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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Off-spec fly ash-based lightweight aggregate properties and their influence on the fresh, mechanical, and hydration properties of lightweight concrete: A comparative study

An off-spec fly ash-based spherical lightweight aggregate (LWA), designated as Spherical Porous Reactive Aggregate (SPoRA), was manufactured through a lab pilot-scale production and its engineering properties, including specific gravity, dry rodded unit weight, water absorption, mechanical performance, and pore structure, were evaluated. Using SPoRA, lightweight concrete (LWC) samples were made and their fresh, mechanical, and hydration properties were assessed and compared with LWC samples made using two commercial LWA available in the US market. The results indicated that fine and coarse SPoRA had 72 h water absorption capacities of 16.4 % and 20.9 %, respectively, which were higher than that of the two commercial LWAs. Higher saturated surface dry specific gravity of SPoRA compared to commercial LWAs led to a higher fresh density for the corresponding LWC. Using X-ray computed tomography, large spherical type pores were observed in SPoRA similar to those in the commercial slate-based LWA. Additionally, the pore size distribution of SPoRA, characterized by a dynamic vapor sorption analyzer, indicated that more than 97 % of the pores had diameters greater than 50 nm. SPoRA’s average bulk crushing strength was 6.8 MPa which was smaller than commercial LWA and was potentially attributed to the differences in manufacturing processes. Nonetheless, SPoRA LWC with 28 days compressive strength of 29 MPa passed the ASTM C330 requirement and had a comparable strength with LWC prepared with the commercial LWAs indicating lesser importance for the LWA strength. SPoRA LWC had a flowability of 5 % compared to LWC prepared with commercial LWAs having flowability of 18 %. This observation was potentially related to the fluxing agent used in the SPoRA production. SPoRA had a 70 % degree of hydration at 12 days which was comparable with that of LWC prepared with commercial LWA.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types.

58 GEOSCIENCES↗
DOE OSTI2020

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.

Whiteley, J. S.↗
DOE OSTI2021

Evolution of standardization and dissemination of cryo-EM structures and data jointly by the community, PDB, and EMDB

Cryogenic electron microscopy (cryo-EM) methods began to be used in the mid-1970s to study thin and periodic arrays of proteins. Following a half-century of development in cryo-specimen preparation, instrumentation, data collection, data processing and modeling software, cryo-EM has become a routine method for solving structures from large biological assemblies to small biomolecules at near to true atomic resolution. This review explores the critical roles played by the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in partnership with the community to develop the necessary infrastructure to archive cryo-EM maps and associated models. Public access to cryo-EM structure data has in turn facilitated better understanding of structure-function relationships and advancement of image processing and modeling tool development. The partnership between the global cryo-EM community and PDB and EMDB leadership has synergistically shaped the standards for metadata, one-stop deposition of maps and models, and validation metrics to assess the quality of cryo-EM structures. The advent of cryo-electron tomography (cryo-ET) for in situ molecular cell structures at a broad resolution range and their correlations with other imaging data introduces new data archival challenges in terms of data size and complexity in the years to come.

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

Insight into the deformation features and capacity loss mechanisms of lithium-ion pouch cells under spherical indentation conditions

Mechanical deformation under extreme conditions is one of the important reasons for the failure of lithium-ion batteries in automotive application. However, the deformation features and component failure of lithium-ion cells to external loading has never been a design consideration. Here, in this study, we conduct spherical indentation tests on a dozen of lithium-ion cells with different capacities under different control mode conditions to investigate their deformation features and capacity loss mechanisms. The experimental results show that, under mechanical deformation conditions, internal faults of cells occur in stages, and energy accumulation and sudden release are two key processes of cell's mechanical failure. The cells' state of charge is the main factor affecting their thermal runaway behaviors. In addition, a finite element model is developed to simulate the deformation features and the failure mechanism of key components of lithium-ion pouch cells; the 3D x-ray computed tomography is employed to demonstrate its internal configuration. With this model, the force-strain response, the deformation features as well as the size of the failure area of lithium-ion cells under spherical indentation conditions are accurately predicted. In 3D x-ray computed tomography images, unique mud cracks in cooper current collector are observed, and the influence mechanisms of the isolated fragments on the cell capacities are revealed. These results may provide useful information for the mechanical structure design of the components of lithium-ion pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2025

