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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SARS-CoV-2 raw wastewater surveillance from student residences on an urban university campus

The COVID-19 pandemic brought about an urgent need to monitor the community prevalence of infection and detect the presence of SARS-CoV-2. Testing individual people is the most reliable method to measure the spread of the virus in any given community, but it is also the most expensive and time-consuming. Wastewater-based epidemiology (WBE) has been used since the 1960s when scientists implemented monitoring to measure the effectiveness of the Polio vaccine. Since then, WBE has been used to monitor populations for various pathogens, drugs, and pollutants. In August 2020, the University of Tennessee-Knoxville implemented a SARS-CoV-2 surveillance program that began with raw wastewater surveillance of the student residence buildings on campus, the results of which were shared with another lab group on campus that oversaw the pooled saliva testing of students. Sample collection began at 8 am, and the final RT-qPCR results were obtained by midnight. The previous day’s results were presented to the campus administrators and the Student Health Center at 8 am the following morning. The buildings surveyed included all campus dormitories, fraternities, and sororities, 46 buildings in all representing an on-campus community of over 8,000 students. The WBE surveillance relied upon early morning “grab” samples and 24-h composite sampling. Because we only had three Hach AS950 Portable Peristaltic Sampler units, we reserved 24-h composite sampling for the dormitories with the highest population of students. Samples were pasteurized, and heavy sediment was centrifuged and filtered out, followed by a virus concentration step before RNA extraction. Each sample was tested by RT-qPCR for the presence of SARS-CoV-2, using the CDC primers for N Capsid targets N1 and N3. The subsequent pooled saliva tests from sections of each building allowed lower costs and minimized the total number of individual verification tests that needed to be analyzed by the Student Health Center. Our WBE results matched the trend of the on-campus cases reported by the student health center. The highest concentration of genomic copies detected in one sample was 5.06 × 10 7 copies/L. Raw wastewater-based epidemiology is an efficient, economical, fast, and non-invasive method to monitor a large community for a single pathogen or multiple pathogen targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluating various composite sampling modes for detecting pathogenic SARS-CoV-2 virus in raw sewage

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

sampling timing↗
DOE OSTI2022

Development of a universal method for high yield of furfural and hydrogen from raw lignocellulosic biomass

In order to develop a universal method for the co-production of furfural and hydrogen from raw lignocellulosic biomass, three types of substrates (maple wood, wheat straw, and tall fescue) were investigated, integrating chemical and electrochemical technologies. The results show both furfural and pure hydrogen were produced from all the biomass substrates, and the substrate type, composition, concentration, and size all affected furfural and hydrogen production. The highest furfural production of 154 mg g -1 substrate was achieved from wheat straw. The highest conversion rate of 67% for the waste furfural residue was realized, and 335 ml pure hydrogen per gram of furfural residue with a Faradaic efficiency of 99.3% was obtained with a new electrolytic cell. A reaction mechanism for hydrogen evolution from furfural residue and a rule for co-production of furfural and hydrogen from raw biomass were proposed. The facts show that a promising universal method for the co-production of furfural and hydrogen from lignocellulosic biomass has been developed.

08 HYDROGEN↗
DOE OSTI2023

Microstructural diversity and digestion yields of select bituminous and subbituminous coals as raw material candidates for carbon fiber precursor production

