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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 OSTI2022

AIMSim : An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Circular Economy Life Cycle Assessment and Visualization Framework: A Multistate Case Study of Wind Blade Circularity in United States

A circular economy (CE) aims to decouple human activities from economic activities and resource use, and its overall goal is reducing or avoiding negative environmental externalities. The newly developed Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework simulates changes in supply chain environmental impacts as it transitions toward circularity. This study expands CELAVI by incorporating detailed spatial resolution and real-world road routes connecting all facilities within the system. The case study on end-of-life decision making of wind turbine blades in the states of Iowa and Missouri explores how supply chain circularity and environmental impacts are affected by pathway costs and level of wind turbine installations. It demonstrates how high circularity costs might be beneficial for circularity transitions given revenue generated from circular pathways. Finally, impacts have important contributions to the supply chain design and thus show the importance of including detailed spatial resolution in CELAVI and CE studies in general.

circular economy↗
DOE OSTI2022

Visualization and validation of twin nucleation and early-stage growth in magnesium

The abrupt occurrence of twinning when Mg is deformed leads to a highly anisotropic response, making it too unreliable for structural use and too unpredictable for observation. Here, we describe an in-situ transmission electron microscopy experiment on Mg crystals with strategically designed geometries for visualization of a long-proposed but unverified twinning mechanism. Combining with atomistic simulations and topological analysis, we conclude that twin nucleation occurs through a pure-shuffle mechanism that requires prismatic-basal transformations. Also, we verified a crystal geometry dependent twin growth mechanism, that is the early-stage growth associated with instability of plasticity flow, which can be dominated either by slower movement of prismatic-basal boundary steps, or by faster glide-shuffle along the twinning plane. The fundamental understanding of twinning provides a pathway to understand deformation from a scientific standpoint and the microstructure design principles to engineer metals with enhanced behavior from a technological standpoint.

36 MATERIALS SCIENCE↗
DOE OSTI2021

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

A novel, magnetically driven convergent Richtmyer–Meshkov platform

Here, we introduce a novel experimental platform for the study of the Richtmyer–Meshkov instability in a cylindrically converging geometry using a magnetically driven cylindrical piston. Magnetically driven solid liner implosions are used to launch a shock into a liquid deuterium working fluid and, ultimately, into an on-axis rod with a pre-imposed perturbation. The shock front trajectory is tracked through the working fluid and up to the point of impacting the rod through the use of on axis photonic Doppler velocimetry. This configuration allows for precise characterization of the shock state as it impacts the perturbed rod interface. Monochromatic x-ray radiography is used to measure the post-shock interface evolution and rod density profile. The ALEGRA MHD model is used to simulate the dynamics of the experiment in one dimension. We show that late in time the perturbation growth becomes non-linear as evidenced by the observation of high-order harmonics, up to n = 5. Two dimensional simulations performed using a combination of the GORGON MHD code and the xRAGE radiation hydrodynamics code suggest that the late time non-linear growth is modified by convergence effects as the bubbles and spikes experience differences in the pressure of the background flow.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Nanoscale elemental and morphological imaging of nitrogen-fixing cyanobacteria

Nitrogen-fixing cyanobacteria bind atmospheric nitrogen and carbon dioxide using sunlight. This experimental study focused on a laboratory-based model system, Anabaena sp., in nitrogen-depleted culture. When combined nitrogen is scarce, the filamentous prokaryotes reconcile photosynthesis and nitrogen fixation by cellular differentiation into heterocysts. To better understand the influence of micronutrients on cellular function, 2D and 3D synchrotron X-ray fluorescence mappings were acquired from whole biological cells in their frozen-hydrated state at the Bionanoprobe, Advanced Photon Source. To study elemental homeostasis within these chain-like organisms, biologically relevant elements were mapped using X-ray fluorescence spectroscopy and energy-dispersive X-ray microanalysis. Higher levels of cytosolic K + , Ca 2+ , and Fe 2+ were measured in the heterocyst than in adjacent vegetative cells, supporting the notion of elevated micronutrient demand. P-rich clusters, identified as polyphosphate bodies involved in nutrient storage, metal detoxification, and osmotic regulation, were consistently co-localized with K + and occasionally sequestered Mg 2+ , Ca 2+ , Fe 2+ , and Mn 2+ ions. Machine-learning-based k-mean clustering revealed that P/K clusters were associated with either Fe or Ca, with Fe and Ca clusters also occurring individually. In accordance with XRF nanotomography, distinct P/K-containing clusters close to the cellular envelope were surrounded by larger Ca-rich clusters. The transition metal Fe, which is a part of nitrogenase enzyme, was detected as irregularly shaped clusters. The elemental composition and cellular morphology of diazotrophic Anabaena sp. was visualized by multimodal imaging using atomic force microscopy, scanning electron microscopy, and fluorescence microscopy. This paper discusses the first experimental results obtained with a combined in-line optical and X-ray fluorescence microscope at the Bionanoprobe.

