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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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Process design for calcination of nickel-based cathode materials by in situ characterization and multiscale modeling
Developing battery materials towards commercial use, from the early discovery through synthesis, processing, scaling up, and eventually to industrial production, may take decades. A notable example is Ni-based layered oxides, which were discovered as early as 1950s and intensively pursued as cathode active materials (CAMs) since the early 90s but have yet to realize their full commercial potential. Significant efforts have been devoted to materials development aiming at improving performance, far less to process development for large-scale synthesis and processing of CAMs. In this paper, we present a rational design of calcination for scalable production of Ni-based CAMs. We start with an overview of the current understanding and knowledge gaps hindering rational process design and scaling-up of the calcination process. Then with specific examples, we demonstrate how to tackle those fundamental challenges through in situ characterization and multiscale modeling. Finally, we conclude by providing perspectives on the remaining challenges and emerging opportunities in commercial development of Ni-based cathodes, calling for more endeavors in this field.
Human Factors Design for Particle Accelerator Control Room Interfaces
Fermilab, the birthplace of many scientific discoveries in physics and particle accelerator sciences, is in the midst of a widescale modernization effort. The Accelerator Control Operations Research Network (ACORN project’s goal is to modernize the accelerator control system by replacing end-of-life power supplies and enhance future operations of the Fermilab accelerator complex with megawatt particle beams. Within ACORN, opportunities for process improvement concerning software development, human-system interface design, and task performance are also being considered. Human factors researchers from Idaho National Laboratory in collaboration with usability experts from Fermilab, are currently investigating human-centered design improvements for the accelerator control system. For example, substantial tribal knowledge and memory recall are required to effectively operate the accelerator system. This contributes to high cognitive workload and potential burnout of accelerator operators. Developing guidance for consistent visual and functional design enables a more intuitive interaction and relieves operators of cognitive burden. Additionally, developing more intuitive and integrated interfaces can also lead to improved accelerator efficacy by empowering operators with greater understanding and control of the systems. The challenge in developing such interfaces is in designing for a wide variety of user goals, system specifications, and level of experience in users. The challenges need to be met while e also considering the maintainability of the control system. The purpose of this paper is to detail the human factors process and design within the ACORN project, describe results gathered thus far, and discuss the larger implications for this work.
Scientific objectives of a Solar Probe mission
The Solar Probe is a mission of exploration and discovery in an uninvestigated region near the sun. The Solar Probe can enhance general understanding of the corona and the solar wind. The first in situ measurements of coronal particles and fields are expected to provide definitive discriminators among many currently proposed models of processes that shape the structure and dynamics of the outer corona and solar wind. In addition, observations of the development of turbulence, nonlinear wave processes, plasma heating, and particle acceleration in the corona can advance these areas of plasma physics in a regime neither duplicated by earth-based laboratories nor by previous space exploration. It also fills the last gap between knowledge of the terrestrial response to solar output as well as provide insight for interpreting observations of general stellar envelopes.
Controlled Vocabularies Boost International Participation and Normalization of Searches
The Global Change Master Directory's (GCMD) science staff set out to document Earth science data and provide a mechanism for it's discovery in fulfillment of a commitment to NASA's Earth Science progam and to the Committee on Earth Observation Satellites' (CEOS) International Directory Network (IDN.) At the time, whether to offer a controlled vocabulary search or a free-text search was resolved with a decision to support both. The feedback from the user community indicated that being asked to independently determine the appropriate 'English" words through a free-text search would be very difficult. The preference was to be 'prompted' for relevant keywords through the use of a hierarchy of well-designed science keywords. The controlled keywords serve to 'normalize' the search through knowledgeable input by metadata providers. Earth science keyword taxonomies were developed, rules for additions, deletions, and modifications were created. Secondary sets of controlled vocabularies for related descriptors such as projects, data centers, instruments, platforms, related data set link types, and locations, along with free-text searches assist users in further refining their search results. Through this robust 'search and refine' capability in the GCMD users are directed to the data and services they seek. The next step in guiding users more directly to the resources they desire is to build a 'reasoning' capability for search through the use of ontologies. Incorporating twelve sets of Earth science keyword taxonomies has boosted the GCMD S ability to help users define and more directly retrieve data of choice.