Surface smoothing for laser powder-bed Ti-6Al-4V by a transient liquid phase

Surface roughness is the primary driver of fatigue for additively manufactured metals. To address surface roughness, this work introduces a new method to smooth features beyond line-of-sight without material removal. The method applies a coating that triggers local surface remelting by activating a eutectic reaction during heat treatment. The associated liquid phase then wets and isothermally solidifies into a smoother surface. For Ti-6Al-4V fabricated with laser powder bed fusion, samples with and without TLP smoothing (using a Cu coating) were characterized with a suite of techniques, including mechanical testing, electron backscatter diffraction, synchrotron X-ray tomography, and fractography. TLP smoothing reduced surface roughness by 80% and amplified compressive residual stress at the surface by about 50%. With statistically equivalent virtual microstructures, crystal plasticity scrutinized the roles of phases, porosity, and surface roughness. Although the tensile strain-to-failure was reduced to 1% strain, the TLP smoothing process increased high-cycle fatigue strength by about 20% compared to control samples, pointing to future opportunities to optimize the new process through various coating compositions and heat treatment schedules. Overall, this work establishes a new paradigm for treating surfaces of materials for smoothness and compressive residual stress.

Additive manufacturing↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Microstructural investigation of PBX 9501: Comparing wet slurry and resonant acoustic mixing techniques

The microstructure of a high explosive (HE) material directly influences the reactive behavior under a variety of insults. Furthermore, the preparation method of polymer-bonded explosives (PBX), as well as the constituent material particle size distribution and consolidation parameters, will dominate the microstructural features that are observed. Here, we compare batches of PBX 9501 prepared by the typical wet slurry process and those prepared by resonant acoustic mixing. The “prills” produced by the wet slurry process leads to an inherently different microstructure than the coated powder produced by resonant acoustic mixing. Additionally, we observed that the conditions required to consolidate (press) the material to the nominal PBX 9501 density varied depending on the preparation technique, which also dictates the observed binder distribution. Furthermore, X-ray microcomputed tomography and ultra-small-angle X-ray scattering analyses revealed distinct differences in the void structure and distribution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Comparative Assessment of U-Net-Based Deep Learning Models for Segmenting Microfractures and Pore Spaces in Digital Rocks

Segmentation of high-resolution X-ray microcomputed tomography (µCT) images is crucial in digital rock physics (DRP), affecting the characterization and analysis of microscale phenomena in the porous media. The complexity of geological structures and nonideal scanning conditions pose significant challenges to conventional image segmentation approaches. Motivated by the recent increasing popularity of deep learning (DL) techniques in image processing, this work undertakes a comparative study of DL models, specifically U-Net and its variants, for segmenting multiple targets with distinguished features in digital rocks, including discrete fracture networks (DFNs), pore spaces, and solid rock. Particularly, DFNs have a smaller volumetric fraction over others, bringing in a substantial challenge of imbalanced segmentation. The primary focus is to evaluate the architecture and feature enhancement strategies of various DL models, including U-Net, attention U-Net, residual U-Net, U-Net++, and residual U-Net++. The models were designed as 2.5D, utilizing a central 2D image and its two adjacent upper and lower 2D images as input to provide a pseudo-3D context. In addition, because the ground truth of segmentation was unknown for real-world digital rocks, we created a benchmark data set following the inverse operations of segmentation. The data synthesis started from the label images (i.e., solid rock, pore spaces, and DFNs), followed by simulating partial volume blurring, adding random background noise, and introducing ring artifacts to mimic real raw X-ray µCT images. The data set, which included various rock types (i.e., sandstone and artificial data), scanning resolution, and magnitudes of noise and artifacts, was divided into training and testing data sets with a 90% and 10% ratio, respectively. Moreover, in addition to the conventional pixel-wise evaluation metrics, the physics-based metric of the lattice-Boltzmann method (LBM) simulated permeability provided more comprehensive assessments. The results demonstrated that the residual connections, nested architectures, and redesigned skip connections contribute to the model performance and give the residual U-Net++ the highest accuracy. The improvements were mainly on the boundaries and small targets, especially the DFNs, which dominate the interconnectivity and therefore affect the permeability greatly. This study also rigorously evaluated the efficiency and generalization of each model, demonstrating that the sophisticated architectures achieved excellent practicability and maintained robust performance on completely unseen data, ensuring their suitability for diverse and challenging DRP applications.

