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Particle size and surface trap effects on the third order nonlinear optical (NLO) properties of CsPbCl3 and CsPbBr3 perovskite quantum dots (PQD) have been investigated using femtosecond (fs) Z-scan experiment. Firstly, optical, morphological and structural characterizations of the synthesized PQDs were performed using linear absorption and photoluminescence (PL) spectra, XRD and TEM methods. Next, NLO properties of CsPbCl3 and CsPbBr3 PQDs with three different particle sizes and with and without surface trap states were studied at the operating wavelength of 800 nm and 3.564 x 1011 W/cm2 intensity. The calculated values of the nonlinear absorption coefficient, β, (~10-11 m/W), the nonlinear refraction coefficient,γ, (~ 10-17 m2/W), and third order susceptibilities, χ(3), (~ 10-11 esu) for both sample types were seen to be in line with the literature. The maximum γ values for CsPbCl3 and CsPbBr3 PQDs were observed in the large and medium particle sizes, respectively. In the case of surface trap free nanomaterials, the unitless figure of merits were found to be increased more than 100 folds for CsPbCl3 PQDs and 30 folds for CsPbBr3 PQDs. These results strongly indicate the significant potential of CsPbCl3 and CsPbBr3 PQDs for numerous nonlinear applications.

Quantum dot-filled polymer nanocomposites are recent of interest due to the improvement of the properties and possibility of use in diverse application areas. In this study, poly(HEMA) cryogels and CsPbCl3 and CsPbBr3 perovskite quantum dots were separately synthesized and then perovskite quantum dots/cryogel nanocomposites were formed for the first time. After comprehensive characterizations of the nanocomposites, the dielectric properties of those structures were studied using impedance spectroscopy. All characterization methods revealed the effective formation of the quantum dot/cryogel nanocomposites. The real part of the dielectric components of the nanocomposites decreased concerning the pure cryogel. For the CsPbCl3 quantum dots filled nanocomposites, the imaginary part of the dielectric component and the loss tangent parameter increased almost by the factors of similar to 1.8 and similar to 2.2, respectively. On the other hand, for the CsPbBr3 quantum dots filled cryogel, the imaginary part of the dielectric component and the loss tangent parameter values decreased by the factors of similar to 2.6 and similar to 2 respectively. The large dielectric losses in the case of CsPbCl3 quantum dot-based nanocomposite were attributed to the high existence of surface traps on the surface of the CsPbCl3 quantum dots.

Recently, the advantages of perovskite solar cells (PSCs) and a significant increase in power conversion efficiency (PCE) have played an essential role in the preference for these materials. Although different methods are used to increase PCE and reduce losses at the interfaces in PSCs, placing a new layer between the absorber/hole transfer layer (HTL) or between the absorber/electron transfer layer (ETL) stands out as one of the most common methods. In this study, considering stability, sustainability, mobility, and non-toxicity, Cs4CuSb2Cl12 (CCSC) perovskite quantum dots (PQDs) were preferred as the interface layer between absorber and HTL in CsPbI3 and formamidinium lead iodide (FAPI)-based PSC devices. While SnO2, Cu2O, and nickel were used as ETL, HTL, and back contact, respectively, CsPbI3 and FAPI perovskites were utilized as absorber materials separately. Simulations were conducted on Solar Cell Capacitance Simulator (SCAPS-1D) software and the current density (J) and voltage characteristics were compared. By choosing different interface layer thicknesses, different radiative recombination coefficients (RRCs), and different defect sites, the cell efficiency of the PQD interlayer solar cells were simulated. Simulations were also carried out using different series resistance (Rs) and different shunt resistance (Rsh) values to show the effect of parasitic losses on cell efficiency, and it was observed that device efficiency increased where Rs was low and Rsh was high. In addition, in FAPI-based structure, with the addition of a PQD layer between the absorber and HTL, it was observed that the short circuit current density increased from 17.6 mA/cm2 to 25.67 mA/cm2, while the cell efficiency increased by 30%. Furthermore, according to the results obtained using CsPbI3 as an absorber, adding a PQD layer between CsPbI3 and HTL increased the short circuit current density from 17.8 mA/cm2 to 20.7 mA/cm2 and cell efficiency by 16%. To sum up, these simulation results demonstrate that inserting a PQD layer between the absorber and HTL significantly enhances the efficiency and charge carrier capacity of solar cells.

Gestures are one of the most important agents for human-computer interaction. They play a mediator role between human intention and the control of machines. Electromyography (EMG) pattern recognition has been studied for gesture recognition for years to control of prostheses and rehabilitation systems. EMG data gives information about the electrical activity related to muscles. It is obtained from the arm and helps to understand hand gestures. For this work, hand gesture data taken from UCI2019 EMG dataset obtained from myo Thalmic armband were classified with six different machine learning algorithms. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were applied for comparison based on several performance metrics which are accuracy, precision, sensitivity, specificity, classification error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. Splitting ratio in the classification was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in the testing phase of the classifier. NB was found to be the best among other methods because of high accuracy. Classification accuracy varies between 97.52% to 100% for each gesture. Considering the results of the performance metrics, it can be said that this study recognizes and classifies seven hand gestures successfully in comparison with the literature. The proposed method can easily be used for human-machine interaction and smart device controlling like prosthesis, wheelchair, and smart entertainment applications. WOS:000704721300002

Today, the world's primary energy demand has been met by the burning of fossil-based fuels at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase in the release of greenhouse gases (GHG) all across the world. The largest share in total GHG emissions belongs to the electricity and heat production sector with a rate of 25%. With this viewpoint, this paper is aiming to forecast the GHG emissions (CO2, CH4, N2O, F-gases, and total GHG) using deep learning (DL), support vector machine (SVM), and artificial neural network (ANN) algorithms from the electricity production sector in Turkey. The dataset is supplied from the Turkish Statistical Institute and covers the years 1990-2018. In the study, the last four years (2015-2018) is being forecasted. To evaluate the performance success of the algorithms, five metrics (RMSE, MBE, rRMSE, R-2, and MAPE) are discussed in detail. In the results, this research is reporting that all algorithms used in the study are giving separately satisfying results for the forecasting of GHG emissions in Turkey. Based on the forecasting results, it is seen that the highest R-2 value for the emissions varies from 0.861 to 0.998 and all results are categorized as excellent in terms of rRMSE (all rRMSE values < 10%). Besides, MBE changes between -2.427 and 2.235, and all MAPE values are smaller than 1.2%. Total GHG emission is forecasted in DL algorithm with very satisfied R-2, RMSE, MBE, rRMSE, and MAPE of 0.998, 2.046, 0.419, 0.406%, and 0.021%, respectively. On the other hand, CO2 accounted for 69.05% of total GHG emissions of Turkey in 1990 but rising by 80.48% in the year 2018. In comparison with those of 1990, electricity production and total GHG emissions of Turkey in 2018 increased by 429.7% and 137.4%, respectively. Total GHG emission corresponding to electricity production is calculated to be 0.3813 Mt-total GHG/MWh in 1990 and 0.1709 Mt-total GHG/MWh in 2018. In conclusion, GHG emissions have recently increased at a high rate, but it is noticed that this increase is considerably higher as compared to the increase in energy production for Turkey. (C) 2020 Elsevier Ltd. All rights reserved. WOS:000611934600022 2-s2.0-85097710385