Here, this work investigates the use of coals as raw materials for carbon fiber precursor production as a new alternative to coal utilization. Coal being a highly complex and heterogeneous material with different macerals and minerals complicates this task. Extensive microstructural characterization as well as preliminary digestion studies are performed on three bituminous coals (Herrin and Springfield from the Illinois Basin and the Blue Gem from the Central Appalachians) and one subbituminous coal from the Powder River Basin (Monarch), as raw material candidates. The subbituminous coal was richer in oxygen, lower in sulfur, and had a higher volatile matter yield. Microstructural investigations were performed using X-ray diffraction, X-ray/neutron computed tomography, scanning electron microscopy with energy dispersive spectroscopy, and petrology. Mild solvent extraction studies were conducted using creosote and decant oil as solvents in microreactors. The extraction yield was sensitive to temperature and time (350 to 450 °C between 30 and 120 mins.) for both creosote and decant oil digestions. While the Blue Gem coal had more desirable microstructural properties with less mineral content and cleaner macerals, it had the lowest coal conversion to quinoline soluble ‘liquid’ (of the bituminous coals). The lower coal conversion yield was hypothesized to be connected to the lack of FeS2 which could act as a catalyst when Fe is liberated from the structure under solvent extraction conditions. The Herrin and Springfield coals revealed similar microstructures and coal conversion efficiencies (higher than Blue Gem). These coals were the most promising candidates for further examination from this first approximations study. The subbituminous Monarch coal, however, was deemed less suitable due to poor coal conversion and less desirable microstructures. Additionally, the presented results with combined microstructural data from multiple length scales established a framework necessary for a first approximations study in this new coal utilization approach.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Raw electron microscopy images for "The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra"

This dataset contains the raw transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images used in the main figures of the paper “The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra (2026).” All the images were acquired at the Materials Research Laboratory, University of Illinois at Urbana-Champaign, by Qian Chen group. 1. We provide five subfolders, each named according to the corresponding figure numbers in the paper. 2. All files in the subfolders for Figures 1–3 and 5 are named as "Panel [letter]_*", where [letter] (e.g., a, b, c) represents the raw images used for the corresponding panels. 3. All files in the subfolder for Figure 4 correspond to panel f and show the configurations of patchy tetrahedra synthesized at varying concentrations of iodide and 2-naphthalenethiol. They are named "Experiment_[number]", where [number] represents the corresponding data points in the phase diagram. 4. In TEM images, the bright and dark regions indicate the polymer patches and nanoparticle cores, respectively. 5. In SEM images, the bright and dark regions indicate the nanoparticle cores and polymer patches, respectively. 6. Abbreviations in file names: HAADF-STEM (high-angle annular dark-field scanning transmission electron microscopy), PINEM (photon-induced near-field electron microscopy), and RCP/LCP (left-/right-handed circularly polarized).

chirality↗
DOE OSTI2026

Raw Lidar and Camera Data Synchronized with Precipitation and Present Weather Data

As part of the sensor characterization task of the SMART 2.0 project, this dataset includes raw data from three spinning lidars ([Ouster OS2-128](https://ouster.com/products/scanning-lidar/os2-sensor/), [Velodyne Puck (VLP-16)](https://velodynelidar.com/products/puck/), and [Velodyne Ultra Puck (VLP-32)](https://velodynelidar.com/products/ultra-puck/)), one camera ([Mako G-319](https://www.alliedvision.com/en/camera-selector/detail/mako/g-319/)), and one present weather sensor ([Vaisala FD-70](https://www.vaisala.com/en/products/weather-environmental-sensors/forward-scatter-fd70)). All data were synchronized, with the log start time indicated in the file name (HHMMSS). The data can be filtered by date, log time (HHMMSS), sensor, frame ID, and weather classification. These data were gathered statically at the Argonne Testbed for Multiscale Observational Science (ATMOS). Two target stop signs were placed in view of the sensors to contribute a target for comparing sensor data under different conditions. The weather data for each day are stored in netCDF “.nc” files. The lidar data contain the X, Y, Z, intensity, reflectivity, and ring from Ouster OS2-128 rev6, Velodyne VLP-16, and Velodyne VLP-32 lidars. ![raw lidar image](LiDAR_pointcloud_ATMOS.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Raw Data published in PeerJ, 2020 for Lower Subalpine needle decomposition field study 2017-2018