58 GEOSCIENCES↗
DOE OSTI2020

Transport Simulations on Scanning Transmission Electron Microscope Images of Nanoporous Shale

Digital rock physics is an often-mentioned approach to better understand and model transport processes occurring in tight nanoporous media including the organic and inorganic matrix of shale. Workflows integrating nanometer-scale image data and pore-scale simulations are relatively undeveloped, however. In this paper, a workflow is demonstrated progressing from sample acquisition and preparation, to image acquisition by Scanning Transmission Electron Microscopy (STEM) tomography, to volumetric reconstruction to pore-space discretization to numerical simulation of pore-scale transport. Key aspects of the workflow include (i) STEM tomography in high angle annular dark field (HAADF) mode to image three-dimensional pore networks in µm-sized samples with nanometer resolution and (ii) lattice Boltzmann method (LBM) simulations to describe gas flow in slip, transitional, and Knudsen diffusion regimes. It is shown that STEM tomography with nanoscale resolution yields excellent representation of the size and connectivity of organic nanopore networks. In turn, pore-scale simulation on such networks contributes to understanding of transport and storage properties of nanoporous shale. Interestingly, flow occurs primarily along pore networks with pore dimensions on the order of tens of nanometers. Smaller pores do not form percolating pathways in the sample volume imaged. Apparent gas permeability in the range of 10−19 to 10−16 m2 is computed.

42 ENGINEERING↗
DOE OSTI2024

Characterization of Soil Thermal and Electrical Properties along Multiple Hillslope Transects at Teller Road Site, Seward Peninsula, Alaska, 2017

This dataset has been acquired along five-119 m long transects located on the bottom part of the watershed hillslope at the NGEE Arctic Teller Road site at mile marker 27 (TL_MM27) on the Seward Peninsula, Alaska in July and September 2017. The Distributed Temperature Profiling (DTP) system dataset consist in vertically-resolved profile of soil temperature covering the top 0.8 m of soil with 8 cm interval. In addition to DPT data, electrical resistivity tomography (ERT) data, soil moisture, depth to rock or thaw layer thickness (no differentiation) and ground elevations data have been acquired along each of the transects. A UAV-based geotiff mosaic of the investigated site is also provided. The four data types provided with this dataset of 37 files (*.csv, *.tif, *.DATA): (1) soil temperature profiles, (2) ERT data, (3) the physical measurements of the thaw layer, and (4) an orthomosaic GeoTIFF of the transect study area.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), 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

Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis

An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.

Additive manufacturing↗
DOE OSTI2024

Cost-Effective Thermomechanical Processing of Nanostructured Ferritic Alloys: Microstructure and Mechanical Properties Investigation

Nanostructured ferritic alloys (NFAs), such as oxide-dispersion strengthened (ODS) alloys, play a vital role in advanced fission and fusion reactors, offering superior properties when incorporating nanoparticles under irradiation. Despite their importance, the high cost of mass-producing NFAs through mechanical milling presents a challenge. This study delves into the microstructure-mechanical property correlations of three NFAs produced using a novel, cost-effective approach combining severe plastic deformation (SPD) with the continuous thermomechanical processing (CTMP) method. Analysis using scanning electron microscopy (SEM)-electron backscatter diffraction (EBSD) revealed nano-grain structures and phases, while scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) quantified the size and density of Ti-N, Y-O, and Cr-O fine particles. Atom probe tomography (APT) further confirmed the absence of finer Y-O particles and characterized the chemical composition of the particles, suggesting possible nitride dispersion strengthening. Correlation of microstructure and mechanical testing results revealed that CTMP alloys, despite having lower nanoparticle densities, exhibit strength and ductility comparable to mechanically milled ODS alloys, likely due to their fine grain structure. However, higher nanoparticle densities may be necessary to prevent cavity swelling under high-temperature irradiation and helium gas production. Further enhancements in uniform nanoparticle distribution and increased sink strength are recommended to mitigate cavity swelling, advancing their suitability for nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling the nucleation and growth of Zr oxide precipitates in internally oxidized Nb3Sn superconductors