External Dependencies-Driven Architecture Discovery and Analysis of Implemented Systems
A method for architecture discovery and analysis of implemented systems (AIS) is disclosed. The premise of the method is that architecture decisions are inspired and influenced by the external entities that the software system makes use of. Examples of such external entities are COTS components, frameworks, and ultimately even the programming language itself and its libraries. Traces of these architecture decisions can thus be found in the implemented software and is manifested in the way software systems use such external entities. While this fact is often ignored in contemporary reverse engineering methods, the AIS method actively leverages and makes use of the dependencies to external entities as a starting point for the architecture discovery. The AIS method is demonstrated using the NASA's Space Network Access System (SNAS). The results show that, with abundant evidence, the method offers reusable and repeatable guidelines for discovering the architecture and locating potential risks (e.g. low testability, decreased performance) that are hidden deep in the implementation. The analysis is conducted by using external dependencies to identify, classify and review a minimal set of key source code files. Given the benefits of analyzing external dependencies as a way to discover architectures, it is argued that external dependencies deserve to be treated as first-class citizens during reverse engineering. The current structure of a knowledge base of external entities and analysis questions with strategies for getting answers is also discussed.
Organic Ices in Titan's Stratosphere
Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both observational and experimental standpoints.
Organic Ices in Titan's Stratosphere
Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both observational and experimental standpoints.
Organic Ices in Titan’s Stratosphere
Titan’s stratospheric ice clouds are by far the most complex of any observed in the solar system, with over a dozen organic vapors condensing out to form a suite of pure and co-condensed ices, typically observed at high winter polar latitudes. Once these stratospheric ices are formed, they will diffuse throughout Titan’s lower atmosphere and most will eventually precipitate to the surface, where they are expected to contribute to Titan’s regolith. Early and important contributions were first made by the InfraRed Interferometer Spectrometer (IRIS) on Voyager 1, followed by notable contributions from IRIS’ successor, the Cassini Composite InfraRed Spectrometer (CIRS), and to a lesser extent, from Cassini’s Visible and Infrared Mapping Spectrometer (VIMS) and the Imaging Science Subsystem (ISS) instruments. All three remote sensing instruments made new ice cloud discoveries, combined with monitoring the seasonal behaviors and time evolution throughout Cassini’s 13-year mission tenure. A significant advance by CIRS was the realization that co-condensing chemical compounds can account for many of the CIRS-observed stratospheric ice cloud spectral features, especially for some that were previously puzzling, even though some of the observed spectral features are still not well understood. Relevant laboratory transmission spectroscopy efforts began just after the Voyager encounters, and have accelerated in the last few years due to new experimental efforts aimed at simulating co-condensed ices in Titan’s stratosphere. This review details the current state of knowledge regarding the organic ice clouds in Titan’s stratosphere, with perspectives from both an observational and experimental standpoint.
Scientific User Behavior and Data-Sharing Trends in a Petascale File System
The Oak Ridge Leadership Computing Facility (OLCF) runs the No. 4 supercomputer in the world, supported by a petascale file system, to facilitate scientific discovery. In this paper, using the daily file system metadata snapshots collected over 500 days, we have studied the behavioral trends of 1, 362 active users and 380 projects across 35 science domains. In particular, we have analyzed both individual and collective behavior of users and projects, highlighting needs from individual communities and the overall requirements to operate the file system. We have analyzed the metadata across three dimensions, namely (i) the projects' file generation and usage trends, using quantitative file system-centric metrics, (ii) scientific user behavior on the file system, and (iii) the data sharing trends of users and projects. To the best of our knowledge, our work is the first of its kind to provide comprehensive insights on user behavior from multiple science domains through metadata analysis of a large-scale shared file system. We envision that this OLCF case study will provide valuable insights for the design, operation, and management of storage systems at scale, and also encourage other HPC centers to undertake similar such efforts.