Anabaena sp↗
DOE OSTI2025

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗
DOE OSTI2021

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

In-Situ Blade Strain Measurements of a Crossflow Turbine Operating in a Tidal Flow

This data was collected between October 25 and December 12 of 2022 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). The goal was to collect blade strain data from a crossflow turbine operating in a tidal flow. A table in ('Deployment Schedule.PNG') outlines the various dates when each instrument was operational, and more details can be found via literature listed in 'Related Publications'.txt. This dataset includes zipped folders for each instrument containing data in .csv files for the relevant duration specific to each instrument, along with separate README file for each measurement. Some instrument files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. All data contained in this submission is unfiltered/unprocessed data unless otherwise noted in the README file. Blade strain was measured using 8 foil-based strain gauges along the span of a single turbine blade. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) both upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal flow with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Chancey 2019. The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. A single blade of this turbine was further modified to accommodate 8 full-bridge strain gauges (Bharath et al 2023, Bichanich et al 2024). For power performance and other relevant details on the turbine and its characteristics, see O'Byrne 2022.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2022

Overview of the COMPASS results *

COMPASS addressed several physical processes that may explain the behaviour of important phenomena. This paper presents results related to the main fields of COMPASS research obtained in the recent two years, including studies of turbulence, L–H transition, plasma material interaction, runaway electron, and disruption physics: Tomographic reconstruction of the edge/SOL turbulence observed by a fast visible camera allowed to visualize turbulent structures without perturbing the plasma. Dependence of the power threshold on the X-point height was studied and related role of radial electric field in the edge/SOL plasma was identified. The effect of high-field-side error fields on the L–H transition was investigated in order to assess the influence of the central solenoid misalignment and the possibility to compensate these error fields by low-field-side coils. Results of fast measurements of electron temperature during ELMs show the ELM peak values at the divertor are around 80% of the initial temperature at the pedestal. Liquid metals were used for the first time as plasma facing material in ELMy H-mode in the tokamak divertor. Good power handling capability was observed for heat fluxes up to 12 MW m –2 and no direct droplet ejection was observed. Partial detachment regime was achieved by impurity seeding in the divertor. The evolution of the heat flux footprint at the outer target was studied. Runaway electrons were studied using new unique systems—impact calorimetry, carbon pellet injection technique, wide variety of magnetic perturbations. Radial feedback control was imposed on the beam. Forces during plasma disruptions were monitored by a number of new diagnostics for vacuum vessel (VV) motion in order to contribute to the scaling laws of sideways disruption forces for ITER. Current flows towards the divertor tiles, incl. possible short-circuiting through PFCs, were investigated during the VDE experiments. The results support ATEC model and improve understanding of disruption loads.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER’s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Post-occupancy evaluation of indoor environmental quality in ten nonresidential buildings in Chongqing, China