We report on atomic-scale analyses of nucleation and growth of Zr oxide precipitates and the microstructural evolution of internally oxidized Nb3Sn wires for high-field superconducting magnet applications, utilizing atom probe tomography (APT), transmission electron microscopy (TEM), and first-principles calculations. APT analyses reveal that oxygen and zirconium are already segregated at grain boundaries (GBs) in the unreacted Nb-1Zr-4Ta (at%) alloy prior to forming Nb3Sn through reacting the Nb alloy with Sn and SnO2. After forming Nb3Sn, Zr oxide precipitates nucleate both at the Nb3Sn/Nb heterophase interfaces and in the Nb3Sn grains, driven by the small solubilities of Zr and O in Nb3Sn compared to their value in Nb. A high number density (Nv) of Zr oxide nanoprecipitates is observed in the Nb3Sn layers, ∼10 23 m −3 , with a mean diameter <10 nm for a heat treatment at 625 °C. Quantitative APT and TEM analyses of the Zr oxide precipitates in the reacted Nb3Sn layers elucidate details of the nucleation, growth, and coarsening processes of the Zr oxide precipitates in Nb3Sn. First-principles calculations and classical nucleation theory are employed to study the nucleation of Zr oxide precipitates in Nb3Sn and to estimate the maximum energy barrier and critical radius for nucleation. Our research unveils the kinetic pathways for nucleation and growth of Zr oxide precipitates and the microstructural evolution of Nb3Sn layers, which helps to understand and improve the superconducting properties of internally oxidized Nb3Sn wires for use in high-field superconducting magnets. •We investigated the nucleation and growth of Zr oxide nanoprecipitates in Nb3Sn superconductors for magnet applications.•APT and TEM analyses reveals the nucleation and growth process of Zr oxide precipitates in Nb3Sn layers.•Classical nucleation theory with DFT is employed to describe the nucleation process of Zr oxide precipitates in Nb3Sn.•We find that the interfacial reactions at Nb/Nb3Sn interface play a critical role in the nucleation of Zr oxide precipitates.•Current study provides a pathway to improve the properties of internally oxidized Nb3Sn superconducting wires.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Positron emission tomography harmonization in the Alzheimer's Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community

Neurosciences & Neurology↗
DOE OSTI2023

Integrating fiber optic sensors into metallic components for sensing in harsh environments

The integration of fiber optic sensors into high-temperature materials is critical for real-time monitoring and autonomous operation of engineering systems. This study demonstrated a spark plasma sintering (SPS)-assisted embedding process for integrating sapphire fiber optic sensors into stainless steel components during part fabrication. Optical fibers were encapsulated in stainless steel 316L powders which were sintered at different fabrication conditions using SPS to investigate the effects of sintering parameters on the embedment. Measurements of optical transmittance, combined with microstructural analysis (X-ray computed tomography and scanning electron microscopy) and mechanical testing (tensile and microhardness), were conducted to examine the fiber functionality, fiber–matrix bonding quality, and properties of the sintered materials. Here, the results show that under suitable fabrication conditions, intact optical fibers can be encapsulated in highly-densified (>98 % relative density) stainless steel components. These conditions also led to a good bond at the fiber–matrix interface with micron-sized material interdiffusion across the interface. The sintering parameters were observed to affect fiber optical attenuation, where high temperature, pressure, and hold time during SPS enhanced fiber–matrix bonding and adversely affected optical transmission. Tensile testing confirmed the superior tensile strength and ductility of the matrix fabricated by SPS. Furthermore, the materials exhibited limited strength reduction (~70 MPa) upon the integration of fibers. This study demonstrates the effectiveness of SPS for fiber-material integration for high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 2. Evaluating Controls on Flow Persistence in an Urbanized Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.

ATS↗
DOE OSTI2021

Progressive growth of the solid–electrolyte interphase towards the Si anode interior causes capacity fading

The solid–electrolyte interphase (SEI), a layer formed on the electrode surface, is essential for electrochemical reactions in batteries and critically governs the battery stability. Active materials, especially those with extremely high energy density, such as silicon (Si), often inevitably undergo a large volume swing upon ion insertion and extraction, raising a critical question as to how the SEI interactively responds to and evolves with the material and consequently controls the cycling stability of the battery. Here, by integrating sensitive elemental tomography, an advanced algorithm and cryogenic scanning transmission electron microscopy, we unveil, in three dimensions, a correlated structural and chemical evolution of Si and SEI. Furthermore, corroborated with a chemomechanical model, we demonstrate progressive electrolyte permeation and SEI growth along the percolation channel of the nanovoids due to vacancy injection and condensation during the delithiation process. Consequently, the Si–SEI spatial configuration evolves from the classic ‘core–shell’ structure in the first few cycles to a ‘plum-pudding’ structure following extended cycling, featuring the engulfing of Si domains by the SEI, which leads to the disruption of electron conduction pathways and formation of dead Si, contributing to capacity loss. The spatially coupled interactive evolution model of SEI and active materials, in principle, applies to a broad class of high-capacity electrode materials, leading to a critical insight for remedying the fading of high-capacity electrodes.

36 MATERIALS SCIENCE↗