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- Title
Parametric optimization and prediction of enhanced thermoelectric performance in co-doped CaMnO<sub>3</sub> using response surface methodology and neural network.
- Authors
Pandey, Binay Kumar; Pandey, Digvijay
- Abstract
In this work, two different combinations of materials are prepared, and the effects on dual doping in Ca1−x−yGdxSrxMnO3 and Ca1−x−yCexSrxMnO3 (x = 0, 0.025, 0.05, y = 0, 0.025, 0.05) materials are evaluated, and its parameters are optimized and predicted by the Box-Benhken design in the RSM method. The activation energy was measured with respect to different thermoelectrical material concentrations. RSM design is validated using hybrid DBN-RSO. The results show that increasing temperature, increases the amount of doping, decreases the thermal conductivity (k) and increases the electrical conductivity (σ) and power factor (PF). A bigger number of merits was also reached by increasing the amount of doping and the temperature. At 1000 K, the Ca0.95Gd0.05Sr0.05MnO3 material has a low thermal conductivity and the highest figure of merit (ZT) value of 0.24, which is more than Ca0.95Ce0.05Sr0.05MnO3. The predicted values from the DBN-RSO method provide results that are closer to the experimental observations. The highest score (ZT) that the DBN-RSO prediction received was 0.26. Besides, the regression value of 99% is obtained from the experimented and predicted values. It shows the confidence and fitness of values. Also, the DBN-RSO achieves closer results to the experimental design with the lowest error value.
- Publication
Journal of Materials Science: Materials in Electronics, 2023, Vol 34, Issue 21, p1
- ISSN
0957-4522
- Publication type
Article
- DOI
10.1007/s10854-023-10954-1