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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NEWTS Integrated Dataset (version 1.0)

The National Energy Water Treatment and Speciation (NEWTS) Integrated Dataset v1.0 provides water researchers, community leaders, and regulators with a unified and standardized energy-related wastewater stream database. This resource is derived from 27 state and federal entities, and scientific publications, and contains more than 400,000 sample records, many of which also provide geospatial information. The dataset includes data for several different energy-related wastewater types including produced water, other oil and gas wastewaters, mine drainage, coal ash leachate, and power plant wastewater. The NEWTS Integrated Dataset was built to support environmentally prudent decision-making, explore treatment opportunities, and identify potential critical mineral sources. A subset of this novel resource is also featured on NETL NEWTS State-Level Database Dashboard. Additional data can be found in the NEWTS EDX Group and the NEWTS Federal Database Dashboard.

abandoned mine drainage↗
DOE OSTI2025

CO2 Storage Economic Analysis: CarbonSAFE Use Case

Poster on “CO2 Storage Economic Analysis: CarbonSAFE Use Case” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The cost of designing, permitting, constructing, operating, and closing a CO2 storage project is of vital importance to project developers. The National Energy Technology Laboratory has developed the NRAP/SMART Technoeconomic and Liability Evaluation for Storage (TALES) Model to provide quantitative cost-based insights to support developers planning CO2 injection and storage projects. This study presents a collaborative economic analysis applying TALES with data from the San Juan Basin CarbonSAFE Phase III project led by the New Mexico Institute of Mining and Technology to estimate potential costs incurred during the implementation of a real-world commercial-scale carbon storage project. Scenario analysis was implemented in which different operational and cost attributes were varied and the associated cost implications observed. Key results data and project cost summary metrics, first-year breakeven price of CO2 ($/tonne) and net present value (NPV), are presented for base and alternative cases. Output provides a unique perspective for project stakeholders towards evaluating the influence of different operational strategies and financing approaches on overall project cost and financial viability.

carbon storage↗
DOE OSTI2026

BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters

Microbial metabolic gene clusters encode the biosynthesis or catabolism of metabolites that facilitate ecological specialization, mediate microbiome interactions and constitute a major source of medicines and crop protection agents. Here, we present BiG-SCAPE and BiG-SLiCE 2.0, next-generation methods that facilitate scalable, accurate and interactive gene cluster analyses. BiG-SCAPE 2.0 updates its classification, alignment methods, and visualizations, enabling more accurate analysis, up to 8x faster runtimes and halved memory requirements. BiG-SLiCE 2.0 updates its distance metric, pHMM database, and classification logic, resulting in increased sensitivity nearing that of BiG-SCAPE. Analysis of 260,630 biosynthetic gene clusters from publicly available genomes reveals that both tools generate concurring estimates of gene cluster diversity, thus providing significantly extended methodological support for recent evidence indicating that the vast majority of natural product diversity remains unexplored. Together, these updates will facilitate global genome mining efforts for natural product discovery and microbiome analyses scalable with current data sizes.

Draisma, Arjan [Wageningen University & Research (↗
DOE OSTI2025

MINE: a new way to design genetics experiments for discovery

Abstract The Maximally Informative Next Experiment or MINE is a new experimental design approach for experiments, such as those in omics, in which the number of effects or parameters p greatly exceeds the number of samples n (p > n). Classical experimental design presumes n > p for inference about parameters and its application to p > n can lead to over-fitting. To overcome p > n, MINE is an ensemble method, which makes predictions about future experiments from an existing ensemble of models consistent with available data in order to select the most informative next experiment. Its advantages are in exploration of the data for new relationships with n < p and being able to integrate smaller and more tractable experiments to replace adaptively one large classic experiment as discoveries are made. Thus, using MINE is model-guided and adaptive over time in a large omics study. Here, MINE is illustrated in two distinct multiyear experiments, one involving genetic networks in Neurospora crassa and a second one involving a genome-wide association study in Sorghum bicolor as a comparison to classic experimental design in an agricultural setting.

