Isothermal Ageing of SnAgCu Solder Alloys: Three-Dimensional Morphometry Analysis of Microstructural Evolution and Its Effects on Mechanical Response

Due to the high homologous temperature and fast cooling rates, the microstructures of SnAgCu (SAC) solders are in a meta-stable state in most applications, which is the cause of significant microstructural evolution and continuous variation in the mechanical behavior of the joints during service. The link between microstructures evolution and deformation behavior of Sn-4.0Ag-0.5Cu solder during isothermal ageing is investigated. The evolution of the microstructures in SAC solders are visualized at different scales in 3D by using a combination of synchrotron x-ray and focused ion beam/scanning electron microscopy tomography techniques at different states of ageing. The results show that, although the grain structure, morphology of dendrites, and overall volume fraction of intermetallics remain almost constant during ageing, considerable coarsening occurs in the Ag₃Sn and Cu₆Sn₅ phases to lower the interfacial energy. The change in the morphometrics of sub-micron intermetallics is quantified by 3D statistical analyses and the kinetic of coarsening is discussed. The mechanical behavior of SAC solders is experimentally measured and shows a continuous reduction in the yield resistance of solder during ageing. For comparison, the mechanical properties and grain structure of β-tin are evaluated at different annealing conditions. Finally, the strengthening effect due to the intermetallics at different ageing states is evaluated by comparing the deformation behaviors of SAC solder and β-tin with similar grain size and composition. The relationship between the morphology and the strengthening effect due to intermetallics particles is discussed and the causes for the strength degradation in SAC solder during ageing are identified.     

Damage Classification of Sandwich Composites Using Acoustic Emission Technique and k-means Genetic Algorithm

In this study acoustic emission (AE) technique was used for monitoring mode I delamination test of sandwich composites. Since, during mode I delamination test various damage mechanisms appear, their classification is of major importance. Hence, integration of \(k\) -means algorithm and genetic algorithm was applied as an efficient clustering method to discriminate different failure modes. Performing primary experiments to find the relationship between AE parameters and damage mechanisms, the AE signals of obtained clusters were assigned to distinct damage mechanisms. Also, the dominance of damage mechanisms was determined based on the distribution of AE signals in different clusters. Finally SEM observation was employed to verify obtained results. The results indicate the efficiency of the proposed method in damage classification of sandwich composites.     

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.     

Experimental study on heat transfer and pressure drop of in-house synthesized graphene oxide nanofluids

Abstract

In this article, first, graphene oxide nanosheets were synthesized in-house according to the modified Hummers method, and these nanosheets were used to prepare graphene oxide nanofluids at two concentrations. Then the thermophysical properties of nanofluids were characterized using X-ray diffraction analysis, a scanning electron microscope, and UV–Vis spectrophotometry. The particle size distribution was investigated using dynamic light scattering. Then, a fundamental study was conducted on the thermal-hydraulic characteristics of graphene oxide nanofluids flowing through a straight copper tube. An experimental setup was developed to find the heat transfer characteristics and pressure drop of nanofluids in the test section consisting of a copper tube with constant heat flux. The flow regimes and associated pressure drop and heat transfer characteristics at varying flow rate were investigated at three different heat flux conditions of 7.4, 9.1, and 12.6?kW/m². Due to the increase in viscosity, flowrate and Reynolds number decreased from 0.01 to 0.1?wt% of graphene oxide nanofluids at constant pump frequency. Experimental data obtained for water were validated with the findings from the literature, and the correlations were formulated for the Nusselt number and Reynolds number by considering the multiple regression analysis. The convective heat transfer coefficient for graphene oxide at 0.01?wt% was higher when compared to graphene oxide at 0.1?wt% and water. The variation of Nusselt number with the heat flux and velocity was insignificant.     

