functional theory of material design: fundamentals and applications—II

Abstract This is the second and the final part of the review on density functional theory (DFT), referred to as DFT-II. In the first review, DFT-I, we have discussed wavefunction-based methods, their complexity, and basics of density functional theory. In DFT-II, we focus on fundamentals of DFT and their implications for the betterment of the theory. We start our presentation with the exact DFT results followed by the concept of exchange-correlation (xc) or Fermi-Coulomb hole and its relationship with xc energy functional. We also provide the exact conditions for the xc-hole, xc-energy and xc-potential along with their physical interpretation. Next, we describe the extension of DFT for non-integer number of electrons, the piecewise linearity of total energy and discontinuity of chemical potential at integer particle numbers, and derivative discontinuity of the xc potential, which has consequences on fundamental gap of solids. After that, we present how one obtains more accurate xc energy functionals by going beyond the LDA. We discuss the gradient expansion approximation (GEA), generalized gradient approximation (GGA), and hybrid functional approaches to designing better xc energy functionals that give accurate total energies. However, these functionals fail to predict properties like the ionization potential and the band gap. Thus, we next describe different methods of modelling these potentials and results of their application for calculation of the band gaps of different solids to highlight accuracy of different xc potentials. Finally, we conclude with a glimpse on orbital-free density functional theory and the machine learning approach.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery

Here, the advent of high-throughput Density Functional Theory (DFT) calculations has supported the creation of large databases containing the quantitative output necessary for constructing theoretical phase diagrams and predicting physical properties. In this Article, we present a complementary resource, the Intermetallic Reactivity Database (IRD), focused on the chemical bonding features of solid-state structures and indicators of potential structural transformations. Each IRD entry augments common features, such as band structures and density of states (DOS) distributions, with chemically motivated information including DFT-Chemical Pressure (CP) schemes and visualizable representations of the atomic charges. Together, these data types enable the rationalization and prediction of potential structural phenomena encountered in intermetallic chemistry, as we illustrate with four examples: the origins of the Y 2 Ni 2 Mg structure in terms of CP features of its parent structures, the anticipation of intergrowth phases from the net atomic CPs collected in Al-containing binary phases, the correlation between trends in the CP schemes of CaCu 5 -type phases and experimentally observed structural variations, and finally, the development of theoretical methodology with the testing of a streamlined method generating DFT-CP schemes. Altogether, these examples highlight how the IRD supports the creation of models of structural chemistry that extend beyond the bounds of its entries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Porous transport electrodes for oxygen evolution reaction in proton exchange membrane water electrolysis -cells: Materials, designs, and diagnoses

H 2 production using proton exchange membrane (PEM) water electrolysis (PEMWE) cells has received considerable attention because of the high efficiencies of these cells and no harmful emissions from the related process. In PEMWE cells, porous transport electrodes (PTEs) composed of a catalyst layer (CL) comprising O 2 evolution reaction (OER) catalysts, porous transport layer (PTL), and PEM play key roles in the stack performance and lifetime. Herein, Ir-based and non-precious-metal OER catalysts that are highly active and stable at low pH values and high anodic potentials are reviewed to understand their OER mechanisms. Various strategies are proposed for engineering CLs and PTLs to improve the interfacial properties and mass transfers of reactants and products to and from the active sites. Additionally, diagnoses of PTEs is significantly crucial for interpreting electrochemical processes and addressing their current challenges. Therefore, half-cell analyses, including diffusion electrode (DE), floating electrode (FE), and modified rotating disk electrode (MRDE) techniques, are explored, and membrane electrode assembly (MEA)-based analyses, such as the polarization technique, electrochemical impedance spectroscopy, and magnetic field analysis, are established. In conclusion, this study aims to provide an overview of recent technologies used for the engineering and diagnostic tools of PEMWE cells and insights into the advanced components and systems to be developed in this field.

Diagnosis of PEMWE Cells↗
DOE OSTI2025

System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression

Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, in this study, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook.

alloy optimization↗