Simultaneously realizing ultrahigh energy storage density and efficiency in BaTiO3-based dielectric ceramics by creating highly dynamic polar nanoregions and intrinsic conduction
Source abstract
Abstract
Nowadays, it is urgent to explore advanced and eco-friendly energy storage capacitors based on lead-free relaxor ferroelectric (RFE) ceramics in order to meet the ever-increasing requirements in pulsed power systems. BaTiO 3 (BT)-based RFE ceramics are considered as ones of the best high-temperature energy storage materials due to their good thermal stability. However, relatively low recoverable energy storage density ( W rec<5 J/cm 3) has been a key bottleneck restricting the practical applications of them. Here, a novel strategy is proposed to create highly dynamic PNRs and the intrinsic conduction by introducing Bi(M 1-0.015 xTa 0.015 x)O 3+0.015 x (BMO-Ta, M=Mg 2/3Ta 1/3) to BT matrix. As a consequence, the designed (1- x)BT- x(BMO-Ta) ceramics exhibit dramatically enhanced energy storage properties including ultrahigh W rec and efficiency ( η), because of the coexistence of very slim polarization hysteresis ( P-E) loops, large polarization difference (Δ P ) and giant dielectric breakdown electric strength ( E b). W rec and η of the x=0.25 ceramic reach up to 9.03 J/cm 3 and 95.2% under 720 kV/cm, respectively. Furthermore, it shows excellent temperature/frequency/cycling stability over a wide range of 20−200 °C, 1−500 Hz and 1−3.3 × 10 5 cycles, respectively (the variations of W rec and η are < 3% and < 4%, respectively). The findings in this paper not only indicate excellent comprehensive properties achieved in the novel (1- x)BT- x(BMO-Ta) system, but also provide an effective approach to explore advanced energy storage capacitors in other lead-free ceramic systems.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Ferroelectric and Piezoelectric Materials · Dielectric materials and actuators · Microwave Dielectric Ceramics Synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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