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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DEVELOPMENT OF PLASMA SIMULATION TOOL FOR THE ACCEL-ERATING CAVITIES

Plasma processing of superconducting radio frequency (SRF) cavities has shown an improvement in accelerating gradient by reducing the radiation due to field emission and multipacting. Plasma processing is a common tech-nique where the free oxygen produced by the plasma breaks down and removes hydrocarbons from surfaces. This increases the work function and reduces the second-ary emission coefficient. The hydrocarbon fragments of H2, CO, CO2, and H2O are removed from the system with the process gas which is flowing through the system. Here, we present COMSOL for the first time to simulate the plasma processing of an SRF cavity. In this work, we use Jefferson Lab's C75 SRF cavities design as our case study. Using simulation, we predict the condition of plasma ignition inside the SRF cavity. The simulation provides information about the optimal rf coupling to the cavity, mode for plasma ignition, choice of gas concen-tration, power, and pressure.

Senevirathne, I.↗
DOE OSTI2020

Interactions Between Microturbulence and Magnetohydrodynamic Instabilities in Magnetized Plasmas (Final Technical Report)

This document details the work accomplished during the period 04/15/2012-01/31/2020 for the collaborative project “Interactions Between Microturbulence and Magnetohydrodynamic Instabilities in Magnetized Plasmas,” and serves as the final report on the University of California, San Diego part of this collaborative project for Department of Energy Office of Science - Fusion Energy Sciences Grant No. DE- SC0007783. This project focused on the development of analytic and computational tools, seeking to better understand the mutual interactions between small-scale temperature-gradient driven microturbulence and macroscopic magnetohydrodynamic (MHD) instabilities, particularly tearing modes, in magnetically confined plasmas relevant for the generation of fusion energy. This project advanced understanding through numerical simulations of different sets of model equations, corresponding to different microturbulence instabilities. Questions addressed included how the magnitudes, spatial structures, and scaling's of turbulent forcing's and drags impact magnetic island dynamics, as well as quantifying the “region of influence” of an island on the microturbulence dynamics. A key finding of the project is that the microturbulence fluctuations and associated cross-field particle and energy fluxes exhibit spatial asymmetries for sufficiently large islands. The results have improved our fundamental understanding of plasma dynamics, motivated new experimental investigations, and will help inform future fusion predictive modeling efforts.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120–600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
DOE OSTI2023

Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels

Abstract U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Since the Experimental Breeder Reactor-II in the late 1960s, researchers accumulated a considerable amount of experience and knowledge on fuel performance at the engineering scale. However, a mechanistic understanding of fuel microstructure evolution and property degradation during in-reactor irradiation is still missing due to a lack of appropriate tools for rapid fuel microstructure assessment and property prediction based on post irradiation examination. This paper proposed a machine learning enabled workflow, coupled with domain knowledge and large dataset collected from advanced post-irradiation examination microscopies, to provide rapid and quantified assessments of the microstructure in two reactor irradiated prototypical annular metal fuels. Specifically, this paper revealed the distribution of Zr-bearing secondary phases and constitutional redistribution across different radial locations. Additionally, the ratios of seven different microstructures at various locations along the temperature gradient were quantified. Moreover, the distributions of fission gas pores on two types of U-10Zr annular fuels were quantitatively compared.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of compact tokamak fusion reactor use cases to inform future transport studies

The OMFIT STEP (Meneghini et al. , Nucl. Fusion , vol. 10, 2020, p. 1088) workflow has been used to develop inductive and steady-state H-mode core plasma scenario use cases for a $B_0 = 8 \, {\rm T}$ , $R_0 = 4 \, {\rm m}$ machine to help guide and inform future higher-fidelity studies of core transport and confinement in compact tokamak reactors. Both use cases are designed to produce 200 MW or more of net electric power in an up-down symmetric plasma with minor radius $a = 1.4 \, {\rm m}$ , elongation $\kappa = 2.0$ , triangularity $\delta = 0.5$ and effective charge $Z_{{\rm eff}} \simeq 2$ . Additional considerations based on the need for compatibility of the core with reactor-relevant power exhaust solutions and external actuators were used to guide and constrain the use case development. An extensive characterization of core transport in both scenarios is presented, the most important feature of which is the extreme sensitivity of the results to the quantitative stiffness level of the transport model used as well as the predicted critical gradients. This sensitivity is shown to arise from different levels of transport stiffness exhibited by the models, combined with the gyroBohm-normalized fluxes of the predictions being an order of magnitude larger than other H-mode plasmas. Additionally, it is shown that although heating in both plasmas is predominantly to the electrons and collisionality is low, the plasmas remain sufficiently well coupled for the ions to carry a significant fraction of the thermal transport. As neoclassical transport is negligible in these conditions, this situation inherently requires long-wavelength ion gyroradius-scale turbulence to be the dominant transport mechanism in both plasmas. These results are combined with other basic considerations to propose a simple heuristic model of transport in reactor-relevant plasmas, along with simple metrics to quantify coupling and core transport properties across burning and non-burning plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Laser-free trapped-ion entangling gates with simultaneous insensitivity to qubit and motional decoherence

