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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Entropy-stabilized single-atom Pd catalysts via high-entropy fluorite oxide supports

Single-atom catalysts (SACs) have attracted considerable attention in the catalysis community. However, fabricating intrinsically stable SACs on traditional supports (N-doped carbon, metal oxides, etc.) remains a formidable challenge, especially under high-temperature conditions. Here, we report a novel entropy-driven strategy to stabilize Pd single-atom on the high-entropy fluorite oxides (CeZrHfTiLa)O x (HEFO) as the support by a combination of mechanical milling with calcination at 900 °C. Characterization results reveal that single Pd atoms are incorporated into HEFO (Pd1@HEFO) sublattice by forming stable Pd–O–M bonds (M = Ce/Zr/La). Compared to the traditional support stabilized catalysts such as Pd@CeO 2 , Pd 1 @HEFO affords the improved reducibility of lattice oxygen and the existence of stable Pd–O–M species, thus exhibiting not only higher low-temperature CO oxidation activity but also outstanding resistance to thermal and hydrothermal degradation. This work therefore exemplifies the superiority of high-entropy materials for the preparation of SACs.

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

Reversing sintering effect of Ni particles on γ-Mo 2 N via strong metal support interaction

Reversing the thermal induced sintering phenomenon and forming high temperature stable fine dispersed metallic centers with unique structural and electronic properties is one of the ever-lasting targets of heterogeneous catalysis. Here we report that the dispersion of metallic Ni particles into under-coordinated two-dimensional Ni clusters over γ-Mo 2 N is a thermodynamically favorable process based on the AIMD simulation. A Ni-4nm/γ-Mo 2 N model catalyst is synthesized and used to further study the reverse sintering effect by the combination of multiple in-situ characterization methods, including in-situ quick XANES and EXAFS, ambient pressure XPS and environmental SE/STEM etc. The under-coordinated two-dimensional layered Ni clusters on molybdenum nitride support generated from the Ni-4nm/γ-Mo 2 N has been demonstrated to be a thermally stable catalyst in 50 h stability test in CO 2 hydrogenation, and exhibits a remarkable catalytic selectivity reverse compared with traditional Ni particles-based catalyst, leading to a chemo-specific CO 2 hydrogenation to CO.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Room-temperature valence transition in a strain-tuned perovskite oxide

Abstract Cobalt oxides have long been understood to display intriguing phenomena known as spin-state crossovers, where the cobalt ion spin changes vs. temperature, pressure, etc. A very different situation was recently uncovered in praseodymium-containing cobalt oxides, where a first-order coupled spin-state/structural/metal-insulator transition occurs, driven by a remarkable praseodymium valence transition. Such valence transitions, particularly when triggering spin-state and metal-insulator transitions, offer highly appealing functionality, but have thus far been confined to cryogenic temperatures in bulk materials (e.g., 90 K in Pr 1- x Ca x CoO 3 ). Here, we show that in thin films of the complex perovskite (Pr 1- y Y y ) 1- x Ca x CoO 3-δ , heteroepitaxial strain tuning enables stabilization of valence-driven spin-state/structural/metal-insulator transitions to at least 291 K, i.e., around room temperature. The technological implications of this result are accompanied by fundamental prospects, as complete strain control of the electronic ground state is demonstrated, from ferromagnetic metal under tension to nonmagnetic insulator under compression, thereby exposing a potential novel quantum critical point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A quinary WTaCrVHf nanocrystalline refractory high-entropy alloy withholding extreme irradiation environments

In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission & fusion reactors, space applications, etc.), design, prediction and control of advanced materials beyond current material designs become paramount. Here, through a combined experimental and simulation methodology, we design a nanocrystalline refractory high entropy alloy (RHEA) system. Compositions assessed under extreme environments and in situ electron-microscopy reveal both high thermal stability and radiation resistance. We observe grain refinement under heavy ion irradiation and resistance to dual-beam irradiation and helium implantation in the form of low defect generation and evolution, as well as no detectable grain growth. The experimental and modeling results—showing a good agreement—can be applied to design and rapidly assess other alloys subjected to extreme environmental conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identification of triacylglycerol remodeling mechanism to synthesize unusual fatty acid containing oils

Abstract Typical plant membranes and storage lipids are comprised of five common fatty acids yet over 450 unusual fatty acids accumulate in seed oils of various plant species. Plant oils are important human and animal nutrients, while some unusual fatty acids such as hydroxylated fatty acids (HFA) are used in the chemical industry (lubricants, paints, polymers, cosmetics, etc.). Most unusual fatty acids are extracted from non-agronomic crops leading to high production costs. Attempts to engineer HFA into crops are unsuccessful due to bottlenecks in the overlapping pathways of oil and membrane lipid synthesis where HFA are not compatible. Physaria fendleri naturally overcomes these bottlenecks through a triacylglycerol (TAG) remodeling mechanism where HFA are incorporated into TAG after initial synthesis. TAG remodeling involves a unique TAG lipase and two diacylglycerol acyltransferases (DGAT) that are selective for different stereochemical and acyl-containing species of diacylglycerol within a synthesis, partial degradation, and resynthesis cycle. The TAG lipase interacts with DGAT1, localizes to the endoplasmic reticulum (with the DGATs) and to puncta around the lipid droplet, likely forming a TAG remodeling metabolon near the lipid droplet-ER junction. Each characterized DGAT and TAG lipase can increase HFA accumulation in engineered seed oils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

