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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine-Learned Structure-Property Relationship in Metallic Glasses (Final Report)

The goal of the proposed work was to gain the ability to predict properties from the known coordinates (relative positions) of atoms in a metallic glass, presuming that information about which regions of the glass are fertile for rearrangment (defects) are encoded in this structural information. This goal is important to the science of metallic glasses. This research of applied machine learning methods to automate the development of a relationship between structural measures and thermally and stress-activated events in the metallic glass. In this way, the work aimed to building a bridge between structure and properties for this class of materials. The work is expected to have timely impact on guiding the tuning of internal structures of metallic glasses for desired properties. It also represents an advance in applying machine learning techniques to discerning the properties of materials.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Application of MAC-Europe AVIRIS data to the analysis of various alteration stages in the Landdmannalauger Hydrothermal Area (South Iceland)

In June 1991 extensive airborne remote sensing data-sets have been acquired over Iceland in the framework of the joint NASA/ESA Multisensor Airborne Campaign Europe (MAC-Europe). The study area is located within the Torfajokull central volcanic complex in South Iceland. This complex is composed by anomalously abundant rhyolitic acid volcanics, which underwent intensive hydrothermal alteration. Detailed studies of surface alteration of rhyolitic rocks in the area showed that all the major elements are leached as the rock is affected by complex mineralogical changes. Montmorillonite appears during the earliest stages of alteration. In the ultimate alteration product montmorillonite is absent and the rock consists mostly of amorphous silica, anatase, up to a volume of 50% kaolinite and variable amounts of native sulphur and pyrite. The case study presented shall endeavor to assess the potential of MAC-Europe AVIRIS and TMS data in determining a possible zonation of hydrothermal alteration in relationship to the active geo-thermal fields and structural features. To this end, the airborne data is analysed in comparison with laboratory spectral measurements of characteristics rock, soil, and vegetation samples collected in the study areaduring the summer of 1992. Various spectral mapping algorithms as well as unmixing approaches are tested and evaluated. Detailed geological and structural mapping as well as geochemical analysis of the main rock and soil types were performed to underpin the analysis of the airborne data.

Sommer, S.↗
DOE OSTI2023

Effect of the Molecular Structure of Surface Vanadia on Activity and Regenerability of VO $x$ /In 2 O 3 Catalysts for CO 2 -Assisted Oxidative Dehydrogenation of Propane

Our recent work has reported that higher propylene selectivity and improved stability can be achieved by combining redox-active VO $x$ and basic In 2 O 3 for CO 2 -assisted oxidative dehydrogenation of propane (CO 2 -ODHP). In the present work, we continued to explore the stability and regenerability of V/In catalysts. In particular, our interest lies in identifying the effect of mono- and polyvanadate on catalytic performance and regenerability. A V/In catalyst with an increased proportion of monovanadate was prepared using the Schlenk line under moisture-free conditions (V/In–S), while the fully polymerized vanadate catalyst was prepared through a regular impregnation (V/In) for comparison. The Schlenk-line-prepared catalyst, namely, V/In–S, not only exhibits a 17–30% enhanced propylene yield at high temperatures (500–540 °C) over V/In but also presents improved stability and regenerability with nearly 88% activity recovered after regeneration in O 2 . Detailed characterizations have been performed to reveal the catalyst structure–performance relationship, including chemisorption (NH 3 /CO 2 -temperature-programmed desorption, NH 3 /CO 2 -TPD), H 2 -temperature-programmed reduction (H 2 -TPR), and spectroscopic studies [Raman spectroscopy, UV–vis diffuse reflectance spectroscopy (UV–vis DRS), near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS), and high-sensitivity low-energy ion scattering (HS-LEIS)]. Characterization results demonstrate that compared with polyvanadates, monovanadates lead to strengthened interaction with In 2 O 3 and a more stabilized V/In surface and subsurface, as well as improved redox properties of VO $x$ . These advantages give rise to the observed enhancement in activity, stability, and regenerability. In conclusion, these findings advance the understanding of the relationship between the activity/stability and the molecular structure of surface oxide species (vanadia) and the interplay between acid–base interactions and redox properties of mixed metal-oxide catalysts for efficient CO 2 -ODHP.

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

Transactional Knowledge Graph Generation To Model Adversarial Activities

A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. Traditionally, KGs are used to describe metadata about entities and to provide additional context to target application results. Many real-world domains also involve temporal interactions between entities in addition to the metadata data. Modeling these attributed transactions is a critical requirement when using KGs in complex real-world applications. Modeling adversarial activities is one such application that develops methodology and tools to produce realistic large-scale background activity graphs that include embedded Weapons of Mass Destruction (WMD) activity patterns. We present a novel platform for constructing a transactional knowledge graph from a diverse set of sources. We present the core components and architecture of the framework, and a use case for generating a background knowledge graph and WMD activity template to evaluate network alignment and subgraph matching algorithms.

