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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Twin-pulse seeding enables pump-probe capabilities in the EUV to soft X-ray spectrum at synchrotron light sources

Having previously reported that separating the two stages of echo-enabled harmonic generation (EEHG) with one or more bending magnet (BM) sections allows the BMs to serve as the desired source of momentum compaction, here we demonstrate that this arrangement can greatly reduce the total energy modulation required by any 4th generation synchrotron light source, leading to higher repetition rates as well as stronger coherent radiation output power, with significant benefits. Since the EEHG beamline performance is mainly determined by the momentum compaction, beam emittances and beta functions of a storage ring lattice, allowing for different separations between the two stages is a straightforward way to increase the momentum compaction of chicane 1. This also enables pump-probe capabilities in a novel context, where twin-pulse seeding on the same electron bunch would allow two distinct radiation pulses with an adjustable delay in the range of 0.1 to 10 ps. In the twin-pulse seeding scheme, the same electron bunch could undergo modulation from two distinct laser pulses. Later stages would produce independent harmonics in subsequent straight sections. There are two variations of this twin-pulse seeding scheme, supporting different scientific applications. With a common modulation in stage 1, the first option allows simultaneously two independent radiation sources, with a full coverage of the EUV (2.5 to 50 nm) to soft X-ray (1.25 to 2.5 nm) spectrum; for the second option, the same stage 2 undulator could generate two coherent pulses both fitting within the FEL bandwidth, or at distinct harmonics. We present particle tracking simulation studies based on the APS-U lattice, including quantum excitation and radiation damping. These simulations indicate that there is no degradation of the modulated longitudinal phase space even when the two stages are separated by as many as 10 BM sections.

36 MATERIALS SCIENCE↗
DOE OSTI2023

MXene/graphene oxide nanocomposites for friction and wear reduction of rough steel surfaces

Development of solid lubricant materials that render reliable performance in ambient conditions, are amenable to industrial size and design complexities, and work on engineered surfaces is reported. These coatings are composed of Ti 3 C 2 T x -Graphene Oxide blends, spray-coated onto bearing steel surfaces. The tribological assessment was carried out in ambient environmental conditions and high contact pressures in a ball-on-disc experimental set-up. The evaluation yielded that the use of Ti 3 C 2 T x -Graphene-Oxide coatings led to substantial reduction in friction down to 0.065 (at 1 GPa contact pressure and 100 mm/s) in comparison to the uncoated of single-component-coated surfaces, surpassing the state-of-the-art. The coatings also provided excellent protection against wear loss of the substrate and counter-face. The results were explained based on the observations from Raman spectroscopy, scanning electron microscopy, transmission electron microscopy, and nanoindentation measurements. In operando formation of a dense, hard and stiff, dangling-bond-saturated tribolayer was observed to be the reason for the sustained lubricity even at high test loads and sliding speeds. This report presents the holistic exploration and correlation of structure-property-processing pertaining to the advancement of solid lubrication science.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient crystal structure materials as reactive sorbent for the CO 2 and CH 4 adsorption and storage

The efficient dirubidium cobalt bis(dihydrogendiphosphate) dihydrate compound is successfully synthesized in a solution and used as a reactive sorbent for the CO 2 and CH 4 gases adsorption and storage. A crystal of this Rb 2 Co(H 2 P 2 O 7 ) 2 ·2H 2 O compound has been isolated and characterized by single X-ray diffraction analysis and was found to crystallize in the triclinic system (P$\overline{1}$) with the cell parameters (Å): 6.980(1), 7.370(1), 7.816(1), 81.74(1), 70.35(1), 86.34(1); V = 374.68(9) Å3, Z = 2. The crystal-packing consists of a three-dimensional framework made upon corners and edges sharing of [RbO 7 ], [H 2 P 2 O 7 ] and [CoO 6 ] entities, furthermore linked by a network of H-bonds. The UV–Vis spectroscopy revealed usual transitions between the ground state 4T1g and the upper levels 4T2g, 4A2g and 4T1g (P). Moreover, the CO 2 and CH 4 gases sorption measurements were successfully performed at two different temperatures (25 and 45 °C) and various pressures ranging from vacuum to 50 bar. Our results show that rate of CO 2 and CH 4 capturing was 3.10 mmol/g and 2.35 mmol/g at temperature 25 °C and pressure 50 bar, respectively. This compound showed a clear potential for CO 2 /CH 4 adsorption and storage thereby paving the way towards its exploration and adaptation for capturing and collecting carbon dioxide and greenhouse gases from the air, and their conversion into hydrocarbon fuels using existing mature technologies. We have also conducted density functional theory calculations to study the CO 2 and CH 4 adsorption properties of Rb 2 Co(H 2 P 2 O 7 ) 2 ·2H 2 O. The simulation results show enhanced adsorption of both types of molecules on the surface of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Migration and aggregation of Pt atoms on metal oxide-supported ceria nanodomes control reverse water gas shift reaction activity