LaCl 3 -based sodium halide solid electrolytes with high ionic conductivity for all-solid-state batteries

To enable high performance of all solid-state batteries, a catholyte should demonstrate high ionic conductivity, good compressibility and oxidative stability. Here, a LaCl 3 -based Na + superionic conductor (Na 1–x Zr x La 1–x Cl 4 ) with high ionic conductivity of 2.9 × 10 –4 S cm –1 (30 °C), good compressibility and high oxidative potential (3.80 V vs. Na 2 Sn) is prepared via solid state reaction combining mechanochemical method. X-ray diffraction reveals a hexagonal structure (P6 3 /m) of Na 1–x Zr x La 1–x Cl 4 , with Na + ions forming a one-dimensional diffusion channel along the c-axis. First-principle calculations combining with X-ray absorption fine structure characterization etc. reveal that the ionic conductivity of Na 1–x Zr x La 1–x Cl 4 is mainly determined by the size of Na + -channels and the Na + /La 3+ mixing in the one-dimensional diffusion channels. When applied as a catholyte, the NaCrO 2 ||Na 0.7 Zr 0.3 La 0.7 Cl 4 ||Na 3 PS 4 ||Na 2 Sn all-solid-state batteries demonstrate an initial capacity of 114 mA h g –1 and 88% retention after 70 cycles at 0.3 C. In addition, a high capacity of 94 mA h g –1 can be maintained at 1 C current density.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unusual plastic strain-induced phase transformation phenomena in silicon

Pressure-induced phase transformations (PTs) in Si, the most important electronic material, have been broadly studied, whereas strain-induced PTs have never been studied in situ. Here, we reveal in situ various important plastic strain-induced PT phenomena. A correlation between the direct and inverse Hall-Petch effect of particle size on yield strength and pressure for strain-induced PT is predicted theoretically and confirmed experimentally for Si-I→Si-II PT. For 100 nm particles, the strain-induced PT Si-I→Si-II initiates at 0.3 GPa under both compression and shear while it starts at 16.2 GPa under hydrostatic conditions. The Si-I→Si-III PT starts at 0.6 GPa but does not occur under hydrostatic pressure. Pressure in small Si-II and Si-III regions of micron and 100 nm particles is ~5–7 GPa higher than in Si-I. For 100 nm Si, a sequence of Si-I → I + II → I + II + III PT is observed, and the coexistence of four phases, Si-I, II, III, and XI, is found under torsion. Retaining Si-II and single-phase Si-III at ambient pressure and obtaining reverse Si-II→Si-I PT demonstrates the possibilities of manipulating different synthetic paths. The obtained results corroborate the elaborated dislocation pileup-based mechanism and have numerous applications for developing economic defect-induced synthesis of nanostructured materials, surface treatment (polishing, turning, etc.), and friction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-performance 2D electronic devices enabled by strong and tough two-dimensional polymer with ultra-low dielectric constant

As the feature size of microelectronic circuits is scaling down to nanometer order, the increasing interconnect crosstalk, resistance-capacitance (RC) delay and power consumption can limit the chip performance and reliability. To address these challenges, new low-k dielectric (k < 2) materials need to be developed to replace current silicon dioxide (k = 3.9) or SiCOH, etc. However, existing low-k dielectric materials, such as organosilicate glass or polymeric dielectrics, suffer from poor thermal and mechanical properties. Two-dimensional polymers (2DPs) are considered promising low-k dielectric materials because of their good thermal and mechanical properties, high porosity and designability. Here, we report a chemical-vapor-deposition (CVD) method for growing fluoride rich 2DP-F films on arbitrary substrates. We show that the grown 2DP-F thin films exhibit ultra-low dielectric constant (in plane k = 1.85 and out-of-plane k = 1.82) and remarkable mechanical properties (Young’s modulus > 15 GPa). We also demonstrated the improved performance of monolayer MoS 2 field-effect-transistors when utilizing 2DP-F thin films as dielectric substrates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mitigating CaCO 3 crystal nucleation and growth through continuous ion displacement via alternating electric fields

Mineral crystal formation poses a challenge on surfaces (e.g., heat exchangers, pipes, membranes, etc.) in contact with super-saturated fluids. Applying alternating currents (AC) to such surfaces can prevent surface crystallization under certain conditions. Here, we demonstrate that ion displacement induced by periodic charging and discharging of the electrical double layer (EDL) inhibits both heterogeneous and homogeneous nucleation (and crystal growth) of CaCO 3 . Titanium sheets (meant to simulate metallic heat exchanger surfaces) are immersed in super-saturated CaCO 3 solutions with a saturation index >11. We show that at relatively high AC frequencies, incomplete EDL formation leads to an alternating electric field that propagates far into the bulk solution, inducing rapid ion migration that overwhelms the Brownian motion of ions. Electrochemical characterization reveals EDL charging/discharging under AC conditions that greatly inhibits precipitation. Operating at 4 V pp , 0.1–10 Hz reduces turbidity by over 96% and reduces CaCO 3 coverage on the metal plates by over 92%. Based on electrokinetic and crystallization models, the ion displacement velocity (exceeding the mean Brownian velocity) and displacement length disrupts ion collision and crystal nucleation. Overall, the technique has potential for preventing mineral crystal formation in heat exchangers and many other industrially relevant systems.