Purohit, Sumit↗
DOE OSTI2022

High‐Field NMR, Reactivity, and DFT Modeling Reveal the γ‐Al 2 O 3 Surface Hydroxyl Network**

Abstract Aluminas are strategic materials used in many major industrial processes, either as catalyst supports or as catalysts in their own right. The transition alumina γ‐Al 2 O 3 is a privileged support, whose reactivity can be tuned by thermal activation. This study provides a qualitative and quantitative assessment of the hydroxyl groups present on the surface of γ‐Al 2 O 3 at three different dehydroxylation temperatures. The principal [AlOH] configurations are identified and described in unprecedented detail at the molecular level. The structures were established by combining information from high‐field 1 H and 27 Al solid‐state NMR, IR spectroscopy and DFT calculations, as well as selective reactivity studies. Finally, the relationship between the hydroxyl structures and the molecular‐level structures of the active sites in catalytic alkane metathesis is discussed.

Merle, Nicolas↗
DOE OSTI2022

High‐Field NMR, Reactivity, and DFT Modeling Reveal the γ‐Al 2 O 3 Surface Hydroxyl Network**

Abstract Aluminas are strategic materials used in many major industrial processes, either as catalyst supports or as catalysts in their own right. The transition alumina γ‐Al 2 O 3 is a privileged support, whose reactivity can be tuned by thermal activation. This study provides a qualitative and quantitative assessment of the hydroxyl groups present on the surface of γ‐Al 2 O 3 at three different dehydroxylation temperatures. The principal [AlOH] configurations are identified and described in unprecedented detail at the molecular level. The structures were established by combining information from high‐field 1 H and 27 Al solid‐state NMR, IR spectroscopy and DFT calculations, as well as selective reactivity studies. Finally, the relationship between the hydroxyl structures and the molecular‐level structures of the active sites in catalytic alkane metathesis is discussed.

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

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗
DOE OSTI2021

A measure of active interfaces in supported catalysts for high-temperature reactions

Formulating knowledge of structure-function relationships in heterogeneous catalysis is central to the design of efficient catalysts; yet, the elucidation of dominant reaction sites has remained as a challenge. Here, in this paper, we present a methodology that can be used to visualize metal-gas and metal-oxide-gas interfaces in three dimensions and to quantify their catalytic activity levels. As a case study, CH 4 oxidation occurring in a Pt/CeO 2 system is chosen. By employing thermally robust Pt@CeO 2 model catalysts with size-tunable and monodisperse cores, and gas-permeable shells, we reconstruct a series of structures in 3D via electron tomography and match the information to activity data and theoretical calculations. This strategy reveals that two different interfaces catalyze the CH 4 oxidation and that their contribution to the overall rate changes with the Pt size, temperature, and gas atmosphere. Our results provide an analytic platform on which to explore reaction pathways and mechanisms applicable to multiple reactions and materials.

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

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
NASA NTRS1992

A near-infrared imaging survey of interacting galaxies - The small angular-size Arp systems

Near-IR images of a large sample of interacting galaxies selected from the Atlas of Peculiar Galaxies by Arp (1966) have been obtained. Approximately 180 systems have been imaged in at least two, and usually three of the standard JHK bands. The survey and the observing and data reduction procedures, are described, and contour plots and aperture photometry are presented. Future papers will analyze the imaging data by groupings based on interaction type, stage, and progenitors. The goals of the analysis are to explore the relationships between galaxy interactions, activity, and morphology by studying the structure of the near-IR luminosity distribution, where extinction effects are much reduced relative to the optical and the major stellar mass component of galaxies dominates the observed light.

Bushouse, Howard A.↗
NASA NTRS1998

The Magnetic Roots of Enhanced Coronal Heating

Building a description of the sites of enhanced heating in the solar atmosphere has been a primary interest of the solar group at MSFC. For locations within and between active regions, as well as in the quiet Sun, this work shows that extended magnetic structures are heated from active compact magnetic structures at their feet. This talk will review what we have learned about the magnetic characteristics of these active footpoint sites, and the relationships of the intensity variations in the low-lying and higher structures, in the context of current theories and observations of magnetic reconnection and jets. One puzzling result is the poor correlation of intensity variations in the low and high loops.

Porter, J. G.↗
DOE OSTI2024

Revealing Structural Evolution of Single Atom Catalysts during Electrochemical CO 2 Reduction by in Situ X-ray Absorption Spectroscopy

Investigating the structural variation of single-atom catalysts (SACs) is crucial to reveal the reaction mechanism under working conditions. Different in situ techniques, especially X-ray absorption spectroscopy (XAS), have been reported to study the structural changes in active sites. However, a systematic study of the relationship between the coordination environment and the catalytic ability of different kinds of SACs is still lacking. Herein, we established isolated transition metal atoms (Fe, Co, Ni, Cu) on N-doped carbon (M-N-C) and employed them in electrocatalytic CO 2 reduction reaction (CO 2 RR). Significantly, Ni-N-C exhibits the highest selectivity (similar to 97.9%) toward CO at -0.8 V vs RHE. In situ XAS characterization discloses the coordination number dependent catalytic performance. Further, the decreased average coordination number of Ni in Ni-N-C at the voltage point with maximum Faradaic efficiency was observed. Density functional theory further provides the possible mechanism of CO 2 -to-CO over the undercoordinated Ni-N-C structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Quantifying the Influences of Epoxide Binding in Epoxide/CO 2 Ring Opening Copolymerization Catalysis

Understanding and predicting the effect of epoxide structure on the rate of polymerization in epoxide/CO 2 ring opening copolymerization catalysis is a long-standing challenge. Here, a known highly active Co(III)K(I) catalyst is used to investigate the influences of six different epoxides' binding strengths on their rates of copolymerization. Since calculations and experiments indicate that studying the catalytically relevant Co(III)−epoxide adduct directly is experimentally challenging, epoxide−catalyst binding interactions are quantified using a Co(II)K(I) complex to model the key catalytic intermediate. Epoxide−catalyst coordination is investigated using UV−vis spectroscopy titrations which provide fast and effective determination of association or binding constants. The epoxide−catalyst equilibrium constants show a clear exponential correlation with copolymerization rates and a new catalyst performance linear free energy relationship is revealed. Epoxides exhibiting stronger catalyst binding constants show higher copolymerization rates. The structure−activity correlation is consistent with the polymerization kinetics, mechanism and DFT calculations. Both the methods to investigate epoxide−catalyst coordination and the linear free energy relationship are shown to apply to the series of six epoxides and a second Co(III)K(I) catalyst. These structure− performance relationships are likely applicable to other transition metal catalysts and should expedite future epoxide and catalyst selection to make useful poly(carbonate) materials.

catalysts↗
NASA NTRS2011

What Turns Galaxies Off? the Different Morphologies of Star-Forming and Quiescent Galaxies Since z Approximates 2 from CANDELS

We use HST/WFC3 imaging from the CANDELS multicyc1e treasury survey, in conjunction with the Sloan Digital Sky Survey, to explore the evolution of galactic structure for galaxies with stellar masses > 3 x 10(exp 10) Solar Mass from Z= 2.2 to the present epoch, a time span of 10 Gyr. We explore the relationship between rest-frame optical color, stellar mass, star formation activity and the structural parameters of galaxies as determined from parametric fits to the surface brightness profiles of galaxies. We confirm the dramatic evolution from z= 2.2 to the present day in the number density of non-star-forming galaxies above 3 x 10(exp 10) Solar Mass reported by other authors. We find that the vast majority of these quiescent systems have concentrated light profiles, as parameterized by the Sersic index, and the population of concentrated galaxies grows similarly rapidly. We examine the joint distribution of star formation activity, Sersic index, stellar mass, mass divided by radius (a proxy for velocity dispersion), and stellar surface density. Quiescence correlates poorly with stellar mass at all z < 2.2 (given the approx < 0.2 dex scatter between halo mass and stellar mass at z approximates 0 inferred by More et al, this argues against halo mass being the only factor determining quiescence). Quiescence correlates better with Sersic index, 'velocity dispersion' and stellar surface density, where Sersic index correlates the best (increasingly so at lower redshift). Yet, there is significant scatter between quiescence and galaxy structure: while the vast majority of quiescent galaxies have prominent bulges, many of them have significant disks, and a number of bulge-dominated galaxies have significant star formation. Noting the rarity of quiescent galaxies without prominent bulges, we argue that a prominent bulge (and, perhaps by association, a supermassive black hole) is a necessary but not sufficient condition for quenching star formation on galactic scales over the last 10 Gyr; such a result is qualitatively consistent with the expectations of the AGN feedback paradigm.

Bell, Eric F.↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Carotenoid cleavage enzymes evolved convergently to generate the visual chromophore

The retinal light response in animals originates from the photoisomerization of an opsin-coupled 11-cis-retinaldehyde chromophore. This visual chromophore is enzymatically produced through the action of carotenoid cleavage dioxygenases. Vertebrates require two carotenoid cleavage dioxygenases, β-carotene oxygenase 1 and retinal pigment epithelium 65 (RPE65), to form 11-cis-retinaldehyde from carotenoid substrates, whereas invertebrates such as insects use a single enzyme known as Neither Inactivation Nor Afterpotential B (NinaB). RPE65 and NinaB couple trans–cis isomerization with hydrolysis and oxygenation, respectively, but the mechanistic relationship of their isomerase activities remains unknown. Here we report the structure of NinaB, revealing details of its active site architecture and mode of membrane binding. Structure-guided mutagenesis studies identify a residue cluster deep within the NinaB substrate-binding cleft that controls its isomerization activity. Our data demonstrate that isomerization activity is mediated by distinct active site regions in NinaB and RPE65—an evolutionary convergence that deepens our understanding of visual system diversity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