Copper effects in mechanical,thermal and electrical properties of rapidly solidified eutectic Sn–Ag alloy

The Sn–3.5 wt%Ag alloy considered as a good alternative to Pb–Sn alloys. This study aims to investigate the effects of Cu or Sb additions by 3 or 5 wt% to melt-spun Sn–3.5%Ag alloy. Ternary melt-spun Sn–Ag–Cu and Sn–Ag–Sb alloys investigated using X-ray diffractions (XRD), Scanning electron microscope (SEM), Dynamic resonance technique (DRT), Instron machine, Vickers hardness tester and Differential scanning calorimetry (DSC). The results revealed that the microstructures of the β-Sn phase, Ag₃Sn and Cu₃Sn intermetallic compounds (IMCs) in the solder matrices were refined due to the effect of Cu additions and melt-spun process. Moreover, increasing Cu content promotes Ag₃Sn intermetallic compound (IMC) formation. Consequently, the addition of “3 wt%” of Cu reduced the creep rate ? from (3.79?×?10^?3) to (1.65?×?10^?3) and delayed the fracture point. The tensile results showed an improvement in Young’s modulus by 47% (30.3 GPa), ultimate tensile strength (UST) by 11.6% (23.9 MPa), and in toughness by 20.5% (952.32 J/m³) compared to the eutectic Sn–Ag alloy. Vickers hardness has improved by 3.3% (136.71 MPa) and thermal activation energy by 54% (90.40 KJ/mol) when compared with that of eutectic Sn–Ag alloy. Those improvements are related to the lack of lattice strain from 7.56?×?10^?4 without “3 wt%” of Cu to 5.26?×?10^?4 with “3 wt%” of Cu. Its melting temperature (T_m) increased by 3 °C due to Ag₃Sn IMC increased and Cu₃Sn formation, but the pasty rang (mushy zone) decreased by 4 °C with “3 wt%” of Cu. The small lattice strains resulted with “3 wt%” of Cu made the electrical resistivity of this alloy more stable at elevated temperatures. The mechanical, thermal and electrical improvements of Sn_93.5–Ag_3.5–Cu₃ alloy provide good physical performance for soldering process and electronic assembly.     

Cutset enumeration of network systems with link and node failures

Network reliability analysis has received considerable attention and is thus widely studied to predict and prevent any network failure. However, most of such works presume perfectly reliable nodes. Although a few studies have considered both link and node failures, none of these methods has utilized the minimal paths or cuts, which are considered as fundamental approaches in the network reliability evaluation. An efficient method for deducing the minimal cutsets of a system subject to both link and node failures from the minimal cutsets of the system, which assumes perfect node reliability, is presented. The proposed method does not require re-enumeration of minimal cutsets for the additional consideration of the node failures. For a simple extension of such a method, the proposed approach can be embedded in any exact or approximate algorithm to account for link failures as well as node failures. As a result, the application of this method would be more realistic and valuable in practice for the reliability evaluation of networks with unreliable nodes.     

Hierarchical TiO2/ZnO Nanostructure as Novel Non-precious Electrocatalyst for Ethanol Electrooxidation

Metal oxides have a higher chemical stability in comparison to metals,so they can be utilized as electrocatalysts if the activity could be enhanced.Besides the composition,the morphology of the nanostructures has a considerable impact on the electrocatalytic activity.In this work,zinc oxide nano branches-attached titanium dioxide nanofibers were investigated as an economic and stable catalyst for ethanol electrooxidation in the alkaline media.The introduced material has been synthesized by electrospinning process followed by hydrothermal technique.Briefly,electrospinning of colloidal solution consisting of titanium isopropoxide,poly(vinyl acetate) and zinc nanoparticles was performed to produce nanofibers embedding solid nanoparticles.In order to produce TiO_2 nanofibers containing ZnO nanoparticles,the obtained electrospun nanofiber mats were calcined in air at 600 °C.The formed ZnO nanoparticles were exploited as seeds to outgrow ZnO branches around the TiO_2 nanofibers using the hydrothermal technique at sub-critical water conditions in the presence of zinc nitrate and bis-hexamethylene triamine.The morphology of the final product,as well as the electrochemical measurements indicated that zinc nanoparticles content in the original electrospun nanofibers has a significant influence on the electrocatalytic activity as the best performance was observed with the nanofibers synthesized from electrospun solution containing 0.1 g Zn,and the corresponding current density was 37 mA/cm~2.Overall,this study paves a way to titanium dioxide to be exploited to synthesize effective and stable metal oxide-based electrocatalysts.     

