The ferroelectric cooling technology based on electrocaloric effect (ECE) has broad application scenarios in solid-state refrigeration due to its various advantages including high refrigeration efficiency, environmental friendliness, convenient operation and easy miniaturization. The ECE refers to the isothermal entropy change (ΔS) or adiabatic temperature change (ΔT) induced by the change of polar dipole state in ferroelectric and other polar materials under external electric fields, which can be classified as positive and negative ECEs. The positive (negative) ECE refers to the phenomenon that the ferroelectrics exude heat when the electric field is applied (removed) and absorb heat when the electric field is removed (applied). The process of absorbing heat from external environment of positive and/or negative ECE can be used in ferroelectric cooling. In addition to the traditional approach of optimizing the pyroelectric coefficient and breakdown field strength, the combination of positive and negative ECE is also considered an effective means to enhance the cooling capacity of ferroelectric materials. Due to short research history, few suitable materials and unclear physical mechanism, the usage of negative ECE is limited in the practical applications.

Firstly, based on the development of ECE, the relevant research progress on ECE cooling materials, devices and theories are introduced. Secondly, the physical mechanism of negative ECE are summarized. Both phase transition and domain rotation can induce negative ECE. In ferroelectric single crystals and antiferroelectric materials, the negative ECE can be induced by the phase transition from low temperature phase (low entropy) to high temperature phase (high entropy) under the external electric field. While, for some ferroelectric materials with rectangular hysteresis loops, polarization disorder induced by a reverse electric field can also produce smaller negative ECE. Thirdly, we discuss the effective strategy for the combination of positive and negative ECEs to enhance the cooling performance in ferroelectric materials. They confirm the feasibility of combining positive and negative ECEs, but how to reasonably regulate the negative ECE to coexist and alternate with the positive ECE at the same or similar temperatures is still an urgent problem. Finally, the perspectives and prospects of future research for the negative ECE are presented.

This review can provide new ideas and directions for the researches of negative ECE and the improvement of the cooling capability of ferroelectric materials, which can promote the study of ECE

An issue of current interest in the use of machine learning models to predict compositions of materials is their reliability in predicting outcomes with elements not included in the training data. We show that the phase diagram of the ceramic (Ba_1–x–yCa_xSr_y)(Ti_{1–u–v–w}Zr_uSn_vHf_w)O₃ can be accurately predicted if the feature values of unknown elements do not exceed the range of values for existing elements in the training data. In particular, we employ physical features as descriptors and compositions as weights to show that by excluding an element, such as Zr, Sn or Hf from the training set and treating it as an unknown element, the machine learning model accurately predicts the property only if the feature values of the unknown element does not exceed the range of values of existing elements in the training set. By adding a small amount of data for the unknown element restores the prediction accuracy. We demonstrate this for BaTiO₃ ceramics doped with rare earth elements where the prediction accuracy is restored if the physical feature space is suitably enlarged with training data. The prediction error increases with the Euclidean distance of the testing sample relative to the nearest training sample in the physical feature space. Our work provides an effective strategy for extending machine learning models for material compositions beyond the scope of available data.

In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, i.e., (Ba_1−x−yCa_xSr_y)(Ti_{1−u−v−w}Zr_uSn_vHf_w)O₃. The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov–Batsanov electronegativity, ratio of valence electron to nuclear charge, and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO₃-based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction is also experimentally validated by typical samples of (Ba_0.85Ca_0.15)(Ti_0.98–xZr_xHf_0.02)O₃. This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

