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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 OSTI2017

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Identify the Removable Substructure in Carbon Activation

Activated carbon plays a pivotal role in achieving critical functions, such as separation, catalysis, and energy storage. A remaining question of carbon activation is which substructures in amorphous carbon are preferentially removed during activation. Herein, we report the first structure–activation correlation elucidated on the basis of unprecedented comprehensive characterization on carbon activation. We discover that activation under CO2 preferentially removes graphenic layers that are more defective. Therefore, the resulting activated carbon contains thinned turbostratic nanodomains that are of a higher local graphenic order. The mechanistic insights explain why more defective soft carbon is “burned” under CO2 at a much faster rate than hard carbon. The mechanism leads to an activation-based design principle of mesoporous carbon. Guided by this principle, a bimodal micromesoporous carbon is prepared simply by CO2 activation. Our findings may cause a paradigm shift for the rational design of nanoporous carbon.

Xing, Zhenyu↗
DOE OSTI2021

Identifying Boron Active Sites for the Oxidative Dehydrogenation of Propane

Oxidative dehydrogenation of propane (ODHP) to propylene could have a significant impact on the production of this critical chemical intermediate, if appropriate catalysts can be discovered. Recently, heterogeneous catalysts based on boron (oxides and nitrides) have been demonstrated to be promising for ODHP, but their active sites have not been conclusively identified. Here, we report that the deposition of differently sized boronic acids into the micropores of silica supports results in different distributions of surface borate species after calcination. Furthermore, these materials, in turn, display a wide range of rates in ODHP but similar selectivity, suggesting that they differ only in the numbers of active sites. Features identified by in situ Raman, IR, and magic-angle-spinning 11 B solid-state NMR spectroscopies are compared to catalyst activity. This correlation identifies the S2 borate species, a hydroxylated nonring boron, as the likely active site and provides a target for directed syntheses of future catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averaging methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.

Dai, Heng↗
DOE OSTI2018

Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors

Identifying tumor antigen-specific T cells from cancer patients has been a goal of tumor immunologists for several decades. Here we show that co-expression of CD103 and CD39 on CD8 TIL highly enriched for tumor-reactive T cells. This cell population, which is only present in TIL from primary and metastatic tumors, exhibited features of exhausted cells and displayed characteristics of tissue-resident memory T cells. CD39+CD103+ CD8 TIL had a distinct TCR repertoire compared to other CD8 TIL subsets and were clonally expanded within the tumor. They were highly enriched for tumor antigen recognition and efficiently killed autologous tumor cells. Finally, patients with head and neck cancer whose CD8 TIL contained a higher frequency of CD39+CD103+ cells experienced a greater overall survival. This work describes a simple method for detecting tumor-reactive CD8 TIL, which should help define mechanisms of current immunotherapies and may lead to the development of future immunotherapies.

cancer, immunotherapy, transcriptomics, t-cells, h↗
DOE OSTI2021

Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response

Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.

systems biology, hypergraph, viral infection, biol↗
DOE OSTI2018

CSGID Solves Structures and Identifies Phenotypes for Five Enzymes in Toxoplasma gondii

Toxoplasma gondii, an Apicomplexan parasite, causes significant morbidity and mortality, including severe disease in immunocompromised hosts and devastating congenital disease, with no effective treatment for the bradyzoite stage. To address this, we used the Tropical Disease Research database, crystallography, molecular modeling, and antisense to identify and characterize a range of potential therapeutic targets for toxoplasmosis. Phosphoglycerate mutase II (PGMII), nucleoside diphosphate kinase (NDK), ribulose phosphate 3-epimerase (RPE), ribose-5-phosphate isomerase (RPI), and ornithine aminotransferase (OAT) were structurally characterized. Crystallography revealed insights into the overall structure, protein oligomeric states and molecular details of active sites important for ligand recognition. Literature and molecular modeling suggested potential inhibitors and druggability. The targets were further studied with vivoPMO to interrupt enzyme synthesis, identifying the targets as potentially important to parasitic replication and, therefore, of therapeutic interest. Targeted vivoPMO resulted in statistically significant perturbation of parasite replication without concomitant host cell toxicity, consistent with a previous CRISPR/Cas9 screen showing PGM, RPE, and RPI contribute to parasite fitness. PGM, RPE, and RPI have the greatest promise for affecting replication in tachyzoites. These targets are shared between other medically important parasites and may have wider therapeutic potential.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Estimation of the Fuel Efficiency Potential of Six Gasoline Blendstocks Identified by the U.S. Department of Energy’s Co-Optimization of Fuels and Engines Program

Six blendstocks identified by the Co-Optimization of Fuels & Engines Program were used to prepare fuel blends using a fixed blendstock for oxygenate blending and a target RON of 97. The blendstocks included ethanol, n-propanol, isopropanol, isobutanol, diisobutylene, and a bioreformate surrogate. The blends were analyzed and used to establish interaction factors for a non-linear molar blending model that was used to predict RON and MON of volumetric blends of the blendstocks up to 35 vol%. Projections of efficiency increase, volumetric fuel economy increase, and tailpipe CO2 emissions decrease were produced using two different estimation techniques to evaluate the potential benefits of the blendstocks. Ethanol was projected to provide the greatest benefits in efficiency and tailpipe CO2 emissions, but at intermediate levels of volumetric fuel economy increase over a smaller range of blends than other blendstocks. A bioreformate surrogate blendstock was projected to provide the greatest increase in volumetric fuel economy and the lowest increase in efficiency. Tailpipe CO2 emissions for blends of the bioreformate surrogate were higher at all blend levels compared to the baseline E10 fuel.

02 PETROLEUM↗
DOE OSTI2017

Identifying attributes of CO 2 leakage zones in shallow aquifers using a parametric level set method: Original Research Article: Identifying attributes of CO 2 leakage zones in shallow aquifers using a parametric level set method

Leakage through abandoned wells and geologic faults poses the greatest risk to CO 2 storage permanence. For shallow aquifers, secondary CO 2 plumes emanating from the leak zones may go undetected for a sustained period of time and has the greatest potential to cause large-scale and long-term environmental impacts. Identification of the attributes of leak zones, including their shape, location, and strength, is required for environmental risk assessment. This study applies a parametric level set (PaLS) method to characterize leak zones. Level set methods are appealing for tracking topological changes and recovering unknown shapes of objects. However, level set evolution using the conventional level set methods is computationally challenging. In PaLS, the level set function is approximated by using a weighted sum of basis functions and the level set evolution problem is replaced by an optimization problem. The efficacy of PaLS is demonstrated by reconstructing attributes of a CO 2 leak zone in a carbonate aquifer. Our results show that PaLS is a robust source identification method that can recover the approximate leak zone locations in the presence of measurement errors, model parameter uncertainty, and inaccurate guesses of source flux strengths. Accurate delineation of source geometry is achievable when a relatively dense observation network is used. The PaLS-based, source recovery framework introduced in this work is generic and can be adapted for any reactive transport model by simply switching the pre- and post-processing routines.

54 ENVIRONMENTAL SCIENCES↗