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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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Processing-dependent chemical ordering in Cu 3 Au characterized via non-destructive Bragg coherent diffraction imaging
Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. Here, in this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu 3 Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.
Synchrotron Micro Computed Tomography for Non-destructive 3D Studies of Fossil Fish
Abstract not provided
3D Non-Destructive Identification of Fossil Fish from the Late Cretaceous of Alberta, Canada using S
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Primary radiation damage in bone evolves via collagen destruction by photoelectrons and secondary emission self-absorption
X-rays are invaluable for imaging and sterilization of bones, yet the resulting ionization and primary radiation damage mechanisms are poorly understood. Here we monitor in-situ collagen backbone degradation in dry bones using second-harmonic-generation and X-ray diffraction. Collagen breaks down by cascades of photon-electron excitations, enhanced by the presence of mineral nanoparticles. We observe protein disintegration with increasing exposure, detected as residual strain relaxation in pre-stressed apatite nanocrystals. Damage rapidly grows from the onset of irradiation, suggesting that there is no minimal ‘safe’ dose that bone collagen can sustain. Ionization of calcium and phosphorous in the nanocrystals yields fluorescence and high energy electrons giving rise to structural damage that spreads beyond regions directly illuminated by the incident radiation. Our findings highlight photoelectrons as major agents of damage to bone collagen with implications to all situations where bones are irradiated by hard X-rays and in particular for small-beam mineralized collagen fiber investigations.
Substantial contribution of iodine to Arctic ozone destruction
Unlike bromine, the effect of iodine chemistry on the Arctic surface ozone budget is poorly constrained. We present ship-based measurements of halogen oxides in the high Arctic boundary layer from the sunlit period of March to October 2020 and show that iodine enhances springtime tropospheric ozone depletion. We find that chemical reactions between iodine and ozone are the second highest contributor to ozone loss over the study period, after ozone photolysis-initiated loss and ahead of bromine.
Novel niobium-doped titanium oxide towards electrochemical destruction of forever chemicals
Electrochemical advanced oxidative processes (EAOP) are a promising route to destroy recalcitrant organic contaminants such as per- and polyfluoroalkyl substances (PFAS) in drinking water. Central to EAOP are catalysis-induced reactive free radicals for breaking the carbon fluorine bonds in PFAS. Generating these reactive species electrochemically at electrodes provides an advantage over other oxidation processes that rely on chemicals or other harsh conditions. Herein, we report on the performance of niobium (Nb) doped rutile titanium oxide (TiO 2 ) as a novel EAOP catalytic material, combining theoretical modeling with experimental synthesis and characterization. Calculations based on density functional theory are used to predict the overpotential for oxygen evolution at these candidate electrodes, which must be high in order to oxidize PFAS. The results indicate a non-monotonic trend in which Nb doping below 6.25 at.% is expected to reduce performance relative to TiO 2 , while higher concentrations up to 12.5 at.% lead to increased performance, approaching that of state-of-the-art Magnéli Ti 4 O 7 . TiO 2 samples were synthesized with Nb doping concentration at 10 at.%, heat treated at temperatures from 800 to 1100 °C, and found to exhibit high oxidative stability and high generation of reactive oxygen free radical species. The capability of Nb-doped TiO 2 to destroy two common species of PFAS in challenge water was tested, and moderate reduction by ~ 30% was observed, comparable to that of Ti 4 O 7 using a simple three-electrode configuration. We conclude that Nb-doped TiO 2 is a promising alternative EAOP catalytic material with increased activity towards generating reactive oxygen species and warrants further development for electrochemically destroying PFAS contaminants.
Destructive reverse bias pinning in perovskite/silicon tandem solar modules caused by perovskite hysteresis under dynamic shading
In this work, we demonstrate how perovskite hysteresis can result in permanent reductions in power output in perovskite/silicon tandem modules—including irreversible hotspot-induced damage—from only brief periods of shading. We show that reverse bias events in which a perovskite cell is biased above a threshold voltage—which in this work we find to be as low as –1.1 V—produces a temporary reduction in power output that is of sufficient magnitude to keep the cell pinned in reverse bias after the shading event ends. As a hysteretic phenomena, this crucial failure mode may be overlooked by static models of perovskite-based solar cells. Higher reverse bias voltages exacerbate the temporary reduction in short-circuit photocurrent, which is also sensitive to the level of illumination under reverse bias. Numerical device modelling demonstrates that this effect is consistent with our understanding of perovskite hysteresis as a consequence of mobile ion-electron coupling controlling rates of non-radiative recombination over time. Measurements of the dynamic response of single-junction perovskite cells are extrapolated to two-terminal and four-terminal perovskite/silicon tandem module modelling. We validate these models with measurements from an equivalent electronic circuit that represents a two-terminal perovskite–silicon tandem mini module. Two module-level solutions are discussed that address this issue, which includes increasing the number of bypass diodes and choosing better suited silicon bottom cells with higher shunter resistance in two-terminal tandem modules.
An automated fast neutron computed tomography instrument with on-line focusing for non-destructive evaluation
A fast neutron tomography imaging instrument has been designed, built, and tested at The Ohio State University 500 kW Research Reactor on a fast neutron beamline with a peak neutron flux ≈5.4 × 107 n·cm−2·s−1 at 1.6 MeV median neutron energy. The instrument and beamline are also configurable for thermal neutron imaging. The imaging apparatus is composed of a lens coupled, water-cooled Electron Multiplying Charge Coupled Device camera, a front-surface mirror, and a high light yield plastic Polyvinyl toluene scintillator. The instrument sits on a mobile cart. A total of 5 motion-control stages are built into the system for XYZ and rotational degrees of freedom for sample positioning; the fifth stage fine tunes the focal distance between the camera and the scintillator to achieve on-line focusing. A Python code with a user-friendly graphical user interface controls the fully automated image acquisition, not requiring user interaction, yet facilitating tracking of the image acquisition. A complete fast neutron computed tomography dataset with 360 projections requires less than 3 h, with 30 s per projection. On-line focusing is accomplished with a commercial, off-the-shelf, dielectrically actuated liquid lens. Finally, tomographic reconstructions are visualized using the Livermore Tomography Tools software package. The effective pixel size (width and height) is ≈0.1058 mm, yielding a minimum voxel size of 0.1058 × 0.1058 × 0.1058 mm3, and produces a spatial resolution of 231 μm when calculated from knife-edge measurements.
