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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 37 records · Page 2

Microbial Biomass in Soils Receiving Varying Levels of Organic Inputs - A Data Compilation

This dataset contains soil microbial biomass data compiled from published laboratory studies and field manipulations of organic inputs to soils. Each study included at least three levels of a given type of organic input applied to a given type of soil. Studies were selected from the literature that had experimentally manipulated organic input rates to soils using unburned, non-synthetic material (e.g. plant litters, manures, and composts). Fifty-five (55) studies, conducted from 1997 to October 2019, were chosen and compiled into a dataset with 358 observations from 96 combinations of a given organic material applied to a given soil in agricultural systems, laboratory incubations, forests, or grasslands. • Studies were selected when biomass measurements used the chloroform fumigation-extraction procedure followed by carbon analysis, or, in two instances, studies estimated microbial biomass using phospholipid fatty acid analysis. • Studies that used fewer than three input rates or characterized microbial biomass via substrate induced respiration were excluded. • The temporal and spatial aspects of the laboratory and field studies were not essential to the analyses (Craig et al., 2021) and were not included with the measurement data. These data were used to assess the relationship between organic input amounts and microbial biomass. Complete reference citations are provided if additional information is needed. The compiled data are provided in two .csv files: (1) soil treatments and microbial biomass data and (2) literature citations. The “Study” field is the cross reference between files. Also provided is the R code for reproducing the results in the related publication.

54 ENVIRONMENTAL SCIENCES↗

Cited Reference Analysis in a National Laboratory to Assess Journal Usage

Librarians at a large, federally funded, multidisciplinary research organization sought to assess the value provided by the library’s collections. One method for capturing this value includes determining how well their library’s collections meet researcher needs, as expressed by the materials those researchers choose to cite in the production of new scholarly communications. A cited reference analysis was performed on the publications originating from the institution to provide the necessary data for the assessment, as well as to aid in the creation of a core list of journals which will inform future collection development efforts. Overall, this paper fills a gap in the library and information science literature by addressing the citation behaviors of researchers at a large multidisciplinary government research facility, a setting that has not been examined previously.

99 GENERAL AND MISCELLANEOUS↗

U.S. Wind Siting Regulation and Zoning Ordinances

A machine readable collection of documented wind siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled from several sources including, DOE's Wind Exchange Ordinance Database (Linked in the submission), National Conference of State and Legislatures Wind Energy Siting (also linked in the submission), and scholarly legal articles. The citations for each ordinance are included in the spreadsheet. This data is an updated to a previously developed database of wind ordinances found in OEDI Submission 1932: "U.S. Wind Siting Regulation and Zoning Ordinances"

analysis↗

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗

Workforce planning: a review of methodologies

Workforce planning deals with determining the number of employees and associated skills necessary to meet the future operational needs of an organization. A workforce system consists of six elements: recruitment, attrition, promotion, training, retention, and scheduling. Historically, several workforce modeling and analysis methodologies have been developed to capture these elements. This paper reviews the results of workforce and manpower models published within peer-reviewed literature between 1959 and 2021 to provide an in-depth analysis of current models. The focus of this review is on analytical, simulation, and empirical models found in literature that were collected based on a citation requirement and keyword search criteria. Results demonstrate the trends in workforce modeling research and discuss the common uses of each model type and the advantages/disadvantages related to each model. Based on the common attributes of workforce systems, the discussion focuses on the most frequently used model type for each element and the best use for each model. Lastly, recommendations are made for the development of workforce models that allow the most comprehensive view of the workforce systems of the future.