The dominant error sources for state-of-the-art laser-free trapped-ion entangling gates are decoherence of the qubit state and the ion motion. The effect of these decoherence mechanisms can be suppressed with additional control fields or with techniques that have the disadvantage of reducing gate speed. In this study, we propose using a near-motional-frequency magnetic field gradient to implement a laser-free gate that is simultaneously resilient to both types of decoherence, does not require additional control fields, and has a relatively small cost in gate speed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2022

Stand and environmental conditions drive functional shifts associated with mesophication in eastern US forests

There is a growing body of evidence that mesic tree species are increasing in importance across much of the eastern US. This increase is often observed in tandem with a decrease in the abundance and importance of species considered to be better adapted to disturbance and drier conditions (e.g., Quercus species). Concern over this transition is related to several factors, including the potential that this transition is self-reinforcing (termed “mesophication”), will result in decreased resiliency of forests to a variety of disturbances, and may negatively impact ecosystem functioning, timber value, and wildlife habitat. Evidence for shifts in composition provide broad-scale support for mesophication, but we lack information on the fine-scale factors that drive the associated functional changes. Understanding this variability is particularly important as managers work to develop site-and condition-specific management practices to target stands or portions of the landscape where this transition is occurring or is likely to occur in the future. To address this knowledge gap and identify forests that are most susceptible to mesophication (which we evaluate as a functional shift to less drought or fire tolerant, or more shade tolerant, forests), we used data from the USDA Forest Service Forest Inventory and Analysis program to determine what fine-scale factors impact the rate (change through time) and degree (difference between the overstory and midstory) of change in eastern US forests. We found that mesophication varies along stand and environmental gradients, but this relationship depended on the functional trait examined. For example, shade and drought tolerance suggest mesophication is greatest at sites with more acidic soils, while fire tolerance suggests mesophication increases with soil pH. Mesophication was also generally more pronounced in older stands, stands with more variable diameters, and in wetter sites, but plots categorized as “hydric” were often highly variable. Our results provide evidence that stand-scale conditions impact current and potential future changes in trait conditions and composition across eastern US forests. We provide a starting point for managers looking to prioritize portions of the landscape most at risk and developing treatments to address the compositional and functional changes associated with mesophication.

Woodbridge, Margaret↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thermodynamic assessment of the quaternary WTaCrV refractory high entropy alloy as a means to guide experimental approaches

The deployment of fusion energy poses challenges for materials in plasma facing components to withstand high temperatures and thermal gradients, particle implantation and neutron damage. The current material of choice is tungsten, although property degradation limits its consideration in future fusion reactors. Hence, materials with better resistance to harsh environments need to be developed for fusion energy to become a reality. High entropy alloys are being explored as potential candidates with some compositions showing good radiation resistance to defect cluster formation. One of these materials is the WTaCrV system, although only one composition has been tested under ion irradiation. In this work, we study the thermodynamic properties of the entire quaternary alloy composition range. Coupling first principles calculations, cluster expansion approaches, and Monte Carlo methods, we access the free energy functionals, short-range ordering as a function of temperature, and atomic configurations that can be compared to experimental observations. We use this data to inform experiments into compositions with higher propensity to form solid solutions, instead of phase separating. With this formalism we have developed thermodynamic database (TDB) files that can be used to plot quaternary phase diagrams.

Cluster Expansion↗
DOE OSTI2024

Investigation of acoustic waves under subsurface conditions to improve the predictions of rock mechanical properties and natural fracture characteristics

