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Öğe Effects of Electrical Properties on Determining Materials for Power Generation Enhancement in TEG Modules(Springer, 2019) Ozturk, Turgut; Kilinc, Enes; Uysal, Fatih; Celik, Erdal; Kurt, HuseyinThis study aimed to increase the energy efficiency of thermoelectric generators designed by considering the electrical properties of p- and n-type semiconductor materials for reducing the costs associated with the experiments, errors, and long production processes. Accordingly, the estimation of the energy amount to be produced by the thermoelectric materials was achieved by different doping elements using three different parameters such as skin-depth, electrical conductivity and dielectric constant. Additionally, the findings were supported by experimental results. In contrast to the conventionally used black-box type approach and estimation methods, an inference was obtained on the actual values of the materials.Öğe High-Temperature Thermoelectric Properties of Sol-Gel Processed Ca2.5Ag0.3RE0.2Co4O9 (RE: Y and Rare-Earths) Materials(Wiley-V C H Verlag Gmbh, 2020) Kilinc, Enes; Uysal, Fatih; Celik, Erdal; Kurt, HuseyinHerein, dually doped Ca2.5Ag0.3RE0.2Co4O9 (RE: La, Pr, Nd, Sm, Gd, Dy, Er, Yb, Eu, Tb, Ho, Lu, Ce, and Y) samples are synthesized by sol-gel technique and consolidated by cold pressing under high pressure to systematically scrutinize the influences of Y and rare-earth dually doping with Ag on transport properties of Ca3Co4O9 for high-temperature thermoelectric (TE) applications. Characterization results reveal that targeted phase is successfully produced, and doping of the compositions is provided. Doping of Y and rare-earth elements together with Ag into the Ca2+ site is effective in increasing the Seebeck coefficient and decreasing the electrical resistivity of the samples, thanks to the reduction in carrier concentration. Thermal conductivity of the samples is reduced related to the lower relative densities and alloy scattering originated from dually doping. Among the samples, Ca2.5Ag0.3Ho0.2Co4O9 and Ca2.5Ag0.3Eu0.2Co4O9 exhibit the highest power factor (PF) values of 0.65 and 0.62 mW m(-1) K-2 at 800 degrees C, respectively. These results are quite high for bulk oxide TE materials which can be assessed as potential oxide TE materials for high-temperature TE power generation.Öğe A prediction model of artificial neural networks in development of thermoelectric materials with innovative approaches(Elsevier - Division Reed Elsevier India Pvt Ltd, 2020) Kokyay, Seyma; Kilinc, Enes; Uysal, Fatih; Kurt, Huseyin; Celik, Erdal; Dugenci, MuharremThe fact that the properties of thermoelectric materials are to be estimated with Artificial Neural Networks without production and measurement will help researchers in terms of time and cost. For this purpose, figure of merit, which is the performance value of thermoelectric materials, is estimated by Artificial Neural Networks without an experimental study. P-and n-type thermoelectric bulk samples were obtained in 19 different compositions by doping different elements into Ca2.7Ag0.3Co4O9- and Zn0.98Al0.02O-based oxide thermoelectric materials. The Seebeck coefficient, electrical resistivity and thermal diffusivity values of the bulk samples were measured from 200 degrees C to 800 degrees C with an increase rate of 100 degrees C, and figure of merit values were calculated. 7 different Artificial Neural Network models were created using 123 measured results of experimental data and the molar masses of the doping elements. In this system aiming to predict the electrical resistivity, thermal diffusivity and figure of merit values of thermoelectric materials, the average R value and accuracy rate of these values were estimated to be 94% and 80%, respectively. (c) 2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Öğe Prediction of nano etching parameters of silicon wafer for a better energy absorption with the aid of an artificial neural network(Elsevier Science Bv, 2018) Kayabasi, Han; Ozturk, Savas; Celik, Erdal; Kurt, Huseyin; Arcaldioglu, ErolTo enhance energy absorption of photovoltaics, several etching experiments with various parameters were performed. In addition, an Artificial Neural Network (ANN) simulation was utilized to predict chemical nano etching parameters such as masking and etching durations for Silicon (Si) solar cell applications to reach minimum surface reflectance in an optimum etching duration. Experiments were performed with different masking and etching durations to determine the characteristics of surface reflectance of micro textured n-type single crystalline Si wafers in 25mmx25mm width and 300 gm thickness to provide training data for ANN. For this purpose, solutions with identic properties including Ag nanoparticles were applied with different application durations on the surfaces of n-type single crystalline Si wafers to cover partially the Si surfaces with Ag nano-particles at masking step. After, partially masked Si surfaces were exposed to chemical nano etching to develop nano-sized porous structures under different etching durations in an identic acidic etching solution. For the etching of Si wafers, 32 masking and etching processes were performed. Reflectance measurements and SEM images were evaluated to determine the optimum etching duration resulting the best surface quality with minimum reflectance. In addition, reflectance values were utilized as input data for training, testing and validation steps of developed ANN. In the ANN simulation, 70% of reflectance values were used as training, 15% of reflectance values were used as validation and 15% of reflectance values were used to test data in the ANN. Consequently, surface reflectance values under different masking and etching durations were predicted with the new parameter set by using the trained ANN with a success level above 99%.Öğe Steady-state thermal-electric analysis of a ?-shaped 8-pair thermoelectric generator(Elsevier Science Bv, 2019) Kilinc, Enes; Uysal, Fatih; Celik, Erdal; Kurt, HuseyinIn this study, steady-state thermal-electric analysis of a pi-shaped 8-pair TEG was performed by finite element method using thermal-electric module in Ansys Workbench for high temperature applications. TE powders of Ca2.7Ag0.3Co4O9 and Zn0.94Al0.04In0.02O were synthesized by sol-gel method followed by cold pressing (CP) to obtain bulk samples. High temperature thermoelectric properties of the bulk samples were used for p- and n-type legs, respectively. In the model, a Delta T temperature difference of 400 degrees C was applied to obtain a high temperature power output. As a result of the thermal-electric analysis, temperature distribution and total current density along the TEG were evaluated. (C) 2019 Elsevier Ltd. All rights reserved.
