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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Search for Rare Processes: Mu2e and KOTO

The Standard Model (SM) of particle physics has been very successful in describing the fundamental forces, the properties of elementary particles, and predicting particles prior to their discovery, for example the top quark and the Higgs boson. Yet the SM does not explain all phenomena, and discoveries of physics processes beyond the SM will allow progress in our understanding of the fundamental nature of matter and forces. The search for rare processes is one of the paths to look for new physics. Two of these searches are for muon to electron conversion and the decay $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$. Our main effort was in the Mu2e experiment under construction at Fermilab, which has the goal of searching for the neutrinoless conversion of a muon into an electron at a single event sensitivity of 2.87 x 10-17 and, in the process, provide evidence of physics processes beyond the Standard Model. We developed the firmware to be used in the Mu2e tracker readout, assisted in the construction and operation of the Mu2e Vertical Slice Test, and provided input to studies of cosmic event reconstructions in Mu2e using simulated data. Under this reward we also concluded our effort in two KOTO experiment in J-PARC, Japan and set a limit on the rate of the rare decay, $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$ using the data collected in the 2016-2018 period. We also maintained and operated the DAQ system used for data collection thru 2021.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Optimization and stabilization of Fermilab Booster using hybrid Bayesian/RL framework

PIPII project will raise Fermilab Booster intensity and ramp rate. Beam losses will limit average power and are hard to simulate. Presently, Booster uses operator-guided empirical tuning. This task is challenging due to high dimensionality, multiple objectives, critical safety constraints, and drifts. We developed a synergistic suite of Bayesian optimization (BO) and reinforcement learning (RL) tools to optimize and stabilize beam losses. First, active learning was used to build a rough model. Data was collected parasitically using two novel safety constraint types – nonlinear input space restrictions (based on optics model), and uncertainty constraints (to stop bad steps/beam aborts). We then applied online multi-objective BO with scalarized objectives and fitting to improve/rebalance losses, increasing safety margins by 25%. Using BO model as a safety veto, we tried several on/off-policy RL agents for long term stabilization; SAC had best performance. We found that adding contextual (state) information further improved performance, eventually integrating key knobs like linac phase and temperature into the parameter space. Long term testing is ongoing to enable operational use.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2021

Evaluation of Energy Efficiency Performance of Heated Windows

Fenestration systems are widely used across the world. There is expansive research on window configurations, frames, and glazing technology, but not enough research has been published on reducing window heat loss through heat application to a pane. The presented study attempted to evaluate the performance of heated windows by developing an experimental setup to test a window at various temperatures by varying the power input to the window. Heated double pane window was installed in an insulated box. A temperature gradient was developed across the window by cooling one side of the window using gel-based ice packs. The other face of the window was heated by enabling power at different wattages through the window. The temperature of the inside and outside panes, current and voltage input, and temperature of the room and box were recorded. The data was used to calculate the apparent effective resistance of the window when not being heated vs. when being heated. The study concluded that, when window temperature was maintained close to the room temperature, the heated double pane window is effective in reducing heat loss by as much as 50% as compared to a non-heated double pane window. When temperature of the window was much higher than the room temperature, the heat loss through the window increased beyond that of a non-heated window. The issues encountered during the current stages of experiments are noted, and recommendations are provided for future studies.

42 ENGINEERING↗
DOE OSTI2022

IM3 SELECT Urbanization Data

IM3 SELECT Urbanization Data Urban fraction is provided in TIF files projected on the WGS84 datum at coarse (1/8 degree) and downscaled (1km) resolutions across the globe for each of three Shared Socioeconomic Pathway (SSP) scenarios corresponding to SSP2, SSP3, and SSP5; and each of two population scenarios corresponding to the default population scenario and an updated population scenario with more detailed projections for the United States. The population projections are provided as CSV files. Folder structure: default_population population urban_fraction coarse SSP2 SSP3 SSP5 downscaled SSP2 SSP3 SSP5united_states_updated_population population urban_fraction coarse SSP2 SSP3 SSP5 downscaled SSP2 SSP3 SSP5 Urban fraction data was produced using the SELECT model v1.0.0 (Gao, J. & O'Neill, B.C. 2019). Default population data derived from Gao, J. & O'Neill, B.C. 2020. Original model outputs produced using the default population data are available from Gao, J. 2020. Updated United States population data derived from Zoraghein, H. & O'Neill, B.C. 2020. Other SELECT input files available at Gao, J. & O'Neill, B.C. 2022.