Mechanical properties and natural fracture characteristics are critical to investigate for subsurface engineering applications, including carbon storage, well drilling, and stimulation, as they govern rock stability, fluid flow, and mechanical behavior under stress. This dissertation integrates experimental and machine learning approaches to enhance the prediction and understanding of these properties by analyzing acoustic wave behavior under varied subsurface conditions. First, the influence of temperature, pore pressure, and supercritical CO2 (scCO2) saturation on poroelastic properties is examined using Gray Berea sandstone samples. The results show that temperature and pore pressure significantly affect the bulk modulus and Biot’s coefficient, while scCO2 saturation impacts rock compressibility, informing strategies for effective geological carbon storage. The study extends this understanding by experimentally evaluating the impact of reservoir depletion on the dynamic mechanical properties of the emerging Caney shale in South Oklahoma with the employment of unsupervised machine learning to predict static mechanical properties across the Caney shale. Integrating petrophysical data and chemostratigraphy, the workflow—featuring K-means clustering, principal component analysis (PCA), and inverse distance weighting (IDW)—improves stratigraphic characterization and the estimation of static-to-dynamic modulus ratios, which is vital for optimizing drilling and stimulation strategies. Finally, the work explores how natural fracture characteristics in shale influence acoustic waveforms and shear wave splitting (SWS) analysis. Experimental data on fractured samples under different stress and temperature conditions, combined with machine learning models such as K-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), reveal key fracture properties impacting SWS and wave propagation. Together, these studies provide a comprehensive framework for linking acoustic wave behavior with rock properties, advancing the methods for monitoring and predicting geomechanical changes. The insights offered valuable implications for safer, more efficient CO2 injection, hydrocarbon extraction, and subsurface management.

Elkholy, Sherif↗
DOE OSTI2026

Historic climate, cosmogenic 10Be, denudation-rate, and geospatial datasets from the Pikes Peak region, Colorado, USA

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of elevation-dependent denudation rates on Pikes Peak in the Front Range of the Rocky Mountains, Colorado, USA. The package includes GIS layers used to produce the study-area map, including sample locations, sample watershed boundaries, the Pikes Peak batholith, Pleistocene glacier extent, weather station locations, and elevation and hillshade rasters, together with comma-separated value (CSV) tables and matching CSV data dictionaries. These mapped layers provide the geographic framework for interpreting denudation patterns across the Pikes Peak region and for relating sample locations to watershed geometry, bedrock setting, glacial history, and nearby climate stations. The first group of tables reports climate and geospatial context for the study area. These files include station-based temperature and precipitation data used to characterize elevational gradients in mean annual climate and monthly climate seasonality, sample locations, denudation-rate and topographic metrics, fixed frost-cracking model parameters, frost-cracking intensity and precipitation-frequency metrics, and stream-power inversion results. Together, these data provide the basis for evaluating how denudation varies with elevation, climate, and landscape form across sampled catchments on Pikes Peak. The second group of tables reports cosmogenic nuclide and erosion-model results used in the denudation analysis. Included files contain accelerator mass spectrometry (AMS) measurements for in situ-produced cosmogenic beryllium-10 (10Be), including sample identifiers, measured 10Be:9Be ratios, analytical uncertainties, carrier mass, quartz mass, blank corrections, blank-group statistics, and calculated 10Be concentrations and uncertainties. Additional tables summarize stream-power-law inversion results for sampled catchments, including optimized model parameters, predicted erosion rates, residual metrics, channel-pixel counts, and convergence status, as well as regression equations and summary statistics used to evaluate relationships among elevation, climate, frost cracking, precipitation forcing, and denudation rate. The package contains GIS files, comma-separated value files (.csv), Microsoft Excel files (.xlsx), CSV data dictionaries, a file-level metadata table, and a readme text file.

10Be cosmogenic nuclides↗
DOE OSTI2023

Three-Dimensional Reconstruction of Nb 3 Sn Films by Focused Ion Beam Cross Sectional Microscopy

Niobium has been the material of choice for SRF cavities for several decades due to its formability and superconducting properties. The accelerating gradient of niobium cavities is, however, rapidly approaching a theoretical limit. To achieve higher accelerating gradients a new material is needed that can sustain high fields. Nb 3 Sn is a promising competitor with a higher superconducting transition temperature and a higher critical field than pure niobium. However, Nb 3 Sn is very brittle and cannot be formed readily into a cavity. The main method for creating Nb 3 Sn cavities is to form a Nb 3 Sn film into a niobium surface using a tin vapor-diffusion method. This technique creates a microcrystalline Nb 3 Sn thin film on the inner surface of the cavity. Tin depleted regions are known to form in the film during this process. Previous studies have analyzed these regions using transmission electron microscopy on cross-sectional lamellae prepared by focused ion beam/scanning electron microscope (FIB/SEM). This method does not provide any three-dimensional (3-D) information about the distribution of tin-deficient regions. In this study we employ a focused ion beam tomographic technique to analyze the 3-D structure of the film. Electron dispersive X-ray spectroscopy is used to image the tin concentration of the film in 3-D. Tin-deficient regions are discovered close to the surface of the Nb 3 Sn film.

Viklund, E.↗