Generally, first and second generations of photovoltaic (PV) cells are including mono‐crystalline silicon, amorphous silicon, and dye‐synthesized solar cells. Investigating the electrical current behavior of these sorts of PV cells shows that a modified multi‐ or single diode(s) model with shunt and series resistance can use as a good choice in a specific range of the current. For other constructions such as multi‐junction PV cells that consist of the third generation, the network models are the best choice. Also, the network models can be used for the first and second generations to improve the validity of model in the wider range of current. In addition to existing generations, PV array and modules, which are serial and parallel arrays of PV cells, will lead to a higher current or voltage. The main disadvantage of using these sorts of systems is their more space occupation. This problem can be solved by using the concentrated PV (CPV) systems that focus the received irradiation on a smaller surface. By considering the generated current, voltage, power, temperature effect, and financial analysis, it seems third‐generation PV systems are more efficient among all the generations. Finally, by considering the ratio of generated current on the occupied space, the CPV systems will be a better choice.

Abstract-The influence of chip layout and architecture on the pattern dependency of selective epitaxy of B-doped SiGe layers has been studied. The variations of Ge-, B-content, and growth rate have been investigated locally within a wafer and globally from wafer to wafer. The results are described by the gas depletion theory. Methods to control the variation of layer profile are suggested.Index Terms-Loading effect, pattern dependency, selective epitaxy, SiGe.

Recently, selective epitaxial growth (SEG) of B-doped SiGe layers has been used in recessed source/drain (S/D) of pMOSFETs. The uniaxial induced strain enhances the carrier mobility in the channel. In this work, a detailed model for SEG of SiGe has been developed to predict the growth rate and Ge content of layers in dichlorosilane(DCS)-based epitaxy using a reduced-pressure CVD reactor. The model considers each gas precursor contributions from the gas-phase and the surface. The gas flow and temperature distribution were simulated in the CVD reactor and the results were exerted as input parameters for Maxwell energy distribution. The diffusion of molecules from the gas boundaries was calculated by Fick's law and Langmuir isotherm theory (in non-equilibrium case) was applied to analyze the surface. The pattern dependency of the selective growth was also modeled through an interaction theory between different subdivisions of the chips. Overall, a good agreement between the kinetic model and the experimental data were obtained.

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Recently, selective epitaxial growth (SEG) of B-doped SiGe layers has been used in recessed source/drain (S/D) of pMOSFETs. The uniaxial induced strain enhances the carrier mobility in the channel. In this work, a detailed model for SEG of SiGe has been developed to predict the growth rate and Ge content of layers in dichlorosilane(DCS)-based epitaxy using a reduced-pressure CVD reactor. The model considers each gas precursor contributions from the gas-phase and the surface. The gas flow and temperature distribution were simulated in the CVD reactor and the results were exerted as input parameters for Maxwell energy distribution. The diffusion of molecules from the gas boundaries was calculated by Fick's law and Langmuir isotherm theory (in non-equilibrium case) was applied to analyze the surface. The pattern dependency of the selective growth was also modeled through an interaction theory between different subdivisions of the chips. Overall, a good agreement between the kinetic model and the experimental data were obtained.

Buildings are one of the primary consumers of energy. In addition to the electricity grids, renewable energies can be used to supply the energy demand of buildings. Intelligent systems such as the Internet of Things (IoT) and wireless sensor technologies can also be applied to manage the energy consumption in buildings. Fortunately, integrating renewable energies with these intelligent systems enables creating nearly zero-energy buildings. In this paper, we present the results of our experimentation to demonstrate forming such a building and showing the benefits for building users and the society. We create a system by integrating photovoltaic (PV) technology with an IoT-based control mechanism to supply and consume energy. We further illustrate “how the integration of IoT and PV technology can bring added value to the users?”. To this end, we evaluate the performance of our system against conventional ways of energy supply and consumption for a lighting use case in a dairy store. We also investigate the environmental and economic impacts of our system. In our implementation, for the IoT-based control system, we have used a set of sensors, a server, and a wireless network to control the energy consumption. We developed a web application for user interaction and software-based settings. To control the lighting system, we developed an algorithm that utilizes the ambient light, users’ movements inside the store and a historical dataset. The historical dataset was collected from the users’ behaviour as a training set for the algorithm for turning on and off the lights. We also designed an electricity management system that computes the energy generation by the PV panels, controls the energy supply, and imports and exports electricity to the grid. The results show that our system is an efficient approach for creating energy-independent buildings by integrating renewable energies with IoT-based control systems. The results also show that our system not only responds to the internal demand by using domestic supply, but it also (i) offers economic benefit by exporting extra renewable electricity to the grid, and (ii) prevents producing huge amounts of CO2. Our system is one of the first works to achieve a nearly zero-energy building in the developing countries with low electricity accessibility.

Abstract. Carbon sequestration has been proposed as a means of slowing the atmospheric and marine accumulation of greenhouse gases. This study used observed and simulated land use/cover changes to investigate and predict carbon sequestration rates in the city of Karaj. Karaj, a metropolis of Iran, has undergone rapid population expansion and associated changes in recent years, and these changes make it suitable for use as a case study for rapidly expanding urban areas. In particular, high quality agricultural space, green space and gardens have rapidly transformed into industrial, residential and urban service areas. Five classes of land use/cover (residential, agricultural, rangeland, forest and barren areas) were considered in the study; vegetation and soil samples were taken from 20 randomly selected locations. The level of carbon sequestration was determined for the vegetation samples by calculating the amount of organic carbon present using the dry plant weight method, and for soil samples by using the method of Walkley and Black. For each area class, average values of carbon sequestration in vegetation and soil samples were calculated to give a carbon sequestration index. A cellular automata approach was used to simulate changes in the classes. Finally, the carbon sequestration indices were combined with simulation results to calculate changes in carbon sequestration for each class. It is predicted that, in the 15 year period from 2014 to 2029, much agricultural land will be transformed into residential land, resulting in a severe reduction in the level of carbon sequestration. Results from this study indicate that expansion of forest areas in urban counties would be an effective means of increasing the levels of carbon sequestration. Finally, future opportunities to include carbon sequestration into the simulation of land use/cover changes are outlined.

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