Biochemistry & Molecular Biology↗
NASA NTRS1975

The application of remote sensing technology to the solution of problems in the management of resources in Indiana

The use of satellite remote sensing for resources management was investigated in Indiana. The technique was applied to strip mining and reclamation, highway planning, and the detection of dolomite reefs. A data base was created and used to produce land characteristics and suitability maps for land use planning. In addition, a three dimensional model was developed which provides a cross-sectional profile of the thermal plumes emitted by point sources of thermal pollution into rivers and lakes; this model may be used for the design and site selection of electric power plants.

Landgrebe, D. A.↗
NASA NTRS1974

Multidisciplinary applications of ERTS and Skylab data in Ohio

Experimental studies of ERTS-1 and Skylab earth resources data, in combination with correlative aircraft and on-site data, for environmental quality, land use, and resource management applications in Ohio show several areas of operational promise. Prime data use candidates demonstrated to date include definition and enforcement of surface mining (all minerals) legislation; Lake Erie modeling/management; land use classification and mapping studies at state, regional, and localized levels; and resources' inventories particularly of forested areas on both regional (multicounty) and localized scales.

Sweet, D. C.↗
NASA NTRS1982

Assessment of a 40-kilowatt stirling engine for underground mining applications

An assessment of alternative power souces for underground mining applications was performed. A 40-kW Stirling research engine was tested to evaluate its performance and emission characteristics when operated with helium working gas and diesel fuel. The engine, the test facility, and the test procedures are described. Performance and emission data for the engine operating with helium working gas and diesel fuel are reported and compared with data obtained with hydrogen working gas and unleaded gasoline fuel. Helium diesel test results are compared with the characteristics of current diesel engines and other Stirling engines. External surface temperature data are also presented. Emission and temperature results are compared with the Federal requirements for diesel underground mine engines. The durability potential of Stirling engines is discussed on the basis of the experience gaind during the engine tests.

Cairelli, J. E.↗
NASA NTRS2019

MERRA-2 Data and Analytic Services at NASA GES DISC for Climate Extremes Study

NASA's climate reanalysis datasets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) contains numerous long-term atmosphere, land, and ocean data products from 1980-present. MERRA-2 datasets, such as precipitation, soil moisture, and temperature, have been used widely to study extreme events. The native archived MERRA-2 data files are day-file (hourly time interval) and month-file, containing up to 125 parameters in one file. Due to the large number of data files and volumes, it is challenging for users, especially the applications research community, to handle the original hourly data files for long time periods to analyze extreme events. In this presentation, we review MERRA-2 data for studies of extreme conditions, and demonstrate analytic services at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). One of the current operational services, 'subsetter', allows users to download only specific data of interest, i.e. data selected by parameter, region, and time period. New services are under development that will provide more 'on-the-fly' statistical calculations when downloading data; improve efficiency when accessing long time-series data. We will provide additional "How-to" resources that include step-by-step instructions on data access and usage. We have tested restructuring of day-files in an optimized data cube, which has significantly improved system performance for accessing long time-series. Overall performance is associated with cube size and structure, data compression method, and how the data are accessed. The optimized data cube structure will enable better online analytic services for statistical analysis and extreme events mining. To demonstrate the service, we use an extreme drought associated with the anomalous 2016 monsoon over southern Asia. This prototype time-series service may be augmented in the cloud infrastructure in the future.

data access↗
DOE OSTI2024

Enhanced Resistance Pines for Improved Renewable Biofuel and Chemical Production (Technical Report)

