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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A High‐Rate Li–CO 2 Battery Enabled by 2D Medium‐Entropy Catalyst

Abstract Lithium‐air batteries based on CO 2 reactant (Li–CO 2 ) have recently been of interest because it has been found that reversible Li/CO 2 electrochemistry is feasible. In this study, a new medium‐entropy cathode catalyst, (NbTa) 0.5 BiS 3 , that enables the reversible electrochemistry to operate at high rates is presented. This medium entropy cathode catalyst is combined with an ionic liquid‐based electrolyte blend to give a Li–CO 2 battery that operates at high current density of 5000 mA g −1 and capacity of 5000 mAh g −1 for up to 125 cycles, far exceeding reported values in the literature for this type of battery. The higher rate performance is believed to be due to the greater stability of the multi‐element (NbTa) 0.5 BiS 3 catalyst because of its higher entropy compared to previously used catalysts with a smaller number of elements with lower entropies. Evidence for this comes from computational studies giving very low surface energies (high surface stability) for (NbTa) 0.5 BiS 3 and transmission electron microscopystudies showing the structure being retained after cycling. In addition, the calculations indicate that Nb‐terminated surface promotes Li–CO 2 electrochemistry resulting in Li 2 CO 3 and carbon formation, consistent with the products found in the cell. These results open new direction to design and develop high‐performance Li–CO 2 batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Benchmarking boron carbide equation of state using computation and experiment

Boron carbide ( B 4 C ) is of both fundamental scientific and practical interest due to its structural complexity and how it changes upon compression, as well as its many industrial uses and potential for use in inertial confinement fusion (ICF) and high-energy density physics experiments. Here, we report the results of a comprehensive computational study of the equation of state (EOS) of B 4 C in the liquid, warm dense matter, and plasma phases. Our calculations are cross-validated by comparisons with Hugoniot measurements up to 61 megabar from planar shock experiments performed at the National Ignition Facility (NIF). Our computational methods include path integral Monte Carlo, activity expansion, as well as all-electron Green's function Korringa-Kohn-Rostoker and molecular dynamics that are both based on density functional theory. We calculate the pressure-internal energy EOS of B 4 C over a broad range of temperatures ( ~ 6 × 10 3 – 5 × 10 8 K) and densities (0.025–50 g / cm 3 ). We assess that the largest discrepancies between theoretical predictions are ≲ 5 % near the compression maximum at 1– 2 × 10 6 K. This is the warm-dense state in which the K shell significantly ionizes and has posed grand challenges to theory and experiment. By comparing with different EOS models, we find a Purgatorio model (LEOS 2122) that agrees with our calculations. The maximum discrepancies in pressure between our first-principles predictions and LEOS 2122 are ~ 18 % and occur at temperatures between 6 × 10 3 – 2 × 10 5 K, which we believe originate from differences in the ion thermal term and the cold curve that are modeled in LEOS 2122 in comparison with our first-principles calculations. To account for potential differences in the ion thermal term, we have developed three new equation-of-state models that are consistent with theoretical calculations and experiment. We apply these new models to 1D hydrodynamic simulations of a polar direct-drive NIF implosion, demonstrating that these new models are now available for future ICF design studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Electronic and magnetic properties of quasi-one-dimensional osmium halide OsCl 4

Using ab initio density functional theory, we study the electronic and magnetic properties of the van der Waals chain material OsCl 4 . In the nonmagnetic state, a strongly anisotropic band structure was observed, in agreement with its anticipated one-dimensional crystal geometry. Based on Wannier functions, we found that the four electrons of the 5d Os atom form a low-spin S = 1 state, with a large crystal field between the d xz/yz and d xy orbitals, corresponding to a strong Jahn–Teller distortion (Q 3 < 0). As a consequence, the magnetic properties are mainly contributed by the d xz/yz states. Furthermore, when a Mott gap develops after the introduction of the Hubbard U and Hund coupling J, we found that the staggered spin order is the most likely magnetic state, namely, spins arranged as (↑-↓-↑-↓) with π wavevector along the chain. In addition, the energy differences between various spin states are small, suggesting a weak magnetic exchange coupling along the chain. Overall, our results provide guidance to experimentalists and theorists working on quasi-one-dimensional osmium halides chain materials.

