Decoding the Fingerprint of Ferroelectric Loops: Comprehension of the Material Properties and Structures

By Li Jin, Fei Li, Shujun Zhang. Progress in Advanced Dielectrics, 2014

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Due to the nature of domains, ferroics, including ferromagnetic, ferroelectric, and ferroelastic materials, exhibit hysteresis phenomena with respect to external driving fields (magnetic field, electric field, or stress). In principle, every ferroic material has its own hysteresis loop, like a fingerprint, which contains information related to its properties and structures. For ferroelectrics, many characteristic parameters, such as coercive field, spontaneous, and remnant polarizations can be directly extracted from the hysteresis loops. Furthermore, many impact factors, including the effect of materials (grain size and grain boundary, phase and phase boundary, doping, anisotropy, thickness), aging (with and without poling), and measurement conditions (applied field amplitude, fatigue, frequency, temperature, stress), can affect the hysteretic behaviors of the ferroelectrics. In this feature article, we will first give the background of the ferroic materials and multiferroics, with an emphasis on ferroelectrics. Then it is followed by an introduction of the characterizing techniques for the loops, including the polarization–electric field loops and strain–electric field curves. A caution is made to avoid misinterpretation of the loops due to the existence of conductivity. Based on their morphologic features, the hysteresis loops are categorized to four groups and the corresponding material usages are introduced. The impact factors on the hysteresis loops are discussed based on recent developments in ferroelectric and related materials. It is suggested that decoding the fingerprint of loops in ferroelectrics is feasible and the comprehension of the material properties and structures through the hysteresis loops is established.

Subfields: Biomedical Engineering, Electronic, Optical and Magnetic Materials, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/f66533299419181325196c80c8443a865fc383cc

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