Uniting Experiment and Computational Modeling to Elucidate Structure-Property Relationships of Water Splitting Materials.
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Metallic glasses are a new class of structural materials which exhibit exciting mechanical properties including high strength and elasticity. In terms of fracture toughness, the material class of metallic glasses spans a wide range; Some metallic glasses are extremely brittle and exhibit near ideal brittle behavior whereas others can be exceptional tough with values comparable to the toughest metals out there. Such large range of observed fracture toughness within the material class of metallic glasses is surprising as they have seemingly a very similar atomic structure. Therefore, we developed “artificial microstructures” which allows to decouple the various contribution of sample geometry, imperfection and structure. Specifically, we decouple variations in the alloys’ chemical composition and the atomic structure and quantified the resulting fracture toughness. Atomic structure of a metallic glass can be modified by the fictive temperature. The fictive temperature of a glass is the temperature at which the liquid metallic glass falls out of equilibrium upon colling and forms a glass. Upon further cooling the structure is maintained only thermal oscillations decrease due to a lower absolute temperature. We found that the effect of fictive temperature (same chemistry, different structural stages of the glass) is comparable to the variations of fracture toughness when the chemistry is varied. Hence, it appears that the subtle differences in the glass structure are responsible for the large range of fracture toughness’s observed. Our results reveal that fracture toughness within the material class of metallic glasses varies significantly and we found some example alloys with exceptional high resistance to fracture and others that are almost ideally brittle. Significant influences on the fracture toughness have the structure of the glass, its chemistry, and some imperfections in the structure.
Causal inference methods are important to complement predictive machine learning models to improve theoretical understanding of the underlying system in climate science. Granger causality (GC) can determine whether time series value of one variable (e.g., X) can predict the future values of another variable (e.g., Y), but not the other way around.
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we discuss a new facility for CLFV at Fermilab and its relation to the muon collider R&D
The overarching objective of this project was to further our understanding of low-level Arctic cloud behavior in ultra-low aerosol concentration environments. To this end, we used a combination of modeling and analysis of DOE ARM datasets to understand the frequency of aerosol-limited cloud dissipation events, to understand the microphysical processes that lead to aerosol-limited dissipation, and to understand how above-cloud aerosol particles can work to maintain low-level clouds that exist in boundary layers with ultra-low aerosol concentrations. We found that aerosol-limited dissipation is unlikely to occur at the DOE long-term observation facilities at North Slope Alaska and Oliktok Point but possibly does occur at higher latitudes in the Arctic. At these higher latitudes, aerosol concentrations are frequently observed to be higher, often substantially higher, than in the boundary layer. These reservoirs can be sufficient to maintain low-level clouds. When they are insufficient, clouds appear to dissipate due to a depletion of liquid water through precipitation that is reinforced by a shutdown of the dynamical processes that support these clouds.
Cyanobacteria play a large and essential role in the global carbon cycle, while also representing promising microbial platforms for the direct bioproduction of fuels and chemicals from solar energy and carbon dioxide (CO 2 ). In both cases, a critical step in the overall photosynthetic process is the transport of inorganic carbon (Ci) across the cell membrane, which occurs via different mechanisms and associated transporter proteins that target either CO 2 or bicarbonate (HCO 3 - ). In the latter case, BicA (constitutively-expressed, high-flux/low-affinity Na + /HCO 3 - symporter) and SbtA (low-flux/high-affinity Na + /HCO 3 - symporter, expressed only under low CO 2 conditions) have been identified as the two main cyanobacterial HCO 3 - transporters. This project entailed combining detailed functional and high-resolution structural studies to help fill key knowledge gaps regarding the mechanisms of HCO 3 - transport by BicA and SbtA. Specifically, major goals of this project included: i) identifying key residues and structural features of BicA and SbtA that are responsible for modulating their uptake flux and affinity behaviors, and ii) obtaining high-resolution structures of full-length BicA and SbtA.
The Permian Energy Development Lab (PEDL) is a new research coalition in West Texas and Southeastern New Mexico, managed by a consortium of higher education, public sector, civic, community, and philanthropic organizations. PEDL’s mission is to catalyze advanced energy research, prepare new energy professionals and entrepreneurs, and create value for energy communities.
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