36 MATERIALS SCIENCE↗
NASA NTRS1995

An extension of the Lighthill theory of jet noise to encompass refraction and shielding

A formalism for jet noise prediction is derived that includes the refractive 'cone of silence' and other effects; outside the cone it approximates the simple Lighthill format. A key step is deferral of the simplifying assumption of uniform density in the dominant 'source' term. The result is conversion to a convected wave equation retaining the basic Lighthill source term. The main effect is to amend the Lighthill solution to allow for refraction by mean flow gradients, achieved via a frequency-dependent directional factor. A general formula for power spectral density emitted from unit volume is developed as the Lighthill-based value multiplied by a squared 'normalized' Green's function (the directional factor), referred to a stationary point source. The convective motion of the sources, with its powerful amplifying effect, also directional, is already accounted for in the Lighthill format: wave convection and source convection are decoupled. The normalized Green's function appears to be near unity outside the refraction dominated 'cone of silence', this validates our long term practice of using Lighthill-based approaches outside the cone, with extension inside via the Green's function. The function is obtained either experimentally (injected 'point' source) or numerically (computational aeroacoustics). Approximation by unity seems adequate except near the cone and except when there are shrouding jets: in that case the difference from unity quantifies the shielding effect. Further extension yields dipole and monopole source terms (cf. Morfey, Mani, and others) when the mean flow possesses density gradients (e.g., hot jets).

Ribner, Herbert S.↗
DOE OSTI2021

Effect of static local distortions vs. dynamic motions on the stability and band gaps of cubic oxide and halide perovskites

Ternary ABX 3 perovskites made of corner-sharing BX 6 octahedra have long featured prominently in solid-state chemistry and condensed matter physics. Still, the joint understanding of their two main subgroups—halides and oxides—has not been fully developed. Indeed, unlike the case in simpler compounds having a single, robust repeated motif (“monomorphous”), certain cubic perovskites can manifest a non-thermal (= intrinsic) distribution of local motifs (“polymorphous networks”). Such static deformations can include positional degrees of freedom (e.g., atomic displacements and octahedral tilting) or magnetic moment degrees of freedom in paramagnets. Unlike thermal motion, such static distortions do not time-average to zero, being an expression of the intrinsic symmetry breaking preference of the chemical bonding. Here, the present study compares electronic structure features of oxide and halide perovskites starting from the static polymorphous distribution of motifs described by Density Functional Theory (DFT) minimization of the internal energy, continuing to finite temperature thermal disorder modeled via finite temperature DFT molecular dynamics. We find that (i) different oxide vs. halide ABX 3 compounds adopt different energy-lowering symmetry-breaking modes. The calculated pair distribution function (PDF) of SrTiO 3 from the first-principles agrees with recently measured PDF. (ii) In both oxides and halides, such static distortions lead to band gap blueshifts with respect to undistorted cubic Pm-3m structure. (iii) For oxide perovskites, high-temperature molecular dynamics simulations initiated from the statically distorted polymorphous structures reveal that the thermally-induced distortions can lead to a band gap redshift. (iv) In contrast, for cubic halide perovskite CsPbI 3 , both the intrinsic distortions and the thermal distortions contribute in tandem to band gap blueshift, the former, intrinsic effect being dominant. (v) In the oxide SrTiO 3 and CaTiO 3 (but not in halide) perovskites, octahedral tilting leads to the emergence of a distinct Γ–Γ direct band gap component as a secondary valley minimum to the well-known indirect R–Γ gap. Understanding such intrinsic vs. thermal effects on oxide vs. halide perovskites holds the potential for designing target electronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Magnetic Response of Excitons and Excitonic Complexes in Defective Hexyl Ammonium Lead Iodide Self-Assembled Quantum Wells

Correlating the magnetic behavior of low-dimensional semiconductors with the chemistry used to form these excitonic materials remains crucial to the development of devices applied to quantum technologies. In this study, we apply large magnetic fields during the collection of low-temperature photoluminescence (PL) to assess the magnetic properties of excitons in hexyl ammonium lead iodide (HA 2 PbI 4 ) self-assembled quantum wells (SAQWs) formed at liquid−liquid interfaces. The effect of incident laser power and temperature was used to assign lower energy features in these samples’ PL spectra to both excitons trapped at defect sites and trions. Measured peaks shifts allow us to estimate coupling between the applied magnetic fields and the charged defect excitons. Our conclusions are supported by density functional theory calculations on supercells of the proposed defective HA 2 PbI 4 SAQW structure. Additionally, we find an anomalous magnetic response from trion states that resembles the behavior of interacting excitonic complexes in similar, quantum-confined materials at low temperatures. These results highlight the crucial role that chemical conditions can play in the magnetic response of 2D semiconductors applied in quantum technologies.

defect chemistry↗
NASA NTRS1981

Anisotropy of the cosmic microwave background radiation

Theoretical predictions of the angular anisotropy in the cosmic microwave background radiation on both small and large angular scales are presented, and the effect of massive neutrinos on both the background radiation anisotropy and on the galaxy correlation function over very large scales is reviewed. Current observations show that the quadrupole anisotropy provides the greatest constraint on theory, and the values for the gravitational potential fluctuations indicate that small amplitude but sufficiently large-scale density fluctuations, both at the present epoch and on the surface of last scattering, can produce significant large angular scale variations in the radiation temperature. Most importantly, it is proposed that the quadrupole moment is most simply and elegantly interpreted in terms of the density fluctuations on very large scales whose presence is inferred from the requirement that an initial fluctuation spectrum is required in order for structure to develop.

