A High Gain and Low Flicker Noise CMOS Mixer with Low Flicker Noise Corner Frequency Using Tunable Differential Active Inductor

This paper presents the design of a high conversion gain and low flicker noise down conversion CMOS double balanced Gilbert cell mixer using \(0.18\,\upmu \hbox {m}\) CMOS technology. The high conversion gain and low flicker noise mixer is implemented by using a differential active inductor (DAI) circuit and cross-coupled current injection technique within the conventional double-balanced Gilbert cell mixer. A cross-coupled current bleeding circuit is used to inject the current to the switching stage to decrease the flicker noise. Instead of spiral inductor, a DAI with high tunability of the inductor and quality factor is used to tune out the parasitic capacitance effect and decrease the leakage current that has a harmonic component and produce the flicker noise. By tuning the DAI, the flicker noise corner frequency is reduced to 150 Hz. The proposed circuit is simulated with Cadence Spectra and the simulation results shows the NF of 11.2 dB, conversion gain of 23.7 dB and IIP3 of \(-6\)  dB for an RF frequency of 2.4 GHz. The excellent LO-RF, LO-IF, RF-LO and RF-IF isolations of \(-60, -110, -52\) and \(-64\)  dB are achieved respectively. The total power consumption is 10.5 mW from a 1.8 V DC power supply.     

Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68?×?10^?5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.     

Reducing of heat loss of rubber compound using natural zeolite filler: effect of partially substitution of fillers on compound properties

The main objective of this work was reducing the heat loss of styrene butadiene rubber by partial substitution of carbon black with natural zeolite as a filler. Reducing the usage of carbon black in the rubber industry is a good strategy to decrease fossil fuel usage and global warming. There are different mineral fillers like silica and clay to be used instead of carbon black. Effect of application of natural zeolite on reducing the heat loss of rubber compound based on SBR was investigated by melt mixing of natural zeolite in rubber matrix in an internal mixer. Natural zeolite was selected as 5, 10, 15, and 20 phr. Carbon black was partially substituted with zeolite and the effect of natural zeolite content and structure on different aspects of the compound including heat buildup, hardness, elongation, and modulus were evaluated. It was shown that although cross-link density and mechanical properties of the compounds decreased a little, but a significant improvement was observed in the fatigue resistance of the compounds beside a favorable decrease in the heat buildup and abrasion loss with an increase in the natural zeolite loading. The rate of improvement in properties was slowed down at zeolite contents higher than 5 phr.     

Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition

This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse. Coercivity is one of the most important properties of magnetic materials and its value shows the needed magnetic field in a way that magnetization of system is decreased to zero. There are many parameters such as pH of solution, oxidative and reductive times, oxidative and reductive voltages, interval between pulses (off-time), and concentration of deposition solution that have direct effect on materials magnetic properties of. Change of initial conditions to obtain the best results is very time consuming, therefore employing a method which can save both the time and cost is necessary. Hence, it this study Artificial Neural Network (ANN), which has numerous applications and has attracted many attentions in various fields, was applied. Through this study, an ANN was designed to present a template that is capable for predicting output data (coercivity) according to input data (pH, oxidative and reductive times, oxidative and reductive voltages, and off-time). Besides, in this research, the results for pH = 4 and 6 were investigated and the effect of off-time as well as the deposition time on coercivity were studied.     

Experimental investigation of liquid–liquid flow in an inclined pipe using dimensionless numbers

Two-phase flow is a common phenomenon in the energy industry, where flow patterns significantly affect heat transfer and pressure drop in different systems. However, there is no unique or comparable flow map because of its dependency on dimensional parameters. Therefore, an analysis using dimensionless numbers makes the results comprehensive. To do so, a series of liquid–liquid flow experiments (1296 experiments) were conducted in a transparent pipe at the different velocities of the phases. The flow patterns were captured using a high-speed camera. The experiments were performed at eight different inclinations within the range of −20 to +20 degrees. Six flow patterns are observed at different inclinations; stratified flow with mixing at the interface (STMI), dispersion of water in oil (Dw/o), dispersion of oil in water (Do/w), dual continuous (DC), slug, and wavy stratified (WST), where the first five flow patterns are presented in the upward flow and the two last flow patterns disappear in some of the downward flow. The pattern of boundaries for each flow pattern in the upward flow shows dependency on inclination, while in the downward flow condition, a rather general format can be applied to most of the patterns. The analysis illustrates that gravity and buoyancy forces are the dominating forces in the system compared to other forces, such as viscous, inertia, and interfacial tension, which are due to the inclination of the pipe.     

Reactivity stabilization and capacity study of fabricated alumina and zirconia-supported CaO-based sorbents for high-temperature CO2 capture in a fixed-bed reactor

Calcium looping process is a promising approach for CO₂ capture from the flue gas of fossil fuel power plants and the cement industry. Even though the advantages of calcium-based sorbents are low cost and high uptake capacity, they suffer from low durability during cycles. Modified sorbents were fabricated by adding alumina and zirconia and the mixture of alumina and zirconia to calcium oxide via the co-precipitation method. The performance of synthesized sorbents in terms of stability and CO₂ capture capacity were evaluated using a fixed bed reactor in various CO₂ sorption/desorption cycles. The sorbents were fabricated by a co-precipitation methodology using 10% binders (alumina and/or silica). X-ray diffraction (XRD), BET/BJH, and scanning electron microscopy (SEM) were conducted for characterization of synthesized sorbents. CaO-10% ZrO₂ showed the best performance among the fabricated sorbents in terms of stability during 5 cycles and CO₂ capacity (14 mmol CO₂/g sorbent). The formation of CaZrO₃ with a perovskite structure and high-temperature resistance could be attributed to well performance of zirconia-supported sorbent. On the other hand, no sign of aluminum zirconate formation was approved in XRD analysis for the fabricated sorbent using mixed binders of zirconia and alumina to enhance its stability during cycles.     