McManamay, Ryan↗
DOE OSTI2026

A Probabilistic Approach to Load Modeling for Central HVAC Systems in Large Commercial Buildings for Retrofit Decisions Under Uncertainty

Retrofitting central HVAC systems in large commercial buildings with advanced technologies like heat recovery chillers (HRCs) offers a significant opportunity to enhance energy efficiency. However, analyzing these retrofits is challenging with traditional whole-building simulation tools, which require intensive calibration and struggle to model innovative system configurations and controls. To overcome these limitations, this study proposes a load profilebased retrofit analysis framework that provides better decisions under uncertainty. The main focus of this paper is the development of a probabilistic load profile model that can be used in the framework by using exploratory data analysis (EDA) of measured building data to properly quantify its inherent variability. A non-parametric Gaussian Process (GP) model was employed to capture the time- and weather-dependent characteristics of the heating load while explicitly modeling its uncertainty. The model's effectiveness is demonstrated through strong predictive performance on unseen data and physically interpretable insights into load behavior. This data-driven, probabilistic load profile serves as a robust and flexible input for subsequent system simulations, enabling a more confident and statistically sound analysis of retrofit potential.

Ham, S W↗
DOE OSTI2021

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

INGENIOUS Thermal Conductivity Measurement Source Categorization

Thermal conductivity (TC) data taken for different wells at a specified drill depth. This is an abridged version of the complete SMU heat flow database, downloaded from the SMU node of the NGDS at the beginning of INGENIOUS (approximately April 2021), and filtered to the INGENIOUS study area. This National Geothermal Data System (NGDS) project aggregates geothermal data collected and curated by the SMU Geothermal Laboratory and its partner organizations. All columns in this database are the same as the SMU database, except for 2 additions associated with this project. Repeated columns are for data correlation purposes. Column descriptions and data types are the same as previous iterations of the SMU database. The new values that are the addition are two new columns developed as part of the INGENIOUS project: INGENIOUS TC Value | INGENIOUS notes INGENIOUS notes are individual notes that were written for specific data points during the analysis process. There are not always notes associated with each input value. INGENIOUS TC Value includes 4 values: 1. Assumed Measured These are values that are assumed to be measured thermal conductivity values, either within a specific well or within the same study region. Many of these have either a published reference, a reported standard deviation, or a unique thermal conductivity value. 2. Data release - assumed measured These are values in the SMU database that are from proprietary data that were added to the SMU database and are labeled as data release for their reference. These values were searched for in person at the SMU Geothermal Laboratory as well as virtual examination of data available on the NGDS. For many of these, there are reported thermal conductivity values associated with the heat flow data in the database, but no specific table or reference to measurements in the original data release files. 3. Known measured These are values that have a reported measurement, either as an original file in the SMU data files on the NGDS or a reported table in a publication. In the rare circumstances, Maria Richards or David Blackwell confirmed measurement. Confirmation of measurement would be written in the INGENIOUS notes column. 4. Unmeasured Unmeasured values are those that are known to be unmeasured, either estimated from another report or no information given. In the SMU database, there are wells that have a heat flow but no thermal conductivity. These are categorized as unmeasured. There are also heat flow values that are stated to have estimated or generalized average thermal conductivity values for the region and rock type. Because these are known to be unmeasured, they are categorized as such. 5. Blank Blank values are either A quality or X quality. These quality values are stated in the INGENIOUS notes. These values were not going to change associated with the heat flow analysis, so these were not examined.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

OrthoPhyl—streamlining large-scale, orthology-based phylogenomic studies of bacteria at broad evolutionary scales

Abstract There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models

Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble data↗
DOE OSTI2021

Creep-fatigue Behavior and Damage Accumulation of a Candidate Structural Material for Concentrating Solar Power Solar Thermal Receiver (Final Technical Report)

