Second‐order intermodulation cancelation and conversion‐gain enhancement techniques for CMOS active mixers

In this paper, two new techniques are proposed to improve the second‐order input intercept point (IIP2) and conversion‐gain in double‐balanced Gilbert‐cell complementary metal‐oxide semiconductor (CMOS) mixers. The proposed IIP2 improvement technique is based on canceling the common‐mode second‐order intermodulation (IM2) component at the output current of the transconductance stage. Additionally, the conversion‐gain is improved by increasing the fundamental component of the transconductance stage output current and creating a negative capacitance to cancel the parasitic capacitors. Moreover, in the proposed IM2 cancelation technique, by decreasing the bias current of the switching transistors, the flicker noise of the mixer is reduced. The proposed mixer has been designed with input frequency and output bandwidth equal to 2.4 GHz and 20 MHz, respectively. Spectre‐RF simulation results show that the proposed techniques simultaneously improve IIP2 and conversion‐gain by approximately 23.2 and 5.7 dB, respectively, in comparison with the conventional mixer with the same power consumption. Also, the noise figure (NF) at 20 kHz, where the flicker noise is dominant, is reduced by 4.9 dB. The average NF is increased nearly 0.9 dB, and the value of third‐order input intercept point (IIP3) is decreased approximately 1.8 dB. Copyright © 2014 John Wiley & Sons, Ltd.     

Geometric optimization for a thermal microfluidic chip

A geometric design for a microfluidic chip using numerical simulation is presented. Finite element method was employed in order to design the microchannel configuration for a microfluidic chip array. The effect of geometry on the thermal response at the interface between the microfluidic chips and an integrated system, such as a micro-electronic device, was investigated. Dimensionless design charts, obtained from the parametric models, demonstrated that a compromise between a maximum heat transfer and a minimum interface temperature was achieved with an equilateral triangle cross-section at a microchannel spacing to width ratio of two. The transient response of the microfluidic chip implied that the transient analysis corresponded to the steady state results under different boundary conditions.     

Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network

Natural gas commonly contains water as a contaminant that can condense to water or form gas hydrates, which causes a range of problems during gas production, transportation, and processing. Therefore, the removal of gas moisture is of great importance. A common and popular method for removing water contamination from natural gas is using solid dehydrators. Calcium chloride is a nonregenerative desiccant to dehydrate natural gas. With continual water adsorption, CaCl₂ changes to consecutively higher states of hydration, finally producing a CaCl₂ brine solution. This method does not require heating or moving parts. In addition, it does not react with H₂S or CO₂. These features make this method a popular one for drying natural gas. Nevertheless, precise and simple methods are needed to predict the water content of natural gas dried by calcium chloride dehydrator units. In this study, an intelligent method, called the radial basis function neural network, was incorporated to predict the gas moisture dehydrated by calcium chloride in dehydration units. Modeling was performed under different conditions of a fresh recharge and before recharging. The overall correlation factor of 0.9999 for both the fresh charge and before charging conditions showed that the outputs of the proposed models were in agreement with the experimental data. In addition, the developed models were compared with the previously proposed intelligent models and classic correlations. The comparison showed that the developed model is superior to the previously proposed models and correlations regarding the accuracy of prediction.     

How To Quantify the Efficiency Potential of Neat Perovskite Films: Perovskite Semiconductors with an Implied Efficiency Exceeding 28%

Perovskite photovoltaic (PV) cells have demonstrated power conversion efficiencies (PCE) that are close to those of monocrystalline silicon cells; however, in contrast to silicon PV, perovskites are not limited by Auger recombination under 1-sun illumination. Nevertheless, compared to GaAs and monocrystalline silicon PV, perovskite cells have significantly lower fill factors due to a combination of resistive and non-radiative recombination losses. This necessitates a deeper understanding of the underlying loss mechanisms and in particular the ideality factor of the cell. By measuring the intensity dependence of the external open-circuit voltage and the internal quasi-Fermi level splitting (QFLS), the transport resistance-free efficiency of the complete cell as well as the efficiency potential of any neat perovskite film with or without attached transport layers are quantified. Moreover, intensity-dependent QFLS measurements on different perovskite compositions allows for disentangling of the impact of the interfaces and the perovskite surface on the non-radiative fill factor and open-circuit voltage loss. It is found that potassium-passivated triple cation perovskite films stand out by their exceptionally high implied PCEs > 28%, which could be achieved with ideal transport layers. Finally, strategies are presented to reduce both the ideality factor and transport losses to push the efficiency to the thermodynamic limit.     