We completed phenotyping constitutive and inducible oleoresin flow across two seasons, constitutive resin canal number and density and wood terpene content in our ADEPT2 and CCLONES populations. We completed genetic association between 19 oleoresin phenotypes and a total of 523,192 SNP markers from ADEPT2 and 13,883 SNP markers in CCLONES using four mixed linear models. A total of 293 significant SNPs (FDR = 0.20) were identified. We used the MENTOR tool to mine mechanistic connections from a multiplex network constructed from poplar multi-omic data to construct a conceptual model for a subset of these significant SNPs. Our model contains 6 transcriptional regulators in addition to 3 monoterpene synthases. To generate more lines of evidence for these significant SNPs, we completed a time course RNAseq experiment after inducing vascular zone cells to differentiate into new resin canals with a methyl jasmonate treatment, a single nuclei RNAseq that identified differentiating resin canal epithelial cells and are completing analysis for a QTL study in a hybrid pine population. The time course identified 4634 significantly down and 1890 significantly up regulated transcripts after treatment with methyl jasmonate, an inducer of new resin canal formation in the vascular cambial meristem. To analyze this large set of differentially regulated genes, we created a predictive expression network and analyzed it with random walk restart using 6 seed genes coding for transcription factors regulating xylem differentiation in poplar. Of the top ranked 200 transcripts, 119 transcripts were significant differentially expressed supporting these transcripts as potential candidates regulating resin canal formation. Analysis of single nuclei sequencing of shoot tips that contain differentiating resin canals, identified 10 clusters. One cluster was highly enriched in transcripts coding for 9 of the enzymes in the MEP pathway 3 prenyl synthetases, and 3 monoterpene synthases strongly suggesting that this cluster represents resin canal epithelial cells. We are mining the additional transcripts to create a trajectory analysis. In summary, we have identified > 10 novel genes that are strongly supported candidates for further analysis in breeding lines and for genetic engineering over- and under- expressing lines to increase wood terpene content to improve resistance to insect and fungal pathogens while simultaneously increasing terpene supplies for renewable chemicals and biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

NEWTS Integrated Dataset (version 2.0)

The National Energy Water Treatment and Speciation (NEWTS) Integrated Dataset v2.0 provides water researchers, community leaders, regulators, and industry stakeholders with a unified and standardized energy-process wastewater chemistry database. This resource is derived from 39 state and federal entities, and scientific publications, and contains more than 700,000 sample records, many of which also provide geospatial information. The dataset includes chemistry data for several different energy-process wastewater types including produced water, other oil and gas wastewaters, mine drainage, coal ash leachate, power plant wastewater, and geothermal fluids. The NEWTS Integrated Dataset was built to support prudent decision-making, characterization of potential critical mineral sources, and modeling of treatment and valorization options. A subset of this novel resource is also featured on the NEWTS State-Level Database Dashboard. Additional data can be found in the NEWTS EDX Group and the NEWTS Federal Database Dashboard.

AMD↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Data Quality Methodology (4.2.1)

SECARB-USA Deliverable 4.2.1 utilizes the outputs from the Needs Assessment (Subtask 2.1) to develop a data quality methodology. Many types of data are needed to evaluate a site for technical and financial viability. Several inventories of data types have been produced (for example NETL, 2010). The objective here is to organize the data types so that the needs met are specified. From this cross index (Table 1 in the Appendix), it will be possible in future tasks to (1) determine, on a site-specific basis, how much of each data type is required at each stage of a project to meet the need, and conversely (2) to further specify and define the data collection methods applied such that the data are tailored to fit that need. Derivative tables can then be developed to semi-quantitively evaluate the extent to which need is critical for early go/no decision points, or if it is more important than average, requiring faster or larger capitalization and spend to meet the need. In an additional step, the current availability of data for a site can be semi-quantitively assessed. From the table of data criticality and the table of data availability, site-specific cost for meeting the data needs can be determined, and allow a pre-permit spend estimated for a portfolio of projects. All of the data needs to evaluate a site are somewhat interconnected. We used criterion (2) above to determine if the data collection design would have to be modified to meet the need; if this was commonly true a linkage was shown. The application of this cross index to sites in the subsequent tasks will demonstrate that the demand for data types varies site-to-site and project-to-project. Examples of factors to be considered are the complexity of the geology, the injection goals such as rate and duration of the injection, and the types of risk and risk tolerance of key stakeholders. In future tasks the team will compare the demand for data with existing data availability. This will, in turn, determine when data needs to be acquired to support project development. For example, in a project area with complex structure, 3-D seismic data may be needed earlier and more urgently than in an area with simple rock body geometries. In some locations, a 3-D seismic survey has already been collected and can be purchased. In other locations the project developer will need to collect these data. For another example, a project near an urban area or near to a park may generate earlier and more substantive public concern than a site that is developed in mined lands. A calculation using the derivative from table 1 will show the different investment needs. Project cost will vary corresponding to data criticality and data availability.

