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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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NASA NTRS1993

Microscale characterization of dissolved organic matter production and uptake in marine microbial mat communities

Intertidal marine microbial mats exhibited biologically mediated uptake of low molecular weight dissolved organic matter (DOM), including D-glucose, acetate, and an L-amino acid mixture at trace concentrations. Uptake of all compounds occurred in darkness, but was frequently enhanced under natural illumination. The photosystem 2 inhibitor, 3-(3,4-dichlorophenyl)-1,1-dimethyl urea (DCMU) generally failed to inhibit light-stimulated DOM uptake. Occasionally, light plus DCMU-amended treatments led to uptake rates higher than light-incubated samples, possibly due to phototrophic bacteria present in subsurface anoxic layers. Uptake was similar with either 3H- or 14C-labeled substrates, indicating that recycling of labeled CO2 via photosynthetic fixation was not interfering with measurements of light-stimulated DOM uptake. Microautoradiographs showed a variety of pigmented and nonpigmented bacteria and, to a lesser extent, cyanobacteria and eucaryotic microalgae involved in light-mediated DOM uptake. Light-stimulated DOM uptake was often observed in bacteria associated with sheaths and mucilage surrounding filamentous cyanobacteria, revealing a close association of organisms taking up DOM with photoautotrophic members of the mat community. The capacity for dark- and light-mediated heterotrophy, coupled to efficient retention of fixed carbon in the mat community, may help optimize net production and accretion of mats, even in oligotrophic waters.

NASA Discipline Exobiology↗

DOE OSTI2024

Community Geothermal: Soil Conductivity, Borehole Design, Energy Models, and Load Data for a Residential System Development - Hinesburg, VT

This dataset contains materials from the Coalition for Community-Supported Affordable Geothermal Energy Systems (C2SAGES) project, which evaluated the techno-economic feasibility of a community geothermal system for a residential development in Hinesburg, VT. The dataset includes detailed soil conductivity test reports, energy models, borehole design reports, hourly energy loads for heating, cooling, and hot water, and design layouts. EnergyPlus was used to model building energy loads, and Modelica software was applied for geothermal loop sizing based on these loads and soil conductivity results. Python scripts for network design further refined the models. Key files include PDF reports on borehole design (with projections for 1-year, 15-year, and 30-year systems), soil conductivity test results, EnergyPlus modeling outputs, and 2D/3D design drawings in PDF, DWG, and DXF formats. Python notebooks for network design and OnePipe model files are also provided, with Modelica required for viewing certain files. Outputs and modeling data are in various formats including CSV, JPG, HTML, and IDF, with units and data clearly labeled to support understanding of system design and performance for the proposed geothermal solution.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Advances and Challenges in Fluorescence in situ Hybridization for Visualizing Fungal Endobacteria

We report that Several bacteria have long been known to interact intimately with fungi, but molecular approaches have only recently uncovered how cosmopolitan these interactions are in nature. Currently, bacterial–fungal interactions (BFI) are inferred based on patterns of co-occurrence in amplicon sequencing investigations. However, determining the nature of these interactions, whether the bacteria are internally or externally associated, remains a grand challenge in BFI research. Fluorescence in situhybridization (FISH) is a robust method that targets unique sequences of interest which can be employed for visualizing intra-hyphal targets, such as mitochondrial organelles or, as in this study, bacteria. We evaluate the challenges and employable strategies to resolve intra-hyphal BFI to address pertinent criteria in BFI research, such as culturing media, spatial distribution of bacteria, and abundance of bacterial 16S rRNA copies for fluorescent labeling. While these experimental factors influence labeling and detection of endobacteria, we demonstrate how to overcome these challenges thorough permeabilization, appropriate media choice, and targeted amplification using hybridization chain reaction FISH. Such microscopy imaging approaches can now be utilized by the broader research community to complement sequence-based investigations and provide more conclusive evidence on the nature of specific bacterial–fungal relationships.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Vacuum Attachment for XRF Scanner

Vacuum apparatuses have been developed for increasing the range of elements that can be identified by use of x-ray fluorescent (XRF) scanners of the type mentioned in the two immediately preceding articles. As a consequence of the underlying physical principles, in the presence of air, such an XRF scanner is limited to analysis of chlorine and elements of greater atomic number. When the XRF scanner is operated in a vacuum, it extends the range of analysis to lower atomic numbers - even as far as aluminum and sodium. Hence, more elements will be available for use in XRF labeling of objects as discussed in the two preceding articles. The added benefits of the extended capabilities also have other uses for NASA. Detection of elements of low atomic number is of high interest to the aerospace community. High-strength aluminum alloys will be easily analyzed for composition. Silicon, a major contaminant in certain processes, will be detectable before the process is begun, possibly eliminating weld or adhesion problems. Exotic alloys will be evaluated for composition prior to being placed in service where lives depend on them. And in the less glamorous applications, such as bolts and fasteners, substandard products and counterfeit items will be evaluated at the receiving function and never allowed to enter the operation

