The development of patterned graphene metasurface absorbers (PGMAs) offers potential solutions for achieving light weight, thinness, wide absorption bandwidth, and tunable terahertz (THz) absorption properties. In order to optimize the absorption properties of PGMA, the absorption spectrum is usually used as an important evaluation metric, which can provide many important properties of PGMA such as absorption bandwidth, absorption magnitude, etc. However, analysis of the absorption spectra corresponding to a large number of variable structural parameters is required when designing the structure, which consumes a lot of resources, since the electromagnetic (EM) wave absorption in PGMA involves complex impedance matching and electric field excitation processes. To address this issue, this study proposes a machine learning (ML) approach based on the random forest (RF) algorithm to predict the absorption bandwidth and structural parameters for designing PGMA, reducing the need for unnecessary numerical simulation and spectra analysis time. With the RF model, a very large effective absorption bandwidth of 3.83 THz and a perfect absorption bandwidth of 2.52 THz are predicted with the R² of 0.938 and 0.907, and the forecast absolute percentage errors (APEs) are only 1.56% and 1.16%, respectively, which is much better than other classical ML algorithms. Furthermore, the proposed PGMA has the advantages of being thin, polarization insensitive, with a large stable incident angle of 60°, and excellent electrical tuning capabilities. This study provides a feasible and effective approach for the sophisticated design of complex systems related to EM wave propagation of absorption, reflection, and transmission.

Ultra-broadband and efficient terahertz (THz) absorption is of paramount importance for the development of high-performance detectors.

Metasurfaces have garnered significant attention in recent years due to their substantial electromagnetic (EM) wave manipulation capabilities. However, most previously documented metasurfaces have been limited to controlling just a single EM wave mode, encompassing transmission, reflection, or absorption. Such limitations have impeded the broader applications of metasurfaces. To address this issue, this study introduces a multi-functional metasurface (MFM) in the utilization of Ge₂Sb₂Te₅ (GST), vanadium dioxide (VO₂), and graphene. This novel design enables real-time control over the transmission, absorption, and reflection of EM waves as necessitated through thermal control, allowing for seamless transitions from complete transmission to complete reflection. Furthermore, this configuration achieves extensive broadband perfect absorption, spanning up to 1.83 THz. The optical response mechanism of this MFM across distinct operational modes is meticulously analyzed through electric field distribution. Remarkably, this proposed MFM exhibits polarization insensitivity and maintains good optical performance even under conditions of wide-angle incidence. With the ability to switch to different operating modes according to the needs of different environments, the proposed MFM has the potential to be used in a wide range of scenarios, including radar stealth, wireless communications, and military search.

Combining the electrical tunability of graphene and phase transition properties of VO₂, an electrical/thermal dual-controlled quad-functional terahertz metasurface absorber was proposed, providing a new idea for terahertz device design.

In this work, the effect of the sample temperature on the magnesium (Mg) and titanium (Ti) plasmas generated by a Q-switched Neodymium-doped Yttrium Aluminum Garnet (Nd:YAG) laser operating at its fundamental wavelength of 1064 nm has been investigated. We observed that increasing the sample temperature significantly enhanced the emission intensities of the plasmas. Comparing the emission peak intensities of the case of 100 °C to the case of 300 °C, we recorded a substantial enhancement of the peak intensities of the latter compared to the former. From these results it can be observed that increasing the sample temperature has a significant effect on the emission intensities of the plasmas. We also studied the plasma dynamics and found that increasing the sample temperature also decreases the air density around the Mg sample surface. The reduction in the air density resulted in a decrease in the radiation process and lowers collision probability. Furthermore, as the plasma expands, the plasma pressure also decreases. In addition, we also employed circular and square cavities to confine the titanium plasma, and investigated the effect of the sizes of the circular and square cavities on the titanium plasma. We observed a general improvement in the emission intensities with both the circular and square cavities and attributed this improvement to the plasma compression effect of the shock waves produced by the plasma within the cavities.

In this work, we propose a novel approach that combines a bidirectional deep neural network (BDNN) with a multifunctional metasurface absorber (MMA) for inverse design, which can effectively address the challenge of on-demand customization for absorbers. The inverse design of absorption peak frequencies can be achieved from 0.5 to 10 terahertz (THz), covering the quasi-entire THz band. Based on this, the BDNN is extended to broadband absorption, and the inverse design yields an MMA at the desired frequency. This work provides a broadly applicable approach to the custom design of multifunctional devices that can facilitate the evaluation and design of metasurfaces in electromagnetic absorption.