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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Out-of-plane properties

This paper summarizes three areas of research which were performed to characterize out-of-plane properties of composite materials. In the first investigation, a series of tests was run to characterize the through-the-thickness tensile strength for a variety of composites that included 2D braids, 2D and 3D weaves, and prepreg tapes. A new test method based on a curved beam was evaluated. Failures were significantly different between the 2D materials and the 3D weaves. The 2D materials delaminated between layers due to out-of-plane tensile stresses while the 3D weaves failed due to the formation of radial cracks between the surface plies caused by high circumferential stresses along the inner radius. The strength of the 2D textile composites did not increase relative to the tapes. Final failure in the 3D weaves was caused by a circumferential crack similar to the 2D materials and occurred at a lower bending moment than in other materials. The early failures in the 3D weaves were caused by radial crack formation rather than a low through-the-thickness strength. The second investigation focused on the development of a standard impact test method to measure impact damage resistance. The only impact tests that currently exist are compression after impact (CAI) tests which incorporate elements of both damage resistance and damage tolerance. A new impact test method is under development which uses a quasi-static indentation (QSI) test to directly measure damage resistance. Damage resistance is quantified in terms of the contact force to produce a unit of damage where a metric for damage may be area in C-scan, depth of residual dent , penetration, damage growth, etc. A final draft of an impact standard that uses a QSI test method will be presented to the ASTM Impact Task Group on impact. In the third investigation, the impact damage resistance behavior of a variety of textile materials was studied using the QSI test method. In this study, the force where large damage initiates was measured and the delamination size as a function of force was determined. The force to initiate large damage was significantly lower in braids and weaves. The delamination diameter - impact forace relationship was quanitfied using a damage resistance parameter, Q(*), which related delamination diameter to imapct force over a range of delamination sizes. Using this Q(*) parameter to rate the materials, the stitched uniweaves, toughened epoxy tapes, and through-the-thickness orthogonal interlock weave were the most damage resistant.

Jackson, Wade C.↗
DOE OSTI2023

Photoexcitation of Fe 3 O Nodes in MOF Drives Water Oxidation at pH=1 When Ru Catalyst Is Present

Abstract Artificial photosynthesis strives to convert the energy of sunlight into sustainable, eco‐friendly solar fuels. However, systems with light‐driven water oxidation reaction (WOR) at pH=1 are rare. Broadly used [Ru(bpy) 3 ] 2+ (bpy=2,2’‐bipyridine) photosensitizer has a fixed +1.23 V potential which is insufficient to drive most water oxidation catalysts (WOCs) in acid, while Fe 2 O 3 , featuring the highly oxidizing holes, is not stable at low pH. Here, the key examples of Fe‐based metal–organic framework (MOF) water oxidation photoelectrocatalysts active at pH=1 are presented. Fe‐MIL‐126 and Fe MOF‐dcbpy structures were formed with 4,4’‐biphenyl dicarboxylate (bpdc), 2,2’‐bipyridine‐5,5’‐dicarboxylate (dcbpy) linkers and their mixtures. Presence of dcbpy linkers allows integration of metal‐based catalysts via coordination to 2,2’‐bipyridine fragments. Fe‐based MOFs were doped with Ru‐based precursors to achieve highly active MOFs bearing [Ru(bpy)(dcbpy)(H 2 O) 2 ] 2+ WOC. Materials were analyzed with X‐ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infra‐red (FTIR) spectroscopy, resonance Raman, X‐ray absorption spectroscopy, fs optical pump‐probe, electron paramagnetic resonance (EPR), diffuse reflectance and electric conductivity measurements and were modeled by band structure calculations. It is shown that under reaction conditions, Fe III and Ru III oxidation states are present, indicating rate‐limiting electron transfer in MOF. Fe 3 O nodes emerge as photosensitizers able to drive prolonged O 2 evolution in acid. Further developments are possible via MOF's linker modification for enhanced light absorption, electrical conductivity, reduced MOF solubility in acid, Ru‐WOC modification for faster WOC catalysis, or Ru‐WOC substitution to 3d metal‐based systems. The findings give further insight for development of light‐driven water splitting systems based on Earth‐abundant metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

