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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ionospheric Disturbances in GNSS TEC Data: SpaceX Falcon 9 Deorbit Maneuvers Over CONUS in April–May 2024

Traveling ionospheric disturbances (TIDs) driven by a large number of internal and external sources are detectable with dense networks of ground‐based Global Navigation Satellite System (GNSS) receivers' measurements of total electron content (TEC). We present the newly developed System for Rapid Analysis of Ionospheric Dynamics (S‐RAID), providing data and visual GNSS TEC products of TIDs of periods ~2–120 min and horizontal resolution up to tens of kilometers for years 2017–2024. The S‐RAID data reveal myriad natural and anthropogenic TIDs from meteorology and space weather, and from human spaceflight activities. In this report, we focus on new prominent disturbances found during SpaceX second stage deorbit maneuvers in April–May 2024. Signatures include waves emanating from the Falcon's trajectory above California and depletions following its deorbit and passage over Arizona. These findings suggest further opportunities to detect and quantify small‐scale events in the ionosphere as well as to understand the responses of the atmosphere‐ionosphere system to known inputs.

58 GEOSCIENCES↗
DOE OSTI2022

FALCON readout channel for X-ray ptychography applications

This work reports the current state of the development of a pixelated readout for nanometer resolution X-ray ptychography applications. A very dense, low-noise and low-power pixel developed in a commercial 65nm CMOS technology is envisioned for such applications, targeting a pixel area of 150 μm × 150 μm, an overall noise of 200e- rms and a power consumption of 150μW per pixel. In the paper, an introduction to the application and an overview of the experimental setup are given, the designed frontend channel is reported and revised in its components and simulation results of the schematic-level design are shown and discussed.

Lazzaroni, Paolo↗
DOE OSTI2023

Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset

The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ~ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recovery of Rare Earth Oxides from Flotation Concentrates of Bastnaesite Ore by Ultra-Fine Centrifugal Concentration

Historically, the ability to effectively separate carbonate gangue from bastnaesite via flotation has frequently proven to be challenging without sacrificing significant rare earth oxide (REO) grade or recovery. However, in light of the fact that the rare earth bearing minerals often exhibit higher specific gravities than the carbonate gangue, the possibility exists that the use of gravity separation could be used to achieve such a selective separation. This however is complicated by the fact that, in cases such as this study when the liberation size is finer than 50 microns, most traditional gravity separation methods become increasingly challenging. The aim of this study is to determine the applicability of centrifugal concentrators to beneficiate ultra-fine (UF) bastnaesite and calcite bearing flotation concentrates. By using a UF Falcon, it was possible to achieve initial gravity REO recoveries exceeding 90% while rejecting on the order of 25% to 35% of the total calcium from an assortment of rougher and cleaner flotation concentrates. Additionally, when additional stages of cleaner UF Falcon gravity separation were operated in an open circuit configuration, it was possible, from an original fine feed of 35 microns containing 50.5% REO and 5.5% Ca, to upgrade up to approximately 59% REO and 2.0% calcium. While not the goal of this study, these results also support previous limited data to suggest that UF Falcons are potentially capable of treating a wider range of materials than they were originally designed for, including feeds rich in heavy mineral content.

Norgren, Alex↗
DOE OSTI2021

Ultra-Fine Centrifugal Concentration of Bastnaesite Ore

Historically, the ability to effectively separate carbonate gangue from bastnaesite via flotation has frequently proven to be challenging without sacrificing significant rare earth oxide (REO) grade or recovery. However, in light of the fact that the rare earth bearing minerals often exhibit higher specific gravities than the carbonate gangue, the possibility exists that the use of gravity separation could be used to achieve such a selective separation. This however is complicated by the fact that, in cases such as this study when the liberation size is finer than 50 µm, most traditional gravity separation methods become increasingly challenging. The purposes of this study is to determine the applicability of gravity concentrators to beneficiate bastnaesite from deleterious calcite bearing flotation feed material. Via the use of a UF Falcon, it was possible to achieve rougher gravity REO recoveries approaching the upper 80% range while rejecting on the order of 30% of the total calcium. In terms of purely REO recovery, this represents a significant improvement over results obtained via a traditional Falcon in previously reported studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Field Scoping Study of Gamma-Ray Measurement of UF 6 Cristillini Samples with CZT and HPGe Detectors

