scholarly journals Broadband and low loss high refractive index metamaterials in the microwave regime

2013 ◽  
Vol 102 (9) ◽  
pp. 091108 ◽  
Author(s):  
T. Campbell ◽  
A. P. Hibbins ◽  
J. R. Sambles ◽  
I. R. Hooper
Author(s):  
Kristof Lodewijks ◽  
Suseendran Jayachandran ◽  
Tangla David Kongnyuy ◽  
Silvia Lenci ◽  
Sayantan Das ◽  
...  

Nanophotonics ◽  
2020 ◽  
Vol 9 (16) ◽  
pp. 4737-4742
Author(s):  
Anton A. Shubnic ◽  
Roman G. Polozkov ◽  
Ivan A. Shelykh ◽  
Ivan V. Iorsh

AbstractWe establish a simple quantitative criterium for the search of new dielectric materials with high values of refractive index in the visible range. It is demonstrated, that for light frequencies below the bandgap, the latter is determined by the dimensionless parameter η calculated as the ratio of the sum of the widths of conduction and valence bands and the bandgap. Small values of this parameter, which can be achieved in materials with almost flat bands, lead to dramatic increase of the refractive index. We illustrate this rule with a particular example of rhenium dichalcogenides, for which we perform ab initio calculations of the band structure and optical susceptibility and predict the values of the refractive index $n{ >}5$ in a wide frequency range around 1 eV together with comparatively low losses. Our findings open new perspectives in search for the new high-index/low-loss materials for all-dielectric nanophotonics.


2019 ◽  
Vol 125 (15) ◽  
pp. 151609 ◽  
Author(s):  
Doddoji Ramachari ◽  
Chan-Shan Yang ◽  
Osamu Wada ◽  
Takashi Uchino ◽  
Ci-Ling Pan

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>


Sign in / Sign up

Export Citation Format

Share Document