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.     

Measurement of the amount and rate of methane dissolution in pure water and aqueous solution of SDS + multi-wall carbon nanotubes + β-cyclodextrin

In this paper, the impact of the mixture of sodium dodecyl sulfate (SDS) + multi-wall carbon nanotubes (MWCNTs) + β-cyclodextrin on the quantity and initial rate of methane dissolved in water is investigated. The experiments were performed at a temperature range of 278.15–303.15 K and an initial pressure of 0.5 MPa. The experimental results show that simultaneous utilization of β-cyclodextrin (0.01 mass fraction), MWCNTs (0.0005 mass fraction), and SDS (0.001 mass fraction) at 278.15 K increases the amount and the rate of methane dissolution in water by 29.90% and 173.78%, respectively, compared to pure water. An increase in the temperature decreases the quantity and the initial rate of methane dissolution in all solutions containing additives. However, no consistent relationship is observed between the temperature and the enhancement percentage of solubility of methane in solutions containing additives.     

Outage Probability Based Robust Distributed Beam-Forming in Multi-User Cooperative Networks with Imperfect CSI

In this paper, a new robust problem is proposed for relay beamforming in relay system with stochastic perturbation on channels of multi user and relay network. The robust problem aims to minimize the transmission power of relay nodes while the imperfect channel information (CSI) injects stochastic channel uncertainties to the parameters of optimization problem. In the power minimization framework, the relays amplification weights and phases are optimized assuming the availability of Gaussian channel distribution. The power sum of all relays is minimized while the outage probability of the instantaneous capacity (or SINR) at each link is above the outage capacity (or SINR) for each user. The robust problem is a nonconvex SDP problem with Rank constraint. Due to the nonconvexity of the original problem, three suboptimal problems are proposed. Simulation and numerical results are presented to compare the performance of the three proposed solutions with the existing worst case robust method.     

Spatial-Temporal Cooperative Spectrum Sensing in Flat Fading Channels for Cognitive Radio Using Extend Kalman Filter

This paper investigates a new channel gain map tracking by Space-Time Extended Kalman Filtering (STEKF) for a flat channel, and a novel spectrum sensing via Time Spatial Weighted Non-negative Lasso (TSWNL) algorithm. STEKF enables CRs to estimate and interpolate channel gain map for the entire geographical area of interest with a limited number of CRs measurements. In order to sense primary users (PU) activities, include the transmission power by each PU, location and number of active PUs, TSWNL algorithm is proposed. Numerical results illustrate that the proposed STEKF channel estimation and TSWNL sensing algorithms outperforms linear methods.     

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.