Role of disordered bipolar complexions on the sulfur embrittlement of nickel general grain boundaries

Minor impurities can cause catastrophic fracture of normally ductile metals. Here, a classic example is represented by the sulfur embrittlement of nickel, whose atomic-level mechanism has puzzled researchers for nearly a century. In this study, coupled aberration-corrected electron microscopy and semi-grand-canonical-ensemble atomistic simulation reveal, unexpectedly, the universal formation of amorphous-like and bilayer-like facets at the same general grain boundaries. Challenging the traditional view, the orientation of the lower-Miller-index grain surface, instead of the misorientation, dictates the interfacial structure. We also find partial bipolar structural orders in both amorphous-like and bilayer-like complexions (a.k.a. thermodynamically two-dimensional interfacial phases), which cause brittle intergranular fracture. Such bipolar, yet largely disordered, complexions can exist in and affect the properties of various other materials. Beyond the embrittlement mechanism, this study provides deeper insight to better understand abnormal grain growth in sulfur-doped Ni, and generally enriches our fundamental understanding of performance-limiting and more disordered interfaces

Synthesis of high entropy metal diborides

In our recent work, several five-component metal diborides, including (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)B2, (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2, (Hf0.2Zr0.2Mo0.2Nb0.2Ti0.2)B2, (Hf0.2Mo0.2Ta0.2Nb0.2Ti0.2)B2, (Mo0.2Zr0.2Ta0.2Nb0.2Ti0.2)B2, and (Hf0.2Zr0.2Ti0.2Cr0.2Ta0.2)B2, were synthesized [Scientific Reports 6:37946 (2016)]. Here, we critically compare several different synthesis routes to fabricate these refractory high-entropy diborides via spark plasma sintering and conventional sintering, with or without sintering aids. While the majority of the compositions formed single phase AlB2 structures via spark plasma sintering, minor secondary oxide phases (mostly (Zr, Hf)O2), as well as porosity, remained. The utilization of multi-step conventional sintering along with appropriate sintering aids, e.g., boron carbide and carbon, allowed for the removal of secondary oxide phases as well as increasing the densification. Furthermore, conventional sintering led to improved homogenization of the different metal elements within the samples, which were verified by EDS mapping. Results on the process optimization for both spark plasma sintering and conventional sintering of the materials, as well as initial measurements of mechanical properties, will be presented and discussed. Please click Additional Files below to see the full abstract

High-entropy metal diborides: a new class of ultra-high temperature ceramics

Several equimolar, five-component, metal diborides were fabricated via high-energy ball milling and spark plasma sintering [Scientific Reports 6:37946 (2016)] or conventional pressure-less sintering. Most compositions synthesized, e.g., (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)B2, (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2 and several others, processed single solid-solution phases of the hexagonal AlB2 structure, while a few other compositions yielded two or more boride phases. These materials represent a new type of ultra-high temperature ceramic (UHTC) as well as a new class of high-entropy materials that possess a non-cubic (hexagonal) and layered (quasi-2D) crystal structure (Fig. 1). Please click Additional Files below to see the full abstract

The mechanisms of flash sintering of ZnO and TiO2 based ceramics

Flash sintering of ZnO, TiO2 and a few other oxide systems has been investigated. A quantitative model has been developed to forecast the thermal runaway conditions. The predicted thermal runaway temperatures from the measured conductivities are in excellent agreements with the observed onset flash temperatures for at least 15 cases with different base materials, doping and surface treatments, particle sizes, and sintering atmospheres, attesting that the “flash” starts as a thermal runaway. Specifically, using ZnO as a model system, a strong dependence of the onset flash sintering temperature on the atmosphere has been discovered. In a set of optimized conditions, ZnO specimens have been sintered to >97% relative densities in ~30 s at furnace temperatures of <120 °C in Ar + 5 mol. % H2, with uniform microstructures and fine grain sizes of ~1 um. The enhanced conductivities of ZnO powder specimens in reduced atmospheres are responsible for the substantial decreases of the onset flash sintering temperatures. More recently, we have investigated the effects of joule heating and high heating ramp rate on fast densification in flash sintering of ZnO, as well as the electrical field/current effects on densification and microstructural developments. Mimic heating profile in flash sintering by rapid thermal annealing (RTA), which excludes the electric filed effects and has the similar heating ramp rate as flash sintering up to 200 °C per second. The specimens after RTA at similar estimated temperature as flash sintering could reach almost the same density and grain size. Observation of an unusual case of triple-line wetting by a gas phase is another project I involved during my graduate study. The Bi vapor penetrates along the triple lines in the electrodeposited Ni to form open channels at 800 and 900 °C. This is interpreted as a case of triple-line wetting by a gas phase, which has never been reported before. This unusual wetting phenomenon is related to the formation of a bilayer complexion and grain boundary embrittlement in the Ni-Bi system [Science 333: 1730 (2011)]. Further controlled experiments using high-purity Ni specimens with and without S doping suggest that the presence of S impurities is essential for the occurrence of this wetting phenomenon. This discovery has practical importance for understanding and controlling the microstructural stability and corrosion resistance

Research on Anomaly Detection of Surveillance Video Based on Branch-Fusion Net and CSAM

As the monitor probes are used more and more widely these days, the task of detecting abnormal behaviors in surveillance videos has gained widespread attention. The generalization ability and parameter overhead of the model affect how accurate the detection result is. To deal with the poor generalization ability and high parameter overhead of the model in existing anomaly detection methods, we propose a three-dimensional multi-branch convolutional fusion network, named “Branch-Fusion Net”. The network is designed with a multi-branch structure not only to significantly reduce parameter overhead but also to improve the generalization ability by understanding the input feature map from different perspectives. To ignore useless features during the model training, we propose a simple yet effective Channel Spatial Attention Module (CSAM), which sequentially focuses attention on key channels and spatial feature regions to suppress useless features and enhance important features. We combine the Branch-Fusion Net and the CSAM as a local feature extraction network and use the Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract global feature information. The experiments are validated on a self-built Crimes-mini dataset, and the accuracy of anomaly detection in surveillance videos reaches 93.55% on the test set. The result shows that the model proposed in the paper significantly improves the accuracy of anomaly detection in surveillance videos with low parameter overhead

Research on Anomaly Detection of Surveillance Video Based on Branch-Fusion Net and CSAM

As the monitor probes are used more and more widely these days, the task of detecting abnormal behaviors in surveillance videos has gained widespread attention. The generalization ability and parameter overhead of the model affect how accurate the detection result is. To deal with the poor generalization ability and high parameter overhead of the model in existing anomaly detection methods, we propose a three-dimensional multi-branch convolutional fusion network, named “Branch-Fusion Net”. The network is designed with a multi-branch structure not only to significantly reduce parameter overhead but also to improve the generalization ability by understanding the input feature map from different perspectives. To ignore useless features during the model training, we propose a simple yet effective Channel Spatial Attention Module (CSAM), which sequentially focuses attention on key channels and spatial feature regions to suppress useless features and enhance important features. We combine the Branch-Fusion Net and the CSAM as a local feature extraction network and use the Bi-Directional Gated Recurrent Unit (Bi-GRU) to extract global feature information. The experiments are validated on a self-built Crimes-mini dataset, and the accuracy of anomaly detection in surveillance videos reaches 93.55% on the test set. The result shows that the model proposed in the paper significantly improves the accuracy of anomaly detection in surveillance videos with low parameter overhead