Creep-fatigue deformation is an important consideration for a thermal receiver in Concentrating Solar Power systems due to the constant static stress or pressure, diurnal cycling, and elevated service temperatures required for efficient operation. An accurate description of the creep-fatigue behavior, not available for five of the six candidate materials, is important for assessment of preliminary designs. This project has provided a detailed analysis of the creep-fatigue behavior and damage accumulation of Alloy 740H, a candidate structural material for a CSP solar thermal receiver to address a critical knowledge barrier for receiver designs identified in the CSP Gen3 Demonstration Roadmap. This project was divided in to three tasks, split between two general categories: Experimental and design methodology. Tasks 1 and 3, relating to the collection of data on Alloy 740H plate and sheet forms, respectively, were performed at the Idaho National Laboratory and used as input to Task 2, the development of design methodology performed at Argonne National Laboratory. Experimental work covered limited tensile and creep testing to fill gaps in available data that is currently available and to demonstrate how the plate of Alloy 740H tested in this work compared to historical data. A more substantial testing program for fatigue and creep-fatigue was performed, with particular emphasis on creep-fatigue as there is little available data and it is necessary to understand the creep-fatigue interaction and develop related design models. This effort includes the development of rules for the design of solar receiver components against high temperature creep-fatigue and ratcheting failure modes. The ASME Code rules for high temperature nuclear components was used to the basis of the method but adjustments will be made to reflect the generally shorter, diurnal operating cycles of thermal receivers and the relative consequences of failure, comparing nuclear to solar components. This work has provided guidance for the design of components for concentrating power facilities operating at high temperatures and undergoing high, secondary thermal stresses relative to the applied primary pressure stress. The design rules were developed for the design of Generation 3 CSP tubular receivers manufactured from Alloy 740H, but are generally applicable to a wide range of component types undergoing similar loads. Procedural design rules for components to be used in conjunction with the 2019 edition of the ASME Boiler & Pressure Vessel Code and the corresponding design data for Alloy 740H have been developed. A commentary describing the rationale behind the design rules and the data underlying the design material properties, as well as an extensive set of worked sample problems detailing the application of the rules to CSP components were developed as a part of this project, with a separate report covering these aspects in detail and included in the Appendix of this report.

14 SOLAR ENERGY↗
DOE OSTI2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models: Preprint

Uncertainty visualization is a key component in translating important insights from ensemble data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models for computationally expensive simulations, users can interact with more aspects of data spaces than ever before. However, the integration of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.

ensemble visualization↗
DOE OSTI2020

Quantifying Uncertainties of Microscopic Nuclear Theories

The goal of this project was to produce modernized nuclear data libraries of light ion fusion reactions and actinide fission product yields. Such libraries are essential input in both stockpile science but also fundamental science (nucleosynthesis, superheavy nuclei). When such libraries are used to model complex processes involving exotic, very short-lived nuclei that have not been measured experimentally, the underlying nuclear data come from theoretical models, the predictive power of which needs to be quantified, together with its uncertainties. By teaming up nuclear theorists and statisticians, we built comprehensive theoretical frameworks based on the most fundamental theories of fusion and fission that we coupled for the first time with a Bayesian framework with machine learning techniques for uncertainty quantification. Our results include: (1) statistical emulators for neutron-alpha scattering, and the fission product mass yields of neutron-induced fission of 239 Pu, which will enable developers to generate random realizations of the corresponding data for uncertainty quantification (UQ) in applications; (2) the first complete and most accurate ab initio prediction of the S-factor for proton capture on 7 Be, which – combined with UQ – may become the recommended value at solar energies in the next review of solar fusion cross sections; (3) statistical emulators for fission mass yields in the neutron-induced fission of 239 Pu; (4) the first quantification of how uncertainties in nuclear forces impact fission properties that are relevant in nucleosynthesis simulations to explain how heavy elements are formed in the Cosmos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗
DOE OSTI2021

Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin - Task 2 Topical Report

This attachment A is a detailed Topical Report for Task 2 (Advanced Field Characterization and Machine Learning Based Data Integration) under the project "Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin." The overall project activity and finding, including the field pilot testing of CO 2 injection are summarized in the companion Final Technical Report The Advanced Field Characterization and Machine Learning Based Data Integration task (Task 2) involved a systematic geologic characterization of the Trenton-Black River (TBR) play in the SMB which included the development of comprehensive datasets, advanced data analytics, risk assessment, and piggyback field characterization. These activities aimed to address the complex carbonate systems by evaluating the extent of fractures, dolomitization, facies, and reservoir properties with the primary objective of informing the static and dynamic modeling, field injection test, and providing input into the development strategy plan. The task was divided into four subtasks: • Subtask 2.1 – Data compilation, review, and analysis • Subtask 2.2 – Risk Assessment • Subtask 2.3 – Advanced Field Characterization • Subtask 2.4 – Integrated Physics-Based Machine Learning and Advanced Data Analytics

02 PETROLEUM↗
DOE OSTI2024

Novel, active, and uncultured hydrocarbon-degrading microbes in the ocean

ABSTRACT Given the vast quantity of oil and gas input to the marine environment annually, hydrocarbon degradation by marine microorganisms is an essential ecosystem service. Linkages between taxonomy and hydrocarbon degradation capabilities are largely based on cultivation studies, leaving a knowledge gap regarding the intrinsic ability of uncultured marine microbes to degrade hydrocarbons. To address this knowledge gap, metagenomic sequence data from the Deepwater Horizon (DWH) oil spill deep-sea plume was assembled to which metagenomic and metatranscriptomic reads were mapped. Assembly and binning produced new DWH metagenome-assembled genomes that were evaluated along with their close relatives, all of which are from the marine environment (38 total). These analyses revealed globally distributed hydrocarbon-degrading microbes with clade-specific substrate degradation potentials that have not been reported previously. For example, methane oxidation capabilities were identified in all Cycloclasticus . Furthermore, all Bermanella encoded and expressed genes for non-gaseous n -alkane degradation; however, DWH Bermanella encoded alkane hydroxylase, not alkane 1-monooxygenase. All but one previously unrecognized DWH plume member in the SAR324 and UBA11654 have the capacity for aromatic hydrocarbon degradation. In contrast, Colwellia were diverse in the hydrocarbon substrates they could degrade. All clades encoded nutrient acquisition strategies and response to cold temperatures, while sensory and acquisition capabilities were clade specific. These novel insights regarding hydrocarbon degradation by uncultured planktonic microbes provides missing data, allowing for better prediction of the fate of oil and gas when hydrocarbons are input to the ocean, leading to a greater understanding of the ecological consequences to the marine environment. IMPORTANCE Microbial degradation of hydrocarbons is a critically important process promoting ecosystem health, yet much of what is known about this process is based on physiological experiments with a few hydrocarbon substrates and cultured microbes. Thus, the ability to degrade the diversity of hydrocarbons that comprise oil and gas by microbes in the environment, particularly in the ocean, is not well characterized. Therefore, this study aimed to utilize non-cultivation-based ‘omics data to explore novel genomes of uncultured marine microbes involved in degradation of oil and gas. Analyses of newly assembled metagenomic data and previously existing genomes from other marine data sets, with metagenomic and metatranscriptomic read recruitment, revealed globally distributed hydrocarbon-degrading marine microbes with clade-specific substrate degradation potentials that have not been previously reported. This new understanding of oil and gas degradation by uncultured marine microbes suggested that the global ocean harbors a diversity of hydrocarbon-degrading bacteria, which can act as primary agents regulating ecosystem health.

Howe, Kathryn L.↗
DOE OSTI2026

FAIRmaterials: Ontology Tools with Data FAIRification in Development

The bilingual FAIRmaterials package simplifies the creation and visualization of materials and data science ontologies. FAIRmaterials, available in the Python and R languages, addresses the complexities associated with traditional ontology editors based on manual user input such as Protege with an intuitive workflow and easy-to-use templates, making it accessible to users both experienced and inexperienced with ontologies. The FAIRmaterials package is its ability to programatically convert simple and structured CSV inputs into rich, well-defined ontologies. This capability is designed to support the findability, accessibility, interoperability, and reusability (FAIR) of research data and serve as a tool in the process of data FAIRification. Its additional features, such as automated ontology merging, static visualizations, and comprehensive documentation for outputs extend its utility, making it a valuable tool for any researcher engaged in knowledge management.

Bradley, Alexander Harding [Case Western Reserve U↗