A simple perfect absorption structure is proposed to achieve the high efficiency light absorption of monolayer molybdenum disulfide (MoS) by the critical coupling mechanism of guided resonances. The results of numerical simulation and theoretical analysis show that the light absorption in this atomically thin layer can be as high as 98.3% at the visible wavelengths, which is over 12 times more than that of a bare monolayer MoS. In addition, the operating wavelength can be tuned flexibly by adjusting the radius of the air hole and the thickness of the dielectric layers, which is of great practical significance to improve the efficiency and selectivity of the absorption in monolayer MoS. The novel idea of using critical coupling to enhance the light-MoS interaction can be also adopted in other atomically thin materials. The meaningful improvement and tunability of the absorption in monolayer MoS provides a good prospect for the realization of high-performance MoS-based optoelectronic applications, such as photodetection and photoluminescence.

Photonic topological transitions (PTTs) in metamaterials open up a novel approach to design a variety of high-performance optical devices and provide a flexible platform for manipulating light-matter interactions at nanoscale. Here, we present a wideband spectral-selective solar absorber based on multilayered hyperbolic metamaterial (HMM). Absorptivity of higher than 90% at normal incidence is supported over a wide wavelength range from 300 to 2215 nm, due to the topological change in the isofrequency surface (IFS). The operating bandwidth can be flexibly tailored by adjusting the thicknesses of the metal and dielectric layers. Moreover, the near-ideal absorption performance can be retained well at a wide angular range regardless of the incident light polarization. These features make the proposed design hold great promise for practical applications in energy harvesting.

Metallic plasmonic nanosensors that are ultra-sensitive, label-free, and operate in real time hold great promise in the field of chemical and biological research. Conventionally, the design of these nanostructures has strongly relied on time-consuming electromagnetic simulations that iteratively solve Maxwell's equations to scan multi-dimensional parameter space until the desired sensing performance is attained. Here, we propose an algorithm based on particle swarm optimization (PSO), which in combination with a machine learning (ML) model, is used to design plasmonic sensors. The ML model is trained with the geometric structure and sensing performance of the plasmonic sensor to accurately capture the geometry-sensing performance relationships, and the well-trained ML model is then applied to the PSO algorithm to obtain the plasmonic structure with the desired sensing performance. Using the trained ML model to predict the sensing performance instead of using complex electromagnetic calculation methods allows the PSO algorithm to optimize the solutions fours orders of magnitude faster. Implementation of this composite algorithm enabled us to quickly and accurately realize a nanoridge plasmonic sensor with sensitivity as high as 142,500 nm/RIU. We expect this efficient and accurate approach to pave the way for the design of nanophotonic devices in future.

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