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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
DOE OSTI2013

PLANET HUNTERS: NEW KEPLER PLANET CANDIDATES FROM ANALYSIS OF QUARTER 2

We present new planet candidates identified in NASA Kepler Quarter 2 public release data by volunteers engaged in the Planet Hunters citizen science project. The two candidates presented here survive checks for false positives, including examination of the pixel offset to constrain the possibility of a background eclipsing binary. The orbital periods of the planet candidates are 97.46 days (KIC 4552729) and 284.03 (KIC 10005758) days and the modeled planet radii are 5.3 and 3.8 R{sub Circled-Plus }. The latter star has an additional known planet candidate with a radius of 5.05 R{sub Circled-Plus} and a period of 134.49 days, which was detected by the Kepler pipeline. The discovery of these candidates illustrates the value of massively distributed volunteer review of the Kepler database to recover candidates which were otherwise uncataloged.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Improving Ligand Geometry in Protein Data Bank Structures Computationally

Information about the three‐dimensional structures of complex biological molecules is an indispensable research tool for many fields of study, including biology, chemistry, pharmacology, and computer science. The Protein Data Bank (PDB) is an public digital archive providing access to structures of nucleic acids, proteins, and large macromolecular machines determined experimentally by X‐ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and 3‐dimensional electron microscopy (3DEM). PDB structures undergo validation for structural plausibility upon their deposition into the PDB using the program Mogul, which cross references each deposited structure against similar data in the Cambridge Structural Database (CSD). Ligands associated with PDB structures are a common source of structural inaccuracy. In this study, a programmatic pipeline was developed to determine if ligand geometric accuracy could be improved using known computational tools. Ligands with dubious geometry were identified through a Python mining code. A sample set of the identified structures was remodeled into minimum energy conformations using the molecular dynamics program AMBER and then fit to the original experimental density maps using the molecular modeling program Coot. The refined models were compared to the original models in the PDB as well as to similar structures in the CSD using the deposition statistical validation server. Preliminary results suggest that the pipeline effectively identifies and improves ligand structures that possess inaccurate geometry. This method can be used to evaluate and improve the accuracy of specific ligands in structures both archived and pending deposition. Support or Funding Information This work was supported by an NSF REU and the RCSB PDB [NSF (DBI‐1338415), NIH, and DOE.] This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Miller, Beck↗
NASA NTRS2016

Description of the TCERT Vetting Reports for Data Release 25

This document, the Kepler Instrument Handbook (KIH), is for Kepler and K2 observers, which includes the Kepler Science Team, Guest Observers (GOs), and astronomers doing archival research on Kepler and K2 data in NASAs Astrophysics Data Analysis Program (ADAP). The KIH provides information about the design, performance, and operational constraints of the Kepler flight hardware and software, and an overview of the pixel data sets available. The KIH is meant to be read with these companion documents:1. Kepler Data Processing Handbook (KSCI-19081) or KDPH (Jenkins et al., 2016). The KDPH describes how pixels downlinked from the spacecraft are converted by the Kepler Data Processing Pipeline (henceforth just the pipeline) into the data products delivered to the MAST archive. 2. Kepler Archive Manual (KDMC-10008) or KAM (Thompson et al., 2016). The KAM describes the format and content of the data products, and how to search for them.3. Kepler Data Characteristics Handbook (KSCI-19040) or KDCH (Christiansen et al., 2016). The KDCH describes recurring non-astrophysical features of the Kepler data due to instrument signatures, spacecraft events, or solar activity, and explains how these characteristics are handled by the pipeline.4. Kepler Data Release Notes 25 (KSCI-19065) or DRN 25 (Thompson et al., 2015). DRN 25 describes signatures and events peculiar to individual quarters, and the pipeline software changes between a data release and the one preceding it.Together, these documents supply the information necessary for obtaining and understanding Kepler results, given the real properties of the hardware and the data analysis methods used, and for an independent evaluation of the methods used if so desired.