Scoping Analysis of Pebble-Bed Reactors for the Destruction of the Transuranic Inventory of LWR Spent Nuclear Fuel
With the forecasted increase in the construction and operation of nuclear reactors, there will be a corresponding increase in the quantity of spent nuclear fuel (SNF) that requires long-term storage. In SNF, transuranic isotopes contribute the most to the long-term radiotoxicity of the fuel and pose a proliferation risk. One option that has been explored to address these issues is the removal of the transuranic isotopes from SNF and the conversion of these isotopes into transuranic fuel (TRU fuel). Here, this work sought to determine how effective a micro-modular Pebble-Bed High-Temperature Gas-Cooled Reactor (PB-HTGR); the 10-MW High Temperature Gas-cooled Test Reactor (HTR-10); and a salt-cooled small-modular pebble-bed reactor (PBR), i.e. the generic Fluoride-cooled High-temperature Reactor (gFHR), are at reducing the inventory of transuranic isotopes while still maintaining the intrinsic safety features of the PBR designs, such as negative temperature coefficients of reactivity. Optimized pebble designs utilizing TRU fuel were found for both reactors through the adjustment for the packing fraction of fuel in each pebble. The Axial Zone Equilibrium Modeling (A-ZEM) method was used in this work to help select the optimized pebble design. Once an optimized pebble design was selected and an equilibrium model was produced, the results from the deep burn (DB) HTR-10 and gFHR designs were compared to the results of two models from the literature. While both the DB gFHR and the DB HTR-10 were able to reduce the weapons-usable transuranic inventory, the performance of these reactors did not match that of the small-modular PB-HTGRs in the literature. Therefore, a need was identified for further refinement of the gFHR design using TRU fuel, as the results for this model were more promising than those of the DB HTR-10, which was strongly limited by the high leakage intrinsic to micro-modular PB-HTGRs.
Sparse-Data Deep Learning Strategies for Radiographic Non-Destructive Testing
Radiography is an imaging technique used in a variety of applications, such as medical diagnosis, airport security, and nondestructive testing. We present a deep learning system for extracting information from radiographic images. We perform various prediction tasks using our system, including material classification and regression on the dimensions of a given object that is being radiographed. Our system is designed to address the sparse-data issue for radiographic nondestructive testing applications. It uses a radiographic simulation tool for synthetic data augmentation, and it uses transfer learning with a pre-trained convolutional neural network model. Using this system, our preliminary results indicate that the object geometry regression task saw an improvement of 70% in the R-squared value when using a multi-regime model. In addition, we increase the performance of the object material classification tasks by utilizing data from different imaging systems. In particular, using neutron imaging improved the material classification accuracy by 20% when compared to x-ray imaging.
Targeted for destruction: degradation of singlet oxygen-damaged chloroplasts
Not Available
Imaging nuclei by smashing them at high energies: how are their shapes revealed after destruction?
High-energy nuclear collisions have recently emerged as a promising ‘imaging-by-smashing’ approach to reveal the intrinsic shapes of atomic nuclei. Here, I outline a conceptual framework for this technique, explaining how nuclear shapes are encoded during quark–gluon plasma (QGP) formation and evolution, and how they can be decoded from final-state particle distributions. Furthermore, I highlight the method’s potential to advance our understanding of both nuclear structure and QGP physics.
Respiratory energy demands and scope for demand expansion and destruction
Photosynthesis is the primary energy input to plants, but most plant metabolic processes are powered much or all of the time by ATP and NAD(P)H that come from respiratory oxidation of photosynthetically produced carbohydrates, that is, “dark respiration” (Amthor, 1994). The processes of growth, nutrient uptake and assimilation, active transport, and maintenance are thus all clients of respiration and compete for their share of a respiratory energy budget that is capped by photosynthate income. The economic principle of opportunity cost applies to these clients’ competing demands: spending respiratory energy on process A means missing the benefit of investing that energy in process B (Mahmoudabadi et al., 2019). This principle is key to assessing prospects for crop improvement by metabolic engineering. As de Lorenzo (2015) put it: “metabolism…frames and ultimately resolves whether a given genetic program (existing…or engineered) can be deployed or not.” Foreign or reconfigured native processes bolted on to a crop-plant’s metabolic chassis must compete for respiratory energy with native ones without crashing the energy economy. A proviso on plant carbon budgets is that photosynthetic carbon fixation (“source activity”) can in certain cases increase to meet increased carbon demand (“sink activity”), that is, budget envelopes are not always fixed (Smith et al., 2018). However, as there is some consensus that productivity is most often limited or co-limited by carbon supply (Ainsworth and Long, 2005; Körner, 2015; Sonnewald and Fernie, 2018), we make this our basal assumption in the analyses below.
DESIGN MODIFICATIONS TO THE EXPLOSIVE DESTRUCTION SYSTEM CLOSURE SYSTEM.
Abstract not provided.
Quantum ghost imaging for non-destructive plant imaging using highly non-degenerate spontaneous parametric downconversion
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