From directional to omnidirectional: meta-devices for ultrabroadband sound absorption with near-causality-limit performance.

Traditional microperforated panel (MPP)-cavity absorbers and metamaterial-based sound absorbers rely on local resonances or multi-resonator designs, which limit their bandwidth, angular applicability, and ease of fabrication. Leveraging the reciprocity theorem and cavity resonances, we introduce a robust class of MPP absorbers, termed meta-MPPs, capable of achieving ultrabroadband near-total sound absorption across a range of 0.37 to 10 kHz.

Experimental realization of orbital-angular-momentum-selective coherent perfect absorption.

Coherent perfect absorption (CPA) dependent on orbital angular momentum (OAM) has been proposed theoretically but so far has lacked experimental validation. Here, we design an OAM-selective coherent perfect absorber and experimentally validate this intriguing phenomenon. By combining a spiral phase plate, a polarization-conversion metasurface, and a conical cavity, an incident plane wave of linear polarization is converted into cylindrical waves with different OAMs in two dimensions, which impinges on a dual-layer cylindrical absorber composed of a ceramic core and an indium tin oxide (ITO) coating.

Dewdrop Metasurfaces and Dynamic Control Based on Condensation and Evaporation.

Dewdrops, the droplets of water naturally occurring on leaves and carapaces of insects, are a fascinating phenomenon in nature. Here, a man-made array of dewdrops with arbitrary shapes and arrangements, which can function as an electromagnetic metasurface, is demonstrated. The realization of the dewdrop array is enabled by a surface covered by a tailored pattern of hydrophilic and hydrophobic coatings, where tiny droplets of water can aggregate and form dewdrops on the former.

A deep neural network for general scattering matrix.

The scattering matrix is the mathematical representation of the scattering characteristics of any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or cylinders, the scattering matrix does not have any analytical forms and thus can only be calculated numerically, which requires heavy computation. Here, we have developed a well-trained deep neural network (DNN) that can calculate the scattering matrix of scatterers without symmetry at a speed thousands of times faster than that of finite element solvers.