• Energy Research
• 2021-2025close
• TR close

© 2011-2012 IEEE.Achieving low-cost and high-performance solar cells based on heterojunction of metal-oxide-semiconductors with silicon (Si) is a difficult task. We herein report the development of cost-effective and efficient SnO2/p-Si heterojunction-based solar cells using the low-temperature hydrothermal method. The fabrication of SnO2/Si heterojunction-based solar cells is realized with various thicknesses of SnO2 layer deposited by controlling the hydrothermal deposition time. The measurements of scanning electron microscopy, optical spectra, four-point probe conductivity, current-voltage (I-V), and capacitance-voltage (C-V) reveal that the thickness of SnO2 emitter layer significantly influences the electrical properties and the photovoltaic performance of the devices. The best power conversion efficiency of 3.09% (Jsc = 20.28 mA/cm2, Voc = 0.312 V, and FF = 48.84%) is achieved for the solar cell having n-SnO2 thickness of 391 nm. The experimental findings disclose that the efficiency of the cells is extremely dependent on the emitter layer thickness, which plays a vital role in determining light-harvesting characteristics and carrier collective capabilities of the cells.

The COVID-19 pandemic has been affecting our lives in many ways, not only the healthcare systems in the countries but the whole societies worldwide. Meantime, a considerable number of studies have been conducted and lots of medical techniques have been tried to overcome the pandemic. In this work, making use of real-world images, we applied Convolutional Neural Networks to chest X-ray images to predict whether a patient has the COVID-19 virus or not. Initially, we used transfer learning to fine tune a number of pre-trained ResNet, VGG, and Xception models, which are very well-known architectures due to their success in image processing tasks. While the achieved performance with these models was encouraging, we ensembled three models to obtain more accurate and reliable results. Finally, our ensemble model outperformed all other models with an F-Score of 97%.

The requisition for maintainable constructions has been greatly raising over the last several years. To fulfil the maintainability necessities of a construction, decisions or changes must be done to a construction in the course of the preconstruction and design steps. This can be plausible utilizing building information modelling. To indicate the utilize of building information modelling in maintainable planning, an example nursing-house is received for modelling research. The energy efficiency of nursing-home is analysed utilizing Autodesk Revit and Green Building Studio simulation which contained different characteristics such as annual heating and cooling loads, annual energy usage. Through using the utilize of different building, insulation and roof materials in the nursing-home modelling, the nursing-home modelling is changed into a greener construction modelling. In addition, the effects of using green walls on the facade of the building on the energy performance were analysed. Utilizing simulation, the utilize of non-natural sources can be dramatically decreased through substituting for them with the utilize of sustainable natural sources by that means energy saving. Building information modelling has substantiated to be effective in providing maintainability with alternative material’s assessment and earlier decision-making. Furthermore, this study employed an integrated new MCDM model to evaluate the performance of four natural stones for utilize in a nursing home setting.

Solidity is one of the main factors that influence the performance of a wind turbine. The solidity of an H-Darrieus wind turbine is optimized in terms of blade number, chord length, and rotor radius to get the maximum power coefficient using Taguchi L16 (4^3) array. Power coefficient values are calculated by the Double Multiple Stream Tube model by in house built MATLAB code according to Taguchi orthogonal array. The maximum power coefficient is found as 0.4728 for the two-bladed rotor with a chord length of 0.2 m and a radius of 2 m. Relation of maximum power coefficient respect to blade number, chord length, and rotor radius is derived. Predicted mean values of the maximum power coefficient are in agreement with calculated values. Furthermore, an equation for the maximum power coefficient is obtained for rotor solidity. Derived equations can be useful to estimate the maximum power coefficient values of H-Darrieus wind turbines.

In this study, an Internet of Things (IoT) communication network is considered, where mobile users in a dense urban environment communicate via a stationary Unmanned Aerial Vehicle (UAV) that acts as a base station. It is thought that the users in the network are designed with a single antenna and the UAV with a double antenna. Users estimate the channel gain parameters according to the pilot symbols they receive from the UAV and send their symbols to the UAV by encoding them with the Space-Time Line Coding (STLC) technique according to these channel gain parameters. In the UAV, on the other hand, symbols are retrieved intensively without the need for channel gain information that may come from users. Monte Carlo simulations were created under various assumptions and the error performance of a fixed user on the network was examined. It is assumed that the envelopes of the communication channels in the system follow the Ricean fading channel in small-scale fading.

Biogas is one of the most attractive renewable resources due to its ability to convert waste into energy. Biogas is produced during an anaerobic digestion process from different organic waste resources with a combination of mainly CH4 (~50 mol/mol), CO2 (~15 mol/mol), and some trace gasses. The percentage of these trace gases is related to operating conditions and feedstocks. Due to the impurities of the trace gases, raw biogas has to be cleaned before use for many applications. Therefore, the cleaning, upgrading, and utilization of biogas has become an important topic that has been widely studied in recent years. In this review, raw biogas components are investigated in relation to feedstock resources. Then, using recent developments, it describes the cleaning methods that have been used to eliminate unwanted components in biogas. Additionally, the upgrading processes are systematically reviewed according to their technology, recovery range, and state of the art methods in this area, regarding obtaining biomethane from biogas. Furthermore, these upgrading methods have been comprehensively reviewed and compared with each other in terms of electricity consumption and methane losses. This comparison revealed that amine scrubbing is one the most promising methods in terms of methane losses and the energy demand of the system. In the section on biogas utilization, raw biogas and biomethane have been assessed with recently available data from the literature according to their usage areas and methods. It seems that biogas can be used as a biofuel to produce energy via CHP and fuel cells with high efficiency. Moreover, it is able to be utilized in an internal combustion engine which reduces exhaust emissions by using biofuels. Lastly, chemical production such as biomethanol, bioethanol, and higher alcohols are in the development stage for utilization of biogas and are discussed in depth. This review reveals that most biogas utilization approaches are in their early stages. The gaps that require further investigations in the field have been identified and highlighted for future research.

Over the last decade, there has been a constant development in control techniques for DC-DC power converters which can be classified as linear and nonlinear. Researchers focus on obtaining maximum efficiency using linear control techniques to avoid complexity although nonlinear control techniques may achieve full dynamic capabilities of the converter. This paper has a similar purpose in which a novel hybrid metaheuristic optimization algorithm (AEONM) is proposed to design an optimal PID controller for DC-DC buck converter’s output voltage regulation. The AEONM employs artificial ecosystem-based optimization (AEO) algorithm with Nelder-Mead (NM) simplex method to ensure optimal PID controller parameters are efficiently tuned to control output voltage of the buck converter. Initial evaluations are performed on benchmark functions. Then, the performance of AEONM-based PID is validated through comparative results of statistical boxplot, non-parametric test, transient response, frequency response, time-domain integral-error-performance indices, disturbance rejection and robustness using AEO, particle swarm optimization and differential evolution. A comparative performance analysis of transient and frequency responses is also performed against simulated annealing, whale optimization and genetic algorithms for further performance assessment. The comparisons have shown the proposed hybrid AEONM algorithm to be superior in terms of enhancing the buck converter’s transient and frequency responses.