A field scoping study was conducted to evaluate the ability of Cd-Zn-Te (CZT) detectors and High Purity Germanium (HPGe) detectors to quantify the U-235 enrichment in UF 6 samples collected on alumina in P-10 tubes, similar to the Cristillini method of sample collection for analysis by mass spectrometry. With in-field gamma-ray spectroscopy methods, international safeguards and verification missions could benefit from quick in-field confirmation of declared enrichment values prior to or without the need for additional shipping, sample preparation and analysis. Samples of depleted (DU), low-enriched (LEU) and high-assay low-enriched (HALEU) uranium were collected on Cristillini-type alumina in P-10 tubes and measured with two H3D, Inc. M400 CZT detectors as well as a Canberra Falcon HPGe detector. The M400 detectors were able to confirm enrichment in the DU and LEU samples but not the HALEU sample in measurements 8-40 hrs after collection. The Falcon system was able to confirm enrichment in all three samples. Improvements in detector geometry, increase in number of detectors as well as increase sample mass loading may allow measurement of U-235 enrichment in these types of samples within 4-8hrs of collection with 5-10% uncertainty.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2022

Investigating Performance of Geothermal Reservoirs With Bi-Directional Heat Transfer for Seasonal Thermal Storage Using Thermal-Hydraulic Reservoir Simulations

This paper presents initial simulation results of deep subsurface seasonal thermal storage of waste heat from a natural gas-fired power plant during the summer months for providing heating during winter months at the Cornell University main campus in Ithaca, New York. An injection temperature profile is calculated with the MEnU tool and numerical reservoir simulations are conducted with the simulator FALCON. Both a porous medium and large single fracture geothermal reservoir are considered at about 3 km depth with initial reservoir temperature of 75 degrees C. Simulations indicate that a much smaller reservoir - which is technically easier and less risky to develop - is sufficient when operating the reservoir for seasonal thermal storage instead of for continuous heat production. For example, storing 13 GWh of heat seasonally in a 100 m thick porous medium based on the Galway formation requires about 65 m of well spacing to limit the yearly variation in production temperature to 10 degrees C. Operating this reservoir for continuous heat production would require a well spacing of 365 m to limit the thermal decline to 10 degrees C after 10 years. Neglecting wellbore heat losses and assuming uniform and homogeneous properties, a round-trip thermal efficiency of 100% can be obtained if the yearly averaged injection temperature matches the initial reservoir temperature.

district heating↗
DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The NASA ACTIVATE Mission

The NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) conducted 162 joint flights with two aircraft over the northwest Atlantic to study aerosol–cloud interactions (ACIs), which represent the largest uncertainty in estimating total anthropogenic radiative forcing. The combination of a high-flying King Air and low-flying HU-25 Falcon, equipped with remote sensing and in situ instruments, characterized trace gases, aerosol particles, clouds, and meteorological variables with data collected nearly simultaneously below, within, and above marine boundary layer (MBL) clouds. Flights spanning warm and cold seasons across 3 years (2020–22) provided a broad range of conditions associated with aerosol particles, cloud properties (including particle size and phase), and meteorology, ideally suited for robust ACI calculations and assessing how well models simulate a wide range of MBL clouds from stratiform to cumulus. ACTIVATE data suggest that drivers of cloud droplet number concentration N d , including aerosol particles and MBL dynamics, vary between winter and summer months with a stronger potential to convert aerosol particles into cloud droplets in winter. Models of varying complexity not only highlight some skills in simulating winter and summer cloud types but also identify challenges that still need to be addressed such as treatment of turbulence, wet scavenging, and mesoscale organization. Remote sensing advances range from new retrieval methods for N d , cloud phase classification, vertically resolved aerosol and cloud condensation nuclei number concentration, and ocean surface wind speed. This work describes these scientific and technological advances along with efforts in outreach and open data science.