Abstract Single-atom catalysts (SACs) are particularly sensitive to external conditions, complicating the identification of catalytically active species and active sites under in situ or operando conditions. We developed a methodology for tracing the structural evolution of SACs to nanoparticles, identifying the active species and their link to the catalytic activity for the reverse water gas shift (RWGS) reaction. The new method is illustrated by studying structure-activity relationships in two materials containing Pt SACs on ceria nanodomes, supported on either ceria or titania. These materials exhibited distinctly different activities for CO production. Multimodal operando characterization attributed the enhanced activity of the titania-supported catalysts at temperatures below 320 ˚C to the formation of unique Pt sites at the ceria-titania interface capable of forming Pt nanoparticles, the active species for the RWGS reaction. Migration of Pt nanoparticles to titania support was found to be responsible for the deactivation of titania-supported catalysts at elevated temperatures. Tracking the migration of Pt atoms provides a new opportunity to investigate the activation and deactivation of Pt SACs for the RWGS reaction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Topological orders of monopoles and hedgehogs: From electronic and magnetic spin-orbit coupling to quarks

Topological states of matter are, generally, quantum liquids of conserved topological defects. We establish this by constructing and analyzing topological field theories which introduce gauge fields to describe the dynamics of singularities in the original field configurations. In this work, homotopy groups are utilized to identify topologically protected singularities, and the conservation of their protected number is captured by a topological action term that unambiguously obtains from the given set of symmetries. Stable phases of these theories include quantum liquids with emergent massless Abelian and non-Abelian gauge fields, as well as topological orders with long-range quantum entanglement, fractional excitations, boundary modes, and unconventional responses to external perturbations. This paper focuses on the derivation of topological field theories and basic phenomenological characterization of topological orders associated with homotopy groups π n (S n ), n ≥ 1 . These homotopies govern monopole and hedgehog topological defects in d = n + 1 dimensions, and enable the generalization of both weakly interacting and fractional quantum Hall liquids of vortices to d > 2 . Hedgehogs have not been in the spotlight so far, but they are particularly important defects of magnetic moments because they can be stimulated in realistic systems with spin-orbit coupling, such as chiral magnets and d = 3 topological materials. We predict topological orders in systems with U(1) × Spin(d) symmetry in which fractional electric charge attaches to hedgehogs. Monopoles, the analogous defects of charge or generic U(1) currents, may bind to hedgehogs via Zeeman effect, or effectively emerge in purely magnetic systems. The latter can lead to spin liquids with different topological orders than that of the resonant valence bond spin liquid. Charge fractionalization of quarks in atomic nuclei is also seen as possibly arising from the charge-hedgehog attachment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Low-energy magnons in the chiral ferrimagnet Cu 2 OSeO 3 : A coarse-grained approach

We report a comprehensive neutron scattering study of low energy magnetic excitations in the breathing pyrochlore helimagnetic Cu2OSeO3. Fully documenting the four lowest energy magnetic modes that leave the ferrimagnetic configuration of the “strong tetrahedra” intact (| ℏ ω| < 13 meV), we find gapless quadratic dispersion at the Γ point for energies above 0.2 meV, two doublets separated by 1.6(2) meV at the R point, and a bounded continuum at the X point. Our constrained rigid spin cluster model relates these features to Dzyaloshinskii-Moriya (DM) interactions and the incommensurate helical ground state. Combining conventional spin wave theory with a spin cluster form factor accurately reproduces the measured equal time structure factor through multiple Brillouin zones. An effective spin Hamiltonian describing complex anisotropic intercluster interactions is obtained.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Tunable Magnon Interactions in a Ferromagnetic Spin-1 Chain

NiNb 2 O 6 is an almost ideal realization of a 1D spin-1 ferromagnetic Heisenberg chain compound with weak unidirectional anisotropy. Using time-domain THz spectroscopy, we measure the low-energy electrodynamic response of NiNb 2 O 6 as a function of temperature and external magnetic field. At low temperatures, we find a magnonlike spin excitation, which corresponds to the lowest energy excitation at q ~ 0. At higher temperatures, we unexpectedly observe a temperature-dependent renormalization of the spin-excitation energy, which has a strong dependence on field direction. Using theoretical arguments, exact diagonalizations, and finite temperature dynamical Lanczos calculations, we construct a picture of magnon-magnon interactions that naturally explains the observed renormalization. In this work, we show how magnetic field strength and direction may be used to directly tune the sign of the magnon-magnon interaction. This unique scenario is a consequence of the spin-1 nature and has no analog in the more widely studied spin-1/2 systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

