scholarly journals Dynamic inversion of planar-chiral response of terahertz metasurface based on critical transition of checkerboard structures

Nanophotonics ◽  
2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Yoshiro Urade ◽  
Kai Fukawa ◽  
Fumiaki Miyamaru ◽  
Kunio Okimura ◽  
Toshihiro Nakanishi ◽  
...  
Keyword(s):  
Time Domain ◽  
Vanadium Dioxide ◽  
Dynamic Inversion ◽  
Critical Transition ◽  
Terahertz Time Domain Spectroscopy ◽  
Metallic Structures ◽  
Circularly Polarized ◽  
Resonant Structures ◽  
Planar Chirality ◽  
Polarized Waves

Abstract Dynamic inversion of the planar-chiral responses of a metasurface is experimentally demonstrated in the terahertz regime. To realize this inversion, the critical transition of the checkerboard-like metallic structures is used. Resonant structures with planar chirality and their complementary enantiomeric patterns are embedded in the checkerboard. Using vanadium dioxide as a variable resistance, the metasurface is implemented in the terahertz regime. The responses of the metasurface to circularly polarized waves are then characterized by terahertz time-domain spectroscopy. Further, the sign of the circular conversion dichroism, which is closely related to the handedness of the planar chirality of the metasurface, is observed to be inverted at 0.64 THz by varying the temperature. Such invertible planar-chiral responses can be applied practically to the handedness-invertible chiral mirrors.

scholarly journals Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 109
Author(s):  
Jimy Oblitas ◽  
Jorge Ruiz
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Electromagnetic Pulse ◽  
Machine Learning Algorithms ◽  
Classification Models ◽  
Terahertz Time Domain Spectroscopy ◽  
Time Domain Spectroscopy ◽  
Svm Algorithm ◽  
Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

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