Understanding the surface segregation of solute atoms in Sn-Bi–based solder from first principles

Low-temperature Sn-Bi solder has wide application in the field of electronic packaging due to its low melting point and good wettability. The formation of Bi-rich phase and intermetallic compound is the major concern for the reliability of Sn-Bi solder joints. We employed first-principles calculations to understand the segregation of Bi and the third elements to the surface of Sn. The effects of alloying elements on inhibiting the Bi surface segregation were described. Our calculations show that the Bi surface segregation could be effectively alleviated by the addition of Ag, Ga, Ni, and In, along with the reduction of further possible formation of intermetallic compounds in the Sn-Bi–based solders. The results could be interpreted by the enhanced bond orders between Bi and its neighboring Sn, alloying elements

The Effect of Fe/Al Ratio and Substrate Hardness on Microstructure and Deposition Behavior of Cold-Sprayed Fe/Al Coatings

Fe/Al composite coatings with compositions of Fe-25 wt.% Al, Fe-50 wt.% Al and Fe-75 wt.% Al were deposited on pure Al and P91 steel plates by a cold spray, respectively. The microstructure of the cross-section of the fabricated coatings was characterized by SEM and EDX. The bonding strength between the coatings and substrates was measured and analyzed. The effects of the Fe/Al ratios and substrate hardness on the deposition behavior were investigated. It was interesting to find fragmented zones in all fabricated coatings, which were composed of large integrated Al particles and small fragmented Al particles. Meanwhile, the fraction of fragmented zones varied with the fraction of the actual Fe/Al ratio. An Fe/Al ratio of 50/50 appeared to be an optimized ratio for the higher bonding strength of coatings. The in situ hammer effect caused by larger and harder Fe particles played an important role in the cold spray process. The substrate with the higher hardness strengthened the in situ hammer effect and further improved the bonding strength

A Statistical Model of Cleavage Fracture Toughness of Ferritic Steel DIN 22NiMoCr37 at Different Temperatures

It is a conventional practice to adopt Weibull statistics with a modulus of 4 for characterizing the statistical distribution of cleavage fracture toughness of ferritic steels, albeit based on a rather weak physical justification. In this study, a statistical model for cleavage fracture toughness of ferritic steels is proposed according to a new local approach model. The model suggests that there exists a unique correlation of the cumulative failure probability, fracture toughness and yield strength. This correlation is validated by the Euro fracture toughness dataset for 1CT specimens at four different temperatures, which deviates from the Weibull statistical model with a modulus of four

Different-Shaped Ultrafine MoNbTaW HEA Powders Prepared via Mechanical Alloying

Different-shaped ultrafine MoNbTaW high-entropy alloy powders were firstly prepared by a convenient mechanical alloying method. The phase composition and microstructure of the powders were characterized. The powders are ultrafine with nano-sized grains and a good homogeneous microstructure. All the powders have a single body-centered cubic solid solution phase and form the high-entropy alloy during mechanical alloying. These powders with different shapes are quite attractive for developing high-performance MoNbTaW high-entropy alloy bulk and coatings combined with a following sintering, spraying, or additive manufacturing technique

The structure and formation of diapirs in the Yinggehai-Song Hong Basin, South China Sea

The occurrence of shale diapirs in the Yinggehai-Song Hong (YGH-SH) Basin is well documented, as is their association with big petroleum fields. In order to better understand how and why the diapirs form we performed a detailed geophysical analysis using a new regional compilation of high-resolution two- and three-dimensional seismic reflection data, as well as drilling data that cover the diapirs in YGH-SH Basin. As many as 18 diapirs were identified and are arranged in six N-S-striking vertical en échelon zones. On seismic reflection sections gas chimney structures, diapiric faults and palaeo-craters are genetically linked with the process of diapirism. Here we use geophysical and geological observations to propose a three-stage model for diapirism: initiation, emplacement, and collapse. During these three stages, different diapiric structure styles are formed, which we describe in detail. These include buried diapirs, piercing diapirs and collapsed diapirs. We link the diapirism to activity on the offshore continuation of the Red River Fault, as shown on our high-resolution seismic reflection data, which is also related to a high paleogeothermal gradient caused by crustal thinning. We also recognize the role of loading by the very large volume of sediment eroded from the edges of the Tibetan Plateau and delivered by the Red River to the basin. © 2011 Elsevier Ltd

BTS: a binary tree sampling strategy for object identification based on deep learning

Object-based convolutional neural networks (OCNNs) have achieved great performance in the field of land-cover and land-use classification. Studies have suggested that the generation of object convolutional positions (OCPs) largely determines the performance of OCNNs. Optimized distribution of OCPs facilitates the identification of segmented objects with irregular shapes. In this study, we propose a morphology-based binary tree sampling (BTS) method that provides a reasonable, effective, and robust strategy to generate evenly distributed OCPs. The proposed BTS algorithm consists of three major steps: 1) calculating the required number of OCPs for each object, 2) dividing a vector object into smaller sub-objects, and 3) generating OCPs based on the sub-objects. Taking the object identification in land-cover and land-use classification as a case study, we compare the proposed BTS algorithm with other competing methods. The results suggest that the BTS algorithm outperforms all other competing methods, as it yields more evenly distributed OCPs that contribute to better representation of objects, thus leading to higher object identification accuracy. Further experiments suggest that the efficiency of BTS can be improved when multi-thread technology is implemented