Fluid flow increases mineralized matrix deposition in 3D perfusion culture of marrow stromal osteoblasts in a dose-dependent manner

Bone is a complex highly structured mechanically active 3D tissue composed of cellular and matrix elements. The true biological environment of a bone cell is thus derived from a dynamic interaction between responsively active cells experiencing mechanical forces and a continuously changing 3D matrix architecture. To investigate this phenomenon in vitro, marrow stromal osteoblasts were cultured on 3D scaffolds under flow perfusion with different rates of flow for an extended period to permit osteoblast differentiation and significant matrix production and mineralization. With all flow conditions, mineralized matrix production was dramatically increased over statically cultured constructs with the total calcium content of the cultured scaffolds increasing with increasing flow rate. Flow perfusion induced de novo tissue modeling with the formation of pore-like structures in the scaffolds and enhanced the distribution of cells and matrix throughout the scaffolds. These results represent reporting of the long-term effects of fluid flow on primary differentiating osteoblasts and indicate that fluid flow has far-reaching effects on osteoblast differentiation and phenotypic expression in vitro. Flow perfusion culture permits the generation and study of a 3D, actively modeled, mineralized matrix and can therefore be a valuable tool for both bone biology and tissue engineering.

Non-NASA Center↗
DOE OSTI2021

Mechanical Performance and Mesostructure Analysis of Proton-Irradiated Fused Filament Fabrication Acrylonitrile Butadiene Styrene Material

The use of fused filament fabrication (FFF) acrylonitrile butadiene styrene (ABS) and other thermoplastics in radiation environments is beginning to be studied as possible replacements for traditionally manufactured parts and tools. Interlayer adhesion within the mesostructure has been shown in published literature to be an integral component in the strength of FFF ABS. Research of irradiated 3D printed polymers has primarily focused on the influence of gamma irradiation on the mechanical properties of FFF ABS samples, without evaluating its impact on the mesostructure of the samples. The purpose of this paper is to understand the mechanical damage caused by proton radiation on FFF ABS samples through the evaluation of the mesostructure of the ABS samples. To achieve this objective, proton radiation at 40 MeV was applied to FFF ABS samples at radiation doses up to 1.0 MGy at a dose rate of 1 MGy/hr. Following the irradiation, tensile testing was performed on the samples. Here, the fractured surfaces of the tested samples were subsequently observed using a scanning electron microscope. The mechanical test results show an increase in the ultimate tensile strength (UTS) and a decrease in the ductility of the irradiated samples. Statistical analysis on the results shows that there is a statistically significant difference in the UTS and the ductility of unirradiated and 1.0 MGy irradiated samples and between irradiated samples and 1.0 MGy samples. In addition, the percentage elongation at break has statistically significant differences in the means between irradiated samples and 1.0 MGy samples. The UTS has statistically significant differences in the means between 0.1 MGy and 1.0 MGy. The difference in the means between the unirradiated and the 1.0 MGy samples is the most significant for both the UTS and elongation at break. The scanning electron microscopy (SEM) results indicated that interlayer adhesion improved as a function of radiation dose corresponding to the increase in tensile strength. The SEM results also showed that crazing and plastic deformation were reduced; aligning with the loss in ductility observed in the tensile tests results. The proton radiation is causing these mechanical and physical changes through two mechanisms: (1) dose rate effects on ionizing radiation-induced oxidative degradation; and (2) radiation heating effects of high energy (>1 MeV) charged particles.

electron microscopy↗
DOE OSTI2021

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Anomalous and planar Hall effects in Co 1−x Ho x thin films across the magnetic sublattice compensation temperature