Probing nuclear effects in neutrino induced pion production
The discovery of neutrino oscillations has opened the door to searches for matterantimatter asymmetries in the lepton sector. Current accelerator-based longbaseline neutrino oscillation experiments, like T2K and NOnA, probing this phenomenon are moving into the realm of high statistics but systematically limited measurements. As a result work in reducing the dominant uncertainties in neutrino oscillations has become crucial in developing our understanding of leptonic CP Violation. One of the dominant uncertainties lies in our understanding of neutrinonucleus interactions. These affect our ability to determine the selection efficiencies for the respective signal and backgrounds, and the reconstruction of the neutrino energy spectra. This is due to our limited knowledge of nuclear effects and has renewed interest in neutrino-nucleus scattering measurements. This thesis presents work on a novel method of isolating nuclear effects which can be applied to isolate interactions on hydrogen. Using Double Transverse Momentum— a kinematic imbalance of the final state hadronic system transverse to the neutrino direction and muon momentum plane, phenomenological studies are performed into its ability to probe nuclear effects. We assess the feasibility of isolating neutrino-interactions on hydrogen in composite nuclear targets using the T2K near detector, ND280. This work is extended on to the MINERnA Experiment where work is undertaken in order to understand the heavy nuclear background. The first differential dpTT cross section measurement for final states containing at least one proton and p0 using the MINERnA detector is reported.
Three Decades of Magnetars
Magnetars are magnetically powered rotating neutron stars with extreme magnetic fields (over 10^14 Gauss). They were discovered in the X- and gamma-rays where they predominantly emit their radiation. Very few sources (roughly 24) have been found since their discovery in 1987. NASA's Fermi Gamma-ray Space Telescope was launched June 11, 2009; since then the Fermi Gamma-ray Burst Monitor (GBM) recorded emission from several magnetar sources. In total, six new sources were discovered between 2008 and 2011, with a synergy between Swift, RXTE, Fermi and the Interplanetary Network (IPN). In my talk I will give a short history of magnetars and describe how this, once relatively esoteric field, has emerged as a link between several astrophysical areas including Gamma-Ray Bursts. Finally, I will describe the exciting new results of Fermi and Chandra in this field and the current status of our knowledge of the magnetar population properties and magnetic fields.
DOE Office of Scientific and Technical Information (OSTI) Artificial Intelligence and Machine Learning
The Department of Energy (DOE) Office of Scientific and Technical Information (OSTI) established its artificial intelligence (AI) team in the summer of 2019. The AI Team's work and research in this space are new endeavors for OSTI; identifying the appropriate areas of research and investigation are priorities for the team and will ensure results and products that support OSTI and the collection, preservation, and dissemination of R&D results. To support OSTI’s strategic plan, the AI Team has started an assessment of the current R&D results corpus (e.g., metadata and full text) collected through ingest products such as E-Link and DOE CODE and disseminated through OSTI.GOV and other discovery applications. This presentation will present applied AI and Machine Learning (ML) approaches to assess and address data challenges and discuss how these data challenges are being evaluated to establish a comprehensive corpus of R&D results, support the reuse of R&D results and its data, and extend these findings to the broader DOE community. This presentation can be presented live or via a recorded presentation.
South Dakota Region Scientific Deep Dive
EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Questions crafted to explore this space include the following: 1) How, and where, will new data be analyzed and used? 2) How will the process of doing science change over the next 5–10 years? and 3) How will changes to the underlying hardware and software technologies influence scientific discovery? Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders
Materials characterization: Can artificial intelligence be used to address reproducibility challenges?
Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.