Instrument↗
DOE OSTI2023

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. In conclusion, the proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

The PhyloFacts FAT-CAT web server: ortholog identification and function prediction using fast approximate tree classification

The PhyloFacts ‘Fast Approximate Tree Classification’ (FAT-CAT) web server provides a novel approach to ortholog identification using subtree hidden Markov model-based placement of protein sequences to phylogenomic orthology groups in the PhyloFacts database. Results on a data set of microbial, plant and animal proteins demonstrate FAT-CAT’s high precision at separating orthologs and paralogs and robustness to promiscuous domains. We also present results documenting the precision of ortholog identification based on subtree hidden Markov model scoring. The FAT-CAT phylogenetic placement is used to derive a functional annotation for the query, including confidence scores and drill-down capabilities. PhyloFacts’ broad taxonomic and functional coverage, with >7.3 M proteins from across the Tree of Life, enables FAT-CAT to predict orthologs and assign function for most sequence inputs. Four pipeline parameter presets are provided to handle different sequence types, including partial sequences and proteins containing promiscuous domains; users can also modify individual parameters. PhyloFacts trees matching the query can be viewed interactively online using the PhyloScope Javascript tree viewer and are hyperlinked to various external databases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Development of vegetative oil sorghum: From lab‐to‐field

Summary Biomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Unveiling the unseen with the Dark Energy Survey: gravitational waves and dark matter

In this thesis I show how large galaxy surveys, in particular the study of the properties of galaxies, can shed light on gravitational wave sources and dark matter. This is achieved using the latest data from the Dark Energy Survey, an on-going $5000 ~{\rm deg}^2$ optical survey. Galaxy properties such as photometric redshifts and stellar masses are derived through spectral energy distribution fitting methods. The results are used to study host galaxies of gravitational wave events and how light traces dark matter in galaxy clusters. Gravitational wave (GW) science, and particularly the electromagnetic follow up of these events, is transforming what had never been seen into a new astronomical field able to unveil the nature of cataclysmic events. Identifying the galaxies that host these events, and estimating their redshift, stellar mass, and star--formation rate, is crucial for cosmological analysis with gravitational waves, for follow up studies and to understand the format ion of the binary systems that are thought to produce observable gravitational wave signals. This thesis describes how host galaxies are important for the DES--GW pipeline used in identifying optical counterparts to GW events. In occasion of the exciting discovery of the first electromagnetic counterpart to a gravitational wave event by our DES--GW team among others, we showed how observations of NGC 4993, the galaxy host of the event, provide important information about possible formation scenarios for binary neutron stars. In particular, we find that NGC 4993 presents shell structures and we relate their formation to the binary formation. The same galaxy properties are used to derive an observable mass proxy for galaxy clusters. I show that this mass observable correlates well with the total mass of clusters, which is mainly composed of dark matter. It can therefore be used for cosmological studies with galaxy clusters. The measurement of stellar--to--halo mass relations i n clusters provides insights on the connection between the s! tar content and the total matter content in clusters, and how this evolves over cosmic time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

BSSD 2019 Performance Metric Q4

The LANL SFA in Terrestrial Microbial Carbon Cycling aims to inform climate modeling and enable carbon management in terrestrial ecosystems by discovering widespread biological processes that control carbon storage and release in temperate biome soils. To achieve these goals, computational approaches are essential to analyze increasingly complex ‘omics’ data from microbial communities in environmental samples. The SFA continues to leverage advances in ‘omics' measurement technology and computation tools to decipher microbial community function in environmental samples. The computational approach is aligned with the recent shift in the SFA’s research approach. When the SFA began 10 years ago, computational approaches principally addressed “who is there” with extremely limited insight into function. Accessible ‘omics' data for complex soil communities were primarily amplicon DNA sequences of taxonomic or functional marker genes obtained by PCR, cloning, and Sanger sequencing. Computational tools for community analysis were nascent. Over the past decade, genome resources (DOE-Integrated Microbial Genomics database) and data acquisition increased 1000-fold, common computational pipelines for many analysis tasks have arisen, and the variety of ‘omics’ measurements has expanded to routinely include shotgun metagenomics, metatranscriptomics, and versions of metabolomics. The SFA is capitalizing on these advances while also developing new computational techniques to decipher how communities with different patterns of carbon cycling function from the species to ecosystem level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Automating methods for estimating metabolite volatility

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc , we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Development of Novel Small-Molecule Activators of Pyruvate Kinase Muscle Isozyme 2, PKM2, to Reduce Photoreceptor Apoptosis

Treatment options are lacking to prevent photoreceptor death and subsequent vision loss. Previously, we demonstrated that reprogramming metabolism via the pharmacologic activation of PKM2 is a novel photoreceptor neuroprotective strategy. However, the features of the tool compound used in those studies, ML-265, preclude its advancement as an intraocular, clinical candidate. This study sought to develop the next generation of small-molecule PKM2 activators, aimed specifically for delivery into the eye. Compounds were developed that replaced the thienopyrrolopyridazinone core of ML-265 and modified the aniline and methyl sulfoxide functional groups. Compound 2 demonstrated that structural changes to the ML-265 scaffold are tolerated from a potency and efficacy standpoint, allow for a similar binding mode to the target, and circumvent apoptosis in models of outer retinal stress. To overcome the low solubility and problematic functional groups of ML-265, compound 2’s efficacious and versatile core structure for the incorporation of diverse functional groups was then utilized to develop novel PKM2 activators with improved solubility, lack of structural alerts, and retained potency. No other molecules are in the pharmaceutical pipeline for the metabolic reprogramming of photoreceptors. Thus, this study is the first to cultivate the next generation of novel, structurally diverse, small-molecule PKM2 activators for delivery into the eye.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
DOE OSTI2016

