Grain size engineered (Ba,Sr)(Zr,Ti)O3 ceramics with excellent energy storage properties for high-voltage pulsed capacitors

Tai, Bin; Yang, Jian; Wang, Jinfeng; Peng, Fadong; Li, Xin; Peng, Xiangyang; Yao, Yao

doi:10.1016/j.ceramint.2022.02.260

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…In this work, experiments were conducted to investigate the doping flexibility around SrTiO3 (STO), with Sr 2+ ions being doped at A-sites by Ba 2+ , Ca 2+ , Pb 2+ , Bi 3+ , etc., and Ti 4+ being doped at B-sites by Zr 4+ , Al 3+ , etc. [41][42][43]. Furthermore, some rare earth elements such as Nd 3+ , Sm 3+ , Pr 3+ and Dy 3+ have been shown to improve the performance of the STO system [44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Luo,

Hao,

Liu

2024

R. Soc. Open Sci.

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The perovskite crystal structure represents a semiconductor material poised for widespread application, underpinned by attributes encompassing heightened efficiency, cost-effectiveness and remarkable flexibility. Notably, strontium titanate (SrTiO 3 )-type perovskite, a prototypical ferroelectric dielectric material, has emerged as a pre-eminent matrix material for enhancing the energy storage capacity of perovskite. Typically, the strategy involves augmenting its dielectric constant through doping to enhance energy storage density. However, SrTiO 3 doping data are plagued by significant dispersion, and the small sample size poses a formidable research hurdle, hindering the investigation of dielectric property and energy storage density enhancements. This study endeavours to address this challenge, our foundation lies in the compilation of 200 experimental records related to SrTiO 3 -type perovskite doping, constituting a small dataset. Subsequently, an interactive framework harnesses deep neural network models and a one-dimensional convolutional neural network model to predict and scrutinize the dataset. Distinctively, the mole percentage of doping elements exclusively serves as input features, yielding significantly enhanced accuracy in dielectric performance prediction. Lastly, rigorous comparisons with traditional machine learning models, specifically gradient boosting regression, validate the superiority and reliability of deep learning models. This research advances a novel, effective methodology and offers a valuable reference for designing and optimizing perovskite energy storage materials.

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Section: Introductionmentioning

confidence: 99%

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Luo,

Hao,

Liu

2024

R. Soc. Open Sci.

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“…The energy‐storage properties of BFO can be improved by modifying the ferroelectrics to induce relaxor behavior. However, the number and size of nanodomains in relaxors cannot be accurately measured, as a result, it is easy to substantially sacrifice polarization when introducing the relaxor component in ferroelectrics 17 …”

Section: Introductionmentioning

confidence: 99%

“…However, the number and size of nanodomains in relaxors cannot be accurately measured, as a result, it is easy to substantially sacrifice polarization when introducing the relaxor component in ferroelectrics. 17 Combing advantages of NBT and BFO, Sr 0.85 Bi 0.1 TiO 3 and NaTaO 3 co-doping is expected to break the long-range ferroelectric order of NBT-BFO and induce short-range polar nanoregions, obtaining a large P max value while reducing the P r value, which is promising to improve energy-storage properties (Figure 1A). Therefore, in this work, we selected 0.7Na 0.5 Bi 0.5 TiO 3 -0.3BiFeO 3 (NBT-BFO) as the basic component and then modified this component by introducing relaxor ferroelectric Sr 0.85 Bi 0.1 TiO 3 (SBT) and incipient ferroelectrics NaTaO 3 (NT) into the system, forming (1 − x)(0.7Bi 0.65 Na 0.35 Fe 0.3 Ti 0.7 O 3 -0.3Sr 0.85 Bi 0.1 TiO 3 )-xNaTaO 3 (abbreviated as 100xNT) solid solution.…”

Section: Introductionmentioning

confidence: 99%

Energy‐storage properties of (0.7Bi_0.65Na_0.35Fe_0.3Ti_0.7O₃–0.3Sr_0.85Bi_0.1TiO₃)–NaTaO₃ relaxor under low electric fields

Yang,

Ren,

et al. 2023

Int J Applied Ceramic Tech

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Currently, the energy‐storage properties of dielectric ceramic capacitors have aroused wide attention; however, most materials exhibiting excellent energy‐storage properties are based on high electric fields, which increase the cost of insulation technology. Herein, the energy‐storage and charge–discharge properties of (1 − x)(0.7Bi0.65Na0.35Fe0.3Ti0.7O3–0.3Sr0.85Bi0.1TiO3)–xNaTaO3 (x = 0.03–0.18, abbreviated as 100xNT) ceramics are investigated. 9NT achieves superior energy‐storage properties under a low electric field of 210 kV/cm, with an energy‐storage density (Wrec) of 3.44 J/cm3 and efficiency (η) of 86.6%. The energy‐storage properties exhibit excellent frequency stability in 3–100 Hz as well as temperature stability between 25 and 175°C. Furthermore, the charge–discharge performance features a high‐power density (PD = 67.04 MW/cm3), and an ultrafast discharge speed (t0.9 = 52 ns). This work paves a new way to explore energy‐storage competitive materials under low electric fields.

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Effect of (Bi, Li) co-doping on dielectric and ferroelectric properties in BaZr0.15Ti0.85O3 ceramics

Wang

Zhang

et al. 2023

Appl. Phys. A

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Grain size engineered (Ba,Sr)(Zr,Ti)O3 ceramics with excellent energy storage properties for high-voltage pulsed capacitors

Cited by 7 publications

References 52 publications

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Energy‐storage properties of (0.7Bi_0.65Na_0.35Fe_0.3Ti_0.7O₃–0.3Sr_0.85Bi_0.1TiO₃)–NaTaO₃ relaxor under low electric fields

Effect of (Bi, Li) co-doping on dielectric and ferroelectric properties in BaZr0.15Ti0.85O3 ceramics

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Grain size engineered (Ba,Sr)(Zr,Ti)O3 ceramics with excellent energy storage properties for high-voltage pulsed capacitors

Cited by 7 publications

References 52 publications

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification

Energy‐storage properties of (0.7Bi0.65Na0.35Fe0.3Ti0.7O3–0.3Sr0.85Bi0.1TiO3)–NaTaO3 relaxor under low electric fields

Effect of (Bi, Li) co-doping on dielectric and ferroelectric properties in BaZr0.15Ti0.85O3 ceramics

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Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO ₃ -type perovskite with doping modification

Energy‐storage properties of (0.7Bi_0.65Na_0.35Fe_0.3Ti_0.7O₃–0.3Sr_0.85Bi_0.1TiO₃)–NaTaO₃ relaxor under low electric fields