Simultaneous Elucidation of Solid and Solution Manganese Environments via Multiphase Operando Extended X-ray Absorption Fine Structure Spectroscopy in Aqueous Zn/MnO 2 Batteries
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Surface albedo can be highly variable in both space and time. The Department of Energy’s Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field study provides a unique opportunity to characterize the variability over the Southern Great Plains of the United States using integrated tower, aircraft, and satellite observations of the surface albedo and Normalized Difference Vegetation Index (NDVI). The primary advantage of the aircraft and satellite observations is the ability to examine the spatial distribution of surface albedo and NDVI over a large area, while the tower measurements have the ability to examine both diurnal and day-to-day variability at a single location. In general, consistency was found between both the broadband (BB) albedo and NDVI measured from the surface, air and space. There was a small increase from 0.186 to 0.194 in the aircraft BB surface albedo between May and September (about 4% change), while the MODIS black-sky BB surface albedo increased from 0.151 to 0.166 over the same period (about 10% change) while the standard deviations in the aircraft and MODIS BB albedo were similar. The largest seasonal differences in both the aircraft BB albedo and NDVI were found for areas with winter wheat or forest, while areas with pasture or grasses showed a smaller seasonal diversity. The Weather Research and Forecasting (WRF) model was used to simulate the BB surface albedo. In comparison with the aircraft and satellite observations, the WRF-simulated BB surface albedo had no seasonal change and a much narrower distribution.
Nanostructures similar to those found in the vividly blue wings of Morpho butterflies and colorful photonic crystals enable structural color through constructive interference of light waves. Different from commonly studied structure-colored materials using periodic structures to manipulate optical properties, we report a previously unrecognized approach to precisely control the structural color and light transmission via a novel photonic colloidal gel without long-range order. Nanoparticles in this gel form micrometer-sized bicontinuous domains driven by the microphase separation of binary solvents. This approach enables dynamic coloration with a precise wavelength selectivity over a broad range of wavelengths extended well beyond the visible light that is not achievable with traditional methods. The dynamic wavelength selectivity is thermally tunable, reversible, and the material fabrication is easily scalable.
Abstract Moiré superlattices of transition metal dichalcogenide (TMD) heterostructures give rise to rich excitonic phenomena associated with the interlayer twist angle. Theoretical calculations of excitons in such systems are typically based on model moiré potentials that mitigate the computational cost. However, predictive understanding of the electron-hole coupling dominating the excitations is crucial to realize the twist-induced modifications of the optical selection rules. In this work, we use many-body perturbation theory to evaluate the relation between twist angle and exciton properties in TMD heterostructures. We present an approach for unfolding excitonic states from the moiré Brillouin zone onto the separate-layer ones. Applying this method to a large-angle twisted MoS 2 /MoSe 2 bilayer, we find that the optical spectrum is dominated by mixed electron–hole transitions with different momenta in the separate monolayers, leading to unexpected hybridization between interlayer and intralayer excitons. Our findings offer a design pathway for exciton layer-localization in TMD heterostructures.
Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.
Abstract Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.
Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07 wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06 wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.
Abstract For a deeper and comprehensive understanding of the composition and function of rhizosphere microbiomes, we need to focus at the scale of individual roots in standardized growth containers. Root exudation patterns are known to vary along distinct parts of the root even in juvenile plants giving rise to spatially distinct microbial niches. To address this, we analyzed the microbial community from two spatially distinct zones of the developing primary root (tip and base) in young Brachypodium distachyon grown in natural soil using standardized fabricated ecosystems known as EcoFABs as well as in more conventional pot and tubes. 16S rRNA based community analysis showed a strong rhizosphere effect resulting in significant enrichment of several OTUs belonging to Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria. However, microbial community composition did not differ between root tips and root base or across different growth containers. Functional analysis of bulk metagenomics revealed significant differences between root tips and bulk soil. The genes associated with different metabolic pathways and root colonization were enriched in root tips. On the other hand, genes associated with nutrient-limitation and environmental stress were prominent in the bulk soil compared to root tips, implying the absence of easily available, labile carbon and nutrients in bulk soil relative to roots. Such insights into the relationships between developing root and microbial communities are critical for judicious understanding of plant-microbe interactions in early developmental stages of plants.
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An asymmetric acceptor 5T-2C8-2Cl has been designed and an efficiency of 13.02% with simultaneously improved J sc and V oc has been achieved.
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In a recent paper, we reported that the position and depth of the minima in the harmonic spectra of CO 2 molecules can be dramatically manipulated by a slight change of the degree of alignment, thus providing a convenient method of shaping attosecond pulses which could lead to the splitting of a typical single attosecond burst into two. Here, we demonstrate that harmonic minima can also be dramatically controlled by changing the pump-probe angles to shape attosecond pulses. In the meanwhile, since the pump-probe angle-dependent harmonic spectra are very sensitive to the degree of alignment, harmonic spectra can also be used to calibrate the alignment distribution of molecules. In this work we identify such robust control of harmonic generation is due to the optical property of CO 2 and the coherent harmonic emission from the oriented molecules. The photoionization transition dipoles (PITDs) of CO 2 exhibit deep minima that change rapidly with molecular orientations accompanied by large phase changes of π in PITDs, thus leading to strong interference when the dipoles are added up coherently.
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Knowledge of the local coordination environment around atomic species in functional materials is critical for understanding their mechanisms of operation. Heterogeneous mixtures of metal complexes are ubiquitous in catalysts, ionic liquids, molten salts, biological enzymes, and geochemical systems, among many others. Extracting information from ensemble-average measurements about the structural and compositional descriptors of each type of coordination complex comprising the mixture is not generally possible, especially when they possess multimodal bond-length distributions. Here, we developed a method that enables the mapping of an x-ray absorption spectrum on the radial distribution function describing the average environment of the metal ions. The supervised neural network based method utilizes an objective training set, for which the choice of the local structural motifs is completely agnostic to the theoretically expected structure and dynamics of the modeled system. The method was validated using first-principles modeling of structural dynamics of nickel complexation in molten salts, and it applies to a large class of heterogeneous systems, including those studied under in situ and operando conditions.
The products of the 203, 205 Tl ( 50 Ti, 2n) fusion-evaporation reactions were studied using the recently commissioned Argonne Gas-Filled Analyzer at Argonne National Laboratory. Two α-decay activities with energies of 9210(19) and 9246(19) keV and half-lives of $42^{+42}_{-14}$ and $24.4^{+7.0}_{-4.5}$ ms were observed which were followed by the known α decays of 247 Md and 243 Es . They are interpreted as originating from the 1/2 ⁻ [521] and 7/2 ⁻ [514] single-proton Nilsson states in the hitherto unknown isotope 251 Lr . From the measured Q α values the 1/2 ⁻ level was placed 117(27) keV above the 7/2 - level in 251 Lr in contrast to 255 Lr where the 1/2 ⁻ level is the lowest. Also, the α decay of 253 Lr was studied in more detail and a new α line at 8660(20) keV was found and a new half-life value of 2.46(32) s for an isomeric state in 253 Lr was measured. The 251, 253, 255 Lr Q α values were compared with predictions of various mass models. The relative energies of the 1/2 ⁻ [521] and 7/2 ⁻ [514] single-proton Nilsson states in 251, 253, 255 Lr isotopes were compared with results of the cranking shell model with pairing treated using the particle-number-conserving method. The level separation and, in particular, the level order change between 251 Lr and 255 Lr was reproduced only when the hexacontetrapole deformation ϵ 6 was included in the calculations.