This data package contains text files that describe geochemical measurements collected from 2017-2018 during isolated conifer needle decomposition field studies at the Lower Subalpine region of Washington Gulch in Crested Butte, CO. The files within include total carbon and nitrogen (CN) content and fourier-transform infrared spectroscopy (FTIR) results for the initial needles collected in 2016 and after decomposition in October 2018. DI water extractions were conducted with the initial harvested needles in 2016 to determine extractable dissolved organic carbon (DOC), total nitrogen (TN), UV absorbance at 254 nm (UV254), and specific UV absorbance (SUVA). This dataset also includes soil extractable DOC, TN, nitrate, nitrite, ammonium, total organic nitrogen (TON), and pH results from soil collections in 2017. Soil porewater results for DOC, TN, UV254, and SUVA analyses at 15 cm soil depth are also provided from 2017-2018. Gas flux raw data provides CO2, CH4, N2O, and NH3 measurements above needle decomposition over the two study years. Finally, soil samples for microbial DNA extractions were collected from the upper soil depth. This raw data is available in the NCBI SRA database under SRA accession number PRJNA605259. These datasets were generated to investigate the isolated decomposition of spruce and lodgepole conifer needles. The goal of this work was to understand how needle litter impacts terrestrial biogeochemical carbon and nitrogen cycling. Results from this work is detailed in the reference paper "A comparison of lodgepole and spruce needle chemistry impacts on terrestrial biogeochemical processes during isolated needle decomposition. DOI: 10.7717/peerj.9538."

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Raw Data published in PeerJ, 2021 for Crested Butte decomposition field study 2017-2019.

This data package contains text files that describe geochemical measurements collected from 2017-2019 during isolated conifer needle decomposition field studies in Crested Butte, Colorado. The geochemical measurements were collected across three elevations (2,800–3,500 m) ranging from montane to subalpine ecoregions. The data sets within include total carbon and nitrogen content and fourier-transform infrared spectroscopy (FTIR) results for the initial needles collected in 2016 and after decomposition in 2019. Data collection results from August 2020 are also included from when the experimental plots were removed to understand final concentrations of soil extractable carbon and nitrogen content as well as mass balances from litter bag deployments. Soil porewater results are also provided from 2017-2019 for DOC, TN, UV254, and specific UV absorbance (SUVA) analyses at 15 cm soil depth. Gas flux raw data provides CO2, CH4, N2O, and NH3 measurements above needle decomposition over the three study years. Finally, soil samples for microbial DNA extractions were collected from the upper soil depth. This raw data is available in the NCBI SRA database under SRA accession numbers PRJNA605259 and PRJNA715914. These data sets were generated to investigate the isolated decomposition of spruce and lodgepole conifer needles. The goal of this work was to determine the roles of elevation, soil type, seasonal changes in soil moisture, and snowmelt timing on litter decomposition processes. Results from this work are detailed in the reference paper "Effect of elevation, season and accelerated snowmelt on biogeochemical processes during isolated conifer needle litter decomposition. DOI: 10.7717/peerj.11926."

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-June 2024. (DE-SC0021139)

This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data, a row of column descriptors, and units of measurements. some data processing and QA/QC was done to filter out data from sensors that went bad and extreme outliers. CO2 sensors that went bad were replaced with new sensors as soon as possible. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input. This is the second version of this data set and was modified on 10/01/2024. The primary change in the data was the addition of data from the fall of 2022 to June of 2024. In addition, minor QA/QC was done to filter out data from sensors that went bad and extreme outliers. THe filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. This is now the third version of the data set, and was modified 03/25/2026. The primary change in the data was the addition of data from the June of 2024 to December 2025. Further r QA/QC was done with the new data to filter out bad data from faulty sensors and extreme outliers. The filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. ##This additional data was funded under DE-SC0024218( Responses of Plant and Microbial Respiration Sources to Changing Cold Season Climate Drivers in the East River Watershed)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Soil temperature and soil moisture raw data, permafrost table depths, and accompanying environmental variable data, Kenai Wildlife Refuge, 2019-2022

Data package purpose: This data package was created to contain all data used in an upcoming article, "Canopy Cover and Microtopography Control Precipitation-Enhanced Thaw of Ecosystem-Protected Permafrost." In review.This data package includes: Raw output from 19 distributed temperature profilers with a thermistor every 10 cm along a 160 cm length at a measurement interval of 15 minutes (.CSV). Raw output from two soil moisture and temperature profilers (90 cm length and 120 cm length) that took composite soil moisture readings every 15 cm along the sensor length at a measurement interval of 30 minutes (.CSV). Permafrost depths were measured annually in mid-September at DTP sensor locations (.CSV) and along an across-site transect (.CSV). Environmental variables (snow depth, canopy closure, moss depth, and elevation) for all sensor locations. Real-time kinetic (RTK) GPS points showing site microtopography (.CSV).Analysis software: Our analysis was done in Matlab. File types can be used with any software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Leaf gas exchange raw data, 2015 - 2017, at Manaus, Brazil

This data package contains raw leaf gas exchange data from the NGEE Tropics K34 tower site on a plateau near Manaus, Brazil. Data was collected using sensor and leaf sample measurements. Leaves on a target tree were chosen by accessing a branch near the tower. In some cases, the branch was naturally positioned near the tower and in other cases, a small rope was used to pull the branch closer to the tower for better access to leaves. Mature leaves were targeted in nearly all cases. The gas exchange system was hauled with a climbing rope to the level of the branch on the tower and the sensor head/leaf enclosure was installed near the branch of interest using a magic arm. We measured leaf gas exchange from 5:00 AM to 5:00 PM, randomly cycling through the approximately 20 leaves in the targeted branch(es). Data were collected over two time periods, detailed within the files. The attached data files contain raw data, sensor manuals, and PDFs with detailed methods and procedures separated into multiple folders. Data is in Excel file format. See data references for data package containing related metadata. This dataset replaces the leaf gas exchange data of two retired packages, NGT0019 and NGT0040.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Raw leaf gas exchange data in the Amazon basin, 2014-2016

This data package contains raw leaf gas exchange data used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. Data was collected from 162 individuals, distributed among 25 botanical families and 113 species, from four sites in the Amazon. The data was collected using a modified portable photosynthesis system for environmental control coupled to a thermal desorption tube for volatile isoprenoid collections. Collected TD tube samples were analysed in Manaus, Brazil, using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The dataset includes both photosynthesis and isoprene/monoterpene emission rates from leaves under standard environmental conditions as well as controlled light response curves. For detailed methods and raw data, refer to the publication and related material cited in Dataset References.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Raw leaf isoprene and monoterpene emission GC-MS chromatograms/calibrations for MassHunter software, Brazil, 2014-2016

This data package contains raw gas chromatography–mass spectrometry (GC-MS) data for isoprene/monoterpene emission rates used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. The attached data files require use of the Agilent MassHunter software. Alternatively, data has been exported into Excel files and can be accessed at [DOI]. For further information on how to Raw isoprene and monoterpene emission rates data

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Raw leaf temperature data from May 2017- Aug 2017 at the B34 and K34 towers in Manaus, Brazil

This data package contains raw data from leaf temperature measurements. Additional metadata, such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s) along with all related data packages. Leaf temperature measurements were made using Teflon insulated type T thermocouples (OM-CP-OCTTEMP-A Nomad®, Omega Engineering) directly attached to the abaxial side of the leaf using a breathable white tape and configured to register measurements every 15 s. No data processing or QA/QC was done on the raw datasets. Processed datasets will be uploaded separately. Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Air temperature and relative humidity raw data from June 2017-Oct 2018 at the B34 tower in Manaus, Brazil

This data package contains raw data from stomatal conductance and leaf temperature measurements from the B34 tower at the NGEE Tropics site in Manaus, Brazil. Related data packages, including additional metadata such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s). Air temperature and relative humidity data were obtained using a thermohygrometer (HC2S3, Campbell Scientific®) installed in tower K34 making a profile of (HR_: 7m, HR_2: 14m, HR_3: 21m). Data files are included in CSV and Excel file formats. No data processing or QA/QC was done on the raw datasets. Processed datasets will be uploaded separately. Contact israelmdt@gmail.com if you need to use this dataset for additional information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AWAKEN Site A5 - ARM Scanning Lidar (Halo XR+ #236) / Raw Data

These data include raw scanning Doppler lidar measurements from the deployment of the ARM HALO XR+ (s/n 236) at the A5 site. The raw measurements include uncalibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate.

17 WIND ENERGY↗