In the Search of Fundamental Inner Bond Strength of Solid Elements

In order to understand the physics behind the surface properties and nano-scale phenomena, we are motivated first to investigate the inner bond strengths as well as the effect of number of neighboring atoms and their relative distance in addition to space positions (crystallography). Therefore, in order to study the effect of the nature of metallic bond on their physico-chemical properties, we first tried to investigate and introduce a mathematical model for transforming the bulk molar cohesion energy into microscopic bond strengths between atoms. Then an algorithm for estimating the nature of bond type including the materials properties and lattice scale “cutoff” has been proposed. This leads to a new fundamental energy scale free from the crystallography and number of atoms. The results of our model in case of fundamental energy scale of metals not only perfectly describe the inter relation between binding and melting phenomena but also adequately reproduce the bond strength for different bond types with respect to other estimations reported in literatures. The generalized algorithm and calculation methodology introduced here by us are suggested to be used for developing energy scale of bulk crystal materials to explain or predict any particular materials properties related to bond strengths of metallic elements

Continuous Separation of Colloidal Particles using Dielectrophoresis.

Dielectrophoresis is the movement of particles in nonuniform electric fields and has been of interest for application to manipulation and separation at and below the microscale. This technique has the advantages of being noninvasive, nondestructive, and noncontact, with the movement of particle achieved by means of electric fields generated by miniaturized electrodes and microfluidic systems. Although the majority of applications have been above the microscale, there is increasing interest in application to colloidal particles around a micron and smaller. This paper begins with a review of colloidal and nanoscale dielectrophoresis with specific attention paid to separation applications. An innovative design of integrated microelectrode array and its application to flow-through, continuous separation of colloidal particles is then presented. The details of the angled chevron microelectrode array and the test microfluidic system are then discussed. The variation in device operation with applied signal voltage is presented and discussed in terms of separation efficiency, demonstrating 99.9% separation of a mixture of colloidal latex spheres

Effect of spark plasma sintering and high-pressure torsion on the microstructural and mechanical properties of a Cu–SiC composite

This investigation examines the problem of homogenization in metal matrix composites (MMCs) and the methods of increasing their strength using severe plastic deformation (SPD). In this research MMCs of pure copper and silicon carbide were synthesized by spark plasma sintering (SPS) and then further processed via highpressure torsion (HPT). The microstructures in the sintered and in the deformed materials were investigated using Scanning Electron Microscopy (SEM) and Scanning Transmission Electron Microscopy (STEM). The mechanical properties were evaluated in microhardness tests and in tensile testing. The thermal conductivity of the composites was measured with the use of a laser pulse technique. Microstructural analysis revealed that HPT processing leads to an improved densification of the SPS-produced composites with significant grain refinement in the copper matrix and with fragmentation of the SiC particles and their homogeneous distribution in the copper matrix. The HPT processing of Cu and the Cu-SiC samples enhanced their mechanical properties at the expense of limiting their plasticity. Processing by HPT also had a major influence on the thermal conductivity of materials. It is demonstrated that the deformed samples exhibit higher thermal conductivity than the initial coarse-grained samples

SURFACE MELTING PHENOMENA MODELING OF DIFFERENT CRYSTALLOGRAPHIC PLANES, LAYER BY LAYER FOR COPPER

In this research, in order to study the surface melting phenomena of metals for different crystallographic planes, we present a thermodynamic model on the example of copper. This model includes interfacial energies of thin layers of solid with gas, surface of melted layers with gas and interface between solid and liquid of metals. Moreover, the effect of surface energy of different planes, number of layers and crystallographic orientation of metallic layers on melting transformation was considered. Therefore, by applying the effect of energy and orientation of surface layers on the variation of Gibbs free energy value of solid phase (thin films) and molten metal, surface melting point for solid thin films has been evaluated. Furthermore the effect of surface energies of (100), (110) and (111) planes has been investigated. The results of our calculations show good agreement with experimental results and other theoretical predictions in literature. By using this model we are also able to calculate and analyze the melting phenomena of metallic thin films layer by layer from some nm scale up to thick (bulk metallic) cases

In the Search of Fundamental Inner Bond Strength of Solid Elements

In order to understand the physics behind the surface properties and nano-scale phenomena, we are motivated first to investigate the inner bond strengths as well as the effect of number of neighboring atoms and their relative distance in addition to space positions (crystallography). Therefore, in order to study the effect of the nature of metallic bond on their physico-chemical properties, we first tried to investigate and introduce a mathematical model for transforming the bulk molar cohesion energy into microscopic bond strengths between atoms. Then an algorithm for estimating the nature of bond type including the materials properties and lattice scale “cutoff” has been proposed. This leads to a new fundamental energy scale free from the crystallography and number of atoms. The results of our model in case of fundamental energy scale of metals not only perfectly describe the inter relation between binding and melting phenomena but also adequately reproduce the bond strength for different bond types with respect to other estimations reported in literatures. The generalized algorithm and calculation methodology introduced here by us are suggested to be used for developing energy scale of bulk crystal materials to explain or predict any particular materials properties related to bond strengths of metallic elements

An Optimal Model for Medicine Preparation Using Data Mining

Introduction: Lack of financial resources and liquidity are the main problems of hospitals. Pharmacies are one of the sectors that affect the turnover of hospitals and due to lack of forecast for the use and supply of medicines, at the end of the year, encounter over-inventory, large volumes of expired medicines, and sometimes shortage of medicines. Therefore, medicine prediction using available retrospective data leads to improved resource management in hospitals. Due to the high capability of data mining in modeling medical problems, selected algorithms were used to determine the optimal model of medicine preparation.   Method: In this cross-sectional study, to investigate different types of data mining algorithms, an information form was developed based on the design objectives and then defined in the form of reports in the hospital information system. The data were extracted using Crystal Report software. To develop the model, the accuracy of the data mining prediction algorithms including KNN, SVM, NN, Random Forest, LR, and Adaboost was examined based on MSE, RMSE, MAE, and R2 criteria in Weka software. Results: Concerning R2, MAE, and RMSE criteria, Adaboost method (0.78, 247, 827) and random forest method (0.6, 1170, 1868) had the highest accuracy compared to other models and reduced the error rate more. Other methods with the above criteria had poorer performance in predicting the research problem. Conclusion: The results of this study indicated that the Adaboost and random forest methods are more accurate than other methods. A small percentage of hospitals plan to manage the preparation of medicines; thus, it is suggested that managers of hospitals and pharmacies use data mining in the management of their respective units

Comparing the growth of Paulownia fortunei and Populus deltoides plantations under different spacing in northern Iran

This study was carried out to investigate poplar and paulownia’s growth under different spacing in a 5-year plantation located in Mazandaran wood and paper company’s fields in Sari-Iran. The trees were planted in three different spacing: 3×3m, 4×4m, and 5×5 m, each established in 3 replications. The quantitative parameters including DBH and Height were measured and a comparison was made between the stands height and diameter growth. Survival percentage was also calculated across the stands. The averages were statistically compared in a randomized block design in two forms: 1) two species were separately compared in their threefold spacing, and 2) two species were crossed in each spacing. Tukey’s test was used to compare the averages. Results showed a promising rate of survival over the stands. Moreover, the growth was significantly different amongst different spacing of the stands, representing the best growth in 3×3m spacing. In addition, the height of poplar was significantly more than those of paulownia, though the diameter growth assessment showed a reverse trend between the stands