42 ENGINEERING↗

Improved Cumulative Fission Yield Measurements with Fission Spectrum Neutrons on 238U

Two isotopically-characterized targets of high-purity (99.961±0.002%) 238U metal and a neutron dosimetry package were exposed to a pulsed neutron irradiation using the Godiva IV critical assembly at the National Criticality Experiments Research Center to measure the integral fission product yields produced in a Watt fission neutron-energy spectrum. Following irradiation, the fissioned targets were counted with two broad energy germanium detectors. Estimates of the total fissions for each target were made using measurements of well-known fission products. The total number of fissions in each target were then used to measure the fission yields of 33 fission products, 18 of which are published as new measurements. Twelve of the 18 isotopes were in agreement with ENDF/B- VIII.0 using the zeta test and treating ENDF/B-VIII.0 as the true mean: 84mBr, 93Y, 94Y, 104Tc, 134I, 135I, 138Xe, 141Ba, 142Ba, 142La, 146Ce, and 149Nd. The ENDF/B-VIII.0 fission yield for 139Ba was only slightly outside of 1-s relative to the uncertainty measured in this work. Uncertainties for 5 fission product yields were markedly improved over those reported in ENDF/B-VIII.0: 128Sn, 129Sb, 130gSb, 131mTe, and 133I. The fission yields measured for 128Sn, 131mTe, and 133I were in good agreement with the ENDF/B-VIII.0 predicted fission yields. However, fission yields measurements of the isotopes 129Sb, and 130gSb deviated from the ENDF/B-VIII.0 value. Both of these isotopes reside near the doubly magic nuclear shell closures of 50 protons and 82 neutrons. These fission yields may be suppressed because of the odd-odd nuclear structure of 130gSb and the odd-even structure of 129Sb and isomer splitting. Comparisons to other recent work and a brief review with citations are included to support this conclusion.

Fission Yields↗

Knowledge acquisition and innovation quality: The moderating role of geographical characteristics of technology

Acquiring and absorbing knowledge at different geographic levels is an important step in the technological learning and innovation processes for firms. Geographically bounded knowledge searching and spillover have been shown in various network formats, industries, geographic scopes, and country contexts. This study finds evidence of localized learning in citation networks in the solar photovoltaic balance-of-systems (PV BOS) innovations, and that the levels of localized learning vary based on the degree to which technologies are relevant to local contexts. Thus, this study proposes that the value of local and non-local knowledge on patent quality is contingent on technological characteristics. Further, leveraging the different degrees of local dependence on four types of technologies in the PV BOS industry, we find that the impact of local knowledge on innovation quality is higher for technologies that are more rooted in local contexts and that the impact of non-local knowledge is higher for technologies that are less relevant to local contexts.

99 GENERAL AND MISCELLANEOUS↗

Grid resolution requirement of chemical explosive mode analysis for large eddy simulations of premixed turbulent combustion

Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article Abstract The grid resolution requirement for trustworthy Chemical Explosive Mode Analysis (CEMA) in Large Eddy Simulation (LES) of premixed turbulent combustion is proposed. Explicit filtering, to emulate the effect of the LES filter, is applied to one-dimensional laminar flame and three-dimensional planar turbulent flames across a wide range of Karlovitz numbers (5 - 239). The identification of the flame front by CEMA is found relatively insensitive to the cell size (Δ), while the combustion mode identification shows more significant sensitivity. Specifically, increasing Δ falsely enhances the auto-ignition and local extinction modes and suppresses the diffusion-assisted mode. Limited dependence of the CEMA performance on the turbulent combustion regime (Karlovitz number) is observed. A simple grid size criterion for reliable CEMA mode identification in LES is proposed as Δ ≲ δ L /2; The criterion can be relaxed to Δ ≲ δ L in the laminar flame limit. Furthermore, theoretical analysis is conducted on an idealised chemistry-diffusion system. The effects of the filtering process and turbulence on the local combustion mode are demonstrated, which is consistent with the numerical observations. Further, by incorporating turbulent combustion models in CEMA, potential improvement in identifying local combustion modes can be expected.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bibliometric review and recent advances in total scattering pair distribution function analysis: 21 years in retrospect