42 ENGINEERING↗
DOE OSTI2020

Nevada Higher Education Benefits from US Department of Energy (DOE) Environmental Management (EM) Nevada Program's Transfer of Geologic Samples - 20442

The Nevada National Security Site (NNSS), formerly the Nevada Test Site, was the location of 100 historic atmospheric and 828 historic underground nuclear tests from 1951 to 1992. Related to this historic nuclear testing, geologic and hydrologic studies of the site were conducted utilizing the skills and the expertise of the U.S. Geological Survey (USGS) and national laboratories (Los Alamos National Laboratory, Lawrence Livermore National Laboratory), who were principal in leading the development of weapons and were responsible for specific underground testing programs. The subsurface samples and technical/scientific data associated with NNSS geologic studies (past and present) are preserved and stored at the USGS Mercury Core Library and Data Center located at the NNSS. Currently, the facility stores over 2,000,000 linear feet of cores and cuttings from more than 2,600 drill holes that can be accessed for study. Most of the samples and historic geologic work was focused directly on subsurface geologic settings that relate directly to historic underground nuclear testing. Since 1992, the United States has observed a unilateral moratorium on full-scale nuclear testing, and the U.S. Department of Energy (DOE) Environmental Management (EM) Nevada Program is now responsible for hydrogeologic characterization of the potential impacts to the natural groundwater systems that may have resulted from historic underground nuclear tests. As part of this effort, the EM Nevada Program Underground Test Area (UGTA) Activity has drilled and completed over 58 deep (2,000 - 7,000 ft.) characterization wells, totaling in excess of 170,000 linear feet of cuttings and core samples. Cutting samples were collected as triplicate samples for each respective depth interval, to account for potential later nondestructive/destructive analysis and to preserve samples for regulatory purposes. Recently, it was recognized by the EM Nevada Program that opportunities may exist to reduce the cost and floor space required for the storage of geologic samples at the Mercury Core Library without impacting the integrity and representative nature of the samples necessary for project execution. An initiative was sponsored by EM Nevada Program to evaluate several options: 1) disposal of a portion of cuttings and cores in a land fill setting; 2) reduction in sample volume through skeletonizing core and cuttings from wells to a representative but much smaller number of samples; and 3) solicit potential interest in the academic community where other geoscientists could freely access the samples for studies. The Nevada state university system through the Nevada Bureau of Mines and Geology responded positively to the opportunity to receive these NNSS samples from the EM Nevada Program. In June 2019, approximately 17,000 geologic samples, representing greater than 170,000 linear feet of drilling and weighing over 20,000 pounds, were shipped from the NNSS to the Great Basin Science Sample and Records Library located in Reno, Nevada. The benefits resulting from this transfer were realized by the EM Nevada Program in terms of cost and space savings for geologic sample storage at the NNSS. The transfer did not impact the EM Nevada Program mission as access to representative geologic samples for regulatory and scientific purposes was preserved. Moreover, the Nevada Bureau of Mines and Geology acquired a significant resource of subsurface geologic samples and supporting technical data to support academic and scientific studies in a complex volcanic setting in southwest Nevada. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Impact of neutron irradiation on the thermophysical properties of additively manufactured stainless steel and inconel