42 ENGINEERING↗
DOE OSTI2026

Carbon monoxide oxidation expands the known metabolic capacity in anaerobic methanotrophic consortia

Consortia of anaerobic methane-oxidizing archaea (ANME-2) and sulphate-reducing bacteria (SRB) represent globally relevant syntrophic associations capable of growing with minimal amounts of free energy and can persist when methane becomes limiting. Carbon monoxide (CO) has been reported in seep environments and represents a thermodynamically favourable alternative electron donor due to its low reduction potential. Here, we show that environmental ANME-SRB consortia can oxidize CO in the absence of methane, in anoxic microcosm experiments using a combination of stable isotope geochemical tracers, metatranscriptomics, and single cell activity measurements (FISH–nanoSIMS). The oxidation of CO was coupled with sulphate-reduction by syntrophic consortia, and, in the absence of sulphate, through CO 2 reduction to methane by ANME-2. Under these conditions, the production of methane was one ninth the rate of methanotrophy coupled to sulphate-reduction. Paired single cell FISH-nanoSIMS analysis of anabolic activity indicates that CO respiration appears to support cell maintenance rather than active growth, consistent with the observed down-regulation of energy generating complexes in ANME (e.g., mtr, rnf, etc.). The versatile capability of CO oxidation by anaerobic methanotrophic consortia broadens our understanding of carbon cycling in methane seeps and highlights potential mechanisms of resilience by methanotrophic archaea under changing geochemical regimes.

03 NATURAL GAS↗
DOE OSTI2021

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality

Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes

Quantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and flexibility. Here, we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM), by controlling the length of side chains along the backbone. Coarse-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of PNIPAM. Conventional analyses (free volume, asphericity, etc.) show that lengths of side chains and their immediate environments dictate the compactness and bending in these architectures. We further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different shapes. Thus, our study provides insights into the shapes of complex architectures as well as a general method to analyze them. The shapes presented here may inspire the synthesis of new bottlebrushes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Insights on the corrosion thermodynamics of chromium in molten LiF-NaF-KF eutectic salts

The thermodynamic stability of chromium in the Cr 0 , Cr 2+ , and Cr 3+ oxidation states considering various F - ion coordination compounds was investigated in molten LiF–NaF-KF (FLiNaK) eutectic salt. Potential fluoride ion activity (F - and CrF 3 - ) diagrams were constructed to predict the most stable Cr oxidation states as a function of anion activity, solvation state of chromium ions and potential at 600 °C. The Gibbs free energies of these compounds were estimated by utilizing a combined cyclic voltammetry – Nernst theory analysis approach in FLiNaK salt. To verify the constructed diagrams, X-ray diffraction was utilized after exposure of Cr at various applied potentials to determine whether compounds detected in solidified FLiNaK salts agreed with thermodynamic calculations. This work aims to identify key thermodynamic factors that are significant to chromium corrosion for molten salt nuclear reactor applications. The F - stability region spans the entire region where spontaneous corrosion of Cr occurs. Cr may be oxidized to Cr 2+ and Cr 3+ in the presence HF (due to moisture as an impurity) spontaneously except some conditions of p 1/2 H 2 /a HF etc. This situation is not changed qualitatively for various states of solvation between oxidized Cr solute in F - solvent and this aspect is substantially similar for the two cases (pair 1: Cr 0 /CrF 3 - / CrF 6 3- ; pair 2: Cr 0 /CrF 4 2- /CrF 5 2- ) considered in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A two-dimensional type I superionic conductor

Superionic conductors (SICs) possess liquid-like ionic diffusivity in the solid state, finding wide applicability from electrolytes in energy storage to materials for thermoelectric energy conversion. Type I SICs (e.g., AgI, Ag 2 Se, etc.) are defined by a first-order transition to the superionic state and have so far been found exclusively in three-dimensional crystal structures. Here, we reveal a two-dimensional type I SIC, α-KAg 3 Se 2 by scattering techniques and complementary simulations. Quasi-elastic neutron scattering and ab initio molecular dynamics simulations confirm that the superionic Ag + ions are confined to sub-nanometre sheets, with the simulated local structure validated by experimental X-ray powder pair-distribution-function analysis. Finally, we demonstrate that the phase transition temperature can be controlled by chemical substitution of the alkali metal ions that comprise the immobile charge-balancing layers. Our work thus extends the known classes of SICs and will facilitate the design of new materials with tailored ionic conductivities and phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

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

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