Silk, J.↗
NASA NTRS1992

Electromagnetic ion/ion cyclotron instability - Theory and simulations

Linear theory and 1D and 2D hybrid simulations are employed to study electromagnetic ion/ion cyclotron (EMIIC) instability driven by the relative streaming of two field-aligned ion beams. The characteristics of the instability are studied as a function of beam density, propagation angle, electron-ion temperature ratios, and ion beta. When the propagation angle is near 90 deg the EMIIC instability has the characteristics of an electrostatic instability, while at smaller angles electromagnetic effects play a significant role as does strong beam coupling. The 2D simulations point to a narrowing of the wave spectrum and accompanying coherent effects during the linear growth stage of development. The EMIIC instability is an important effect where ion beta is low such as in the plasma-sheet boundary layer and upstream of slow shocks in the magnetotail.

Winske, D.↗
DOE OSTI2022

Development of Porous Crystalline Materials for Selective Binding of O 2 from Air

Porous materials that take advantage of different chemisorptive behaviors of O 2 and N 2 could be an effective alternative to approaches based on physisorption for this important separation. Recently, the tetra-nuclear cobalt complex [(Co(III) 2 (bpbp)O 2 ) 2 bdc](PF 6 ) 4 (CSD code: GAMVIB; bpbp – = 2,6-bis(N,N-bis(2-pyridylmethyl)aminomethyl)-4-tert-butylphenolato; bdc 2– = 1,4-benzenedicarboxylato) was shown to have potential for O 2 /N 2 separations based on this concept. This observation raises the question of what other known materials have similar properties. Here, we combine structure screening with a high-throughput periodic density functional theory (DFT) workflow to investigate O 2 and N 2 adsorption in materials from validated crystal structure databases (e.g., CoRE-MOF database and CSD database). These calculations identify multiple materials that have similar di-Co clusters to GAMVIB that are predicted to selectively bind O 2 over N 2 , and suggest design rules that can be used to tune the O 2 and N 2 affinities in materials of this kind.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds

Quantum-mechanics-(QM)-based simulations now routinely aid in understanding and even discovering new chemistries involving molecules and materials exhibiting desired functionalities. Ab initio correlated wavefunction (CW) theories systematically improve QM methods, with many exhibiting high accuracy. However, execution of CW methods requires expensive computations that typically scale poorly with system size. Divide-and-conquer approaches partition large systems into smaller fragments; a lower level of theory treats fragment interactions while a preferred higher level of theory describes the important fragment. These methods offer ways to incorporate CWs into chemical simulations of large systems, e.g., biomolecules, surfaces, large inorganic clusters, bulk crystals, etc. Here we propose a partitioning protocol that utilizes capping atoms to saturate severed covalent bonds at fragment interfaces and density functional embedding theory (DFET) to describe fragment interactions. The capping groups in each fragment provide an ad-hoc potential that approximates the effects of the environment. An embedding potential optimized via DFET then serves as an augmentation of the capping group to simulate the effects of the environment. We concurrently use an auxiliary fragment (a separate system comprised of only the combined capping groups) to account for, and thereby correct, the electron density contributions of all the capping groups added to all of the fragments. This method depends only on the capped-subsystem and auxiliary-fragment electron densities, forgoing, as with the original DFET developed for metallic systems, orbital-based projector approaches that determine a nonlocal action of the embedding potential onto the fragment electrons. By using an auxiliary fragment, the method maintains a purely electron-density-dependent embedding potential, substantially lessening the cost and leading to simpler implementation. In this work, we demonstrate the utility of our capped-DFET and ensuing capped embedded CW method in two contrasting systems, namely, an organic molecule and an ionic metal oxide cluster.

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

Mechanistic study of a CO-free pathway in the methanol oxidation reaction over oxygen vacancies in NiOOH

The methanol oxidation reaction is a key reaction in direct methanol fuel cells. Prior research indicates that if oxygen vacancies in NiOOH serve as the active sites, the methanol oxidation mainly proceeds through the formate-involving pathway, which is a CO-free pathway, distinct from the conventional path over transition metal catalysts, though the fundamental reason for this suppressed CO formation is unclear. Herein, we report density functional theory calculations, through which we uncover the underlying reasons for this alternate path of methanol oxidation over the oxygen vacancies in NiOOH. We find that the existence of oxygen vacancies in NiOOH affects the adsorption configuration of adsorbates and that the interfacial charge transfer is minimal for CHO and CO intermediates. In addition, CHO, a key intermediate to form CO, adsorbs at the oxygen vacancy through the oxygen atom, leading to low stability due to the incomplete valence saturation of the carbon atom. This weak electronic interaction and instability effectively inhibit CHO formation and, consequently, CO formation. These insights provide valuable guidance for the development of efficient and CO-tolerant catalysts for methanol oxidation.