A pyridoxal 5'-phosphate-dependent Mannich cyclase
Pyridoxal 5'-phosphate (PLP)-dependent enzymes catalyse a diverse range of chemical transformations. Despite their extraordinary functional diversity, no PLP-dependent enzyme is known to catalyse Mannich-type reactions, an important carbon–carbon bond-forming reaction in synthetic organic chemistry. Here we report the discovery of a biosynthetic enzyme LolT, a PLP-dependent enzyme catalysing a stereoselective intramolecular Mannich reaction to construct the pyrrolizidine core scaffold in loline alkaloids. Importantly, its versatile catalytic activity is harnessed for stereoselective synthesis of a variety of conformationally constrained α,α-disubstituted α-amino acids, which bear vicinal quaternary–tertiary stereocentres and various aza(bi)cyclic backbones, such as indolizidine, quinolizidine, pyrrolidine and piperidine. Furthermore, crystallographic and mutagenesis analysis and computational studies together provided mechanistic insights and structural basis for understanding LolT’s catalytic activity and stereoselectivity. As a whole, this work expands the biocatalytic repertoire of carbon–carbon bond-forming enzymes and increases our knowledge of the catalytic versatility of PLP-dependent enzymes.
Isovalent Alloying and Heterovalent Substititution as Routes to Accelerate the Development and Optimization of Super-Efficient Halide Perovskite PV Solar Cells
Halide perovskites represent one of the fastest growing photovoltaic (PV) device technologies in the last decade, with research efficiencies among the highest confirmed. However, the research needed to advance tandem perovskite solar cells requires a specialized understanding base, but there is no such base for this young group of materials with the same depth as in semiconductor alloy research. Whereas IV-IV, II-V and II-VI semiconductors have a sophisticated science foundation refined over decades due of their use in electronics, lasers, LEDs and night vision applications, this is not yet the case for halide perovskites. At the same time, these materials behave rather differently than conventional semiconductors. Within the rapidly unfolding field of PV, the emerging halide-perovskite alloy research has largely been based on empiricism. Clearly, a concerted and comprehensive application to halide perovskite alloys of the modern quantum theory of solids brought us over the years a broad understanding of the critical factors enabling conventional inorganic PV could leapfrog the Edisonian, trial-and-error search for solutions to critical problems that are poorly understood. These theory approaches are required to accelerate the progress toward commercially ready hybrid-perovskites by providing a theory-guided experiment component to the EERE investments in this high-priority R&D. During this project we have studied three different rungs on the ladder of knowledge about hybrid perovskites. This kind of single halide perovskites represent the first level of development (rung 1) of solar cells, and includes materials such as APbI 3 , ASnI 3 , APbBr 3 , ASnBr 3 with different A cations, such as methylammonium (MA) with formamidinium (FM). Rung 2 is obtained by mixing B cations (Sn and Pb), and X anions (Cl and Br or I) producing a disordered alloy (A, A’)(M,M’)(X,X’) 3 -space. The most advanced rung corresponds to heterovalent-substitution of halide perovskites, creating an ordered double-perovskite compound. Although the focus of the project was on Rungs 2 and 3, we have made important discoveries regarding the nature of pure perovskites. We’ve observed that cubic perovskites should be modeled by a polymorphous network, instead of the basic single unit cell approach. This leads to the conclusion that perovskites have a fluid-like nature. An article regarding this work has been submitted to Nature Materials. Using the approach described above, we have also simulated alloys of halide perovskites, representing rung 2. We have focused on mixtures of [Cs,FA][Sn,Pb]I 3 with different concentrations, being able to disentangle the structural and electronic factors that determines the variation (bowing) of properties such as band gaps, mixing enthalpies and decomposition energies. For rung 3 compounds, we’ve closely interacted with experimental groups clarifying the chemical routes to obtain a certain compound, and what should be the most stable structure for them. As the final product, and consequent dissemination of the research, we’ve submitted two papers for publication, that are currently under evaluation. The interaction with experimental groups was also an important focus. We have participated in several local, national and international meetings, and reached out more closely to groups from CU Boulder and Arkansas.
Serving Fisheries and Ocean Metadata to Communities Around the World
NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://qcrnd.nasa.qov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage. The directory also offers data holders a means to post and search their data through customized portals, i.e. online customized subset metadata directories. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1994. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as a coordinating node of the International Directory Network (IDN), has been active at the international, regional and national level for many years through its involvement with the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and partnerships (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP), sharing experience, knowledge related to metadata and/or data management and interoperability.