Showing papers by "Iran University of Science and Technology published in 2021"

Guanine-Based DNA Biosensor Amplified with Pt/SWCNTs Nanocomposite as Analytical Tool for Nanomolar Determination of Daunorubicin as an Anticancer Drug: A Docking/Experimental Investigation

Abstract: Daunorubicin is a famous anthracycline anticancer chemotherapy drug with many side effects that is very important to measure in biological samples. A daunorubicin electrochemical biosensor was fabr...

Optimization of hybrid nanoparticles with mixture fluid flow in an octagonal porous medium by effect of radiation and magnetic field

Abstract: Investigation of fluid behavior in a cavity enclosure has been a significant issue from the past in the field of fluid mechanics. In the present study, hydrothermal evaluation of hybrid nanofluid with a water–ethylene glycol (50–50%) as the base fluid which contains MoS2–TiO2 hybrid nanoparticles, in an octagon with an elliptical cavity in the middle of it, has been performed. In this problem, the effects of the radiation parameter, porosity, and the magnetic parameter have been analyzed on temperature distribution and fluid flow streamlines and also, on the local and average Nusselt numbers. The governing equations have been solved by the finite element method (FEM). As a novelty, the Taguchi method has been utilized for test design. Further, the response surface method (RSM) has been applied to achieving the optimum value of the involved parameters. The obtained results illustrate that with an augment in the Rayleigh number from 10 to 100, the average Nusselt number will improve by about 61.82%. Additionally, regarding the correlation, it is indeed transparent that the Rayleigh number has the most colossal contribution comparing other factors on the achieved equation, by about 61.88%.

Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans.

Abstract: COVID-19 has infected millions of people worldwide. One of the most important hurdles in controlling the spread of this disease is the inefficiency and lack of medical tests. Computed tomography (CT) scans are promising in providing accurate and fast detection of COVID-19. However, determining COVID-19 requires highly trained radiologists and suffers from inter-observer variability. To remedy these limitations, this paper introduces an automatic methodology based on an ensemble of deep transfer learning for the detection of COVID-19. A total of 15 pre-trained convolutional neural networks (CNNs) architectures: EfficientNets(B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50 and Inception_resnet_v2 are used and then fine-tuned on the target task. After that, we built an ensemble method based on majority voting of the best combination of deep transfer learning outputs to further improve the recognition performance. We have used a publicly available dataset of CT scans, which consists of 349 CT scans labeled as being positive for COVID-19 and 397 negative COVID-19 CT scans that are normal or contain other types of lung diseases. The experimental results indicate that the majority voting of 5 deep transfer learning architecture with EfficientNetB0, EfficientNetB3, EfficientNetB5, Inception_resnet_v2, and Xception has the higher results than the individual transfer learning structure and among the other models based on precision (0.857), recall (0.854) and accuracy (0.85) metrics in diagnosing COVID-19 from CT scans. Our study based on an ensemble deep transfer learning system with different pre-trained CNNs architectures can work well on a publicly available dataset of CT images for the diagnosis of COVID-19 based on CT scans.

Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model

Abstract: Artificial intelligence techniques are excessively used in computing for training, forecasting and evaluation purposes Among these techniques, artificial neural network (ANN) is widely used for developing prediction models ANNs use various Meta-heuristic algorithms including approximation methods for training the neural networks ANN plays a significant role in this area and can be helpful in determining the neural network input coefficient The main goal of presented study is to train the neural network using meta-heuristic approaches and to enhance the perceptron neural network precision In this article, we used an integrated algorithm to determine the neural network input coefficients Later, the proposed algorithm was compared with other algorithms such as ant colony and invasive weed optimization for performance evaluation The results reveal that the proposed algorithm results in more convergence with neural network coefficient as compared to existing algorithms However the proposed method resulted in reduction of prediction error in the neural network

Integration of metal-organic frameworks and covalent organic frameworks: Design, synthesis, and applications

Abstract: Summary Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), featuring porous crystalline materials, have attracted tremendous attention for various applications due to their periodic and well-defined structures, high surface area, and tunable pore architectures. In particular, the facile modification of MOFs and COFs enables their intermesh into MOF/COF hybrids to enhance performance and/or extend scope toward diverse applications. In this review, we provide an overview of significant progress in the design and synthesis of MOF/COF hybrids, including MOF@COF, COF@MOF, MOF + COF, C-MOF, and COF-in-MOF, and their various applications in catalysis, gas adsorption, sensing, energy storage, and photodynamic therapy. The challenges and prospects of the construction of MOF/COF hybrids for various applications are also briefly discussed.

Defect engineering of oxide perovskites for catalysis and energy storage: synthesis of chemistry and materials science.

