Microwave dielectric ceramics (MWDCs) with a high Q×f value can improve the performance of radio frequency components like resonators, filters, antennas and so on. However, the quantitative structure-property relationship (QSPR) for the Q×f value is complicated and unclear. In this study, machine learning methods were used to explore the QSPR and build up Q×f value prediction model based on a dataset of 164 ABO₄-type MWDCs. We employed five commonly-used algorithms for modeling, and 35 structural features having correlations with Q×f value were used as input. In order to describe structure from both global and local perspectives, three different feature construction methods were compared. The optimal model based on support vector regression with radial basis function kernel shows good performances and generalization capability. The features contained in the optimal model are primitive cell volume, molecular dielectric polarizability and electronegativity with A- and B-site mean method. The relationships between property and structure were discussed. The model used for the Q×f value prediction of tetragonal scheelite shows excellent performances (R² = 0.8115 and RMSE = 8362.73 GHz), but it needs auxiliary features of average bond length, theoretical density and polarizability per unit volume for monoclinic wolframite ceramics to improve model prediction ability.

To date, most of the reported piezoelectric energy harvesters (PEHs) use lead-based Pb(Zr,Ti)O₃ (PZT) piezoceramic family, which is obviously harmful to the environment. In recent years, the PEHs constructed with lead-free piezoceramics have been developed rapidly. However, their force-to-electric (F–E) output performances are still unsatisfactory. To address this issue, here we present a PEH assembled with lead-free potassium sodium niobate (KNN) based co-fired multilayered piezoceramics (MLPCs), which show a high output current and power. First, high-quality KNN-based MLPCs are prepared by tape-casting process. Each MLPC contains 11 piezoceramic layers, and the cross-section SEM image of the MLPC indicates that the ceramic layers are well connected with the Ag/Pd inner electrode layers. The d₃₃ of a single MLPC reaches up to 4675 pC/N. The F–E output performance of KNN-MLPC based PEH is then tested. The inherent advantages of multilayered ceramics enable the PEH to achieve a peak-to-peak output current of up to 1.48 mA and a peak-to-peak output power of 2.19 mW under a harmonic force load of 6 kN at 14 Hz. Finally, the PEH is tested to validate its practical application in real road environments, demonstrating its promising for the use of self-powered monitoring sensors for collecting traffic data.

Local electric-field around multitype pores (dielectric pore, interface pore, electrode pore) in multilayer ceramic capacitors (MLCCs) was investigated using Kelvin probe force microscopy combined with the finite element simulation to understand the effect of pores on the electric reliability of MLCCs. Electric-field is found to be concentrated significantly in the vicinity of these pores and the strength of the local electric-field is 1.5–5.0 times of the nominal strength. Unexpectedly, the concentration degree of the pores in the inner electrode is much higher than that in the dielectrics and dielectric-electrode interfaces. Meanwhile, geometry orientations are found to have a remarkable influence on the local electric field strength. The pores act as an insulation degradation precursor via local electric, thermal center, and oxygen vacancies accumulation center. Such unusual local electric field concentration of multitype pores can provide new insights into the understanding of insulation degradation evolution, processing tailoring and design optimization for MLCCs.

Low permittivity microwave dielectric ceramics (MWDCs) are attracting great interest because of their promising applications in the new era of 5G and IoT. Although theoretical rules and computational methods are of practical use for permittivity prediction, unsatisfactory predictability and universality impede rational design of new high-performance materials. In this work, based on a dataset of 254 single-phase microwave dielectric ceramics (MWDCs), machine learning (ML) methods established a high accuracy model for permittivity prediction and gave insights of quantitative chemistry/structure-property relationships. We employed five commonly-used algorithms, and introduced 32 intrinsic chemical, structural and thermodynamic features which have correlations with permittivity for modeling. Machine learning results help identify the permittivity decisive factors, including polarizability per unit volume, average bond length, and average cell volume per atom. The feature-property relationships were discussed. The optimal model constructed by support vector regression with radial basis function kernel was validated its superior predictability and generalization by verification dataset. Low permittivity material systems were screened from a dataset of ~3300 materials without reported microwave permittivity by high-throughput prediction using optimal model. Several predicted low permittivity ceramics were synthesized, and the experimental results agree well with ML prediction, which confirmed the reliability of the prediction model.