Plant‐mediated green synthesis of silver nanoparticles using Trifolium resupinatum seed exudate and their antifungal efficacy on Neofusicoccum parvum and Rhizoctonia solani

In recent years, biosynthesis and the utilisation of silver nanoparticles (AgNPs) has become an interesting subject. In this study, the authors investigated the biosynthesis of AgNPs using Trifolium resupinatum (Persian clover) seed exudates. The characterisation of AgNPs were analysed using ultraviolet–visible spectroscopy, X‐ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infra‐red spectroscopy. Also, antifungal efficacy of biogenic AgNPs against two important plant‐pathogenic fungi (Rhizoctonia solani and Neofusicoccum Parvum) in vitro condition was evaluated. The XRD analysis showed that the AgNPs are crystalline in nature and have face‐centred cubic geometry. TEM images revealed the spherical shape of the AgNPs with an average size of 17 nm. The synthesised AgNPs were formed at room temperature and kept stable for 4 months. The maximum distributions of the synthesised AgNPs were seen to range in size from 5 to 10 nm. The highest inhibition effect was observed against R. solani at 40 ppm concentration of AgNPs (94.1%) followed by N. parvum (84%). The results showed that the antifungal activity of AgNPs was dependent on the amounts of AgNPs. In conclusion, the AgNPs obtained from T. resupinatum seed exudate exhibit good antifungal activity against the pathogenic fungi R. solani and N. Parvum.Inspec keywords: silver, nanoparticles, botany, ultraviolet spectra, visible spectra, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectra, nanobiotechnology, biological techniquesOther keywords: plant‐mediated green synthesis, silver nanoparticles, Trifolium resupinatum seed exudate, antifungal efficacy, Neofusicoccum parvum, Rhizoctonia solani, biosynthesis, ultraviolet–visible spectroscopy, X‐ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, plant‐pathogenic fungi, XRD analysis, TEM images, antifungal activity, temperature 293 K to 298 K, Ag     

Sn-adopted fullerene $$(\hbox {C}_{60})$$ nanocage as acceptable catalyst for silicon monoxide oxidation

In recent years, the discovery of metal catalysts for the oxidation of silicon monoxide (SiO) has become extremely important. In first step, the Sn adoption of fullerene (\(\hbox {C}_{60})\) was investigated and then activation of surface of \(\hbox {Sn-C}_{60}\) via \(\hbox {O}_{2}\) molecule was examined. In second step, the SiO oxidation on surface of \(\hbox {Sn-C}_{60}\) via Langmuir Hinshelwood (LH) and Eley Rideal (ER) mechanisms was investigated. Results show that \(\hbox {O}_{2}\hbox {-Sn-C}_{60}\) can oxidize the SiO molecule via \(\hbox {Sn-C}_{60}\hbox {-O-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60}\hbox {-O-O}^{*}\hbox {-SiO} \rightarrow \hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}_{2}\) and \(\hbox {Sn-C}_{60}\hbox {-O}^{*} + \hbox {SiO}\rightarrow \hbox {Sn-C}_{60} + \hbox {SiO}_{2}\) reactions. Results show that SiO oxidation via the LH mechanism has lower energy barrier than ER mechanism. Finally, \(\hbox {Sn-C}_{60}\) is an acceptable catalyst with high performance for SiO oxidation in normal temperature.     

Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model

In recent years, BOT approach has provided an increasingly popular project financing to move toward infrastructure development in Asian countries such as Iran. There are many complexities in projects because of the variety of factors in project’s trend and also the dependence of project on mainly national factors. Due to these complexities and their long-term operating, the projects meet with uncertainty and numerous risks. In this paper, an attempt is made at identifying common risks in BOT projects. A novel hierarchical structure of risks is presented on the basis of the project-oriented point of view; next, some effective criteria for risk ranking in BOT projects are introduced.Then, the problem is defined in Fuzzy Multi Attribute Decision Making (FMADM) field. Therefore, Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods are presented in order to rank high risks in BOT projects. In this study, these two methods are compared in four respects: separation among alternatives, fuzzy error in criteria’s weights, risk response planning, and numerousness of alternatives in proportion to criteria. The proposed model is used for identifying and assessing risks in Iran BOT power plant project. Finally, the rankings of high risks are determined according to their results at the end of this paper.     

A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.     

Optical and morphological properties of silicon dioxide thin films

The purpose of the present work is to compare the structural and optical properties of the Silicon dioxide films obtained from TMOS Si(OCH₃)₄), N₂O, and NH₃ as precursor gases, which with respect to their potential optical applications, have been deposited by using inductively coupled plasma-enhanced chemical-vapor deposition method. The optical property as well as the thickness of the films were analyzed by means of variable angle spectroscopic ellipsometry. Morphological studies were carried out by scanning electron microscopy, and chemical composition characterization was performed with the help of energy dispersive spectroscopy unit coupled with the electron microscope. The type of the substance and the precursor composition used for silicon dioxide synthesis are effective on the chemical composition of the films. The refractive index values of these films advocate their use as high refractive index materials while their low extinction coefficients assure the devices transparency. The work presents deposition rates as well as the films optical properties, chemical composition and morphology regarding the operational parameters of their synthesis. It also provides a comparison of the characteristics of the two competitive precursor compounds.     

Ambiguity-driven fuzzy C-means clustering: how to detect uncertain clustered records

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They mainly emerge from making decisions about a subset of records that do not provide sufficient evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate preventing performance loss which is common in similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm.