Ceramic-based dielectric materials are regarded as the best candidates for advanced pulsed power capacitors because of their excellent mechanical and thermal properties. Nevertheless, lead-free bulk ceramics show relatively low recoverable energy storage density (W_rec < 2 J cm⁻³) owing to their low dielectric breakdown strength (DBS < 200 kV cm⁻¹). In order to significantly increase W_rec, we proposed a strategy (compositions drive the grain size to submicrometer) to improve the DBS of lead-free ceramics. In this work, (1 − x)(K_0.5Na_0.5)NbO₃–xSrTiO₃ (KNN–ST) ceramics were chosen as a representative to verify the validity of this strategy. The (1 − x)KNN–xST ceramics (x = 0.15 and 0.20) with submicrometer grains (about 0.3 μm) were prepared using pressureless solid state sintering. A large W_rec (4.03 J cm⁻³) and DBS (400 kV cm⁻¹ with a thickness of 0.2 mm) were achieved for 0.85KNN–0.15ST ceramics. The value of 4.03 J cm⁻³ is superior to all other W_rec in lead-free bulk ceramics and 2–3 times larger than that of other lead-free bulk ceramics. A large W_rec (3.67 J cm⁻³) and energy storage efficiency (72.1%) were simultaneously achieved for 0.80KNN–0.20ST ceramics. The results confirm that the (1 − x)KNN–xST ceramics (x = 0.15 and 0.20) are desirable materials for advanced pulsed power capacitors. The findings in this study could push the development of a series of KNN-based ceramics with enhanced DBS and W_rec in the future. On the other hand, this work could broaden the applications of KNN materials in a new field.

Simultaneous achievement of a large W_rec of 3.51 J cm⁻³ and a high η of 80.1% in 0.86NN–0.14BNH ceramics under 350 kV cm⁻¹, leading to an excellent comprehensive energy storage performance in lead-free bulk ceramics.

Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 2^8×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span.

The power of controlling objects with mind has captivated a popular fascination to human beings. One possible path is to employ brain signal collecting technologies together with emerging programmable metasurfaces (PM), whose functions or operating modes can be switched or customized via on-site programming or pre-defined software. Nevertheless, most of existing PMs are wire-connected to users, manually-controlled and not real-time. Here, we propose the concept of remotely mind-controlled metasurface (RMCM) via brainwaves. Rather than DC voltage from power supply or AC voltages from signal generators, the metasurface is controlled by brainwaves collected in real time and transmitted wirelessly from the user. As an example, we demonstrated a RMCM whose scattering pattern can be altered dynamically according to the user’s brain waves via Bluetooth. The attention intensity information is extracted as the control signal and a mapping between attention intensity and scattering pattern of the metasurface is established. With such a framework, we experimentally demonstrated and verified a prototype of such metasurface system which can be remotely controlled by the user to modify its scattering pattern. This work paves a new way to intelligent metasurfaces and may find applications in health monitoring, 5G/6G communications, smart sensors, etc.

Absorption of electromagnetic waves in a medium is generally manipulated by controlling the frequency dispersion of constitutive parameters. However, it is still challenging to gain the desired constitutive parameters for customized absorption over a broad frequency range. Here, by virtue of spoof surface plasmonic polaritons (SPPs), we demonstrate capabilities of the spatialk-dispersion engineering for producing the customized broadband absorption. Incident waves can be efficiently converted to the spoof SPPs by plasmonic arrays and their propagation and/or absorption can be controlled by engineering the spatial dispersion ofk-vector. Based on this feature, we show how such concept is employed to achieve broadband as well as frequency-selective broadband absorptions as examples. It is expected that the proposed concept can be extended to other manipulations of propagating electromagnetic waves over a broad frequency range.

This work simultaneously achieved a large temperature change (ΔT, ∼0.70 K) at room temperature and ultra-stable ΔT(±1.4% variation between 300 K with 380 K) in 0.78NaNbO₃–0.22BaTiO₃bulk ceramic.