No abstract

Microglia dynamically survey the brain parenchyma. Microglial processes interact with neuronal elements; however, the role neuronal network activity plays in regulating microglial dynamics is not entirely clear. Most studies of microglial dynamics use either slice preparations or in vivo imaging in anesthetized mice. Here we demonstrate that microglia in awake mice have relatively reduced process area and surveillance territory, and that reduced neuronal activity under general anesthesia increases microglial process velocity, extension and territory surveillance. Similarly, reductions in local neuronal activity through sensory deprivation or optogenetic inhibition increase microglial process surveillance. Using pharmacological and chemogenetic approaches, we demonstrate that reduced norepinephrine signaling is necessary for these increases in microglial process surveillance. These findings indicate that under basal physiological conditions noradrenergic tone in awake mice suppresses microglial process surveillance. Our results emphasize the importance of awake imaging for studying microglia-neuron interactions and demonstrate how neuronal activity influences microglial process dynamics.

Grain boundary segregation leads to nanoscale chemical variations that can alter a material's performance by orders of magnitude (e.g., embrittlement). To understand this phenomenon, a large number of grain boundaries must be characterized in terms of both their five crystallographic interface parameters and their atomic-scale chemical composition. We demonstrate how this can be achieved using an approach that combines the accuracy of structural characterization in transmission electron microscopy with the 3D chemical sensitivity of atom probe tomography. We find a linear trend between carbon segregation and the misorientation angle ω for low-angle grain boundaries in ferrite, which indicates that ω is the most influential crystallographic parameter in this regime. However, there are significant deviations from this linear trend indicating an additional strong influence of other crystallographic parameters (grain boundary plane, rotation axis). For high-angle grain boundaries, no general trend between carbon excess and ω is observed; i.e., the grain boundary plane and rotation axis have an even higher influence on the segregation behavior in this regime. Slight deviations from special grain boundary configurations are shown to lead to unexpectedly high levels of segregation.

Grain refinement through severe plastic deformation enables synthesis of ultrahigh-strength nanostructured materials. Two challenges exist in that context: First, deformation-driven grain refinement is limited by dynamic dislocation recovery and crystal coarsening due to capillary driving forces; second, grain boundary sliding and hence softening occur when the grain size approaches several nanometers. Here, both challenges have been overcome by severe drawing of a pearlitic steel wire (pearlite: lamellar structure of alternating iron and iron carbide layers). First, at large strains the carbide phase dissolves via mechanical alloying, rendering the initially two-phase pearlite structure into a carbon-supersaturated iron phase. This carbon-rich iron phase evolves into a columnar nanoscaled subgrain structure which topologically prevents grain boundary sliding. Second, Gibbs segregation of the supersaturated carbon to the iron subgrain boundaries reduces their interface energy, hence reducing the driving force for dynamic recovery and crystal coarsening. Thus, a stable cross-sectional subgrain size <10  nm is achieved. These two effects lead to a stable columnar nanosized grain structure that impedes dislocation motion and enables an extreme tensile strength of 7 GPa, making this alloy the strongest ductile bulk material known.

Complex solid solutions ("high entropy alloys"), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a datadriven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition-spread thin-film libraries are synthesised. High-throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru. The method can identify optimal complex-solid-solution materials for electrocatalytic reactions in an unprecedented manner.

No abstract

No abstract

No abstract