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Colossal dielectric constant of NaNbO3 doped BaTiO3 ceramics

Cao W. Q., Xu L. F., Ismail M. M., Huang L. L.

Materials Science Poland

2016

Vol. 34, No. 2

322--329

BaTiO3 ceramics doped with 0.40 mol% NaNbO3 were prepared using a traditional approach by sintering at temperature of 1250 ºC to 1290 ºC. The prepared ceramics was characterized by very good dielectric properties, such as high dielectric constant (1.5 × 105), low dielectric loss (0.1), and good dielectric temperature stability in the −40 ºC to 100 ºC range for the sample sintered below 1270 ºC. The dielectric characteristics obtained with XPS confirmed that Ti4+ ions remain in the state without any change. The huge increase in dielectric constant in NaNbO3 doped BaTiO3 samples occurs when large amount of Ba2+ ions are excited to a high energy bound state of Ba2+ − e or Ba+ to create electron hopping conduction. For samples with the content of NaNbO3 higher than 0.40 mol%, or sintering temperature higher than 1280 ºC, compensation effect is dominated by cation vacancies with sharply decreasing dielectric constant and increased dielectric loss. The polaron effect is used to explain the relevant mechanism of giant dielectric constant appearing in the ferroelectric phase.

Converter end-point prediction model using spectrum image analysis and improved neural network algorithm

Wen H. Y., Zhao Q., Chen Y. R., Zhou M. C., Zhang M., Xu L. F.

Optica Applicata

2008

Vol. 38, nr 4

693-704

Aiming at the present situation of the steelmaking end-point control at home and abroad, a neural network model was established to judge the end-point. Based on the colour space conversion and the fiber spectrum division multiplexing technology, a converter radiation multi-frequency information acquisition system was designed to analyze the spectrum light and image characteristic information, and the results indicate that they are similar at early-middle stage but dissimilar when approach the steelmaking blowing end. The model was trained and forecasted by using an improved neural network correction coefficient algorithm and some appropriate variables as the model parameters. The experimental results show the proposed algorithm improves the prediction accuracy by 15.4% over the conventional algorithm in 5s errors and the respond time is about 1.688s, which meets the requirements of end-point judgment online.