metamaterial structure
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Z. Li ◽  
J. Li ◽  
Y. Zhang ◽  
Y. Zhai ◽  
X. Chu ◽  

Pasumarthi Suneetha ◽  
Kethavathu Srinivasa Naik ◽  
Pachiyannan Muthusamy

Abstract The μ-negative metamaterial (MNG) two-element MIMO antenna design was proposed in this article for WiMAX (2.5–2.8 GHz), WLAN (3.2–5.9 GHz), and ITU band (8.15−8.25 GHz) applications. The first design of the MIMO antenna operates at 2.7 and 4.9 GHz frequencies. In order to reduce the mutual coupling, a defective ground structure is used. For further isolation improvement, an MNG unit cell is placed in between the two radiating elements at a distance of 10 mm. The designed antenna elements have better than −23 dB coupling isolation between the two radiating elements. Moreover, with MNG an additional frequency of 8.2 GHz is obtained, which is useful for ITU band applications. The proposed antenna bandwidth is expanded by 19% in the lower operational band, 20% in the second operational band, and 32% in the higher frequency band with the MNG unit cell. From the analysis, the proposed antenna is suitable for WiMAX/WLAN/ITU band applications because of its low enveloped correlation coefficient, and highest directive gain and low mutual coupling between the radiating components. The proposed antenna was simulated, fabricated, and measured with the help of the Schwarz ZVL vector network analyzer and anechoic chamber. Both measured and simulated results are highly accurate and highly recommended for WiMAX/WLAN/ITU bands.

2022 ◽  
Vol 128 (1) ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya ◽  
Bijrajsinh Parmar ◽  
Sudipta Das ◽  
Murali Krishna ◽  

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Zichun Li ◽  
Jinhua Li ◽  
Ye Zhang ◽  
Yingjiao Zhai ◽  
Xueying Chu ◽  

Due to the problems that the metal pattern layer on the top of the traditional metamaterial structure is easy to be oxidized and easy to fall off, in this paper, a novel semiconductor metamaterial nanostructure composed of a periodic array of GaAs-SiO2 cubes and a gold (Au) film has been proposed. Using FDTD solutions software to prove this metamaterial structure can achieve ultranarrow dual-band, nearly perfect absorption with a maximum absorbance of 99% and a full-width at half-maximum (FWHM) value that is less than 20 nm in the midinfrared region. The refractive index sensitivity is demonstrated by changing the background index and analyzing the absorption performance. It had been proved that this absorber has high sensitivity (2000/RIU and 1300/RIU). Using semiconductor material instead of the metal material of the top pattern layer can effectively inhibit the performance failure of the metamaterial structure caused by metal oxidation. The proposed narrow, dual-band metamaterial absorber shows promising prospects in applications such as infrared detection and imaging.

2021 ◽  
Mahmoud Abouelatta ◽  
Muhammad Othman ◽  
Ahmed Mahmoud ◽  
Mohamed Swillam

Naufal A. H. Putra ◽  
Edwar ◽  
Wahyudi Hasbi ◽  
Muhammad P. Manggala ◽  
Daffa U. Kusmara ◽  

2021 ◽  
Vol 56 (6) ◽  
pp. 922-929
E. V. Lomakin ◽  
S. A. Yurgenson ◽  
B. N. Fedulov ◽  
A. N. Fedorenko

Abstract— The conventional design in aeronautical engineering is reinforced shell, which for most structures is a thin shell with a stringer set. This article compares the behavior of a conventional reinforced shell and a spaced shell metamaterial structure with a reinforced set using the example of a main airplane pressure bulkhead. The evaluation criterion is to ensure the required level of residual strength when the limiting state is reached.

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2672
Zheyu Hou ◽  
Pengyu Zhang ◽  
Mengfan Ge ◽  
Jie Li ◽  
Tingting Tang ◽  

Metamaterials and their related research have had a profound impact on many fields, including optics, but designing metamaterial structures on demand is still a challenging task. In recent years, deep learning has been widely used to guide the design of metamaterials, and has achieved outstanding performance. In this work, a metamaterial structure reverse multiple prediction method based on semisupervised learning was proposed, named the partially Conditional Generative Adversarial Network (pCGAN). It could reversely predict multiple sets of metamaterial structures that can meet the needs by inputting the required target spectrum. This model could reach a mean average error (MAE) of 0.03 and showed good generality. Compared with the previous metamaterial design methods, this method could realize reverse design and multiple design at the same time, which opens up a new method for the design of new metamaterials.

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