Enhanced breakdown strength of BaTiO_(3)-based multilayer ceramic capacitor by structural optimization

0.5 wt%Nb_(2)O_(5)doped 0.12BiAlO_(3)-0.88BaTiO_(3)(12BA5N)multilayer ceramic capacitor(MLCC-1)was prepared,which satisfied EIA X7R specification(where X is the minimum temperature,R is the percentage of capacitance variation limit)at 1 kHZ.The distribution of internal electric field under breakdown voltage was simulated by finite element method(FEM),indicating that the electric field strength increased significantly at the terminal of internal electrode.These areas may become the headstream of breakdown for MLCC-1 due to the shape mutation.In order to improve the breakdown performance of MLCC-1,it was optimized by 12BA5N+2G green sheets(prepared by 12BA5N ceramic powder with 2 wt%B-Al-Si glass additive),then MLCC-2 was obtained which satisfied EIA X8R specification.Its BDS rose from 20 to29.4 kV·mm^(-1),and the electric field distribution of dielectric layer was also analyzed by FEM.Besides,it was also found that the grain size and the dielectric constants of"core"and"shell"parts for the 12BA5N+2G dielectric layer both contributed to the enhanced BDS of MLCC-2according to the simulation results from FEM.

High-throughput data-driven interface design of high-energy-density polymer nanocomposites

Understanding the interface effect in dielectric nanocomposites is crucial to the enhancement of their performance.In this work,a data-driven interface design strategy based on high-throughput phase-field simulations is developed to study the interface effect and then optimize the permittivity and breakdown strength of nanocomposites.Here,we use two microscopic features that are closely related to the macroscopic dielectric properties,the thickness and permittivity of the interface phases,to evaluate the role of interfaces in experimental configuration,and thus provide quantitative design schemes for the interfacial phases.Taking the polyvinyl difluoride(PVDF)-BaTiO_(3) nanocomposite as an example,the calculation results demonstrate that the interfacial polarization could account for up to 83.6% of the increase in the experimentally measured effective permittivity of the nanocomposite.Based on the interface optimized strategy,a maximum enhancement of ~156% in the energy density could be achieved by introducing an interface phase with d/r=0.55 and ε_(interface)/ε_(filler)=0:036,compared to the pristine nanocomposite.Overall,the present work not only provides fundamental understanding of the interface effect in dielectric nanocomposites,but also establishes a powerful data-driven interface design framework for such materials that could also be easily generalized and applied to study interface issues in other functional nanocomposites,such as solid electrolytes and thermoelectrics.

Designing polymer nanocomposites with high energy density using machine learning

Addressing microstructure-property relations of polymer nanocomposites is vital for designing advanced dielectrics for electrostatic energy storage.Here,we develop an integrated phase-field model to simulate the dielectric response,charge transport,and breakdown process of polymer nanocomposites.Subsequently,based on 6615 high-throughput calculation results,a machine learning strategy is schemed to evaluate the capability of energy storage.We find that parallel perovskite nanosheets prefer to block and then drive charges to migrate along with the interfaces in x-y plane,which could significantly improve the breakdown strength of polymer nanocomposites.To verify our predictions,we fabricate a polymer nanocomposite P(VDF-HFP)/Ca_(2)Nb_(3)O_(10),whose highest discharged energy density almost doubles to 35.9 J cm^(−3) compared with the pristine polymer,mainly benefit from the improved breakdown strength of 853 MVm^(−1).This work opens a horizon to exploit the great potential of 2D perovskite nanosheets for a wide range of applications of flexible dielectrics with the requirement of high voltage endurance.

Machine learning in energy storage materials

With its extremely strong capability of data analysis,machine learning has shown versatile potential in the revolution of the materials research paradigm.Here,taking dielectric capacitors and lithium‐ion batteries as two representa-tive examples,we review substantial advances of machine learning in the research and development of energy storage materials.First,a thorough discussion of the machine learning framework in materials science is presented.Then,we summarize the applications of machine learning from three aspects,including discovering and designing novel materials,enriching theoretical simulations,and assisting experimentation and characterization.Finally,a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science.