A Review on Metamaterials for Device Applications

Abstract

Metamaterials are the major type of artificially engineered materials which exhibit naturally unobtainable properties according to how their microarchitectures are engineered. Owing to their unique and controllable effective properties, including electric permittivity and magnetic permeability, the metamaterials play a vital role in the development of meta-devices. Therefore, the recent research has mainly focused on shifting towards achieving tunable, switchable, nonlinear, and sensing functionalities. In this review, we summarize the recent progress in terahertz, microwave electromagnetic, and photonic metamaterials, and their applications. The review also encompasses the role of metamaterials in the advancement of microwave sensors, photonic devices, antennas, energy harvesting, and superconducting quantum interference devices (SQUIDs).

An Innovative Polarisation-Insensitive Perfect Metamaterial Absorber with an Octagonal-Shaped Resonator for Energy Harvesting at Visible Spectra

“…The expression denoting PMA is formulated as A(ω) = 1 − R(ω) − T(ω), where R(ω) and T(ω) represent the coefficients of reflection and transmission, respectively. The rates of the reflection and transmission of the absorber are contingent upon the scattering parameters, namely R(ω) = S 11 2 and T(ω) = S 21 2 . Accordingly, optimising the geometric structure of PMA to attain resonance frequency, whereby Z(ω) = Z 0 , is deemed essential.…”

“…The concept of metamaterial was initially proposed by Veselago in 1968. However, metamaterials are used considerably in engineering, particularly in perfect lensing, optical cloaking, antenna design, sensing technology, and absorber structures [ 2 ]. The use of metasurfaces for absorptive purposes represents a promising field of application for metamaterials.…”

Artificial Intelligence for Energy Conversion

“…K-Nearest Neighbors, Regression Analysis, Support Vector Machines, and Judgment Trees are examples of key supervised learning algorithms. The training data in unsupervised learning is unlabeled, and the models learn to categories the input data using Clustering, Principal Component Analysis, etc., methods [11].…”

Design of novel microstrip patch antenna for millimeter-wave B5G communications

“…10.3389/fmats.2024.1364159 (Ullah et al, 2019;Kumar et al, 2022), but the surface wave leakage in this planar antenna increases (Liu et al, 2018a;Ge et al, 2018;Hao et al, 2019), resulting in poor radiation efficiency. 2) Improve the feeding method, such as electromagnetic coupling feeding (Fan et al, 2018;Soliman et al, 2022), L-shaped probe (Fan et al, 2018;Farooq et al, 2021;Soliman et al, 2022) or Mshaped probe (Yang et al, 2020a;Kumar et al, 2021) feeding, etc., but the antenna radiation pattern will change with frequency, and the cross-pole higher. 3) Use parasitic elements to combine multiple coupled resonance modes to increase the bandwidth, but its volume will increase a lot (Beguad et al, 2018;Srivastava et al, 2020;Karami et al, 2022).…”