Metallic amorphous ferrimagnets derived from alloying 3d transition metals with 4f-electron rare earths host fascinating effects of compensation between the 3d and 4f magnetic sublattices. Here, a detailed study of the anisotropic magnetoresistance (Δρ xx ), planar Hall effect (ρ xy PHE ), and anomalous Hall effect (ρ xy AHE ) is reported on a series of Co 1−x Ho x thin films over a wide field–temperature (H–T) phase space. Close to the magnetic compensation temperature, the ρ xy AHE –H loops show a double sign reversal and signatures of spin-flop transition at higher fields. The Δρ xx and ρ xy PHE also display strong deviations from the classical angular dependence seen in soft ferromagnets like permalloy as the angle ϕ between in-plane current and magnetic field is scanned from 0 to 2π. It is argued that the non-zero orbital angular momentum of Ho ions in the lattice and stabilization of bubble domains below magnetic saturation may be responsible for such features. Direct imaging of magnetic textures with x-ray photoelectron microscopy shows the formation of stripe domain patterns in the regime of sublattice compensation. Such stripes are likely to transform into magnetic bubbles before full saturation is reached in a large magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electron Tunneling and X-Ray Photoelectron Spectroscopy Studies of the Superconducting Properties of Nitrogen-Doped Niobium Resonator Cavities

Scanning tunneling microscopy and spectroscopy and X-ray photoelectron spectroscopy (XPS) have been used to investigate the differences between the surface electronic structures and chemical structure of typically prepared and N-doped Nb cutouts from superconducting radio frequency (SRF) cavities. The goal of this work is to get insights into the fundamental physics and materials mechanisms behind the striking decrease of the surface resistance with the radiofrequency magnetic field, which has been observed by many groups on N-doped Nb cavities. In particular, we address the effects of N-doping on the superconducting properties at the surface of SRF cavities. XPS measurements reveal a significantly more oxidized Nb 3d states on the N-doped Nb surfaces which is confirmed by tunneling spectroscopy measurements. Analysis of the tunneling spectra in the framework of a recent model (A. Gurevich and T. Kubo Phys. Rev. B 96, 184515 (2017)) shows that the N-doping greatly reduces local distribution of superconducting properties on the surface, causes a significant shrinkage of the metallic suboxide and changes the contact resistance between the metallic suboxide and the bulk niobium toward an optimum value, resulting in a lower surface resistance. Combination of these factors enables one to effectively tune the density of states at the surface and to reveal the decrease of the surface resistance with the radio frequency field which follows from the BCS theory. A slightly reduced average gap and a smaller coherence length, revealed by tunneling spectroscopy in the vortex cores, have been found in the N-doped Nb samples compared to typically prepared Nb samples, indicating a stronger impurity scattering caused by nitrogen doping in a moderately disordered material.

Lechner, Eric M.↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
DOE OSTI2022

NXREFINE

NXRefine implements a complete workflow for both data acquisition and reduction of single crystal diffuse x-ray scattering collected as a series of area detector frames during continuous sample rotation. The data are stored in HDF5 files, which conform to the NeXus data format standard. These files contain a comprehensive set of metadata, including the incident wavelength, powder calibration scans used to define detector distances, beam centers, and yaw, pitch, and roll corrections. Automated peak searches of the three-dimensional data arrays define a set of Bragg peaks that are used to define an orientation matrix, defined according to Busing and Levy, which is then used to transform the data from angular coordinates to reciprocal space coordinates using the CCTW program. Multiple rotations at different detector translations and offset rotation axes are combined to ensure that there are no gaps in the reconstructed data, and allow the construction of masks to eliminate detector artifacts due to Compton scattering within the sensor layers. Finally, the data can be transformed into 3D-ΔPDF maps using the punch-and-fill method after symmetrization.