Abstract: Oxide perovskites have emerged as an important class of materials with important applications in many technological areas, particularly thermocatalysis, electrocatalysis, photocatalysis, and energy storage. However, their implementation faces numerous challenges that are familiar to the chemist and materials scientist. The present work surveys the state-of-the-art by integrating these two viewpoints, focusing on the critical role that defect engineering plays in the design, fabrication, modification, and application of these materials. An extensive review of experimental and simulation studies of the synthesis and performance of oxide perovskites and devices containing these materials is coupled with exposition of the fundamental and applied aspects of defect equilibria. The aim of this approach is to elucidate how these issues can be integrated in order to shed light on the interpretation of the data and what trajectories are suggested by them. This critical examination has revealed a number of areas in which the review can provide a greater understanding. These include considerations of (1) the nature and formation of solid solutions, (2) site filling and stoichiometry, (3) the rationale for the design of defective oxide perovskites, and (4) the complex mechanisms of charge compensation and charge transfer. The review concludes with some proposed strategies to address the challenges in the future development of oxide perovskites and their applications.

Zeolite-based composites for the adsorption of toxic matters from water: A review

Abstract: The adsorption of toxic matters from water using zeolites has been developed due to the low cost of operation and adsorbent. However, their use due to the lower efficiency of natural zeolites and the difficult reusability of nanosized-synthetic zeolites is limited. In recent twenty years, the use of zeolite-based composites in the forms of zeolite/inorganic and zeolite/polymer composites has been accelerated for the adsorption of toxic matters from water sources. This review summarized the recent studies on the capability of zeolite-based composites toward toxic matters adsorption such as heavy metal ions, dyes, herbicides, and drugs from water. The main adsorption mechanisms using the zeolite-based composites are surface adsorption, chelation, ion exchange, electrostatic interaction, diffusion and, complexation. The comparison of the capability of zeolite-based composites and raw zeolites for the removal of toxic matters from the water provides the valuable applicability of the zeolite-based composites for actual wastewater treatment in the future.

Synergistic advanced oxidation process for the fast degradation of ciprofloxacin antibiotics using a GO/CuMOF-magnetic ternary nanocomposite

Abstract: A novel strategy was described to fabricate GO/CuBDC-Fe3O4 ternary nanocomposite using a green solvothermal method. The physicochemical properties of the ternary nanocomposite were probed by ATR-FTIR, WA-XRD, Raman, FE-SEM, TEM/HRTEM, STEM/mapping, and EDS spectroscopy. In this nanocomposite, graphene oxide (GO) nanosheets were used as an ideal platform for CuBDC metal-organic framework (MOF) and Fe3O4 growth, aiming to create a peroxymonosulfate (PMS) activator to degrade ciprofloxacin (CIP) antibiotics expeditiously. The proposed ternary nanocomposite showed highest degradation rate of CIP (98.5%) within 24 min with rate constant of 0.191 min−1. The findings demonstrated that Cu/Fe species and C˭O groups within ternary nanocomposite catalyzed PMS synergistically to the formation of the hydroxyl and sulfate radicals for CIP degradation. Furthermore, the ternary nanocomposite showed good recyclability enabling facile separation of the catalyst from reaction mixtures using an external magnet. On the other hand, radical quenching tests and electron paramagnetic resonance (EPR) reveal that the •OH and SO4•− a vital role in the degradation process. The current protocol can be a useful criterion in designing and fabrication of various ternary nanocomposites and provides new insight into environmental remediation.

Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms

Abstract: The burning of fossil fuels produces large amounts of exhaust gases containing carbon dioxide (CO2). The emission of CO2 into the atmosphere is widely known as the leading cause of global warming and climate change. The separation processes are responsible for capturing the CO2 to reduce its undesirable effects on the environment. Since the conventional processes have their drawbacks, it is crucial to find a more environment-friendly process for CO2 capture. Recently, ionic liquids (ILs) have become an interesting candidate for CO2 capture. In this study, the solubility of CO2 in the 1-n-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) is estimated using six different artificial intelligence (AI) techniques, including four artificial neural networks (ANN), support vector machines (LS-SVM), adaptive neuro-fuzzy interface system (ANFIS). The cascade feed-forward neural network has been found as the best model for the considered matter. This model predicts overall experimental datasets with excellent accuracy of AARD = 6.88%, MSE = 8 × 1 0 − 4 , and R 2 = 0 . 98808 . The maximum mole fraction of CO2 in the ionic liquid (i.e., 0.8) can be obtained at the highest pressure and the lowest temperature.

Max-Min Fair Energy-Efficient Beamforming Design for Intelligent Reflecting Surface-Aided SWIPT Systems With Non-Linear Energy Harvesting Model

Abstract: This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms.