Development of an interferometer for measurement of the diffusion coefficient of miscible liquids

A common-path interferometer (CPI) system was developed to measure the diffusivity of transparent liquid pairs by real-time visualization of the concentration gradient profile. The CPI is an optical technique that can be used to measure changes in the gradient of the refractive index of transparent materials. The CPI is a shearing interferometer that shares the same optical path from a laser light source to the final imaging plane. Molecular diffusivity of liquids can be determined by use of physical relations between changes in the optical path length and the liquid phase properties. The data obtained by this interferometer are compared with similar results from other techniques. This demonstrates that the instrument is reliable for measurement of the diffusivity of miscible liquids and allows the system to be compact and robust. It can also be useful for studies in interface dynamics as well as other applications in a low-gravity environment.     

Preliminary investigation of near-field nondestructive testing of carbon-loaded composites using loaded open-ended waveguides

Composite materials are being utilized in a multitude of industrial and commercial applications. This is due to their desirable features such as light weight, durability and strength. This presents quite a challenge to the field of nondestructive testing and evaluation (NDT&E). Due to the material complexity associated with these materials, many techniques have been shown to be ineffective when inspecting these materials. The ability of microwaves to penetrate deeply inside such dielectric materials and composites makes microwave NDT techniques very attractive for interrogating such materials. Microwaves are also sensitive to the presence of dissimilar layers in these materials which allows for accurate thickness variation measurement in the range of a few micrometers at frequencies as low as 10 GHz. Near-field microwave inspection techniques were successfully used for detecting and locating defects and voids of different sizes and shapes in composites. For optimal detection, the standoff distance between the sensor and the composite and frequency of operation were used as optimization parameters to improve the detection capability. Carbon-loaded composites present a challenge to microwave NDT because of the lossy nature of carbon, especially at high microwave frequencies. Lower frequencies penetrate more (deeper) in carbon-loaded composites, however, the size of the waveguide sensor increases drastically at lower frequencies and consequently the resolution degrades rapidly as well. To overcome this dilemma, open-ended rectangular waveguides loaded with a dielectric material will be used to inspect carbon-loaded composites. The loading of the waveguide reduces the frequency of operation and keeps the small size of the waveguide (i.e. increases the penetration depth and maintains the resolution). Carbon-loaded composites with disbonds will be inspected and the ability of utilizing loaded rectangular waveguides for carbon-loaded composites inspection will be assessed.     

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.     

A basic program for calculating subsurface water temperatures using chemical geothermometers-implication to geothermal reservoir estimation

GEOTHERM is a computer program written in BASIC language to estimate geothermal reservoir temperature using the well-known chemical gèothermometers. The empirical equations used in the program were obtained from the literature. Three different chemical geothermometers are included in the program: Na-K, Na-K-Ca, and silica geothermometers; this gives the user the opportunity not only to select the most reliable geothermometer in estimating subsurface temperature, but also to select the type of geothermometer according to available data. A sample input file of geothermal waters obtained from Iceland has been tested, so as to show the applicability and usefulness of the program.     

Holographic memory-based Bayesian optimization algorithm (HM-BOA) in dynamic environments

This paper presents a new evolutionary dynamic optimization algorithm, holographic memory-based Bayesian optimization algorithm (HM-BOA), whose objective is to address the weaknesses of sequential memory-based dynamic optimization approaches. To this end, holographic associative neural memory is applied to one of the recent successful memory-based evolutionary methods, DBN-MBOA (memory-based BOA with dynamic Bayesian networks). Holographic memory is appropriate for encoding environmental changes since its stimulus and response data are represented by a vector of complex numbers such that the phase and the magnitude denote the information and its confidence level, respectively. In the learning process in HM-BOA, holographic memory is trained by probabilistic models at every environmental change. Its weight matrix contains abstract information obtained from previous changes and is used for constructing a new probabilistic model when the environment changes. The unique features of HM-BOA are: 1) the stored information can be generalized, and 2) a small amount of memory is required for storing the probabilistic models. Experimental results adduce grounds for its effectiveness especially in random environments.     

Determining number of clusters and prototype locations via multi-scale clustering

In clustering algorithms, it is usually assumed that the number of clusters is known or given. In the absence of such a priori information, a procedure is needed to find an appropriate number of clusters. This paper presents a clustering algorithm that incorporates a mechanism for finding the appropriate number of clusters as well as the locations of cluster prototypes. This algorithm, called multi-scale clustering, is based on scale-space theory by considering that any prominent data structure ought to survive over many scales. The number of clusters as well as the locations of cluster prototypes are found in an objective manner by defining and using lifetime and drift speed clustering criteria. The outcome of this algorithm does not depend on the initial prototype locations that affect the outcome of many clustering algorithms. As an application of this algorithm, it is used to enhance the Hough transform technique.