Preparation And Electrical Properties Of BST-based Ceramic Materials

Compared with traditional batteries,energy storage dielectric ceramic capacitors have the advantages of high power density and fast charging and discharging speed.Therefore,energy storage dielectric ceramic materials have become a research hotspot.Most of them are lead-containing ceramic materials.Lead will not only cause huge pollution to the environment,but also damage human health.Barium strontium titanate-based lead-free energy storage ceramic materials have the advantages of flexible dielectric tunability,high dielectric constant and low dielectric loss.The energy consumption in the process is large,which is not conducive to energy saving and emission reduction;at the same time,the maximum breakdown field strength of BST-based ceramics is relatively low,making it difficult to obtain high energy storage density;and the poor temperature stability of BST-based energy storage dielectric ceramics limits its performance.Therefore,in order to reduce the sintering temperature of BST-based ceramics and improve their energy storage properties and temperature stability,the following researches in this thesis were carried out:Ba_0.4Sr_0.6Ti O₃ ceramic samples were prepared by solid phase method.Ba_0.4Sr_0.6Ti O₃ceramic samples with cubic phase structure could be obtained at different pre-sintering and sintering temperatures.When the pre-sintering temperature is 1000°C and the sintering temperature is 1420°C,the obtained Ba_0.4Sr_0.6Ti O₃ saturation polarization P_max is 16.23μC/cm²,and the breakdown field strength E_b is 155 k V/cm².At this time,the energy storage characteristics also reach the best,where the energy storage density W_rec is 0.927J/cm³,and the energy storage efficiencyηis 73.9%.（1-x）Ba_0.4Sr_0.6Ti O₃-x Bi(Mg_2/3Nb_1/3)O₃ ceramic samples,（1-x）BST-x BMN,were prepared by solid-phase method.When x≤0.15,only a single perovskite structure appeared,when x>0.15,the second phase Sr Nb₂O₆ appeared.With the increasing of the BMN doping amount makes the dielectric constant and dielectric loss decrease continuously,the P-E curve becomes more slender,the P_max and P_r gradually decrease.Moreover,according to the hysteresis loop,the energy storage characteristic curve is obtained.When the doping amount of BMN is 0.15 mol and the sintering temperature is 1250°C,the energy storage characteristic of the sample is the best,where the energy storage density W_rec is 0.832J/cm³,and the energy storage efficiencyηwas 89.5%.Under the electric field of 100k V/cm and with the test temperature being increased from 30°C to 120°C,the energy storage density of the 0.85BST-0.15BMN ceramic samples decreased from 0.777 J/cm³ to0.750 J/cm³,and the energy storage efficiency was reduced from 91.96%to 86.15%,where the energy storage density has good temperature stability.（1-x）Ba_0.4Sr_0.6Ti O₃-x Bi(Mg_2/3Ta_1/3)O₃ ceramic samples,（1-x）BST-x BMT,were prepared by solid phase method.When x≤0.15,only perovskite exists in the ceramic samples structure.With increase of the doping amount of BMT,makes the dielectric constant and dielectric loss decrease continuously,the P-E curve also becomes thinner,and the P_max and P_r first increase and then decrease.Moreover,according to the hysteresis loop,the energy storage characteristic curve is obtained.When the BMT doping amount is 0.075 mol and the sintering temperature is 1220°C,the（1-x）BST-x BMT ceramic sample has the best energy storage characteristics,where the W_rec is 0.568 J/cm³andηis 91.4%.Under the electric field of 100 k V/cm and with the test temperature being increased from 30°C to 120°C,the energy storage density of the 0.925BST-0.075BMN ceramic samples decreased from 0.296J/cm³ to 0.263 J/cm³,and the energy storage efficiency decreased from 92.46%to 84.68%.An artificial neural network expert system for BST-based ceramic materials was built to conduct network simulation training on the experimental data of energy storage characteristics of BMT-doped BST ceramic materials.The optimal network training parameters to obtain the energy storage density are:the learning rate is 0.6,the momentum factor is 0.8,the number of hidden layers is 2,and the number of hidden layer nodes is（4,7）;the optimal network training parameters to obtain the energy storage efficiency The learning rate is 0.7,the momentum factor is 0.8,the number of hidden layers is 2,and the number of hidden layer nodes is（4,5）.The optimal network training parameter conditions were set,where the single-point prediction function was used to predict the energy storage characteristic parameters of BMT-doped BST ceramic materials.The average relative error of the predicted value of energy storage efficiency parameters is as follows:3.7850%at maximum and 0.0046%at minimum.The error between the predicted value and the actual value is small,so the system can accurately predict the energy storage characteristics of BMT-doped BST ceramics,which has a certain reference value and can act as a guiding role.