Ho, Thy [University of Oklahoma, Norman, OK (Unite↗
DOE OSTI2024

Digital twins and deep learning segmentation of defects in monolayer MX 2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX 2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX 2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX 2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. Further, this digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of Metal Boride/Carbide Precursors for MOCVD Applications

This report describes the proposed efforts for a three-year (CY23-25) program to develop refractory metal boride/carbide precursors for metal-organic chemical vapor deposition (MOCVD) applications. Reported are the CY24 results on the thermal processing of bis-cyclopentadienyl dialkyl and tetra-alkyl precursors to obtain metal carbide products. Precursors evaluated are commercially available. Materials were processed within in a custom-built MOCVD system at 1000 ⁰C, as well as in a hot isostatic press (HIP) at temperatures of 1000 ⁰C or 1650 ⁰C at pressures of 5000 psi. The products were identified as metal carbide, metal oxide, or a mixture of carbide and oxide phases depending on the starting material and process used. Density functional theory calculations were performed to determine the decomposition mechanism and to inform how ligand choice led to the products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

NNL.Fe.qSNAP-ZBL.2024.1: A Fe Spectral Neighbor Analysis Potential for Radiation Damage Simulations

The NNL.Fe.qSNAP-ZBL.2024.1 machine-learned potential (MLP) has been generated to support the development of an elemental body-centered cubic (BCC) Fe athermal recombination corrected neutron damage model and simulations of primary recoil atom (PRA) cascades in BCC Fe. This MLP is a quadratic spectral neighbor analysis potential (qSNAP) hybridized with the universal Ziegler-Beirsack-Littmark (ZBL) potential at short-range and is named according to Naval Nuclear Laboratory MLP naming conventions (NNL.material-system.MLP-type.year.version). Training set calculations for Fe are presented along with the subsequent MLP fitting procedure. A key criterion of the fitting procedure is that ZBL describes the short-range interaction with minimal impact on the MLP. The MLP is compared to density functional theory (DFT) predicted properties relevant to radiation damage simulation, including threshold displacement energies, for validation. The NNL.Fe.qSNAP-ZBL.2024.1 potential is considered suitable for molecular dynamics (MD) simulations of radiation defects up to 800 K and PRA cascades in BCC Fe up to around 10 keV. The potential can additionally be used on a limited basis for recoils of 10–20 keV, within which range the emergence of structures outside the training set in cascade simulations may cause system instabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

A new adaptive control approach for aerospace vehicles with parameter uncertainties

A new stochastic adaptive control structure is developed for the problem of combined parameter estimation and control of aerospace vehicles with changing parameters. Parameter uncertainties are modeled as first-order Gauss-Markov processes, and are introduced to the system dynamics through a small parameter. It is assumed that an accurate inertial measurement unit gives perfect measurements of the state variables. Since the stochastic system is assumed to be Gauss-Markov, the density function of the parameters given these measurements is conditionally Gaussian. Based on this conditionally Gaussian density, the problem of minimizing a quadratic cost over an infinite time horizon can be set up within the framework of stochastic optimal control theory. The optimal feedback control law is derived from a straightforward expansion of the Hamilton-Jacobi-Bellman equation, based on the LQG solution. The resulting nonlinear controller is applied to the pitch axis control of a space platform with uncertain moments of inertia and is shown to produce marked improvement over a fixed controller.

Hahn, Yungsun↗
NASA NTRS2016

Operational Implementation of a Pc Uncertainty Construct for Conjunction Assessment Risk Analysis

Earlier this year the NASA Conjunction Assessment and Risk Analysis (CARA) project presented the theoretical and algorithmic aspects of a method to include the uncertainties in the calculation inputs when computing the probability of collision (Pc) between two space objects, principally uncertainties in the covariances and the hard-body radius. The output of this calculation approach is to produce rather than a single Pc value an entire probability density function that will represent the range of possible Pc values given the uncertainties in the inputs and bring CA risk analysis methodologies more in line with modern risk management theory. The present study provides results from the exercise of this method against an extended dataset of satellite conjunctions in order to determine the effect of its use on the evaluation of conjunction assessment (CA) event risk posture. The effects are found to be considerable: a good number of events are downgraded from or upgraded to a serious risk designation on the basis of consideration of the Pc uncertainty. The findings counsel the integration of the developed methods into NASA CA operations.

assessment↗