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Metamaterial - Crossref

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Last Updated: 15 May 2022

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A Review: The Functional Materials-Assisted Terahertz Metamaterial Absorbers and Polarization Converters

What will happen if metamaterial structures meet functional materials? The recent introduction of metamaterial structures and functional materials in conjunction with functional materials opens new possibilities for dynamic manipulation of terahertz wave. Depending on the external stimulus, the optical response of functional materials can be greatly enhanced based on highly localized structures in metamaterials, and metamaterial properties can in turn be controlled in a wide dynamic range. In the broader context of the terahertz absorption and polarization conversion, we summarize the latest advancements in the functional materials-based metamaterial frameworks for flexible control of the terahertz absorption and polarization conversion.

Source link: https://doi.org/10.3390/photonics9050335


3D printed metamaterial absorbers for mid-infrared surface-enhanced spectroscopy

The relative long wavelengths of IR light make it possible to produce three-dimensional IR metamaterials using the state-of-the-art 3D fabrication techniques relative to visible and near-infrared. The observed rise in IR detection can also be partially attributed to the MMA's 3D architecture's improved accessibility. In particular, the microscale 3D printed structures for surface-enhanced IR detection lead to selective analyte deposition in high-field regions, giving another degree of freedom in the creation of the 3D printed structures for surface-enhanced IR detection. Our report reveals the versatility of metastructures based on advanced 3D printing techniques in tailoring the interaction between IR light and materials on a subwavelength scale.

Source link: https://doi.org/10.1063/5.0093332


Ultra-wideband and high gain antipodal tapered slot antenna with planar metamaterial lens

This paper discusses ultra-wideband and high-gain antipodal tapered slot antenna with planar metamaterial lens in order to solve the challenges of poor reception, narrow bandwidth, and poor radiation directivity of traditional ground penetrating radar antennas. To greatly raise radiation efficiency, the ATSA's single-layer planar lens made of the designed unit cells of various sizes is introduced in the ATSA's maximum radiation direction, enabling it to greatly increase radiation capability. Both the three planned planar lens antennas have a wide impedance bandwidth of 107. 4% and a dB gain bandwidth of 54. 5%, respectively.

Source link: https://doi.org/10.1017/s1759078721000878


Graphene-based plasmonic metamaterial for terahertz laser transistors

Future 6G- and 7G-class THz wireless communication technologies are investigated and addressed toward unified light sources applicable to future 6G- and 7G-class THz wireless communication systems, with possible physical device configurations and design constraints being discussed and addressed.

Source link: https://doi.org/10.1515/nanoph-2021-0651


Bandgap and mode shape tuning of piezoelectric metamaterial

In order to customize the wave propagation characteristics of these metamaterials, the mode of piezoelectric metamaterials is modified by manipulating spatially the electrical boundary conditions of the piezo-elements, in a desired and controlled manner. The proposed scheme is based on the fact that open-circuit piezo-elements made of lead-zirconate-titanate are twice as stiff as piezo-elements operating under short-circuit conditions. Any desirable spatially distribution of the stiffness over the entire metamaterial volume is achieved by appropriate switching of the boundary conditions of the various piezo-elements between open and short-circuit conditions. However, it would also be possible to modify the metamaterial's mode shape in order to regulate wave propagation's magnitude and direction. A finite element model is designed to describe the bandgap and mode shape of one-dimensional piezo-metamaterial's one-dimensional piezo-metamaterial's one-dimensional piezo-metamaterial's one-dimensional piezo-metamaterial's one-dimensional piezo-metamaterial's one-dimensional piezo-metamaterial's one-dimensional piezometamaterial's one-dimensional piezo.

Source link: https://doi.org/10.1121/10.0010956


Acoustic metamaterial inverse design based on machine learning using Gauss–Bayesian models

The emergence of acoustic metamaterials gives a distinct response to acoustic waves that are unobtainable in nature. Here, we introduce an inverse-design technique based on machine learning and apply it to low-frequency broadband sound attenuating systems. We successively customized ventilated microperforated sound-absorbing structure, sound absorbers with cohesively coupled poor resonances, and ventilated meta-silencers with consecutive Fano resonances by using the GB model's superiority on multi-parameter configuration and optimization effectiveness.

Source link: https://doi.org/10.1121/10.0011230


Acoustic metamaterial design framework using deep learning and generative modeling

This talk discusses our findings and study on the use of deep learning and generative modeling in acoustic metamaterial design. The generative 2-D GLO-Nets and reinforcement learning models that have broadband low scattering results for 2-D planar configurations of scatterers under plane wave incidence will be shown. The current challenges encountered during the application of deep learning techniques in scaled metamaterial design will be addressed.

Source link: https://doi.org/10.1121/10.0011233


Reinforcement learning for acoustic metamaterial design

This talk introduces our findings and methods in using reinforcement learning to refine acoustic metamaterials [1]. Our reinforcement learning agents are capable of determining cylindrical scatterers in water that minimizes the dispersion of an acoustic plane wave. The resultant designs produced by reinforcement learning algorithms such as double deep Q-learning network and deep deterministic policy gradient algorithms are similar to those developed by the gradient-based optimization solver such as fmincon, and in some instances superior to those produced by the gradient-based optimization solver fmincon. To teach reinforcement learning models to completion, however, significant computational resources are still required to prepare reinforcement learning models to be complete.

Source link: https://doi.org/10.1121/10.0011239

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions