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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 865 records · Page 48
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DOE OSTI2019

Revealing the atomic ordering of binary intermetallics using in situ heating techniques at multilength scales

Ordered intermetallic nanoparticles are promising electrocatalysts with enhanced activity and durability for the oxygen-reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs). The ordered phase is generally identified based on the existence of superlattice ordering peaks in powder X-ray diffraction (PXRD). However, after employing a widely used postsynthesis annealing treatment, we have found that claims of “ordered” catalysts were possibly/likely mixed phases of ordered intermetallics and disordered solid solutions. In this work, we employed in situ heating, synchrotron-based, X-ray diffraction to quantitatively investigate the impact of a variety of annealing conditions on the degree of ordering of large ensembles of Pt 3 Co nanoparticles. Monte Carlo simulations suggest that Pt 3 Co nanoparticles have a lower order–disorder phase transition (ODPT) temperature relative to the bulk counterpart. Furthermore, we employed microscopic-level in situ heating electron microscopy to directly visualize the morphological changes and the formation of both fully and partially ordered nanoparticles at the atomic scale. In general, a higher degree of ordering leads to more active and durable electrocatalysts. The annealed Pt 3 Co/C with an optimal degree of ordering exhibited significantly enhanced durability, relative to the disordered counterpart, in practical membrane electrode assembly (MEA) measurements. The results highlight the importance of understanding the annealing process to maximize the degree of ordering in intermetallics to optimize electrocatalytic activity.

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

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The SRT reconstruction algorithm for semiquantification in PET imaging

Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of {sup 18}F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT exhibits higher contrast and lower bias than OSEM at the cold lesions. This improvement is achieved at the expense of increasing the noise in the reconstructed images. For the hot lesions, SRT exhibits a small improvement in contrast and LBR over OSEM with 21 subsets and two iterations; however, this improvement is not statistically significant. As the number of iterations increases, the performance of OSEM improves over SRT but again without statistical significance. The curves of contrast and LBR as a function of IR after Gaussian blurring indicate that the advantage of SRT in the cold regions is maintained even after decreasing the noise level by Gaussian blurring. Conclusions: SRT, at the expense of slightly increased noise in the reconstructed images, reconstructs images of higher contrast and lower bias than the clinical protocol of OSEM. This improvement is particularly evident for images involving cold regions. Thus, it appears that SRT should be particularly useful for the quantification of low-count and cold regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Atomic Fingerprinting of Heteroatoms Using Noncontact Atomic Force Microscopy

We report immense strides have been made in increasing the resolution of scanning probe microscopy. Noncontact atomic force microscopy (n-AFM) now offers one the ability to characterize and visualize single molecules with subatomic resolution. Specifically, nc-AFM with a carbon monoxide (CO) functionalized tip has the ability to discriminate functional groups (–C≡C–, –CH 2 , –C$=$O, …), although discriminating atomic species often remains as an ongoing challenge. Here, real-space pseudopotentials constructed within density functional theory are employed to accurately simulate nc-AFM images of molecules containing heteroatoms (S, I, and N) within dibenzothiophene (DBT), 2-iodotriphenylene (ITP), acridine (ACR) and ferrous phthalocyanine (FePc). It is found that S and I atoms can be easily identified from C based on their unique features. For N atoms, a use of tip functionalization is proposed to effectively discriminate them from C atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗
DOE OSTI2019

Machine Learning-Guided Approach for Studying Solvation Environments

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, in this work, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1–, and 2–, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.

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

Direct Visualization and Manipulation of Tunable Quantum Well State in Semiconducting Nb 2 SiTe 4

We report quantum well states (QWSs) can form at the surface or interfaces of materials with confinement potential. They have broad applications in electronic and optical devices such as high mobility electron transistor, photodetector, and quantum well laser. The properties of the QWSs are usually the key factors for the performance of the devices. However, direct visualization and manipulation of such states are, in general, challenging. In this work, by using angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy/spectroscopy (STM/STS), we directly probe the QWSs generated on the vacuum interface of a narrow band gap semiconductor Nb 2 SiTe 4 . Interestingly, the position and splitting of QWSs could be easily manipulated via potassium (K) dosage onto the sample surface. Our results suggest Nb 2 SiTe 4 to be an intriguing semiconductor system to study and engineer the QWSs, which has great potential in device applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structural basis for gene regulation by a B 12 -dependent photoreceptor

Photoreceptor proteins enable organisms to sense and respond to light. The newly discovered CarH-type photoreceptors use a vitamin B 12 derivative, adenosylcobalamin, as the light-sensing chromophore to mediate light-dependent gene regulation. Here in this paper, we present crystal structures of Thermus thermophilus CarH in all three relevant states: in the dark, both free and bound to operator DNA, and after light exposure. These structures provide visualizations of how adenosylcobalamin mediates CarH tetramer formation in the dark, how this tetramer binds to the promoter -35 element to repress transcription, and how light exposure leads to a large-scale conformational change that activates transcription. In addition to the remarkable functional repurposing of adenosylcobalamin from an enzyme cofactor to a light sensor, we find that nature also repurposed two independent protein modules in assembling CarH. Finally, these results expand the biological role of vitamin B 12 and provide fundamental insight into a new mode of light-dependent gene regulation.

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

Perfect Crystals: microgravity capillary counterdiffusion crystallization of human manganese superoxide dismutase for neutron crystallography

The NASA mission Perfect Crystals used the microgravity environment on the International Space Station (ISS) to grow crystals of human manganese superoxide dismutase (MnSOD)—an oxidoreductase critical for mitochondrial vitality and human health. The mission’s overarching aim is to perform neutron protein crystallography (NPC) on MnSOD to directly visualize proton positions and derive a chemical understanding of the concerted proton electron transfers performed by the enzyme. Large crystals that are perfect enough to diffract neutrons to sufficient resolution are essential for NPC. This combination, large and perfect, is hard to achieve on Earth due to gravity-induced convective mixing. Capillary counterdiffusion methods were developed that provided a gradient of conditions for crystal growth along with a built-in time delay that prevented premature crystallization before stowage on the ISS. Here, we report a highly successful and versatile crystallization system to grow a plethora of crystals for high-resolution NPC.

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

Advanced Functional Membranes for Noble Gas Management: Fabrication and Testing of Membranes on Porous Support

We selected several porous materials including PNW-24, CC3, SAPO-34, SAPO-56 and PNW-6, as the basis for fabricating membranes for Kr/Xe separation. Initially, we synthesized powder samples of the candidates and confirmed their crystallinity and morphology with X-ray diffraction and microscopy. In the case of PNW-24 and PNW-6, we further confirmed its Xe selectivity through Xe and Kr adsorption experiments, while we relied on literature reports for CC3, SAPO-34 and SAPO-56. We subsequently proceeded to fabricate membranes on porous alumina supports using techniques such as seeded-assisted secondary growth, in-situ growth and solution-processed method. PXRD on the membranes verified that the structures were well-grown on the surface of alumina support, while SEM provided visual confirmation of particle morphology. Importantly, defects were not visually observed in the fabricated membranes, which was further verified with pressure retention tests. We analyzed the separation performance of the synthesized membranes using a dilute gas mixture comprising of xenon, krypton and argon. We focused on PNW-6 to optimize the synthesis reaction time and study the effects of time on gas separation. While more research is necessary to reach a comprehensive conclusion, in alignment with our milestones, we have successfully achieved our goals.

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

Measuring and directing charge transfer in heterogenous catalysts

Abstract Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts. While charge transfer is tunable using the atomic structure and chemistry of the catalyst-support interface, direct experimental evidence is missing for three-dimensional catalyst nanoparticles, primarily due to the lack of a high-resolution method that can probe and correlate both the charge distribution and atomic structure of catalyst/support interfaces in these structures. We demonstrate a robust scanning transmission electron microscopy (STEM) method that simultaneously visualizes the atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts using a model Au-catalyst/SrTiO 3 -support system. Using this method, we further reveal the atomic-scale mechanisms responsible for the highly active perimeter sites and demonstrate that the charge transfer behavior can be readily controlled using post-synthesis treatments. This methodology provides a blueprint for better understanding the role of charge transfer in catalyst stability and performance and facilitates the future development of highly active advanced catalysts.

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

Kelvin-Helmholtz instability as one of the key features for fast and efficient emulsification by hydrodynamic cavitation

The paper investigates the oil–water emulsification process inside a micro-venturi channel. More specifically, the possible influence of Kelvin-Helmholtz instability on the emulsification process. High-speed visualizations were conducted inside a square venturi constriction with throat dimensions of 450 µm by 450 µm, both under visible light and X-Rays. We show that cavity shedding caused by the instability results in the formation of several cavity vortices. Their rotation causes the deformation of the oil stream into a distinct wave-like shape, combined with fragmentation into larger drops due to cavitation bubble collapse. Later on, the cavity collapse further disperses the larger drops into a finer emulsion. Thus, it turns out that the Kelvin-Helmholtz instability is similarly characteristic for hydrodynamic cavitation emulsification inside a microchannel as is the Rayleigh-Taylor instability for acoustically driven emulsion formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Assessing sustainability in agricultural landscapes: a review of approaches