OSBORN, RAYMOND↗
NASA NTRS2015

3D Printing in Zero-G Experiment, In Space Manufacturing (LPS, 4)

The 3D Printing in Zero‐G Experiment has been an ongoing effort for several years. In June 2014 the technology demonstration 3D printer was launched to the International Space Station. In November 2014 the first 21 parts were manufactured in orbit marking the beginning of a paradigm shift that will allow astronauts to be more self‐sufficient and pave the way to larger scale orbital manufacturing. Prior to launch the 21 parts were built on the ground with the flight unit with the same feedstock. These ground control samples are to be tested alongside the flight samples in order to determine if there is a measurable difference between parts built on the ground vs. parts built in space. As of this writing, testing has not yet commenced. Tests to be performed are structured light scanning for volume and geometric discrepancies, CT scanning for density measurement, destructive testing of mechanical samples, and SEM analysis for inter‐laminar adhesion discrepancies. Additionally, an ABS material characterization was performed on mechanical samples built from the same CAD files as the flight and ground samples on different machine / feedstock combinations. The purpose of this testing was twofold: first to obtain mechanical data in order to have a baseline comparison for the flight and ground samples and second to ascertain if there is a measurable difference between machines and feedstock.

Bean, Quincy↗
DOE OSTI2020

Nanoscale optical tomography with cathodoluminescence spectroscopy

In one aspect, a cathodoluminescence (CL) spectroscopic tomography device includes a sample stage to support a sample. An electron beam source scans an electron beam over the sample to yield light emission by the sample. A reflective element directs the light emission by the sample to a light detector. A controller controls operation of the sample stage, the electron beam source, and the light detector. In one aspect, a CL spectroscopic tomography device includes an electron beam source which directs an electron beam at an object to yield an emission by the object. A detector detects the emission. A controller receives information from the detector related to the detected emission. The controller derives a two-dimensional (2D) CL map from the information related to the detected emission, and derives a three-dimensional (3D) CL tomogram from the 2D CL map.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Evidence of hexagonal germanium grains on annealed monolayer MoS 2

Growing three-dimensional (3D) materials on two-dimensional (2D) van der Waals surface has shown its effectiveness in overcoming materials incompatibility for stacking transferrable membranes toward advanced device manufacturing. Herein, we demonstrate that the nucleation of hexagonal germanium (Ge) grains within a continuous crystalline film, which has been unfeasible through traditional epitaxy techniques, is realized by chemical vapor deposition on top of n-type monolayer molybdenum disulfide (MoS 2 ) substrates. Suggested by quantum molecular dynamics calculation, the hexagonal Ge nucleation is thermodynamically preferable to cubic Ge when growing on monolayer MoS 2 with sulfur vacancies. The strained hexagonal Ge grains have been confirmed by transmission electron microscopy analyses from both real space and reciprocal space. Scanning probe microscopy shows that the hexagonal Ge film possesses higher reflectivity in infrared spectral range, implying a higher carrier concentration resulted from the narrower band gap, as compared to cubic Ge.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Non-disruptive error field measurement in DIII-D low safety factor plasmas and projection to ITER

Previous experiments in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion 62 126007) introduced a method to identify intrinsic error fields (EFs) in tokamaks with minimal disruption risk by promptly healing driven magnetic islands during the conventional 'compass scan'. This paper presents recent experimental and numerical advancements in extending this approach to low q 95 plasmas, and projects its applicability to ITER. Non-disruptive EF measurement is achieved at q 95 = 4.5 and 3.9 without any initial EF correction (EFC) by reducing the time between the occurrence of the locked mode (LM) and control action to 10 ms and increasing the density 50%–100%. However, 50% correction of the intrinsic EF is required to achieve island healing at q 95 = 3.2 with 10 ms delay for the control action. Nonlinear two-fluid modeling with the TM1 code reproduces the DIII-D experimental observations, indicating that promptly turning off the 3D coil current reduces both magnetic island width and electromagnetic force, while raising the density increases plasma viscosity, facilitating magnetic island healing. The simulations show that for scenarios with q 95 = 3.2, lowering the control action time to 5 ms will lead to island healing without EFC. TM1 simulations are extended to future ITER scenarios with 5 MA and 7.5 MA plasma currents, predicting the dependence of required density rise on action time and EF amplitude. These simulations indicate that, benefiting from the much longer resistive time, island healing can be successfully achieved in ITER when taking control action 100–500 ms after a LM occurrence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Compositional effects on cure kinetics, mechanical properties and printability of dual-cure epoxy/acrylate resins for DIW additive manufacturing