X-Ray Diffraction and Electron Microscopy Studies of the Size Effects on Pressure-Induced Phase Transitions in CdS Nanocrystals

In recent years, investigations of the phase transition behavior of semiconducting nanoparticles under high pressure has attracted increasing attention due to their potential applications in sensors, electronics, and optics. However, current understanding of how the size of nanoparticles influences this pressure-dependent property is somewhat lacking. In particular, phase behaviors of semiconducting CdS nanoparticles under high pressure have not been extensively reported. Therefore, in this work, CdS nanoparticles of different sizes are used as a model system to investigate particle size effects on high-pressure-induced phase transition behaviors. In particular, 7.5, 10.6, and 39.7 nm spherical CdS nanoparticles are synthesized and subjected to controlled high pressures up to 15 GPa in a diamond anvil cell. Analysis of all three nanoparticles using in-situ synchrotron wide-angle X-ray scattering (WAXS) data shows that phase transitions from wurtzite to rocksalt occur at higher pressures than for bulk material. Bulk modulus calculations not only show that the wurtzite CdS nanomaterial is more compressible than rocksalt, but also that the compressibility of CdS nanoparticles depends on their particle size. Furthermore, sintering of spherical nanoparticles into nanorods was observed for the 7.5 nm CdS nanoparticles. Our results provide new insights into the fundamental properties of nanoparticles under high pressure that will inform designs of new nanomaterial structures for emerging applications.

Meng, Lingyao↗
DOE OSTI2022

Investigation and Expression Analysis of R2R3-MYBs and Anthocyanin Biosynthesis-Related Genes during Seed Color Development of Common Bean ( Phaseolus vulgaris )

Anthocyanins are responsible for the coloration of common bean seeds, and their accumulation is positively correlated with the expression level of anthocyanin biosynthetic genes. The MBW (MYB-bHLH-WD40) complex is thought to regulate the expression of these genes, and MYB proteins, which are a key factor in activating anthocyanin pathway genes, have been identified in several plants. This study demonstrated gene structures, chromosomal placements, gene duplications of R2R3-MYBs, miRNAs associated with R2R3-MYBs, and the interaction of these genes with other flavonoid regulatory genes. qRT-PCR was used to investigate the role of specific R2R3-MYBs and flavonoid genes in common bean seed color development. As a result of a comprehensive analysis with the help of in silico tools, we identified 160 R2R3-MYB genes in the common bean genome. We divided these genes into 16 classes on the basis of their intron-exon and motif structures. Except for three, the rest of the common bean R2R3-MYB members were distributed to all chromosomes with different densities, primarily located on chromosomes 3 and 8. We identified a total of 44 duplicated gene pairs dispersed across 11 chromosomes and evolved under purifying selection (Ka/Ks < 1), 19 of which were derived from a whole-genome duplication. Our research uncovered 25 putative repressor PvMYB proteins that contain the EAR motif. Additionally, fifty different cis-regulatory elements regulated by light, stress, and hormone were identified. Within the genome of the common bean, we discovered a total of 36 microRNAs that target a total of 72 R2R3-MYB transcripts. The effect of 16 R2R3-MYB genes and 16 phenylpropanoid pathway genes, selected on the basis of their interaction in the protein-protein interaction map, playing role in the regulation of seed coat color development was evaluated using qRT-PCR in 5 different tissues at different developmental stages. The results revealed that these specific genes have different expression levels during different developmental periods, with higher levels in the pod filling and early pod stages than in the rest of the developmental periods. Furthermore, it was shown that PvTT8 (bHLH), PvTT2 (PvMYB42), PvMYB113, PvTTG1, and PvWD68 genes have effects on the regulation of seed coat color. The findings of this study, which is the first to use whole-genome analysis to identify and characterize the R2R3-MYB genes in common bean, may serve as a reference for future functional research in the legume.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The Importance of the Social Aspects of Agrivoltaics

This presentation features a high-level synthesis of international social science research on agrivoltaics. The main takeaways from this body of scholarship - namely the importance of farmer engagement, cross-sector collaboration, and legal frameworks - is discussed in the context of NREL's "5 C's of Agrivoltaic Success Factors" framework. A nested model approach to the 5 C's is proposed to emphasize the importance of the social aspects of agrivoltaics (Compatibility and Collaboration).

agrivoltaics↗
DOE OSTI2023

Modernist materials synthesis: Finding thermodynamic shortcuts with hyperdimensional chemistry