Ferroelectric (FE) phase transition with a large polarization change benefits to generate large electrocaloric (EC) effect for solid-sate and zero-carbon cooling application. However, most EC studies only focus on the single-physical factor associated phase transition. Herein, we initiated a comprehensive discussion on phase transition in Pb_0.99Nb_0.02[(Zr_0.6Sn_0.4)_1−xTi_x]_0.98O₃ (PNZST100x) antiferroelectric (AFE) ceramic system under the joint action of multi-physical factors, including composition, temperature, and electric field. Due to low energy barrier and enhanced zero-field entropy, the multi-phase coexistence point (x = 0.12) in the composition–temperature phase diagram yields a large positive EC peak of maximum temperature change (ΔT_max) = 2.44 K (at 40 kV/cm). Moreover, the electric field–temperature phase diagrams for four representative ceramics provide a more explicit guidance for EC evolution behavior. Besides the positive EC peaks near various phase transition temperatures, giant positive EC effects are also brought out by the electric field-induced phase transition from tetragonal AFE (AFE_T) to low-temperature rhombohedral FE (FE_R), which is reflected by a positive-slope boundary in the electric field–temperature phase diagram, while significant negative EC responses are generated by the phase transition from AFE_T to high-temperature multi-cell cubic paraelectric (PE_MCC) with a negative-slope phase boundary. This work emphasizes the importance of phase diagram covering multi-physical factors for high-performance EC material design.

For efficient solid-state refrigeration technologies based on electrocaloric effect (ECE), it is a great challenge of simultaneously obtaining a large adiabatic temperature change (ΔT) within a wide temperature span (T_span) in lead-free ferroelectric ceramics. Here, we studied the electrocaloric effect (ECE) in (1-x)(Na_0.5Bi_0.5)TiO₃-xCaTiO₃ ((1-x)NBT-xCT) and explored the combining effect of morphotropic phase boundary (MPB) and relaxor feature. The addition of CT not only constructs a MPB region with the coexistence of rhombohedral and orthorhombic phases, but also enhances the relaxor feature. The ECE peak appears around the freezing temperature (T_f), and shifts toward to lower temperature with the increasing CT amount. The directly measured ECE result shows that the ceramic of x = 0.10, which is in the MPB region, has an optimal ECE property of ΔT_max = 1.28 K @ 60 ℃ under 60 kV/cm with a wide T_span of 65 ℃. The enhanced ECE originates from the electric-field-induced transition between more types of polar nanoregions and long-range ferroelectric macrodomains. For the composition with more relaxor feature in the MPB region, such as x = 0.12, the ECE is relatively weak under low electric fields but it exhibits a sharp increment under a sufficiently high electric field. This work provides a guideline to develop the solid–state cooling devices for electronic components.

Introducing polarization field of piezoelectric materials is an effective strategy to improve photocatalytic performance. In this study, a new type of BaTiO₃/CuO heterostructure catalyst was designed and synthesized to achieve high piezo-photocatalytic activity through the synergy of heterojunction and piezoelectric effect. The BaTiO₃/CuO heterostructure shows a significantly enhanced piezo-photocatalytic degradation efficiency of organic pollutants compared with the individual BaTiO₃ nanowires (NWs) and CuO nanoparticles (NPs). Under the co-excitation of ultrasonic vibration and ultraviolet radiation, the optimal degradation reaction rate constant k of polarized BaTiO₃/CuO heterostructure on methyl orange (MO) dye can reach 0.05 min^-1, which is 6.1 times of photocatalytic rate and 7 times of piezocatalytic rate. The BaTiO₃/CuO heterostructure with remarkable piezo-photocatalytic behavior provides a promising strategy for the development of high-efficiency catalysts for wastewater purification, and it also helps understand the coupling mechanism between piezoelectric effect and photocatalysis.

Y-type hexagonal ferrite with planar magnetocrystalline anisotropy has ultrahigh cut-off frequency up to GHz and excellent magnetic properties in hyper frequency range, so that is regarded as the most suitable material in correpongding inductive devices and components. The technology of low temperature cofired ceramics for surface-mounted multilayer chip components needs ferrite to be sintered well under 900 ℃ to avoid the melting and diffusion of Ag inner electrode during the cofiring process. To lower the sintering temperature of Y-type hexagonal ferrite, there are several methods, (1) using nano-sized starting powders, (2) substitution by low-melting elements, (3) adding sintering additives, and (4) introducing lattice defect. In this paper, the effects of different methods on the sintering behavior and the magnetic properties were discussed in detail.