Nucleation and growth of Pd nanoparticles during electrocrystallization on pencil graphite

The aim of present study was to investigate the mechanism of palladium electrodeposition on a pencil graphite surface from an aqueous solution containing 1 mM PdCl₂ and 500 mM H₂SO₄. Cyclic voltammetry revealed that the electrodeposition process proceeds via an initial underpotential deposition (UPD) and subsequently, it follows by the overpotential deposition (OPD) under diffusion control. Current transients showed a complex deposition trend, i.e. the electrodeposition of Pd includes at least three stages: adsorption on the surface, and two types of nucleation courses; two-dimensional (2D) and three-dimensional (3D) which limited by the lattice incorporation of Pd adatoms and the diffusion of Pd ions, respectively. Therefore the electrocrystallization of Pd on pencil graphite conforms to the Stranski–Krastanov growth mechanism. Furthermore, theoretical models were applied to evaluate each of above mentioned stages for different electrode potentials and to estimate the electrochemical parameters of Pd electrodeposition. The micrographs of electrodeposited Pd at relatively high frequency of the nucleation rate, illustrated a well dispersed Pd nanoparticles with a relatively narrow size distribution.     

Relationship between clinical assessments of function and measurements from an upper-limb robotic rehabilitation device in cervical spinal cord injury

Upper limb robotic rehabilitation devices can collect quantitative data about the user's movements. Identifying relationships between robotic sensor data and manual clinical assessment scores would enable more precise tracking of the time course of recovery after injury and reduce the need for time-consuming manual assessments by skilled personnel. This study used measurements from robotic rehabilitation sessions to predict clinical scores in a traumatic cervical spinal cord injury (SCI) population. A retrospective analysis was conducted on data collected from subjects using the Armeo Spring (Hocoma, AG) in three rehabilitation centers. Fourteen predictive variables were explored, relating to range-of-motion, movement smoothness, and grip ability. Regression models using up to four predictors were developed to describe the following clinical scores: the GRASSP (consisting of four sub-scores), the ARAT, and the SCIM. The resulting adjusted R(2) value was highest for the GRASSP "Quantitative Prehension" component (0.78), and lowest for the GRASSP "Sensibility" component (0.54). In contrast to comparable studies in stroke survivors, movement smoothness was least beneficial for predicting clinical scores in SCI. Prediction of upper-limb clinical scores in SCI is feasible using measurements from a robotic rehabilitation device, without the need for dedicated assessment procedures.     

The EU fusion programme and roadmap

To meet ITER and fusion energy challenges, a gradual transition of the EU fusion programme priorities from today's fusion research to a strongly goal-oriented programme with clear milestones is required. This transition and the programme priorities should be driven by an ambitious, yet realistic roadmap to be agreed by EU fusion stakeholders. The programme must primarily focus on: ensuring the success of ITER; and research and development of reactor-relevant technologies. Industry must be involved early in the implementation and international collaboration should be conducted as part of a strategic approach. Joint programming should be a cornerstone of the EU fusion programme in Horizon 2020. Options for the new structure and current actions required to implement the roadmap during Horizon 2020 will be presented.     

Adsorptive separation of meta-xylene from C8 aromatics

Industrial adsorptive separation process for liquids is most successful when the involved species have very close boiling points, making distillation expensive or are thermally sensitive at convenient distillation temperatures. The adsorption process was studied for separating meta-xylene from a feed mixture containing all C₈ aromatics on binder-free X and Y zeolites in the liquid phase. Zeolitic adsorbents with different SiO₂/Al₂O₃ were synthesized by the hydrothermal method and ion-exchanged with alkaline metal cations like lithium, sodium and potassium. The adsorption process was carried out in a breakthrough system at temperature of 110–160 °C and pressure of 6–8 atm. The influence of adsorbent moisture content on the separation process was studied. The optimization of adsorption process was also investigated by the changing operation conditions. The isotherms for each isomer of C₈ aromatics and the desorbent possess the adsorption characteristics of Langmuir type. The selectivity factor of meta-xylene and the saturation adsorption capacities of adsorbates were determined. It was observed that the selectivity of meta-xylene increased by sodium ion-exchanging of cationic sites in Y zeolite and the selectivity factor of meta-xylene/para-xylene, meta-xylene/ortho-xylene and meta-xylene/ethylbenzene in the optimum conditions was determined to be 2.62, 2.83 and 5.93, respectively.