Realizing improved strength–ductility synergy in eutectic alloys acting as in situ composite materials remains a challenge in conventional eutectic systems, which is why eutectic high-entropy alloys (EHEAs), a newly-emerging multi-principal-element eutectic category, may offer wider in situ composite possibilities. Here, we use an AlCoCrFeNi2.1 EHEA to engineer an ultrafine-grained duplex microstructure that deliberately inherits its composite lamellar nature by tailored thermo-mechanical processing to achieve property combinations which are not accessible to previously-reported reinforcement methodologies. The as-prepared samples exhibit hierarchically-structural heterogeneity due to phase decomposition, and the improved mechanical response during deformation is attributed to both a two-hierarchical constraint effect and a self-generated microcrack-arresting mechanism. This work provides a pathway for strengthening eutectic alloys and widens the design toolbox for high-performance materials based upon EHEAs.

High-entropy herringbone alloy Eutectic high-entropy alloys have a dual phase structure that could be useful for optimizing a material’s properties. Shi et al . found that directional solidification of an aluminum-iron-cobalt-nickel eutectic high-entropy alloy created a herringbone-patterned microstructure that was extremely resistant to fracture (see the Perspective by An). The structure contained lamellae of hard and soft phases, and the cracks that formed in the hard phase were arrested at the boundary of the soft phase. This, along with stress transfer, allowed a tripling of the maximal elongation while retaining high strength. —BG

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Growing evidence indicates brain inflammation has been involved in the genesis of seizures. However, the direct effect of acute inflammation on neuronal circuits is not well known. Lipopolysaccharide (LPS) has been used extensively to stimulate brain inflammatory responses both in vivo and in vitro. Here, we observed the contribution of inflammation induced by 10 μg/mL LPS to the excitability of neuronal circuits in acute hippocampal slices. When slices were incubated with LPS for 30 minutes, significant increased concentration of tumor necrosis factor α and interleukin 1β were detected by enzyme-linked immunosorbent assay. In electrophysiological recordings, we found that frequency of epileptiform discharges and spikes per burst increased 30 minutes after LPS application. LPS enhanced evoked excitatory postsynaptic currents but did not modify evoked inhibitory postsynaptic currents. In addition, exposure to LPS enhanced the excitability of CA1 pyramidal neurons, as demonstrated by a decrease in rheobase and an increase in action potential frequency elicited by depolarizing current injection. Our observations suggest that acute inflammation induced by LPS facilitates epileptiform activity in vitro and that enhancement of excitatory synaptic transmission and neuronal excitability may contribute to this facilitation. These results may provide new clues for treating seizures associated with brain inflammatory disease.

A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN) for detecting defects in tire X-ray images. Owing to the capability of pixel-wise prediction of FCN, the location, and segmentation of defects are completed simultaneously. The network architecture used in the method mainly consists of three phases. The first phase is a traditional deep network, which is used to extract the feature of tire images, and feature maps are obtained at the last layer. By replacing fully connected layers into convolution layers, final feature maps retain sufficient spatial information. By adding up-sampling layers, in the second phase, outputs with the same size as the original image can be generated. After the first two phases, we develop the coarse segmentation results and refine them through fusing multi-scale feature maps. The experimental results show that the proposed method can accurately locate and segment defects in tire images.

The magnetic-field-assisted approach has been used in the shape-controlled synthesis of single Bi nanocrystals. By tuning the magnetic field strength in the solvothermal process, Bi nanowires with dimension of 40-200 nm and lengths up to tens of micrometers were synthesized. Various techniques such as x-ray diffraction, scanning electron microscopy, transmission electron microscopy and Fourier transform infrared spectrometry have been used to characterize the products obtained. The results show that the magnetic field plays a key role in the crystal growth of the Bi nanowires. All nanowires were highly oriented single crystals with the growth direction along the c-axis.

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