Sharif Digital Repository

Dye-sensitized solar cells (DSCCs) in the form of mixed nanostructures containing TiO2 nanoparticles and nanowires with different weight ratios and phase compositions are reported. X-ray diffraction and field emission scanning electron microscopy analyses revealed that the synthesized TiO 2 nanoparticles had average crystallite size in the range 21-39 nm, whereas TiO2 nanowires showed diameter in the range 20-50 nm. The indirect optical band gap energy of TiO2 nanowires, anatase- and rutile-TiO2 nanoparticles was calculated to be 3.35, 3.28 and 3.17 eV, respectively. The power conversion efficiency of the solar cells changed with nanowire to nanoparticle weight ratio, reaching a maximum at a... 

A novel simple synthetic procedure for fabrication of high surface area nanostructured TiO2 electrode with uniform particles for photovoltaic application is reported. Modifying the TiO2 particulate sol by pH adjustment together with employment of a polymeric agent, so-called polymeric gel process, was developed. The polymeric gel process was used to deposit nanostructured thick electrode by dip coating incorporated in dye-sensitized solar cells (DSSCs). X-ray diffraction (XRD) analysis revealed that deposited film was composed of primary nanoparticles with average crystallite size in the range 21-39 nm. Field emission scanning electron microscope (FE-SEM) images showed that deposited film... 

Concentrically braced structures are one of the most common resistant systems in steel structures due to their high stiffness and lateral strength. However, when these structures are subjected to moderate and severe earthquakes, their compression member begins to buckle. Also, buckling of compression members in Concentrically Braced Frames (CBFs) decreases ductility and degradation of the hysteresis curve. Numerous studies have been performed to improve the behavior of CBFs. Using steel dampers is considered one of the appropriate ways to improve the behavior of CBFs. Although steel dampers improve the hysteretic behavior of the CBFs, they impose additional costs on structures. Therefore, in... 

In the present work, we focused on simultaneously control electron injection and electron transport, in dye-sensitized solar cells (DSSCs), aided by introducing Cr3+ and CNTs into a TiO2 photoanode, respectively. X-ray photoelectron spectroscopy (XPS) revealed that, Cr 3+ and CNTs were successfully incorporated into the TiO2 lattice without forming secondary phases. X-ray diffraction (XRD) analysis showed that Cr introduction has perfectly balanced the amount of anatase and rutile phases in order to accomplish a more efficient cell. Field emission scanning electron microscope (FE-SEM) images showed deposited films to have a porous morphology composed of nanoparticles and TiO2 nanoparticles... 

TiO 2 dye-sensitized solar cells (DSSCs) in the form of double-layer films, containing an under-layer and an over-layer, with various crystal structures (i.e., anatase and rutile phases) and morphologies (i.e., nanoparticle and nanowire) were reported. It was found that the photovoltaic performance of TiO 2 DSSCs depends on the morphology, crystal structure, light scattering effect, optical band gap energy and arrangement of the under- and over-layer films. The double-layer solar cell made of anatase-TiO 2 nanoparticles as the under-layer and anatase-TiO 2 nanowires as the over-layer (i.e., AW solar cell) showed the highest power conversion efficiency and fill factor of 6.34% and 62.6%,... 

In a nutshell, this study outlines the efficacy of mixed dimensional (2D/3D) hybrid perovskites by developing a new class of triple-cation quasi-2D perovskites having (S0.97S′0.03)2[Cs0.05(FA0.97MA0.03)0.95]n-1Pbn(I0.07Br0.03)3n+1 general composition, in which a mixture of ý5-ammonium valeric acid ýiodideý (S) and tetra-n-octylammonium ýbromide (S′) was employed ýas a spacer.ý The effect of the 2D and 3D structures molar ratios (i.e., C=2D/2D+3D) in the range of 0-100 % on photovoltaic performance of the deposited photoanodes was systemically studied. Drawing a comparison between such compounds and an analogous triple-cation 3D counterpart (i.e., Cs0.05(FA0.83MA0.17)0.95Pb(I0.83Br0.17)3) as... 

Abstract: The present research comes up with optimizing the layers thickness of a Ruddlesden–Popper perovskite with the general formula of (S0.97S0.03 ')2[Cs0.05(FA0.097MA0.03)0.95]n – 1Pbn(I0.97Br0.03)3n + 1 for efficient, stable solar cell applications. Such a triple-cation quasi-two-dimensional (2D) structure simultaneously contains two spacers, namely 5-ammonium valeric acid iodide (S) and tetra-n-octylammonium bromide (S'). Systematic studies showed that morphology, crystal structure, optical properties, photovoltaic performance, and internal resistances of this compound depended upon the value of the n integer. Field emission scanning electron microscopy set forth that the deposited... 