Global research activities have been driven by the quest to develop and characterize novel materials for technological advancements. The total scattering pair distribution function (TSPDF) is a powerful and versatile characterization technique for examining the structural details of diverse complex materials including liquid, amorphous, disordered crystalline, and nanostructured materials. Thus, it is critical to keep track of research progress, identify research gaps, and future research directions of the application of the TSPDF technique in materials development and discovery. In this work, a bibliometric analysis of literature regarding the TSPDF technique between 2000 and 2021 was conducted using datasets retrieved from the Web of Science database. The research trends based on publication outputs, research subject distribution, co-authorships among institutions, countries/regions, co-citation of referenced sources, and keyword co-occurrence are evaluated and discussed herein. The impact of the TSPDF technique is projected to increase due to its importance in probing emerging functional materials, and the advances in specialized facilities and instrumentation among the scientific communities engaged with it. Finally, current and emerging research hotspots related to TSPDF technique such as catalysis, computer modeling and simulation, pharmaceutics, machine learning, hydrogen storage, battery materials, and layered structured materials are also identified and discussed.

36 MATERIALS SCIENCE↗

Production of muonic kaon atoms at high-energy colliders

We develop a framework for the formation of exotic muonic kaon atoms ($K\mu$) in semileptonic $D^{0}$ decays, using the effective weak Hamiltonian, a helicity-based treatment of the leptonic current, and a nonrelativistic bound-state projection. The resulting branching ratio, $\mathrm{BR}(D^{0}\!\to(K\mu )\nu_{\mu})=2.29\times10^{-10}$, is implemented in a ROOT-based code to estimate yields at RHIC, LHC, and STCF. We show quantitatively that $K\mu$ atoms—also produced through coalescence in the quark--gluon plasma (QGP)—provide a sensitive probe of low-momentum primordial muons and early-time electromagnetic radiation, offering complementary constraints in an otherwise unexplored phase space for thermal dilepton and photon emission. Newly estimated dissociation cross sections in detector material indicate that secondary‑vertex reconstruction should be experimentally feasible, allowing clean experimental identification of the atoms. Projected yields from QGP coalescence in LHC and RHIC heavy-ion collisions, and from $D^{0}$ decays in LHC high-luminosity $p+p$ collisions indicate that the first observation of $K\mu$ atoms is within reach. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Towards a RAG-based summarization for the Electron Ion Collider

Abstract The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed athttps://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found inhttps://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]

Instruments & Instrumentation↗

Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data

Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism.

Hawkins, Charles (ORCID:0000000312849047)↗

Exaggerated Plurivory of Macrophomina phaseolina : Accounting for the Large Host Range Claim and the Shifting of Scientific Language

Macrophomina phaseolina is a plant pathogenic fungus that is frequently described as having a broad host range encompassing more than 500 species. We noticed that citations provided in support of this statement do not actually demonstrate such a broad host range. To elucidate the true documented host range of this fungus, we initiated a literature meta-analysis of 894 publications on M. phaseolina since 1913. We discovered that the first host range summaries did not require Koch's postulates or other experimental demonstrations of pathogenicity. Most of the available early host claims were based on tenuous associations between the fungus and symptoms, sometimes without reporting isolation or morphological examination in vitro. These statements apparently led to a pattern of increasingly exaggerated host range claims, without support from a primary reference, until the claim that M. phaseolina has 500 hosts became common in the early 2000s. At present, the scientific community typically requires Koch's postulates to characterize pathogenicity on a new host. Among all the available literature, we only found primary experimental evidence for M. phaseolina’s pathogenicity on 97 hosts; 74 hosts confirmed by Koch's postulates and 23 hosts with all steps from Koch's postulates completed except for recovery of the pathogen from symptomatic tissues. This study demonstrates how scientific concepts can change over time and necessitate changes to historic axioms. We propose that the hyperbole surrounding the host range of M. phaseolina has obscured an accurate depiction of its biology.

Plant Sciences↗

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, sub­tomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗

Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.