Energy Efficiency Maximization via Joint Active and Passive Beamforming Design for Multiuser MISO IRS-Aided SWIPT

Abstract: This letter studies a multiuser multiple-input single-output (MISO) intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits data along with energy to a set of users to decode data and harvest energy by adopting the power splitting (PS) simultaneously. The energy efficiency indicator (EEI) is introduced to trade off between data rate and harvested energy, which is maximized by jointly optimizing beamforming vectors at the BS, PS ratio at each user, and phase shifts at the IRS. To solve this non-convex optimization problem, we first adopt the majorization-minimization (MM) approach to construct a concave-convex fractional function which can be handled via the Dinkelbach algorithm and then propose an efficient algorithm for solving two subproblems based on the alternating optimization (AO). For the first subproblem, semi-definite relaxation (SDR), MM approach, and Dinkelbach algorithm are adopted, while for the second sub-problem, a new manifold approach is proposed to handle the unit-modulus constraints due to IRS passive reflection. Simulation results demonstrate the superior performance of our proposed algorithm compared to other baseline schemes.

The influence of austenitization temperature on microstructural developments, mechanical properties, fracture mode and wear mechanism of Hadfield high manganese steel

Abstract: In this investigation, the effect of austenitization temperature on microstructural evolution, mechanical properties, fracture mode, and wear mechanism of a high carbon Hadfield steel was studied. Four blocks of the Hadfield steel were cast in an induction furnace. One hour austenitization was performed at 1000 °C, 1075 °C, 1150 °C, and 1225 °C on the cast samples followed by quenching in water. Uniaxial tensile test, pin on disk wear test and Vickers hardness measurements were employed on the processed samples. An optical microscope and a field emission scanning electron microscope were used to study the microstructural evolution. Transmission electron microscope was employed to observe the carbides that were formed. Moreover, scanning electron microscopy technique was used to define the mode of fracture on the tensile test samples. Results showed that increasing austenitization temperature reduced carbides and increased austenite grain size. Mechanical properties measurements also showed that increasing austenitization temperature increased yield/tensile strengths, hardness, and wear resistance of this steel. However, these increments were made at the expense of ductility. Fractography results showed a very ductile mode of fracture. The share of the ductile fracture mode was further increased by reducing the austenitization temperature from 1225 °C to 1000 °C.

Multi-Objective Resource Allocation for IRS-Aided SWIPT

Abstract: In this letter, we study the resource allocation for a multiuser intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits energy and information signals simultaneously to multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) assisted by an IRS. Under this setup, we introduce a multi-objective optimization (MOOP) framework to investigate the fundamental trade-off between the data sum-rate maximization and the total harvested energy maximization, by jointly optimizing the energy/information beamforming vectors at the BS and the phase shifts at the IRS. This MOOP problem is first converted to a single-objective optimization problem (SOOP) via the $\epsilon $ -constraint method and then solved by majorization minimization (MM) and inner approximation (IA) techniques. Simulation results unveil a non-trivial trade-off between the considered competing objectives, as well as the superior performance of the proposed scheme as compared to various baseline schemes.

Natural convection of CuO-water nanofluid in a conventional oil/water separator cavity: Application to combined-cycle power plants

Abstract: The oily water from various sources in combined cycle power plants is collected in oil/water separator in which the oil separates from water due to the density difference. The idea of the presented geometry is taken from conventional oil/water separators. This paper studies the natural convection of the CuO-water nanoliquid in a rectangular cavity with fins attached to the insulated wall and porous media. Discretion of Navier-Stokes equations is done by Finite Element Method and assumptions are laminar, steady and incompressible flow. Heat transfer performance and entropy generation are investigated for distinct Rayleigh numbers (103–105), Darcy numbers (10−2–10−4), and Hartmann numbers (0, 10, 20). Different sizes of the fins are also studied to show consequences of fin size on heat transfer in cavity. This is the first time that these parameters and their impacts on Nusselt number and entropy generation are studied for a conventional oil/water separator cavity. Corollaries demonstrate that increasing Rayleigh number and Darcy number improves heat transfer performance and average Nusselt number. Nevertheless, Hartmann number has a reverse effect with average Nusselt number. Finally, a new equation for average Nusselt number is developed with regard to Rayleigh number, Hartmann number, and Darcy number.

Metal oxide electron transport materials for perovskite solar cells: a review

Abstract: Solar electricity is an unlimited source of sustainable fuels, yet the efficiency of solar cells is limited. The efficiency of perovskite solar cells improved from 3.9% to reach 25.5% in just a few years. Perovskite solar cells are actually viewed as promising by comparison with dye-sensitized solar cells, organic solar cells, and the traditional solar cells made of silicon, GaAs, copper indium gallium selenide (CIGS), and CdTe. Here, we review bare and doped metal oxide electron transport layers in the perovskite solar cells. Charge transfer layers have been found essential to control the performance of perovskite solar cells by tuning carrier extraction, transportation, and recombination. Both electron and hole transport layers should be used for charge separation and transport. TiO2 and 2,2′,7,7′-Tetrakis[N,N-di(4-methoxyphenyl)amino]-9,9′-spirobifluorene are considered as the best electron and hole transport layers. Metal oxide materials, either bare or doped with different metals, are stable, cheap, and effective.