Indoor environmental quality (IEQ) is an essential element of sustainable building performance. To advance knowledge of indoor environmental quality in Chinese nonresidential buildings, this study conducted post-occupancy evaluation (POE) in varied use areas within schools, hospitals, offices, hotels and shopping malls through environmental measurements and occupant surveys in 10 buildings over four seasons in Chongqing, China. Measured thermal conditions were more frequently within comfort standards in winter than in summer according to the predicted mean vote – predicted percentage dissatisfied model; but the model did not reliably predict thermal satisfaction. There was insufficient illuminance in 28% of studied areas and high illuminance in shopping malls led to complaints about glare. Sound pressure exceeded the Chinese guideline in all buildings with shopping malls having significantly higher levels than other buildings. CO 2 was generally below the Chinese national guideline of 1000 ppm; the two percent of measurements that exceeded the guideline occurred in classrooms, hospital wards and underground supermarkets. Formaldehyde did not exceed the limit level of 0.1 mg/m3 in any space. Most of the buildings did not provide effective protection from outdoor fine particulate matter (PM2.5), and infiltration from outdoors was the main factor for exceeding health-based guidelines indoors. Shopping malls had lower IEQ satisfaction than offices, schools and hospitals. Sound was the factor with the lowest satisfaction level among the four principal environmental factors. Overall, this study demonstrates value of POE on identifying deficiencies in indoor environment and provides a reference for assessment of IEQ in nonresidential buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. Here, in order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Pan-Arctic Vegetation Cover (PAVC) database v1.1

The Pan-Arctic Vegetation Cover (PAVC) database contains synthesized field-data observations of vegetation cover from 978 Arctic Alaska plots with observations from 2010 to 2021. The cover datasets contain plot data at both the plant functional type (PFT) and species-level resolution, with standardized PFT definitions and species names. We synthesized publicly available point-intercept and visual estimate plots from the Arctic Vegetation Archive of Alaska, the Alaska Vegetation Plots Database, the North Slope Science Catalog, and the National Ecological Observatory Network; as well as previously unpublished data from the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic).Users will find four synthesized datasets, 4 associated data descriptor (dd) files, and 1 metadata file in the PAVC database:synthesized_species_fcover.csv contains fractional cover (fcover) for unique accepted species names, where names include vegetation identified at the family, genus, species, subspecies, and variety levels, as well as general functional types across all 5 data sources. The synthesized_species_fcover_dd.csv accompanies this dataset with header information.synthesized_pft_fcover.csv contains fcover for the following PFTs: non-vascular plants with lichen and bryophyte subcategories, trees with deciduous and evergreen subcategories, shrubs with deciduous and evergreen subcategories, graminoids (grasses), and forbs (herbaceous flowering plants) measured as total cover. Litter and “other” cover are also included as total cover. Additional “types” include water and bare ground, which were measured as top cover. The synthesized_pft_fcover_dd.csv accompanies this dataset with header information.species_pft_checklist.csv is a lookup table containing the translation from a dataset species name to an accepted species name and to a PFT. This table can be used to clarify our species to PFT adjudications, and to aid users in assigning their own PFTs. Any issues found in this checklist should be reported in the Issues tab of our github.survey_unit_information.csv contains auxiliary information about the plots synthesized in this database. It contains useful information for filtering plots of interest based on temporal, geospatial, and contextual information about the plot surveys.flmd.csv contains metadata information about each file in the database.This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fused filament fabrication of thermoplastic polyurethane composites with microencapsulated phase-change material

Here, the present study examines the thermal energy storage (TES) effectiveness and printability of microencapsulated phase-change material (MEPCM) combined with thermoplastic polyurethane (TPU) for fused filament fabrication (FFF). Two formulations were assessed: 24D MEPCM, which changes phase at 24 ° C, compounded with TPU pellets and 43D MEPCM, which changes phase at 43 ° C, integrated with TPU powder. These combinations are designed to evaluate the effectiveness of the form of the TPU (pellets versus powder) in the FFF process. The investigation includes a comprehensive analysis of thermal characteristics, encompassing phase-change temperature, latent heat of fusion, thermal conductivity, and thermal decomposition, which are assessed through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Additionally, mechanical properties, including stress-strain behavior, are examined to evaluate material suitability for TES applications, while microstructural visualization is used to provide deeper insights into material performance, structural integrity, and the quality of printed components. The 24D MEPCM and TPU pellets formulation experienced a significant loss of approximately 39.6% of PCM during filament extrusion and printing, resulting in a reduced effective latent heat. Therefore, further characterization of the pellet formulation was discontinued due to excessive leakage. In contrast, the 43D MEPCM and TPU powder formulation demonstrated minimal PCM loss, with the 60 wt.% composition achieving an effective latent heat of 132 J/g. This value represents the highest effective latent heat currently documented in the literature for PCM-polymer-composite materials produced using an FFF-based additive manufacturing process.

25 ENERGY STORAGE↗