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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Genome-scale modelling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Restoring Historic Forest Disturbance Frequency Would Partially Mitigate Droughts in the Central Sierra Nevada Mountains

Forest thinning and prescribed fire are expected to improve the climate resilience and water security of forests in the western U.S., but few studies have directly modeled the hydrological effects of multi-decadal landscape-scale forest disturbance. By updating a distributed process-based hydrological model (DHSVM) with vegetation maps from a distributed forest ecosystem model (LANDIS-II), we simulate the water resource impacts of forest management scenarios targeting partial or full restoration of the pre-colonial disturbance return interval in the central Sierra Nevada mountains. In a fully restored disturbance regime that includes fire, thinning, and insect mortality, reservoir inflow increases by 4%–9% total and 8%–14% in dry years. At sub-watershed scales (10–100 km2), thinning dense forests can increase streamflow by >20% in dry years. In a thinner forest, increased understory transpiration compensates for decreased overstory transpiration. Consequentially, 73% of streamflow gains are attributable to decreased overstory rain and snow interception loss. Thinner forests can increase headwater peak flows, but reservoir-scale peak flows are almost exclusively influenced by climate. Uncertainty in future precipitation causes high uncertainty in future water yield, but the additional water yield attributable to forest disturbance is about five times less sensitive to annual precipitation uncertainty. This partial decoupling of the streamflow disturbance response from annual precipitation makes disturbance especially valuable for water supply during dry years. Our study can increase confidence in the water resource benefits of restoring historic forest disturbance frequencies in the central Sierra Nevada mountains, and our modeling framework is widely applicable to other forested mountain landscapes.

Boardman, Eli N. [University of Nevada, Reno, NV (↗
DOE OSTI2026

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. Here, this work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Chen, I-Chi [Ames Laboratory (AMES), Ames, IA (Uni↗
DOE OSTI2024

Transforming Uinta Basin Earth Materials for Advanced Products (TUBE-MAP)

The objectives of this project were to quantify, assess, and plan to enable the transformation of Uinta Basin earth resources, such as coal, oil shale, resin, rare earth elements, and critical minerals into high value metal, mineral, and carbon-based products. The specific major goals were 1) basinal assessments and initial planning (Task 2), 2) basinal assessment for waste stream reuse with associated plan development (Task 3), 3) basinal strategies development for infrastructure, industries, and business (Task 4), 4) technology assessment, development, and field-testing plan (Task 5), 5) technology innovation center plan (Task 6), and 6) stakeholder outreach and education plan (Task 7).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

To date, 48 projects, consisting of 139 wells, are currently under review with the Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program for Class VI – wells used for geologic sequestration of carbon dioxide. The number of applications submitted is expected to increase in coming years with the increase of the 45Q tax credit available to projects that initiate construction prior to 2033. The amount of data collected to submit a Class VI permit is vast, and often disparate, coming from state, federal, and commercial entities, as well as field-specific data collected within an area of interest. When preparing for site selection and permitting, the initial aggregation of relevant public data can be time intensive. The Carbon Storage Site Mapping Inquiry tool (MapIT) was created to support and accelerate the discovery and accessibility of open-source data and information available across the USA. Data was aggregated and organized based on data types described within the EPA UIC Class VI permit documentation. The online tool enables users to explore hundreds of geospatial data layers and connect to additional external resources, leveraging API and REST services where possible to ensure updates to data in real time. MapIT enables users to explore state and federal data related to geologic, geophysical, structural, hydrologic, and contextual information. In addition to displaying spatial data and linking to external resources, MapIT leverages custom widgets to ensure that internal data and external data are discoverable and accessible. The widgets connect users to resources such as the USGS publications and the USGS Earthquake Catalog based on a user-defined location. This talk will describe data aggregation workflows, data types, data preparation, and tool development for MapIT. The Carbon Storage Site Mapping Inquiry Tool and underlying database are valuable, intuitive resources that empower government, academic, commercial and industry stakeholders to explore, analyze, and acquire carbon storage related data.