FutureGen 2.0 Oxy-combustion Large Scale Test – Final Report

The primary objectives of the FutureGen 2.0 CO 2 Oxy-Combustion Large Scale Test Project were to site, permit, design, construct, and commission, an oxy-combustion boiler, gas quality control system, air separation unit, and CO 2 compression and purification unit, together with the necessary supporting and interconnection utilities. The project was to demonstrate at commercial scale (168MWe gross) the capability to cleanly produce electricity through coal combustion at a retrofitted, existing coal-fired power plant; thereby, resulting in near-zeroemissions of all commonly regulated air emissions, as well as 90% CO 2 capture in steady-state operations. The project was to be fully integrated in terms of project management, capacity, capabilities, technical scope, cost, and schedule with the companion FutureGen 2.0 CO 2 Pipeline and Storage Project, a separate but complementary project whose objective was to safely transport, permanently store and monitor the CO 2 captured by the Oxy-combustion Power Plant Project. The FutureGen 2.0 Oxy-Combustion Large Scale Test Project successfully achieved all technical objectives inclusive of front-end-engineering and design, and advanced design required to accurately estimate and contract for the construction, commissioning, and start-up of a commercial-scale "ready to build" power plant using oxy-combustion technology, including full integration with the companion CO 2 Pipeline and Storage project. Ultimately the project did not proceed to construction due to insufficient time to complete necessary EPC contract negotiations and commercial financing prior to expiration of federal co-funding, which triggered a DOE decision to closeout its participation in the project. Through the work that was completed, valuable technical, commercial, and programmatic lessons were learned. This project has significantly advanced the development of near-zero emission technology and will be helpful to plotting the course of, and successfully executing future large demonstration projects. This Final Scientific and Technical Report describes the technology and engineering basis of the project, inclusive of process systems, performance, effluents and emissions, and controls. Further, the project cost estimate, schedule, and permitting requirements are presented, along with a project risk and opportunity assessment. Lessons-learned related to these elements are summarized in this report. Companion reports Oxy-combustion further document the accomplishments and learnings of the project, including: A.01 Project Management Report which describes what was done to coordinate the various participants, and to track their performance with regard to schedule and budget B.02 Lessons Learned - Technology Integration, Value Improvements, and Program Management, which describes the innovations and conclusions that we arrived upon during the development of the project, and makes recommendations for improvement of future projects of a similar nature . B.03 Project Economics, which details the capital and operation costs and their basis, and also illustrates the cost of power produced by the plant with certain sensitivities. B.04 Power Plant, Pipeline, and Injection Site Interfaces, which details the interfaces between the two FutureGen projects B.05 Contractual Mechanisms for Design, Construction, and Operation, which describes the major EPC, and Operations Contracts required to execute the project.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Culture-Independent Fungal Profiling for the International Space Station using Nanopore Sequencing: Method Development

Microbial monitoring of the International Space Station (ISS) environment is a crew health requirement that encompasses both bacterial and fungal identification. To achieve this currently, culture-based methods are used for sample collection, and these samples must be returned to the laboratory for analysis. The use of culture and the need for sample return to Earth results in a bias toward culturable organisms and causes a significant delay between sample collection and delivery of final data (weeks to months), respectively. Recently, advancements in molecular technology have aided a broad range of applications, including medical, industrial, and basic sciences. Additionally, increases in portability and ease-of-use of molecular platforms have provided point-of-use capabilities demonstrated by the miniPCR thermal cycler (miniPCR bio) and the MinION sequencer (Oxford Nanopore Technologies). Together, these devices have been applied to, and validated for, the identification of bacteria onboard the ISS. Building on this work, we have developed a spaceflight-compatible fungal workflow. Molecular-based fungal analysis is complicated by low biomass, difficult-to-lyse spores, debate regarding the region for taxonomic assignment, and the lack of bioinformatic pipelines and reference databases. To overcome these difficulties, primers yielding an ~ 2 Kb amplicon were validated against a wide range of ISS fungal isolates. The current spaceflight library preparation was substantially optimized, a bioinformatic pipeline was created, and refinements to the UNITE database were implemented. To compare this optimized method to the current culture-based standard, 30 sample sets (60 total swabs, two swabs held in tandem) were evaluated. Parallel fungal profiles were obtained between the two methods, with the culture-independent method revealing increased diversity. The addition of this method to the already established bacterial process fulfills the crew health identification requirement. Moreover, the implementation of this method onboard ISS will enhance our understanding of its unique fungal microbiome.