aerosol indirect effect↗
DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Utah FORGE Phase 3 Native State Model: 2022 Update

This is the Phase 3 native state model update. The Phase 3 numerical model represents a significant subsurface volume below the FORGE site footprint. The model domain of 4.0 km x 4.0 km x 4.2 km is located approximately between depths of 4000 to 4200 meters below land surface. This data archive consists of 10 files, 4 of which are simulation input files and the remaining 6 are simulation output files. There is an included readme.txt file that contains details on each of the data files. The input files include meshes, FALCON code inputs, tabulated data of water properties, temperature values, and model boundaries. The output files include simulation outfiles and point data of modeled material properties.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Dataset for 'Ombadi, M. & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States, Water Research'

This package contains data sets and code used to obtain the results in Ombadi, M., & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States. Water Research, 118664. The folder "data" contains 259 .csv files, each of which has daily time series of concurrent streamflow (Q) and specific conductance (SC) for each of the sites used in this study originally downloaded from the USGS National Water Information System (NWIS; USGS, 2016). The number of data points in each of the files is at least 3650 (i.e. 10 years of daily measurements). The folder "RF_single_data" contains 259 .csv files, each of which include data used to train and test the Random Forest models at individual sites for predicting SC during days of floods. The folder "RF_regional_data" contains 3 .csv files, each of which include scaled data compiled from all sites within each climate zone (arid, temperate and wet). "metadata.csv" contains the physical properties of the 259 catchments corresponding to the sites used in this study; this data was extracted from GAGES-II dataset (Falcone et al., 2010). "RF_implementation.ipynb" is a Jupyter notebook with the code needed to implement the analysis using Random Forest models either for individual sites or for the regional models (for each climate zone). The code utilizes the data in the two folders: "RF_single_data" and "RF_regional_data" and the metadata.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto, E., Ombadi, M., Ciulla, F. et al. Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022. Commun Earth Environ 7, 734 (2026). https://doi.org/10.1038/s43247-026-03890-5. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - pyeto: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - calculations: Code used in UCRB_Drought_Workflow_Impacts.ipynb - plotting: Code used in UCRB_Drought_Workflow_Impacts.ipynb - README.md - UCRB_Drought_Workflow_Preprocessing.ipynb: The code used to prep raw data for the analysis - UCRB_Drought_Workflow_Impact.ipynb: The code which uses the prepped raw data for analysis, and plots all figures - requirements_ucrb-drought_v2.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The "RESERVOIRS_RAW" folder contains ".csv" files for each reservoir in the UCRB with daily reservoir storage. There are also two files in the INPUTS folder that have combined reservoir storage data and reservoir metadata. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "Peak95_Min5_Relchange" that has ".csv" files for the relative change in peak (95th %) and minimum (5th %) variables. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE 1: Title and code file updated to match submitted manuscript 10-15-2025. UPDATE 2: Code and data files updated to match revised manuscript 3-4-2026. UPDATE 3: Code and data files updated to match revised manuscript 6-7-2026. ** NOTE: DD and FLMD have not been updated yet. UPDATE 4: Added associated Manuscript information and DD and FLMD have been updated. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Essence of Cryptol: A Denotational Cryptol Interpreter in Coq for Foundational Assurances for Quantum Resistant Cryptosystems