Purohit, Sumit↗

Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters

Abstract—Grid following inverter-based renewable generation has replaced conventional generation in recent years, resulting in lower system inertia. The frequency resilience in such a lower inertia system is critical for emergency mitigation. In this paper, we propose a resilience metric based on frequency recovery to quantitatively represent system resilience in terms of the rate of change of frequency. Application of grid-forming converters provides a means to improve system resilience by providing virtual inertia. An under-frequency load shedding strategy is applied to further support frequency recovery in cases with high penetration of grid-forming inverters. Case studies are designed and performed in a modified IEEE 9-bus test system using the PSCAD/EMTDC platform. Simulation results demonstrate the validity of the proposed resilience metric and the effectiveness of the strategy for reducing frequency excursion in inverter-based power systems. citation: J. Gui, H. Lei, T. R. McJunkin and B. K. Johnson, "Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066357.

14 SOLAR ENERGY↗

Crystal Ball: A Naturalist Perspective of Microbiology: Examples from Methanogenic Archaea

Storytelling has been the primary means of knowledge transfer over human history. The effectiveness and reach of stories are improved when the message is appropriate for the target audience. Oftentimes, the stories that are most well received and recounted are those that have a clear purpose and that are told from a variety of perspectives that touch on the varied interests of the target audience. Whether scientists realize or not, they are accustomed to telling stories of their own scientific discoveries through the preparation of manuscripts, presentations, and lectures. Perhaps less frequently, scientists prepare review articles or book chapters that summarize a body of knowledge on a given subject matter, meant to be more holistic recounts of a body of literature. Yet, by necessity, such summaries are often still narrow in their scope and are told from the perspective of a particular discipline. In other words, interdisciplinary reviews or book chapters tend to be the rarity rather than the norm. Naturalists are scientists that tell stories of organisms and their natural environment within a historical context and from an interdisciplinary perspective. Such stories interweave observations of biological subjects from disparate disciplines into a cohesive story. To this end, the success of naturalists in recounting compelling natural history stories is enhanced when they allow themselves to become vulnerable by knowingly entering a literature base full of unfamiliar ways of observing and describing such subjects. When successful, this approach can broaden the scope of the story, making it of interest to a larger audience and attracting further recounting of that story (e.g., citations). To this end, when we look into our crystal ball, we see a need to tell more holistic stories of microorganisms and their functions from an interdisciplinary, naturalist perspective that combines what is known of their contemporary ecology, physiology, and biochemistry with their evolutionary history thereby placing them within a historical Earth context. With this conviction in mind, we take the opportunity to look back on the natural history of methanogens to identify some of their most profound biochemical innovations and their impact on the trajectory of life and the planet. Our approach was to integrate knowledge from the fields of biochemistry, physiology, ecology, evolution, geochemistry, and atmospheric sciences. We start with the origin of the first autotrophic cells and how this set Earth on a path towards biomass expansion and functional diversification. Next, we discuss the importance of iron and sulfur in methanogen metabolism, and how their innovative ways of accessing these elements allowed for expanded use of iron-sulfur clusters ([Fe-S]) as nanoscale catalysts that power(ed) Earth’s biochemical cycles. We next discuss the origin and evolution of [NiFe]-hydrogenase and how this versatile enzyme chassis was co-opted by a variety of anaerobes and aerobes as the foundation for respiratory complexes capable of sustaining higher forms of life, including humans. Lastly, we discuss the evolution of the enzyme molybdenum nitrogenase, and how this allowed the biosphere to overcome fixed nitrogen limitation by tapping the seemingly endless reserve of atmospheric dinitrogen (N 2 ). In telling these stories, we aimed to generate template(s) to motivate future interdisciplinary natural history studies focused on other organisms and processes. Further, we hope to stimulate scientific discourse and provide impetus to become vulnerable and peruse literature of relevance to understanding the causes and consequences of the evolution of your favorite microorganism or biochemical process.

59 BASIC BIOLOGICAL SCIENCES↗