A new facile strategy for fabrication of high surface area electrode in the form of mixtures of coated carbon nanotubes (CNTs) and TiO2 nanoparticles with various weight ratios is reported. The so-called polymeric gel process was used to deposit thick film containing uniform distribution of TiO2 nanoparticles and coated CNTs with high porosity by dip coating for dye-sensitized solar cells (DSSCs) applications. Based on simultaneous differential thermal analysis, the minimum annealing temperature to obtain inorganic- and organic-free films was determined at 500°C. X-ray diffraction analysis revealed that deposited films were composed of primary nanoparticles with crystallite size in the range... 

The elastic behavior of an edge dislocation, which is positioned outside of a nanoscale elliptical inhomogeneity, is studied within the interface elasticity approach incorporating the elastic moduli and surface tension of the interface. The complex potential function method is used. The dislocation stress field and the image force acting on the dislocation are found and analyzed in detail. The difference between the solutions obtained within the classical-elasticity and interface-elasticity approaches is discussed. It is shown that for the stress field, this difference can be significant in those points of the inhomogeneity-matrix interface, where the radius of curvature is smaller and which... 

Steam/water stratified flow would occur in transient condition (e.g. LOCA) in light water Nuclear Power Plants (NPPs). Due to high gradient of flow characteristics at the interface of steam/water flow, the prediction of flow characteristics (e.g. temperature, pressure, velocity, and Turbulent Kinetic Energy (TKE)) requires further attention and special interfacial models. Also, accurate simulation of these mentioned characteristics needs fine spatial mesh and very small time steps based on Computational Fluid Dynamics (CFD) standard criteria. In order to reduce the computational cost, the combination of thermal–hydraulic modelling and soft computing is considered as a new strategy in this... 

Introduction: It has shown that listening to Mozart music can potentiate spatial tasks in human; and reduce seizure attacks in epileptic patients. A few studies have reported the effects of prenatal plus postpartum exposure of mice to the Mozart music on brain-drived neurotrophic factor (BDNF) in the hippocampus. Here we investigated the effect of postpartum exposure to The Mozart music on BDNF concentration in the hippocampus of rat. Methods: Thirty male one day old newborn Wistar rats divided randomly in two equal experimental and control groups. Experimental group exposed to slow rhythm Mozart music (Mozart Sonata for two pianos KV 448, 6 hour per day; sound pressure levels, between 80... 

We present a new TiO2 morphology, featuring highly crystalline anatase structure, synthesized by a two consecutive hydrothermal operation for the manufacture of dye-sensitized solar cells (DSSCs). This construct is a one-dimensional (1D) nanostructure (i.e., nanowire) with interconnected nanoparticles on the surface with a large percentage of reactive facets, so called corn-like nanowire. Such morphology is produced by a surface modification concept using surface tension stress mechanism. The double layer DSSC made of anatase-TiO2 nanoparticles as the under-layer and corn-like TiO 2 nanowires as the over-layer (as light scattering layer) shows higher conversion efficiency of 7.11% and short... 

This study introduces a new method for forecasting future states of nuclear power plants (NPPs) parameters in abnormal conditions (i.e. transients). The proposed method consists of two steps. First, the type of transients is recognized by the modular EBP based identifier. A hybrid network is then used to forecast the selected parameters of the identified transient. ARIMA model is used to estimate the linear component of the selected parameters. The neural network developed by EBP learning algorithm is then used to estimate the nonlinear component of the selected parameters. Finally, prediction of parameters is obtained by adding the estimated linear and nonlinear components. To analyze the... 

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error back-propagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time... 

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power... 

A transient is defined as an event when a plant proceeds from a normal state to an abnormal state. In nuclear power plants (NPPs), recognizing the types of transients during early stages, for taking appropriate actions, is critical. Furthermore, classification of a novel transient as "don't know", if it is not included within NPPs collected knowledge, is necessary. To fulfill these requirements, transient identification techniques as a method to recognize and to classify abnormal conditions are extensively used. The studies revealed that model-based methods are not suitable candidates for transient identification in NPPs. Hitherto, data-driven methods, especially artificial neural networks... 

This study introduces a new identifier for nuclear power plants (NPPs) transients. The proposed identifier performs its function in two steps. First, the transient is identified by the previously developed supervised classifier combining ARIMA model and EBP algorithm. In the second step, the patterns of unknown transients are fed to the identifier based on the semi-supervised learning (SSL). The transductive support vector machine (TSVM) as a semi-supervised algorithm is trained by the labeled data of transients to predict some unlabeled data. The labeled and newly predicted data is then used to train the TSVM for another portion of unlabeled data. Training and prediction is continued until... 

One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning... 

Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and... 

Up to now, different methods have been developed for estimation of buildup factor (BF). However, either expensive estimation or time-consuming estimation are major restrictions/challenges of these methods. In this study a new technique utilizing combination of Monte Carlo method and the Bayesian regularization (BR) learning algorithm of multilayer feed-forward neural network (FFNN) is employed for estimation of BFs. First, the BFs of the different elements (i.e. Al, Cu, and Fe) at different energies and different mean free paths (MFPs) are modeled by the MCNP code. The results show that the calculated BFs by MCNP code are in good agreement with the reported values of American nuclear society...