Schramm, Harry F.↗

DOE OSTI2021

Soil minerals affect taxon-specific bacterial growth

Abstract Secondary minerals (clays and metal oxides) are important components of the soil matrix. Clay minerals affect soil carbon persistence and cycling, and they also select for distinct microbial communities. Here we show that soil mineral assemblages—particularly short-range order minerals—affect both bacterial community composition and taxon-specific growth. Three soils with different parent material and presence of short-range order minerals were collected from ecosystems with similar vegetation and climate. These three soils were provided with 18O-labeled water and incubated with or without artificial root exudates or pine needle litter. Quantitative stable isotope probing was used to determine taxon-specific growth. We found that the growth of bacteria varied among soils of different mineral assemblages but found the trend of growth suppression in the presence of short-range order minerals. Relative growth of bacteria declined with increasing concentration of short-range order minerals between 25–36% of taxa present in all soils. Carbon addition in the form of plant litter or root exudates weakly affected relative growth of taxa (p = 0.09) compared to the soil type (p < 0.01). However, both exudate and litter carbon stimulated growth for at least 34% of families in the soils with the most and least short-range order minerals. In the intermediate short-range order soil, fresh carbon reduced growth for more bacterial families than were stimulated. These results highlight how bacterial-mineral-substrate interactions are critical to soil organic carbon processing, and how growth variation in bacterial taxa in these interactions may contribute to soil carbon persistence and loss.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Metagenomic stable isotope probing reveals bacteriophage participation in soil carbon cycling

Soil viruses are important components of the carbon (C) cycle, yet we still know little about viral ecology in soils. We added diverse 13 C-labelled carbon sources to soil and we used metagenomic-SIP to detect 13 C assimilation by viruses and their putative bacterial hosts. These data allowed us to link a 13 C-labelled bacteriophage to its 13 C-labelled Streptomyces putative host, and we used qPCR to track the dynamics of the putative host and phage in response to C inputs. Following C addition, putative host numbers increased rapidly for 3 days, and then more gradually, reaching maximal abundance on Day 6. Viral abundance and virus:host ratio increased dramatically over 6 days, and remained high thereafter (8.42 ± 2.94). From Days 6 to 30, virus:host ratio remained high, while putative host numbers declined more than 50%. Putative host populations were 13 C-labelled on Days 3–30, while 13 C-labelling of phage was detected on Days 14 and 30. This dynamic suggests rapid growth and 13 C-labelling of the host fueled by new C inputs, followed by extensive host mortality driven by phage lysis. Furthermore, these findings indicate that the viral shunt promotes microbial turnover in soil following new C inputs, thereby altering microbial community dynamics, and facilitating soil organic matter production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Sensor fusion of pyrometry and acoustic measurements for localized keyhole pore identification in laser powder bed fusion

We report in-situ process monitoring as an aid to part qualification for laser powder-bed fusion (L-PBF) technology is a topic of increasing interest to the additive manufacturing community. In this work, airborne acoustic and inline pyrometry measurements were recorded simultaneously with the laser position. X-ray radiography imaging was used to spatio-temporally register keyhole pore locations to the pyrometry and acoustic signals, enabling binary labeling of data partitions based on pore formation. These labeled partitions were subsequently featurized using a highly comparative time-series analysis toolbox. Acoustic data was found to be much more effective than the pyrometry data for keyhole pore identification. However, when the information contained in both sensing modalities was combined in a sensor fusion strategy, the error rates of the top performing models were significantly reduced.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Freewheeling: What Six Locations, 61,000 Trips, and 242,000 Miles in Colorado Reveal about How E-Bikes Improve Mobility Options

Personal micromobility modes such as bicycles, e-bikes and scooters offer low- or zero-emission transportation alternatives to single occupancy vehicles (SOVs). However, the lack of supporting data has led to a dearth of data-driven research on the usage of personally owned e-bikes, including variations due to weather, geography and demographics. In this paper, we present an overview of the longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use rates across different demographics, trip characteristics, and geographies. The CanBikeCO program recorded travel survey data from late July 2021 to December 31, 2022, from low-income Colorado households who were provided with e-bikes for personal use by the Colorado Energy Office (CEO). This data was collected in six different communities across Colorado following the mini-pilot program that was conducted in Fall 2020. To collect data for the survey, the program used the NREL OpenPATH application, which combines passive data collection with semantic information such as trip mode and purpose labels. To the best of our knowledge, there is no prior travel survey data on personally owned e-bikes with this range and scope. This unique dataset yielded several insights. One is that commute trips among participants had nearly 17% higher shares of e-bikes than all trips combined. E-bikes were stated to most often replace cars (34% of e-bike trips) and personal micromobility (22%). Participants favored walking for trips less than 1 mile, e-bikes for trips 1-3 miles, and e-bikes, cars or shared rides for trips 3-20 miles. Seasonality accounted for a 10% decrease and subsequent recovery in e-bike mileage on a per user basis. E-bikes are also appealing across age groups, even among older individuals, and see decreased utilization similar to regular bikes or walking during winter months. We also find that e-bike use may be related to characteristics of land use and urban form, occupation and income as well as household car ownership. We conclude that, for this population, who are mainly part of low-income households, the emissions added by the use of e-bikes (in the case of replacement of non-motorized modes) are outweighed by the strong single occupancy vehicle (SOV) travel replacement. As a whole, our findings suggest a considerable potential for energy savings and emissions reductions from personal e-bike ownership.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Detecting damaged buildings using real-time crowdsourced images and transfer learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.

58 GEOSCIENCES↗

DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2016

Team Update on North American Proton Facilities for Radiation Testing

In the wake of the closure of the Indiana University Cyclotron Facility (IUCF), this presentation provides an overview of the options for North American proton facilities. This includes those in use by the aerospace community as well as new additions from the cancer therapy regime. In addition, proton single event testing background is provided for understanding the criteria needed for these facilities for electronics testing.

Single event effects testing↗

NASA NTRS2016

Team Update on North American Proton Facilities for Radiation Testing

Proton cyclotrons↗

NASA NTRS2016

Team Update on North American Proton Facilities for Radiation Testing

Single event effects testing↗

NASA NTRS2016

Team Update on North American Proton Facilities for Radiation Testing

Cancer therapy↗

DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗

DOE OSTI2024

Deep unsupervised learning using spike-timing-dependent plasticity

Abstract Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k -means clustering approach.

Lu, Sen↗