Development of Pure Poly Vinyl Chloride (PVC) with Excellent 3D Printability and Macro- and Micro-Structural Properties

Unmodified polyvinyl chloride (PVC) has low thermal stability and high hardness. Therefore, using plasticizers as well as thermal stabilizers is inevitable, while it causes serious environmental and health issues. In this work, for the first time, pure food-grade PVC with potential biomedical applications is processed and 3D printed. Samples are successfully 3D printed using different printing parameters, including velocity, raster angle, nozzle diameter, and layer thickness, and their mechanical properties are investigated in compression, bending, and tension modes. Scanning electron microscopy is also used to evaluate the bonding and microstructure of the printed layers. Among the mentioned printing parameters, raster angle and printing velocity influence the mechanical properties significantly, whereas the layer thickness and nozzle diameter has a little effect. Images from scanning electron microscopy  also reveal that printing velocity greatly affects the final part's quality regarding defective voids and rasters’ bonding. The maximum tensile strength of 88.55 MPa is achieved, which implies the superiority of 3D-printed PVC mechanical properties compared to other commercial filaments. This study opens an avenue to additively manufacture PVC that is the second most-consumed polymer with cost-effective and high-strength features.     

4D Printing of Polyvinyl Chloride (PVC): A Detailed Analysis of Microstructure,Programming, and Shape Memory Performance

In this research, polyvinyl chloride (PVC) with excellent shape-memory effects is 4D printed via fused deposition modeling (FDM) technology. An experimental procedure for successful 3D printing of lab-made filament from PVC granules is introduced. Macro- and microstructural features of 3D printed PVC are investigated by means of wide-angle X-ray scattering (WAXS), differential scanning calorimetry (DSC), and dynamic mechanical thermal analysis (DMTA) techniques. A promising shape-memory feature of PVC is hypothesized from the presence of small close imperfect thermodynamically stable crystallites as physical crosslinks, which are further reinforced by mesomorphs and possibly molecular entanglement. A detailed analysis of shape fixity and shape recovery performance of 3D printed PVC is carried out considering three programming scenarios of cold (T_g −45 °C), warm (T_g −15 °C), and hot (T_g +15 °C) and two load holding times of 0 s, and 600 s under three-point bending and compression modes. Extensive insightful discussions are presented, and in conclusion, shape-memory effects are promising,ranging from 83.24% to 100%. Due to the absence of similar results in the specialized literature, this paper is likely to fill a gap in the state-of-the-art shape-memory materials library for 4D printing, and provide pertinent results that are instrumental in the 3D printing of shape-memory PVC-based structures.     

Synthesis of magnesium hydroxide nanofiller and its use for improving thermal properties of new poly(ether‐amide)

A new poly(ether‐amide; PEA) as a source of polymeric matrix, containing flexible ether group in the main chain was synthesized by direct polycondensation reaction of 1,2‐(4‐carboxy phenoxy)ethane with 4,4‐diaminodiphenyl ether in a medium consisting of N‐methyl‐2‐pyrrolidone, triphenyl phosphite, calcium chloride, and pyridine. The resulting PEA was characterized by gel permeation chromatography (GPC), ¹H NMR and FT‐IR spectroscopy. Magnesium Hydroxide (MH) nanostructure was synthesized by the reaction of magnesium sulfate and sodium hydroxide by sonochemical method. The MH particle was characterized by X‐ray diffraction (XRD) and scanning electron microscopy (SEM). Then Mg(OH)₂ nanostructure was added to poly(ether‐amide) matrix and resulting nanocomposites were characterized by XRD, SEM, and Thermogravimetry Analysis (TGA). Thermal decomposition of the PEA shifted towards higher temperature in the presence of the magnesium hydroxide nanoparticles. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Thermogravimetric Analysis of a Cellulosic Fabric Incorporated by Synthetic Ammonium Magnesium Phosphate as a Flame-Retardant

We have investigated the effect of synthetic struvite (MgNH₄PO₄·6H₂o) on the flammability of a cellulosic fabric. It was synthesized by means of the multiple-bath method and deposited onto a cotton fabric. Its uniformity was ensured by means of squeeze rolls, obtaining the optimum effective add-on value of ammonium magnesium phosphate to impart flame retardancy to cotton fabric in the range of around 12 g anhydrous salt per 100 g fabric. A thermogravimetric analysis of pure cotton, treated cotton, and the salt was accomplished, and their thermograms were compared and commented upon. The results obtained fortified the chemical theory expressing the promotion of the formation of solid char rather than the formation of volatile pyrolysis products, during the fulfillment of thermal decomposition of the cellulosic substrate.