Morkner, Paige↗
DOE OSTI2023

Mapping Inquiry Tool (MapIT) Database

The Mapping Inquiry Tool (MapIT) database consists of a geodatabase and data catalog of geologic, geophysical, structural, hydrologic, and contextual data, based on the data types to support geologic carbon storage activities and other subsurface energy systems resource assessments. The database was aggregated from publicly available data across the USA from state and federal entities. The database is structured by categories including rock unit geology, boundaries, national CS datasets, geophysical data, faults and structural data, infrastructure, surface hydrology, groundwater, and more. The data described in the data catalog is also available in the Mapping Inquiry Tool (https://edx.netl.doe.gov/dataset/mapping-inquiry-tool). Version 3 of the geodatabase and data catalog have been updated as of 5/17/2024. The database was published with a limited number of layers. The Catalog V3 contains many more resources than the geodatabase, documenting all layers that will be included in MapIT, and includes links to the original sources of the data. Within the catalog, in the final column, there is information about if the file is included in the geodatabase or not. Use the links provided in the catalog to download data directly from the original source if not included in the geodatabase. Four resources are included in this submission: 1. Geodatabase 2. ReadMe file 3. Catalog of data layers and additional data resources 4. Web link to a resource describing the motivation and reviewing the content of the geodatabase - DOE NETL Carbon Storage Site Mapping Inquiry Tool Database

carbon storage↗
DOE OSTI2024

Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Array↗
DOE OSTI2024

Efficacious Qubit Mappings for Quantum Simulations of the 12 C Rotational Band

Solving atomic nuclei from first principles places enormous demands on computational resources, which grow exponentially with increasing number of particles and the size of the space they occupy. We present first quantum simulations based on the variational quantum eigensolver for the low-lying structure of the 12C nucleus that provide acceptable bound-state energies even in the presence of noise. Here, we achieve this by taking advantage of two critical developments. First, we utilize an almost perfect symmetry of atomic nuclei that, in a complete symmetry-adapted basis, drastically reduces the size of the model space. Second, we use the efficacious Gray encoding, for which it has been recently shown that it is resource efficient, especially when coupled with a near band-diagonal structure of the nuclear Hamiltonian.

Encoding↗
DOE OSTI2025

lllinois Storage Corridor CarbonSAFE Phase III: Pre-drilling Site Assessment: Prairie State Generating Company

The Illinois Storage Corridor project will drill a stratigraphic test well as part of the Illinois Storage Corridor CarbonSAFE Phase 3 project near the Prairie State Generating Company coal-fired power plant near Marissa, Illinois. The pre-drilling site evaluation has considered the primary target reservoirs, the Potosi Dolomite and St. Peter Sandstone, and primary seal, the Maquoketa Group. Data to be collected from the well include core, fluid samples, in situ well tests, geophysical logs intended to provide information on lithologic, geomechanical, and geophysical characteristics to determine the feasibility for the geologic sequestration of 50 million metric tons or more of injected carbon dioxide. The planned drilling site has been evaluated using available subsurface geologic data and analyses from the Illinois Basin. These data provide lithologic and structural information, shallow groundwater resource distribution, location of known nearby wellbores, and regional drilling characteristics. The data were used to generate geologic structure and isopach maps for the target reservoir and caprock strata and for prognosing the tops of major lithologic units to aid drilling and coring procedures. The regional analyses indicate that no known structural features are expected to negatively impact the target storage reservoir or caprock. No protected and sensitive areas, groundwater resources, or existing resource development are expected to be impacted by the proposed well drilling activities. The well is planned to be drilled to a total depth of approximately 5,600 feet (1,707 m) and terminate in the Precambrian. Cores (up to 5 intervals) will be collected from the Maquoketa Group, confining units above the St. Peter Sandstone, St. Peter Sandstone, confining units of the Potosi Dolomite and the Potosi Dolomite. Water samples will be attempted to be collected from the St. Peter Sandstone and Potosi Dolomite. Potential impact on drilling progress is a lost circulation zone in the Potosi Dolomite, which has been demonstrated to have intermittent cavernous porosity from karstification elsewhere in the Illinois Basin. This document also presents a preliminary coring and sampling program, proposed logging suite, and well testing program, all of which will be reviewed during drilling.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2023

A Novel Resilience-Oriented Cellular Grid Formation Approach for Distribution Systems with Behind-the-Meter Distributed Energy Resources

This paper presents a resilience-oriented cellular grid formation approach to achieve scalable and reconfigurable community microgrid operations for distribution systems with behind-the-meter distributed energy resources. A set of interconnected solar photovoltaics, energy storage systems, and load is termed as a cell, implying a subset of the grid that can operate independently using its own resources. Cells are identified such that each cell inherently has sufficient energy resources to black start and can provide a certain level of backup power for its load under the loss of utility power supply. The proposed cell formation approach builds on a unique self-organizing map-based method (SomRes) to quantify a system's resilience. Using SomRes and a non-dominated sorting-based genetic algorithm (NSGA-II), a fast and efficient cell formation algorithm is developed to identify cells in a distribution system that are resilient against extreme events. The efficacy of the proposed approach is demonstrated on a numerical model of a real distribution feeder in Colorado, United States.

distributed energy resource↗
DOE OSTI2023

Proof of principle study: synchrotron X-ray fluorescence microscopy for identification of previously radioactive microparticles and elemental mapping of FFPE tissues

Biobanks containing formalin-fixed, paraffin-embedded (FFPE) tissues from animals and human atomic-bomb survivors exposed to radioactive particulates remain a vital resource for understanding the molecular effects of radiation exposure. These samples are often decades old and prepared using harsh fixation processes which limit sample imaging options. Optical imaging of hematoxylin and eosin (H&E) stained tissues may be the only feasible processing option, however, H&E images provide no information about radioactive microparticles or radioactive history. Synchrotron X-ray fluorescence microscopy (XFM) is a robust, non-destructive, semi-quantitative technique for elemental mapping and identifying candidate chemical element biomarkers in FFPE tissues. Still, XFM has never been used to uncover distribution of formerly radioactive micro-particulates in FFPE canine specimens collected more than 30 years ago. In this work, we demonstrate the first use of low-, medium-, and high-resolution XFM to generate 2D elemental maps of ~ 35-year-old, canine FFPE lung and lymph node specimens stored in the Northwestern University Radiobiology Archive documenting distribution of formerly radioactive micro-particulates. Additionally, we use XFM to identify individual microparticles and detect daughter products of radioactive decay. The results of this proof-of-principle study support the use of XFM to map chemical element composition in historic FFPE specimens and conduct radioactive micro-particulate forensics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

The Electromagnetic Characteristics of the Tianlai Cylindrical Pathfinder Array

A great challenge for 21 cm intensity mapping experiments is the strong foreground radiation which is orders of magnitude brighter than the 21 cm signal. Removal of the foreground takes advantage of the fact that its frequency spectrum is smooth while the redshifted 21 cm signal spectrum is stochastic. However, a complication is the non-smoothness of the instrument response. This paper describes the electromagnetic simulation of the Tianlai cylinder array, a pathfinder for 21 cm intensity mapping experiments. Due to the vast scales involved, a direct simulation requires a large amount of computing resources. We have made the simulation practical by using a combination of methods: first simulate a single feed, then an array of feed units, finally with the feed array and a cylindrical reflector together, obtain the response for a single cylinder. We studied its radiation pattern, bandpass response and the effects of mutual coupling between feed units, and compared the results with observation. Many features seen in the measurement result are reproduced well in the simulation, especially the oscillatory features which are associated with the standing waves on the reflector. The mutual coupling between feed units is quantified with S-parameters, which decrease as the distance between the two feeds increases. Based on the simulated S-parameters, we estimate the correlated noise which has been seen in the visibility data, and the results show very good agreement with the data in both magnitude and frequency structures. These results provide useful insights on the problem of 21 cm signal extraction for real instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Development of male-sterile lines of Setaria viridis to accelerate C 4 model plant genetics

Setaria viridis is a diploid C 4 grass in the Poaceae family, notable for its rapid life cycle of 6–8 weeks from sowing to seed—much shorter than the 4–5 months required by crops such as Zea mays and Sorghum bicolor . This fast growth makes S. viridis a valuable model for C 4 crop research. Genetic crosses are essential for studying gene function, but manual crossing is labor-intensive and time-consuming. Here, to address this, we developed a male-sterile line by targeting the S. viridis ortholog of Setaria italica NO POLLEN 1 ( SiNP1 ), which encodes a glucose–methanol–choline oxidoreductase required for pollen exine formation. Using Cas9 and TREX2 -mediated genome editing, we generated SiNP1 knockouts in both the S. viridis ME034V and A10.1 backgrounds that were fully male-sterile. Backcrossing T 0 male-sterile plants to ME034V wild-type followed by selfing yielded a stable BC 1 F 2 line homozygous for a 59 bp deletion in the S. viridis NO POLLEN 1 gene, easily genotyped by PCR and maintained by heterozygous siblings. Using this line, we developed a simple and efficient crossing protocol that eliminates the need for emasculation. This method enables a single person to perform up to 100 crosses per day—compared to 15 using traditional methods—and yields 20–32 F 1 hybrid seeds per panicle with 100% genetic purity. We also quantified pollen flow and outcrossing frequencies under greenhouse conditions to develop optimal bagging strategies and prevent unintended pollination. This resource accelerates genetic research in S. viridis , enhancing its utility as a premier C 4 model for mapping and functional genomics.

C4 research↗
DOE OSTI2024

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Engineering↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2024

Carbon Transport and Storage Planning and Viability Support Tools

The EDX disCO2ver Carbon Transport and Storage Planning and Viability Support Tools are made up of the Carbon Storage Planning Inquiry Tool (CS PlanIT, Justman et al. 2024) and the Carbon Storage Technical Viability Approach Support Tool (CS TVA). Together, these tools support data access to support understanding data availability to support planning efforts for carbon transport and storage. The Carbon Storage Planning Inquiry Tool (CS PlanIT) is an online web mapping application designed to help users explore, query, and evaluate multiple data layers to support and accelerate carbon storage resource and feasibility assessments and planning efforts. CS PlanIT currently contains a range of datasets associated with geologic, technical, and infrastructure factors. The data sets can be filtered geographically for an area of interest to update statistics and charts within the dashboard. The dashboard is divided into different sections called widgets, relating to different steps in the carbon storage planning process. The resources in this submission include a link to PlanIT, as well as a data catalog and link to user documentation. The original citation for the CS PlanIT tool, which has now been integrated into the toolset here, was: - Devin Justman, Scott Pantaleone, Maneesh Sharma, Lucy Romeo, Paige Morkner, CS PlanIT (Carbon Storage Planning Inquiry Tool) , 6/28/2024, https://edx.netl.doe.gov/dataset/cs-planit-carbon-storage-planning-inquiry-tool, DOI: 10.18141/2377953 The Carbon Storage Technical Viability Approach Support (CS TVA) Tool displays spatial data availability for the many components of Geologic Carbon Storage (GCS) technical viability assessment (Creason al 2025). Identifying sites suitable for GCS requires evaluating the intersection of myriad factors, including reservoir conditions, subsurface and surface hazards, infrastructure requirements, and energy community metrics. The technical viability of a site can only be confirmed for instances where all these factors have data available, and where those data support viability. Additional Resources related to the Technical Viability Assessment Tool: - Julia Mulhern, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zachary Jackson, Jacob Shay, Gabriel Creason, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Database, 3/26/2025, https://edx.netl.doe.gov/dataset/edx4ccs-carbon-storage-technical-viability-approach-database , DOI:10.18141/1984655 - Julia Mulhern, MacKenzie Mark-Moser, Gabriel Creason, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zach Jackson, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Matrix, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-matrix , DOI: 10.18141/2539979 - Gabriel Creason, Zach Jackson, Neyda Cordero Rodriguez, Julia Mulhern, Casey White, Araceli Lara, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-data-availability-results-database, DOI:10.18141/2538557

Carbon storage↗