Hang N. Nguyen↗
DOE OSTI2019

High-throughput phenotyping of cell-to-cell interactions in gel microdroplet pico-cultures

Microbiomes exert significant influence on our planet's ecology. Elucidating the identities of individual microbes within these communities and how they interact is a vital research imperative. Using traditional plating and culturing methods, it is impractical to assess even a small fraction of the interactions that exist within microbial communities. To address this technology gap, we integrated gel microdroplet technology with microfluidics to generate millions of microdroplet cultures (MDs) that sequester individual cells for phenotyping MDs, facilitating rapid analysis and viable recovery using flow cytometry. Herein, we describe a validated high-throughput phenotyping pipeline that elucidates cell-to-cell interactions for millions of combinations of microorganisms. Through iterative co-culturing of an algae and a pool of environmentally sourced microbes, we successfully isolated bacteria that improved algal growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Leakage Risk Assessment of CO{sub 2} Transportation by Pipeline at the Illinois Basin Decatur Project, Decatur, Illinois

The Illinois Basin Decatur Project (IBDP) is designed to confirm the ability of the Mt. Simon Sandstone, a major regional saline-water-bearing formation in the Illinois Basin, to store 1 million tons of carbon dioxide (CO{sub 2}) injected over a period of three years. The CO{sub 2} will be provided by Archer Daniels Midland (ADM) from its Decatur, Illinois, ethanol plant. In order to transport CO{sub 2} from the capture facility to the injection well (also located within the ADM plant boundaries), a high-pressure pipeline of length 3,200 ft (975 m) has been constructed, running above the ground surface within the ADM plant footprint. We have qualitatively evaluated risks associated with possible pipeline failure scenarios that lead to discharge of CO{sub 2} within the real-world environment of the ADM plant in which there are often workers and visitors in the vicinity of the pipeline. There are several aspects of CO{sub 2} that make its transportation and potential leakage somewhat different from other substances, most notable is its non-flammability and propensity to change to solid (dry ice) upon strong decompression. In this study, we present numerical simulations using Computational Fluid Dynamics (CFD) methods of the release and dispersion of CO{sub 2} from individual hypothetical pipeline failures (i.e., leaks). Failure frequency of the various components of a pipeline transportation system over time are taken from prior work on general pipeline safety and leakage modeling and suggest a 4.65% chance of some kind of pipeline failure over the three-years of operation. Following the Precautionary Principle (see below), we accounted for full-bore leakage scenarios, where the temporal evolution of the mass release rate from the high-pressure pipeline leak locations was simulated using a state-of-the-art Pipe model which considers the thermodynamic effects of decompression in the entire pipeline. Failures have been simulated at four representative locations along the pipeline route within the ADM plant. Leakage scenarios at sites along the route of the pipeline, where plant operations (e.g., vehicular and train transportation) seem to present a higher likelihood of accidental failure, for example due to vehicles or equipment crashing into the pipeline and completely severing it, were modeled by allowing them to have a double source consistent with the pipeline releasing high-pressure CO{sub 2} from both ends of the broken pipe after a full-bore offset rupture. Simulation results show that the built environment of the plant plays a significant role in the dispersion of the gas as leaking CO{sub 2} can impinge upon buildings and other infrastructure. In all scenarios simulated, the region of very high-concentration of CO{sub 2} is limited to a small area around the pipeline failure, suggesting the likelihood of widespread harmful CO{sub 2} exposure to plant personnel from pipeline leakage is low. An additional risk is posed by the blast wave that emanates from a high-pressure pipeline when it is breached quickly. We estimate the blast wave risk as low because it occurs only for a short time in the immediate vicinity of the rupture, and requires an instantaneous large-scale rupture to occur. We recommend consideration of signage and guard rails and posts to mitigate the likelihood of vehicles crashing into the pipeline. A standardized emergency response plan applicable to capture plants within industrial sites could be developed based on the IBDP that would be useful for other capture plants. Finally, we recommend carrying out coupled wellbore-reservoir blowout scenario modeling to understand the potential for hazardous conditions arising from an unexpected blowout at the wellhead.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