Interest in 3D printing of thermoset resins has increased significantly in recent years. One approach to additive manufacturing of thermoset resins is printing dual-cure resins with direct ink write (DIW). Dual-cure resins are multi-component resins which employ an in situ curable constituent to enable net-shape fabrication while a second constituent and cure mechanism contribute to the final mechanical properties of the printed materials. In this work, the cure kinetics, green strength, printability, and print fidelity of dual-cure epoxy/acrylate thermoset resins are investigated. Resin properties are evaluated as a function of acrylate concentration and in situ UV exposure conditions. The acrylate cure kinetics are probed using photo-differential scanning calorimetry and the impacts of resin composition and UV cure profile on the acrylate extent of conversion are presented. Continuous and pulsed UV cure profiles are shown to affect total conversion due to variances in radical efficiency at different UV intensities and acrylate concentrations. The effects of acrylate concentration on the kinetics of the epoxy thermal cure and the final mechanical properties are also investigated using dynamic mechanical analysis and three-point bend measurements. The glass transition temperature is dependent on formulation, with increasing acrylate content decreasing the T g . However, the room temperature shear moduli, flexural moduli, strength, strain-to-failure, and toughness values are relatively independent of resin composition. The similarity of the final properties allows for greater flexibility in resin formulation and in situ cure parameters, which can enable the printing of complex parts that require high green strength. We found that the in situ UV print intensities and exposure profiles that are necessary to achieve the best print quality are not, in most cases, the conditions that maximize conversion of the acrylate network. Lastly, this highlights the importance of developing optimized resin compositions which enable complete cure of the acrylate network by promoting acrylate dark cure or thermal cure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational Tools for Additive Manufacture of Tailored Microstructure and Properties

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Currently most additive builds use a single set of process parameters (e.g. laser power and scan speed) which results in a part with a homogenous microstructure that provides a singular performance level. To move beyond this state, Raytheon Technologies Research Center worked to create a set of computational tools to track material evolution through each step of the additive process. Computational fluid dynamics and phase field models for microstructure evolution as a function of processing parameters, and crystal plasticity models fully coupling microstructure and mechanical properties for performance predictions were leveraged to establish a connection between additive parameters and the final microstructure. This framework was utilized to tailor spatially-varying mechanical properties in a part by appropriately controlling the microstructure evolution during the additive process. Specifically, a turbine blade was 3D printed from nickel superalloy IN718 using laser powder bed fusion with coarse grains in the airfoil section which experiences the highest temperatures and is creep limited while finer grains were printed in the root of the blade which experience higher stresses but at lower temperatures and is therefore fatigue limited. The benefit of being able to intentionally insert coarse grains in the high temperature region of the blade was showcased with a microstructure sensitive creep model that indicates longer creep life for coarser grains.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗
DOE OSTI2021

From casting to 3D printing geopolymers: A proof of concept

The objective of this paper is to define a mix design strategy allowing the implementation of geopolymer in powder bed 3D printing. In a selective binder approach, an aluminosilicate powder bed is activated through the deposit of an alkaline silicate solution. We first show that, in comparison to casting, this technology requires mastering the spreading of the liquid into the powder bed to ensure that the reaction takes place where it is needed. A series of experiments at the drop level, mimicking the 3D printing process, were performed to determine the printing solution composition for a given powder bed packing. We then highlight the relationship between the compressive strength of the printed geopolymer and the powder bed saturation, controlled by the printing parameters. Finally, SEM/EDX analysis confirm the formation of geopolymer with desired Si/Al and Na/Al ratios in the resulting printed material.

36 MATERIALS SCIENCE↗