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In this paper, we report the dielectric and ferroelectric response of compositionally graded bilayer and trilayer composites consisting of BaTiO3 (BT) and 0.975BaTiO3-0.025Ba(Cu1/3Nb2/3)O3 (BTBCN). Two types of graded bilayer samples were synthesized, one with same thickness of BT and BTBCN while other with different layer thicknesses. The graded trilayer sample consisted of BT layer sandwiched between two BTBCN layers of equal thickness. Scanning electron microscopy and transmission electron microscopy images showed a sharp interface with needle-shape domains across the interface. The domain size on BT side was found to be larger than that on BTBCN side. The temperature dependence of dielectric response for all composite systems was found to exhibit shifting in characteristic Curie peak compared to constituent material which was associated to coupling between layers. Moreover, the differences in grain size, tetragonality, domain mobility of each layer was found to perturb the electrical response of composite. The polarization mismatch between uncoupled BT and BTBCN established internal electric field in composite specimen and defined new polarization states in each layer by perturbing free energy functional of the composite specimen. Dynamic hysteresis behaviors and power-law scaling relations of all specimens were determined from polarization-electric field hysteresis loop measurements as a function of frequency. All systems were found to exhibit similar dynamic scaling relationships. Hysteresis area , Pr, and EC decreased with increasing frequency due to delayed response but increased with increasing applied electric field due to enhancement of driving force. Trilayer system was found to exhibit strong internal-bias field and double hysteresis behavior. The coupling effect resulting due to polarization mismatch between layers had substantial influence on the dynamic hysteresis behavior and power-law scaling relations. [ABSTRACT FROM AUTHOR]

In this paper, we report the dielectric and ferroelectric response of compositionally graded bilayer and trilayer composites consisting of BaTiO3 (BT) and 0.975BaTiO3-0.025Ba(Cu1/3Nb2/3)O3 (BTBCN). Two types of graded bilayer samples were synthesized, one with same thickness of BT and BTBCN while other with different layer thicknesses. The graded trilayer sample consisted of BT layer sandwiched between two BTBCN layers of equal thickness. Scanning electron microscopy and transmission electron microscopy images showed a sharp interface with needle-shape domains across the interface. The domain size on BT side was found to be larger than that on BTBCN side. The temperature dependence of dielectric response for all composite systems was found to exhibit shifting in characteristic Curie peak compared to constituent material which was associated to coupling between layers. Moreover, the differences in grain size, tetragonality, domain mobility of each layer was found to perturb the electrical response of composite. The polarization mismatch between uncoupled BT and BTBCN established internal electric field in composite specimen and defined new polarization states in each layer by perturbing free energy functional of the composite specimen. Dynamic hysteresis behaviors and power-law scaling relations of all specimens were determined from polarization-electric field hysteresis loop measurements as a function of frequency. All systems were found to exhibit similar dynamic scaling relationships. Hysteresis area , Pr, and EC decreased with increasing frequency due to delayed response but increased with increasing applied electric field due to enhancement of driving force. Trilayer system was found to exhibit strong internal-bias field and double hysteresis behavior. The coupling effect resulting due to polarization mismatch between layers had substantial influence on the dynamic hysteresis behavior and power-law scaling relations. [ABSTRACT FROM AUTHOR]

This paper proposed an application of Artificial Neural Network (ANN) to concurrently model ferroelectric hysteresis properties of Barium Titanate in both single-crystal and bulk-ceramics forms. In the ANN modeling, there are 3 inputs, which are type of materials (single or bulk), field amplitude and frequency, and 1 output, which is hysteresis area. Appropriate number of hidden layer and hidden node were achieved through a search of up to 2 layers and 30 neurons in each layer. After ANN had been properly trained, a network with highest accuracy was selected. Query file of unseen input data was then input to the selected network to obtain the predicted hysteresis area. From the results, the target and predicted data were found to match very well. This therefore suggests that ANN can be successfully used to concurrently model ferroelectric hysteresis property even though the considered ferroelectrics are with different domains, grains and microscopic crystal structures. [ABSTRACT FROM AUTHOR]