Atom-to-Device Simulation of MoO3/Si Heterojunction Solar Cell.

Metal oxides are commonly used in optoelectronic devices due to their transparency and excellent electrical conductivity. Based on its physical properties, each metal oxide serves as the foundation for a unique device. In this study, we opt to determine and assess the physical properties of MoO3 metal oxide. Accordingly, the optical and electronic parameters of MoO3 are evaluated using DFT (Density Functional Theory), and PBE and HSE06 functionals were mainly used in the calculation. It was found that the band structure of MoO3 calculated using PBE and HSE06 exhibited indirect semiconductor properties with the same line quality. Its band gap was 3.027 eV in HSE06 and 2.12 eV in PBE. Electrons and holes had effective masses and mobilities of 0.06673, -0.10084, 3811.11 cm2V-1s-1 and 1630.39 cm2V-1s-1, respectively. In addition, the simulation determined the dependence of the real and imaginary components of the complex refractive index and permittivity of MoO3 on the wavelength of light, and a value of 58 corresponds to the relative permittivity. MoO3 has a refractive index of between 1.5 and 3 in the visible spectrum, which can therefore be used as an anti-reflection layer for solar cells made from silicon. In addition, based on the semiconducting properties of MoO3, it was estimated that it could serve as an emitter layer for a solar cell containing silicon. In this work, we calculated the photoelectric parameters of the MoO3/Si heterojunction solar cell using Sentaurus TCAD (Technology Computing Aided Design). According to the obtained results, the efficiency of the MoO3/Si solar cell with a MoO3 layer thickness of 100 nm and a Si layer thickness of 9 nm is 8.8%, which is 1.24% greater than the efficiency of a homojunction silicon-based solar cell of the same size. The greatest short-circuit current for a MoO3/Si heterojunction solar cell was observed at a MoO3 layer thickness of 60 nm, which was determined by studying the dependency of the heterojunction short-circuit current on the thickness of the MoO3 layer.

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.

The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms' performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

Geometric Optimization of Perovskite Solar Cells with Metal Oxide Charge Transport Layers.

Perovskite solar cells (PSCs) are a promising area of research among different new generations of photovoltaic technologies. Their manufacturing costs make them appealing in the PV industry compared to their alternatives. Although PSCs offer high efficiency in thin layers, they are still in the development phase. Hence, optimizing the thickness of each of their layers is a challenging research area. In this paper, we investigate the effect of the thickness of each layer on the photoelectric parameters of n-ZnO/p-CH3NH3PbI3/p-NiOx solar cell through various simulations. Using the Sol-Gel method, PSC structure can be formed in different thicknesses. Our aim is to identify a functional connection between those thicknesses and the optimum open-circuit voltage and short-circuit current. Simulation results show that the maximum efficiency is obtained using a perovskite layer thickness of 200 nm, an electronic transport layer (ETL) thickness of 60 nm, and a hole transport layer (HTL) thickness of 20 nm. Furthermore, the output power, fill factor, open-circuit voltage, and short-circuit current of this structure are 18.9 mW/cm2, 76.94%, 1.188 V, and 20.677 mA/cm2, respectively. The maximum open-circuit voltage achieved by a solar cell with perovskite, ETL and HTL layer thicknesses of (200 nm, 60 nm, and 60 nm) is 1.2 V. On the other hand, solar cells with the following thicknesses, 800 nm, 80 nm, and 40 nm, and 600 nm, 80 nm, and 80 nm, achieved a maximum short-circuit current density of 21.46 mA/cm2 and a fill factor of 83.35%. As a result, the maximum value of each of the photoelectric parameters is found in structures of different thicknesses. These encouraging results are another step further in the design and manufacturing journey of PSCs as a promising alternative to silicon PV.

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.