Synthesis remains a challenge for advancing materials science. A key focus of this challenge is how to enable selective synthesis, particularly as it pertains to metastable materials. This perspective addresses the question: how can “spectator” elements, such as those found in double ion exchange (metathesis) reactions, enable selective materials synthesis? By observing reaction pathways as they happen (in situ) and calculating their energetics using modern computational thermodynamics, we observe transient, crystalline intermediates that suggest that many reactions attain a local thermodynamic equilibrium dictated by local chemical potentials far before achieving a global equilibrium set by the average composition. Furthermore, using this knowledge, one can thermodynamically “shortcut” unfavorable intermediates by including additional elements beyond those of the desired target, providing access to a greater number of intermediates with advantageous energetics and selective phase nucleation. Ultimately, data-driven modeling that unites first-principles approaches with experimental insights will refine the accuracy of emerging predictive retrosynthetic models for complex materials synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Preface for the special topic collection honoring Dr. Scott Chambers’ 70th birthday and his leadership in the science and technology of oxide thin films

It is an honor to dedicate this special issue to Dr. Scott A. Chambers, who has had a rewarding and impactful career in surface science, spectroscopy, and thin film synthesis. His research career, spanning from his graduate work in the 1970’s to the present day, was built upon pioneering early work in precision thin film synthesis and spectroscopic characterization that occurred beginning in the 1960’s. Notably, this includes the contributions of both Art Gossard to precision film synthesis by molecular beam epitaxy (MBE) and Chuck Fadley to photoelectron spectroscopy; both Art and Chuck were recently honored with JVSTA commemorative issues of their own. Yet Scott is no mere copycat; he extended and expanded their contributions to further advance the field of surface science, and he applied the same scientific rigor to the emerging field of precision epitaxial oxide synthesis. This rigor was perhaps not always appreciated by the more “enthusiastic” members of the community who tended to draw exciting conclusions from limited data. He was once referred to, fondly, by a collaborator as a “spoilsport” for his penchant for using careful, defensible synthesis and characterization to prove that popular models and assumptions of the day did not stand up to scrutiny.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modeling Catalyzed Growth of Single-Wall Carbon Nanotubes (Final Report)

Single-walled carbon nanotubes (SWCNTs) are among the most promising nanostructures because of their special and unusual physico-chemical properties that make them strong candidates for a variety of applications. Since their discovery, chemical vapor deposition (CVD) methods have become the preferred synthesis method. CVD uses a carbon-containing precursor species, which is decomposed over a catalyst yielding C atoms that eventually evolve in carbon nanostructures. CVD temperatures are in the order of 1000K or lower, and pressures in the range of 1-5 atm, although some processes use higher pressures. Depending on the nature of the catalyst, and on the conditions of pressure, temperature, and presence of other inert compounds, the resultant nanostructures may include nanofibers, nano-onions, multiple-, or single-wall carbon nanotubes, among others. When the CVD synthesis process is appropriately tuned, however, it is possible to obtain only SWCNTs, which are geometrically characterized by their diameter and by their chiral angle. The chiral indexes (n,m) determine the degree of helical twist of the graphite lattice along the nanotube axis, where the term chirality is derived from the left and right-handed helicity of the tubes defined by their chiral indexes (n,m) and (m,n) respectively. The main goal of this research is to build fundamental understanding that will help elucidating the nature and structure of the actual catalyst in a combined catalyst/support system, which we claim is essential especially when the catalyst is of nanodimensions. Given that we are considerably advanced in the understanding of selective single-walled carbon nanotube catalyst synthesis, we take this process as a case study. Thus, we focus on the understanding of the selective growth of SWCNTs on supported nanocatalysts in systems where the catalyst synthesis takes place in the same reactor preceding the nanotube synthesis. Once we understand what type of catalyst structure would be necessary for this specific growth, we will try to elucidate the catalytic synthesis process that leads to such catalytic structure. Thus, our aim of controlling nanostructure synthesis may start to be delineated.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. In this perspective, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni–Ti–Cu–V Quaternary Thin-Film Library

Ni–Ti–based shape memory alloys (SMAs) have found widespread use in the last 70 years, but improving their functional stability remains a key quest for more robust and advanced applications. Named for their ability to retain their processed shape as a result of a reversible martensitic transformation, SMAs are highly sensitive to compositional variations. Alloying with ternary and quaternary elements to fine-tune the lattice parameters and the thermal hysteresis of an SMA, therefore, becomes a challenge in materials exploration. Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques. In this study, a composition spread of Ni–Ti–Cu–V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer. Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy, synchrotron X-ray diffraction, and temperature-dependent resistance measurements. Of the 177 compositions in the materials library, 32 were observed to have shape memory effect, of which five had zero or near-zero thermal hysteresis. These compositions provide flexibility in the operating temperature regimes that they can be used in. A phase map for the quaternary system and correlations of functional properties are discussed with respect to the local microstructure and composition of the thin-film library.

42 ENGINEERING↗