Mechanism of ferroelectric properties of (BaCa)(ZrTi)O3 from first-principles calculations

Luo, Bing; Wang, Xiaohui; Tian, Enke; Song, Hang; Qu, Haimo; Cai, Ziming; Li, Baiwen; Li, Longtu

doi:10.1016/j.ceramint.2018.02.197

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…A first-principles method was performed to confirm the higher spontaneous polarization of the R phase in BNT-based ceramics. , To reduce the computational resources, a simplified ceramic composition of 0.8Bi 0.5 Na 0.5 TiO 3 –0.2NaNbO 3 (0.8BNT-0.2NN) was adopted in our simulations by ignoring a few SrZrO 3 . The optimal crystal structures of the R and T phases in 0.8BNT-0.2NN are presented in Figure a,b, respectively.…”

Section: Resultsmentioning

confidence: 99%

Multiphase Engineered BNT-Based Ceramics with Simultaneous High Polarization and Superior Breakdown Strength for Energy Storage Applications

Zhu

Cai

Luo

et al. 2021

ACS Appl. Mater. Interfaces

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Dielectric ceramics are crucial for high-temperature, pulse-power energy storage applications. However, the mutual restriction between the polarization and breakdown strength has been a significant challenge. Here, multiphase engineering controlled by the two-step sintering heating rate is adopted to simultaneously obtain a high polarization and breakdown strength in 0.8(0.95Bi 0.5 Na 0.5 TiO 3 − 0.05SrZrO 3 )−0.2NaNbO 3 (BNTSZNN) ceramic systems. The coexistence of tetragonal (T) and rhombohedral (R) phases benefits the temperature stability of BNTSZNN ceramics. Increasing the heating rate during sintering reduces the diffusion of SrZrO 3 and NaNbO 3 into Bi 0.5 Na 0.5 TiO 3 , which results in a high proportion of the R phase and a finer grain size. The overall polarization is enhanced by increasing the proportion of the high-polarization R phase, which is demonstrated using a first-principles method. Meanwhile, the finer grain size enhances the breakdown strength. Following this design philosophy, an ultrahigh W dis of 5.55 J/cm 3 and η above 85% is achieved in BNTSZNN ceramics as prepared with a fast heating rate of 60 °C/min given a simultaneously high polarization of 43 μC/cm 2 and high breakdown strength of 350 kV/cm. Variations in the discharge energy density from room temperature to 160 °C are less than 10%. Additionally, such BNTSZNN ceramics exhibit an ultrafast discharge speed with τ 0.9 at approximately 60 ns, which shows great potential in pulse-power system applications.

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Section: Resultsmentioning

confidence: 99%

Multiphase Engineered BNT-Based Ceramics with Simultaneous High Polarization and Superior Breakdown Strength for Energy Storage Applications

Zhu

Cai

Luo

et al. 2021

ACS Appl. Mater. Interfaces

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“…Theoretical research is an effective alternative to experimental research. Over the past decades, conventional first-principles calculations have been a powerful tool for calculating the structures and properties of materials [16][17][18]. For example, they have been used to study the structural and electronic properties of perfect, doped, and defective 2D materials [19][20][21], the interaction between the substrate and 2D materials [22,23], and the interaction between the contacts and 2D materials [24,25].…”

Section: Introductionmentioning

confidence: 99%

Bandgap prediction of two-dimensional materials using machine learning

Liu

Zhang

et al. 2021

PLoS ONE

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The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

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“…Interestingly, Dong et al [33] reported 1.6 wt% ZnO doped in Ba 0.3 Sr 0.7 TiO 3 ceramics with an enhanced W rec of 3.9 J/cm 3 at 40 kV/mm. Taking a theoretical approach, Wang et al [51] reported firstprinciples calculations and molecular dynamic simulations to study the effects of the chemical composition, phase under temperature, and electric fields on the ferroelectric and energy storage properties of ABO 3 perovskite FEs. These simulation results revealed a W rec of 2.8 J/cm 3 and a η of 95% at E b of 350 kV/cm in Ba 0.6 Sr 0.4 TiO 3 ceramics, and, furthermore, a W rec of 30 J/cm 3 and a η of 92% obtained at an E b of 2750 kV/cm in the same composition of Ba 0.6 Sr 0.4 TiO 3 .…”

Section: Ferroelectricsmentioning

confidence: 99%

Ceramic-Based Dielectric Materials for Energy Storage Capacitor Applications

Pattipaka,

Lim,

Son

et al. 2024

Materials

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Materials offering high energy density are currently desired to meet the increasing demand for energy storage applications, such as pulsed power devices, electric vehicles, high-frequency inverters, and so on. Particularly, ceramic-based dielectric materials have received significant attention for energy storage capacitor applications due to their outstanding properties of high power density, fast charge–discharge capabilities, and excellent temperature stability relative to batteries, electrochemical capacitors, and dielectric polymers. In this paper, we present fundamental concepts for energy storage in dielectrics, key parameters, and influence factors to enhance the energy storage performance, and we also summarize the recent progress of dielectrics, such as bulk ceramics (linear dielectrics, ferroelectrics, relaxor ferroelectrics, and anti-ferroelectrics), ceramic films, and multilayer ceramic capacitors. In addition, various strategies, such as chemical modification, grain refinement/microstructure, defect engineering, phase, local structure, domain evolution, layer thickness, stability, and electrical homogeneity, are focused on the structure–property relationship on the multiscale, which has been thoroughly addressed. Moreover, this review addresses the challenges and opportunities for future dielectric materials in energy storage capacitor applications. Overall, this review provides readers with a deeper understanding of the chemical composition, physical properties, and energy storage performance in this field of energy storage ceramic materials.

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Mechanism of ferroelectric properties of (BaCa)(ZrTi)O3 from first-principles calculations

Cited by 13 publications

References 32 publications

Multiphase Engineered BNT-Based Ceramics with Simultaneous High Polarization and Superior Breakdown Strength for Energy Storage Applications

Multiphase Engineered BNT-Based Ceramics with Simultaneous High Polarization and Superior Breakdown Strength for Energy Storage Applications

Bandgap prediction of two-dimensional materials using machine learning

Ceramic-Based Dielectric Materials for Energy Storage Capacitor Applications

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