Role of nano‐size SiO2 additive on the thermal behavior of cyanoacrylate nanocomposite

Nowadays, solvent‐free, one‐part cyanoacrylate adhesive is widely used in medicine and dentistry. According to a literature survey done by the authors, there are few papers concentrated on the role of nano‐sized particles on the thermal behavior of cyanoacrylate glue. Thus the main goal of the current research focused on clarifying the role of nano‐sized SiO₂ on the thermal behavior of cyanoacrylate. Thermal behavior of all materials including cyanoacrylate and its nanocomposites was studied by using Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) methods. The results of DSC analysis showed that an increase in the amount of nano‐sized SiO₂ results in decreases in the duration of cyanoacrylate curing, energy release during polymerization, and incubation time of polymerization. Furthermore, the results of TGA tests illustrated that the weight loss of cyanoacrylate strongly depends on the contents of both caffeine and SiO₂. In fact, an increase in nano‐sized SiO₂ content increases the degradation temperature of cyanoacrylate. J. VINYL ADDIT. TECHNOL., 2010. © 2010 Society of Plastics Engineers     

A new stress-updating algorithm for viscoplasticity

Material behavior beyond the elastic limit can be rate-dependent, and this rate sensitivity can be captured by the viscoplastic material models. To describe the viscoplastic material behavior in structural analysis, an efficient numerical framework is necessary. In this paper an algorithm is proposed for metals for which von Mises yield surface along with Peri?’s viscoplastic model is employed. The efficiency and accuracy of the technique is examined by comparison with different numerical studies. The convergence rate of the proposed algorithm is investigated. Characteristics of the viscoplastic behavior such as relaxation are illustrated in the selected case studies. Finally, application of the algorithm in practice is demonstrated by a boundary value problem.

     

Silicon Nanowires Driving Miniaturization of Microelectromechanical Systems Physical Sensors: A Review

The miniaturization of microelectromechanical systems (MEMS) physical sensors is driven by global connectivity needs and is closely linked to emerging digital technologies and the Internet of Things. Strong technical advantages of miniaturization such as improved sensitivity, functionality, and power consumption are accompanied by significant economic benefits due to semiconductor manufacturing. Hence, the trend to produce smaller sensors and their driving force resemble very much those of the miniaturization of integrated circuits (ICs) as described by Moore's law. In this respect, with its IC-, and MEMS-compatibility, and scalability, the silicon nanowire is frequently employed in frontier research as the sensor building block replacing conventional sensors. The integration of the silicon nanowire with MEMS has thus generated a multiscale hybrid architecture, where the silicon nanowire serves as the piezoresistive transducer and MEMS provide an interface with external forces, such as inertial or magnetic. This approach has been reported for almost all physical sensor types over the last decade. These sensors are reviewed here with detailed classification. In each case, associated technological challenges and comparisons with conventional counterparts are provided. Future directions and opportunities are highlighted.     

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.     

Eggshell, a new bio-filler for polypropylene composites

Chicken eggshell (ES) is an industrial byproduct containing 95% calcium carbonate, and its disposal constitutes a serious environmental hazard. Different proportions of chicken eggshell as bio-filler for polypropylene (PP) composite were compared with different particle sizes and proportions of commercial talc and calcium carbonate fillers by tensile test. The Young's modulus (E) was improved with the increment of ES content, and this bio-filler was better than all types of carbonate fillers with different particle sizes used in this study. Although ES composites showed lower E values than talc composites, talc filler could be replaced by up to 75% with ES while maintaining a similar stiffness and E compared to the talc composites. Scanning electron microscopy showed an improved interfacial bonding on the tensile fractured surface. The improvement in the mechanical properties was attributed to a better ES/matrix interface related to the geometric ratio of the ES particles similar to talc particles.     

The Fast Evaluation Strategy for Evolvable Hardware

An evolutionary algorithm implemented in hardware is expected to operate much faster than the equivalent software implementation. However, this may not be true for slow fitness evaluation applications. This paper introduces a fast evolutionary algorithm (FEA) that does not evaluate all new individuals, thus operating faster for slow fitness evaluation applications. Results of a hardware implementation of this algorithm are presented that show the real time advantages of such systems for slow fitness evaluation applications. Results are presented for six optimisation functions and for image compression hardware.