The deployment of additive manufacturing (AM) technologies to support the nuclear energy industry is limited by the current lack of data related to the irradiation performance of AM parts. The Colorado School of Mines is conducting a multi-year irradiation and post irradiation examination project to study the impacts of neutron irradiation on stainless steel 316L and Inconel 718 and 625 specimens produced by several commercially available AM techniques and irradiated in the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL). This paper presents initial results on the impact of neutron irradiation on the heat capacity, the thermal diffusivity, and the thermal conductivity of the specimens at temperatures between 100 oC and 400 oC. The unirradiated AM specimens have heat capacity values similar to, or lower than, the reference values, regardless of manufacturing method or material. The irradiated AM specimens have average heat capacities greater than the unirradiated specimens and reference values in every case. Additionally, the thermal diffusivities of the unirradiated AM specimens are either larger or statistically similar to the reference values. The irradiated AM specimens exhibit lower thermal diffusivities compared to the reference thermal diffusivity values. One of the stainless steel 316L laser powder bed fusion samples exhibited thermal diffusivity values with statistically significant anisotropy prior to and after irradiation. The thermal conductivities of the unirradiated AM stainless steel 316L specimens and the unirradiated Inconel 718 specimens are lower or statistically similar to the reference values. The unirradiated Inconel 625 specimens exhibit slightly higher thermal conductivity values than the reference values. The irradiated specimens show a significant increase in thermal conductivity compared to the unirradiated specimens and the reference values for all materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Sequestering Rare Earth Elements and Precious Metals from Seawater Using a Highly Efficient Polymer Adsorbent Derived from Acrylic Fiber

An amidoxime and carboxylate containing polymer adsorbent derived from acrylic fiber has shown extremely high efficiencies for extracting critical materials and precious metals from seawater. Among 50 extractable elements, the lanthanides, cobalt, and palladium were ranked near the top with KD values in the order of 107, about an order of magnitude higher than that of uranium. The KD value of the lanthanides increased linearly with the atomic number indicating charge density is a factor controlling trivalent lanthanide extractability in seawater. The data given in this report provides crucial information regarding the strategies of ocean mining of critical materials and precious metals.

36 MATERIALS SCIENCE↗
NASA NTRS1976

Further laboratory study of the diffuse reflectance spectra of frosts occurring on astronomical objects

Oligoclase and bloedite, two mined samples, have been investigated, and the diffuse reflectance spectra are presented. These data are for powdered material, 50 microns to 5 microns size mixture, cooled to 160 K. The reflectivity of the oligoclase sample was also measured at room temperature, about 290 K, and the results at these two temperatures do indicate some tentative differences. A frost of ordinary water was prepared and its spectral reflectance is presented. This result compares reasonably well with measurements made by other investigators.

Glaser, F. M.↗
NASA NTRS1975

Interdisciplinary applications and interpretations of ERTS data within the Susquehanna River basin

The author has identified the following significant results. The full potential of high quality data is achieved only with the application of efficient and effective interpretation techniques. An excellent operating system for handling, processing, and interpreting ERTS-1 and other MSS data was achieved. Programs for processing digital data are implemented on a large nondedicated general purpose computer. Significant results were attained in mapping land use, agricultural croplands, forest resources, and vegetative cover. Categories of land use classified and mapped depend upon the geographic location, the detail required, and the types of lands use of interest. Physiographic and structural provinces are spectacularly displayed on ERTS-1 MSS image mosaics. Geologic bedrock structures show up well and formation contacts can sometimes be traced for hundreds of kilometers. Large circular structures and regional features, previously obscured by the detail of higher resolution data, can be seen. Environmental monitoring was performed in three areas: coal strip mining, coal refuse problems, and damage to vegetation caused by insects and pollution.

Mcmurtry, G. J.↗