Research and development agencies, as well as policy makers and agri-food enterprises, need reliable data to support informed decisions that can improve the sustainability of agricultural landscapes. We present a review of agricultural sustainability assessment frameworks (ASAF) that identifies the features most relevant to monitoring progress towards sustainability goals for agricultural landscapes. This qualitative review considers a variety of approaches for defining goals and for selecting stakeholders, spatial and temporal boundaries, indicators, and analytical approaches. We focused on assessment frameworks that ( i ) include environmental, social, and economic implications of agriculture; ( ii ) are applicable to multiple, non-specified farm system types; ( iii ) are described in an English language, peer-reviewed publication; ( iv ) have been developed for use at a farm system to regional spatial scale; ( v ) engage stakeholders; ( vi ) provide case studies; and ( vii ) could be used in a variety of contexts across the globe. Based on the review, we provide recommendations for further development and use of assessment frameworks to better address the needs of agricultural research, extension, and development organizations. We recommend an agro-ecosystem approach to help stakeholders identify appropriate indicators for their situation. Assessment methods need to be flexible enough for adaptation to a spectrum of agricultural landscapes and changing environmental conditions, and remain relevant as farmers and other stakeholders acquire new information, resources, and different management techniques. We find that to address information gaps across different scales from farm to region will require creativity and some reliance on local knowledge systems to support adaptive management. Assessment results should communicate relationships among ecosystem services and socio-economic activities affected by agricultural landscapes. Furthermore visualization tools can facilitate understanding of trade-offs and synergies among sustainability goals as reflected by individual indicators.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Imaging chiral symmetry breaking from Kekule bond order in graphene

Chirality—or ‘handedness’—is a symmetry property crucial to fields as diverse as biology, chemistry and high-energy physics. In graphene, chiral symmetry emerges naturally as a consequence of the carbon honeycomb lattice. This symmetry can be broken by interactions that couple electrons with opposite momenta in graphene. Here we directly visualize the formation of Kekulé bond order, one such phase of broken chiral symmetry, in an ultraflat graphene sheet grown epitaxially on a copper substrate. We show that its origin lies in the interactions between individual vacancies in the copper substrate that are mediated electronically by the graphene. We show that this interaction causes the bonds in graphene to distort, creating a phase with broken chiral symmetry. Furthermore, the Kekulé ordering is robust at ambient temperature and atmospheric conditions, indicating that intercalated atoms may be harnessed to drive graphene and other two-dimensional materials towards electronically desirable and exotic collective phases.

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 OSTI2015

Systematic characterization of protein folding pathways using diffusion maps: Application to Trp-cage miniprotein

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories in a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.

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

Imaging Charged Exciton Localization in van der Waals WSe 2 /MoSe 2 Heterobilayers

Exciton localization in transition-metal dichalcogenide monolayers is behind a variety of interesting phenomena and applications, including broad-spectrum solar cells and singlephoton emissions. Strain fields at the periphery of topographically distinct features such as nanoscopic bubbles were recently associated with localized charge-neutral excitons. Here, we use tip-enhanced photoluminescence (PL) to visualize excitons in WSe 2 /MoSe 2 heterobilayers (HBL). We find strong optical emission from charged excitons, particularly positively charged trions, in HBL supported by interlayer charge transfer. Our results reveal strong trion confinement, with a localization length scale comparable to the trion size, at the apex region inside individual nanoscopic bubbles. Nano-PL mapping also shows sub-10 nm spatial variations in the localized trion emission spectra, which stem from atomic-scale potential energy fluctuations. These findings demonstrate the possibility of confining charged exciton complexes that are electrically tunable, opening up further opportunities to probe many-body exciton physics, and to explore additional possible sites for strong exciton localization that can lead to quantum emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Cellulose Microfibril Formation by Surface-Tethered Cellulose Synthase Enzymes

Cellulose microfibrils are pseudocrystalline arrays of cellulose chains that are synthesized by cellulose synthases. The enzymes are organized into large membrane-embedded complexes in which each enzyme likely synthesizes and secretes a β-(1→4) glucan. The relationship between the organization of the enzymes in these complexes and cellulose crystallization has not been explored. To better understand this relationship, we used atomic force microscopy to visualize cellulose microfibril formation from nickel-film-immobilized bacterial cellulose synthase enzymes (BcsA-Bs), which in standard solution only form amorphous cellulose from monomeric BcsA-B complexes. Fourier transform infrared spectroscopy and X-ray diffraction techniques show that surface-tethered BcsA-Bs synthesize highly crystalline cellulose II in the presence of UDP-Glc, the allosteric activator cyclic-di-GMP, as well as magnesium. The cellulose II cross section/diameter and the crystal size and crystallinity depend on the surface density of tethered enzymes as well as the overall concentration of substrates. Furthermore, our results provide the correlation between cellulose microfibril formation and the spatial organization of cellulose synthases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