Systems of the utmost consequence need a means to establish authenticity of software and data. Cryptosystems implement authentication, but can be vulnerable to cryptographic and implementation attacks. With the threat of quantum cryptographic attacks, “post-quantum” cryptosystems (PQCs) must be henceforth used in these systems. However, the new cryptography needs new ways to, rigorously and machine-checkably, prove systems free of vulnerabilities. We propose a retargetable capability to rapidly instantiate proven correct postquantum cryptosystems through novel proof-carrying synthesis and proof-automation technique, extending those proven successful on existing systems. This capability is crucial to meeting the cryptographic requirements for future high-consequence systems. Since specifications for high consequence cryptography are presently captured in a domain specific language known as Cryptol. While this can enable convenient fully automated reasoning about Cryptol specificaitons and implementations via the Software Analysis Workbench (SAW), Cryptol has expressivity gaps, so that cryptosystems with probabilistic programming features like Falcon cannot be fully expressed in the language. Moreover, SAW’s automation fails for programs and specificaitons with inductive and recursive structure, as in the Sphincs+ PQC. Finally, Cryptol and SAW together represent some 200,000 lines of unverified Haskell, so that the any guarantees about high consequence cryptography are presently contingent on a large, unverified, yet trusted computing base. The first step of the larger project of agile, assured crpytography is therefore to provide a formal, mechanized semantics for Cryptol, so that the specifications expressed by cryptographers in Cryptol can be reasoned about and compiled into performant implementations with a foundational, machine checkable certificate of correctness. This report describes our work on this first step, culminating in the design of a certified denotational interpreter, in Coq, for core Cryptol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Root image collection from a genotype by environment (GxE) root architecture study of the bioenergy species, Populus trichocarpa

Here we present a curated dataset; raw images of fresh roots collected from a genotype by environment (growth matrix) greenhouse root architecture study of the bioenergy species, Populus trichocarpa. Five separate experiments were performed to generate the dataset. Biological replicates (ramets, vegetative propagants) of Populus trichocarpa genotypes BESC 24 and BESC 375 were used for all experiments. The first two experiments were an eight-week and a six-week pot experiment where plants with grown in 4" X 14" sized pots were filled with Farfard 3B potting soil (Fafard Sungro #3B), and watered twice daily. Average greenhouse humidity and growth temperature were around 50-60% and 23°C, respectively. Ramets of P. trichocarpa genotypes BESC 24 and BESC 375 were used in various experiments. The source of this material was dormant field cuttings grown in greenhouse. The second experiment was a Microcosm root architecture experiment. This was done in a microcosm built with double-decker tissue culture Magenta box. Tissue culture plants with new root initials were transferred to microcosm with pro choice rapid dry growth matrix with 1x Hoagland’s solution pH5.7 and grown for six weeks until harvest. Temperature was held at ~22°C. The closed container trapped moisture. The source material was tissue culture stock plants established from shoot tips of greenhouse stock plants (obtained from same set of dormant field cuttings as rest of the study). For the remaining two experiments, greenwood stem cuttings from greenhouse grown plants were grown in six-week rhizotron and two-week hydroponics experiments in the greenhouse. For rhizotron experiments, plants were grown in 2-D rhizotron with black panels on both sides (31cm H x 30cm L x 1.2cm W) and watered daily. For hydroponics experiment, plants were grown in a 15ml falcon tube with distilled water and covered in foil and shaken a few times a day. Greenhouse temperature was held at ~ 23°C and humidity was between 50-60%. Stems were cut off and the root system was separated, gently washed to rid of matrix, immersed in a water tray and scanned for obtaining raw root images reported here. All scans of roots floating in a water tray were performed using an Epson Perfection V850 Pro Photo Scanner. Additional meta data is embedded in the raw image files.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Lessons Learned for Responsible Use of Cloud in the Cirrus Project, Following the CrowdStrike Outage Event

A disruption in CrowdStrike’s Falcon cybersecurity platform on July 19th, 2024, caused worldwide chaos. This event highlights the imperative need for cloud security measures for networks that are critically reliant on cloud technology. This incident negatively impacted air travel, government networks, and critical infrastructure sectors such as hospitals and financial institutions. While no electric utilities had a physical impact, and few had an IT impact, there were issues created by loss of cloud services, and other interrelated industries. For utilities and energy distribution organizations, understanding and mitigating these risks is essential. The Cirrus tool offers a strategic solution engineered to weave cloud integration seamlessly into the fabric of operational management, thereby enhancing resilience and streamlining efficiency in the face of digital challenges.

25 ENERGY STORAGE↗